Dynamic programming for QFD in PES optimization
Energy Technology Data Exchange (ETDEWEB)
Sorrentino, R. [Mediterranean Univ. of Reggio Calabria, Reggio Calabria (Italy). Dept. of Computer Science and Electrical Technology
2008-07-01
Quality function deployment (QFD) is a method for linking the needs of the customer with design, development, engineering, manufacturing, and service functions. In the electric power industry, QFD is used to help designers concentrate on the most important technical attributes to develop better electrical services. Most optimization approaches used in QFD analysis have been based on integer or linear programming. These approaches perform well in certain circumstances, but there are problems that hinder their practical use. This paper proposed an approach to optimize Power and Energy Systems (PES). A dynamic programming approach was used along with an extended House of Quality to gather information. Dynamic programming was used to allocate the limited resources to the technical attributes. The approach integrated dynamic programming into the electrical service design process. The dynamic programming approach did not require the full relationship curve between technical attributes and customer satisfaction, or the relationship between technical attributes and cost. It only used a group of discrete points containing information about customer satisfaction, technical attributes, and the cost to find the optimal product design. Therefore, it required less time and resources than other approaches. At the end of the optimization process, the value of each technical attribute, the related cost, and the overall customer satisfaction were obtained at the same time. It was concluded that compared with other optimization methods, the dynamic programming method requires less information and the optimal results are more relevant. 21 refs., 2 tabs., 2 figs.
Efficient dynamic optimization of logic programs
Laird, Phil
1992-01-01
A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.
Markdown Optimization via Approximate Dynamic Programming
Directory of Open Access Journals (Sweden)
Cos?gun
2013-02-01
Full Text Available We consider the markdown optimization problem faced by the leading apparel retail chain. Because of substitution among products the markdown policy of one product affects the sales of other products. Therefore, markdown policies for product groups having a significant crossprice elasticity among each other should be jointly determined. Since the state space of the problem is very huge, we use Approximate Dynamic Programming. Finally, we provide insights on the behavior of how each product price affects the markdown policy.
Pareto optimization in algebraic dynamic programming.
Saule, Cédric; Giegerich, Robert
2015-01-01
Pareto optimization combines independent objectives by computing the Pareto front of its search space, defined as the set of all solutions for which no other candidate solution scores better under all objectives. This gives, in a precise sense, better information than an artificial amalgamation of different scores into a single objective, but is more costly to compute. Pareto optimization naturally occurs with genetic algorithms, albeit in a heuristic fashion. Non-heuristic Pareto optimization so far has been used only with a few applications in bioinformatics. We study exact Pareto optimization for two objectives in a dynamic programming framework. We define a binary Pareto product operator [Formula: see text] on arbitrary scoring schemes. Independent of a particular algorithm, we prove that for two scoring schemes A and B used in dynamic programming, the scoring scheme [Formula: see text] correctly performs Pareto optimization over the same search space. We study different implementations of the Pareto operator with respect to their asymptotic and empirical efficiency. Without artificial amalgamation of objectives, and with no heuristics involved, Pareto optimization is faster than computing the same number of answers separately for each objective. For RNA structure prediction under the minimum free energy versus the maximum expected accuracy model, we show that the empirical size of the Pareto front remains within reasonable bounds. Pareto optimization lends itself to the comparative investigation of the behavior of two alternative scoring schemes for the same purpose. For the above scoring schemes, we observe that the Pareto front can be seen as a composition of a few macrostates, each consisting of several microstates that differ in the same limited way. We also study the relationship between abstract shape analysis and the Pareto front, and find that they extract information of a different nature from the folding space and can be meaningfully combined.
Dynamic programming approach to optimization of approximate decision rules
Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata
2013-01-01
This paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure R(T) which is the number
Dynamic Programming Approach for Exact Decision Rule Optimization
Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata
2013-01-01
This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from
Stochastic dynamic programming model for optimal resource ...
Indian Academy of Sciences (India)
M Bhuvaneswari
2018-04-11
Apr 11, 2018 ... handover in VANET; because of high dynamics in net- work topology, collaboration ... containers, doctors, nurses, cash and stocks. Similarly, ... GTBA does not take the resource types and availability into consideration.
Optimal Operation of Radial Distribution Systems Using Extended Dynamic Programming
DEFF Research Database (Denmark)
Lopez, Juan Camilo; Vergara, Pedro P.; Lyra, Christiano
2018-01-01
An extended dynamic programming (EDP) approach is developed to optimize the ac steady-state operation of radial electrical distribution systems (EDS). Based on the optimality principle of the recursive Hamilton-Jacobi-Bellman equations, the proposed EDP approach determines the optimal operation o...... approach is illustrated using real-scale systems and comparisons with commercial programming solvers. Finally, generalizations to consider other EDS operation problems are also discussed.......An extended dynamic programming (EDP) approach is developed to optimize the ac steady-state operation of radial electrical distribution systems (EDS). Based on the optimality principle of the recursive Hamilton-Jacobi-Bellman equations, the proposed EDP approach determines the optimal operation...... of the EDS by setting the values of the controllable variables at each time period. A suitable definition for the stages of the problem makes it possible to represent the optimal ac power flow of radial EDS as a dynamic programming problem, wherein the 'curse of dimensionality' is a minor concern, since...
Optimization of a pump-pipe system by dynamic programming
DEFF Research Database (Denmark)
Vidal, Rene Victor Valqui; Ferreira, Jose S.
1984-01-01
In this paper the problem of minimizing the total cost of a pump-pipe system in series is considered. The route of the pipeline and the number of pumping stations are known. The optimization will then consist in determining the control variables, diameter and thickness of the pipe and the size of...... of the pumps. A general mathematical model is formulated and Dynamic Programming is used to find an optimal solution....
Dynamic Programming Approach for Exact Decision Rule Optimization
Amin, Talha
2013-01-01
This chapter is devoted to the study of an extension of dynamic programming approach that allows sequential optimization of exact decision rules relative to the length and coverage. It contains also results of experiments with decision tables from UCI Machine Learning Repository. © Springer-Verlag Berlin Heidelberg 2013.
Adaptive dynamic programming with applications in optimal control
Liu, Derong; Wang, Ding; Yang, Xiong; Li, Hongliang
2017-01-01
This book covers the most recent developments in adaptive dynamic programming (ADP). The text begins with a thorough background review of ADP making sure that readers are sufficiently familiar with the fundamentals. In the core of the book, the authors address first discrete- and then continuous-time systems. Coverage of discrete-time systems starts with a more general form of value iteration to demonstrate its convergence, optimality, and stability with complete and thorough theoretical analysis. A more realistic form of value iteration is studied where value function approximations are assumed to have finite errors. Adaptive Dynamic Programming also details another avenue of the ADP approach: policy iteration. Both basic and generalized forms of policy-iteration-based ADP are studied with complete and thorough theoretical analysis in terms of convergence, optimality, stability, and error bounds. Among continuous-time systems, the control of affine and nonaffine nonlinear systems is studied using the ADP app...
SEWER NETWORK DISCHARGE OPTIMIZATION USING THE DYNAMIC PROGRAMMING
Directory of Open Access Journals (Sweden)
Viorel MINZU
2015-12-01
Full Text Available It is necessary to adopt an optimal control that allows an efficient usage of the existing sewer networks, in order to avoid the building of new retention facilities. The main objective of the control action is to minimize the overflow volume of a sewer network. This paper proposes a method to apply a solution obtained by discrete dynamic programming through a realistic closed loop system.
Optimization of Algorithms Using Extensions of Dynamic Programming
AbouEisha, Hassan M.
2017-04-09
We study and answer questions related to the complexity of various important problems such as: multi-frontal solvers of hp-adaptive finite element method, sorting and majority. We advocate the use of dynamic programming as a viable tool to study optimal algorithms for these problems. The main approach used to attack these problems is modeling classes of algorithms that may solve this problem using a discrete model of computation then defining cost functions on this discrete structure that reflect different complexity measures of the represented algorithms. As a last step, dynamic programming algorithms are designed and used to optimize those models (algorithms) and to obtain exact results on the complexity of the studied problems. The first part of the thesis presents a novel model of computation (element partition tree) that represents a class of algorithms for multi-frontal solvers along with cost functions reflecting various complexity measures such as: time and space. It then introduces dynamic programming algorithms for multi-stage and bi-criteria optimization of element partition trees. In addition, it presents results based on optimal element partition trees for famous benchmark meshes such as: meshes with point and edge singularities. New improved heuristics for those benchmark meshes were ob- tained based on insights of the optimal results found by our algorithms. The second part of the thesis starts by introducing a general problem where different problems can be reduced to and show how to use a decision table to model such problem. We describe how decision trees and decision tests for this table correspond to adaptive and non-adaptive algorithms for the original problem. We present exact bounds on the average time complexity of adaptive algorithms for the eight elements sorting problem. Then bounds on adaptive and non-adaptive algorithms for a variant of the majority problem are introduced. Adaptive algorithms are modeled as decision trees whose depth
Dynamic programming approach to optimization of approximate decision rules
Amin, Talha
2013-02-01
This paper is devoted to the study of an extension of dynamic programming approach which allows sequential optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure R(T) which is the number of unordered pairs of rows with different decisions in the decision table T. For a nonnegative real number β, we consider β-decision rules that localize rows in subtables of T with uncertainty at most β. Our algorithm constructs a directed acyclic graph Δβ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most β. The graph Δβ(T) allows us to describe the whole set of so-called irredundant β-decision rules. We can describe all irredundant β-decision rules with minimum length, and after that among these rules describe all rules with maximum coverage. We can also change the order of optimization. The consideration of irredundant rules only does not change the results of optimization. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository. © 2012 Elsevier Inc. All rights reserved.
Optimization of decision rules based on dynamic programming approach
Zielosko, Beata
2014-01-14
This chapter is devoted to the study of an extension of dynamic programming approach which allows optimization of approximate decision rules relative to the length and coverage. We introduce an uncertainty measure that is the difference between number of rows in a given decision table and the number of rows labeled with the most common decision for this table divided by the number of rows in the decision table. We fix a threshold γ, such that 0 ≤ γ < 1, and study so-called γ-decision rules (approximate decision rules) that localize rows in subtables which uncertainty is at most γ. Presented algorithm constructs a directed acyclic graph Δ γ T which nodes are subtables of the decision table T given by pairs "attribute = value". The algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The chapter contains also results of experiments with decision tables from UCI Machine Learning Repository. © 2014 Springer International Publishing Switzerland.
Convergence of Sample Path Optimal Policies for Stochastic Dynamic Programming
National Research Council Canada - National Science Library
Fu, Michael C; Jin, Xing
2005-01-01
.... These results have practical implications for Monte Carlo simulation-based solution approaches to stochastic dynamic programming problems where it is impractical to extract the explicit transition...
Dynamic programming approach for partial decision rule optimization
Amin, Talha
2012-10-04
This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between number of rows in a decision table T and number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δ γ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δ γ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.
Optimization of control poison management by dynamic programming
International Nuclear Information System (INIS)
Ponzoni Filho, P.
1974-01-01
A dynamic programming approach was used to optimize the poison distribution in the core of a nuclear power plant between reloading. This method was applied to a 500 M We PWR subject to two different fuel management policies. The beginning of a stage is marked by a fuel management decision. The state vector of the system is defined by the burnups in the three fuel zones of the core. The change of the state vector is computed in several time steps. A criticality conserving poison management pattern is chosen at the beginning of each step. The burnups at the end of a step are obtained by means of depletion calculations, assuming constant neutron distribution during the step. The violation of burnup and power peaking constraints during the step eliminates the corresponding end states. In the case of identical end states, all except that which produced the largest amount of energy, are eliminated. Among the several end states one is selected for the subsequent stage, when it is subjected to a fuel management decision. This selection is based on an optimally criterion previously chosen, such as: discharged fuel burnup maximization, energy generation cost minimization, etc. (author)
Dynamic programming approach for partial decision rule optimization
Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata
2012-01-01
This paper is devoted to the study of an extension of dynamic programming approach which allows optimization of partial decision rules relative to the length or coverage. We introduce an uncertainty measure J(T) which is the difference between number of rows in a decision table T and number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules (partial decision rules) that localize rows in subtables of T with uncertainty at most γ. Presented algorithm constructs a directed acyclic graph Δ γ(T) which nodes are subtables of the decision table T given by systems of equations of the kind "attribute = value". This algorithm finishes the partitioning of a subtable when its uncertainty is at most γ. The graph Δ γ(T) allows us to describe the whole set of so-called irredundant γ-decision rules. We can optimize such set of rules according to length or coverage. This paper contains also results of experiments with decision tables from UCI Machine Learning Repository.
INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization
Energy Technology Data Exchange (ETDEWEB)
Groer, Christopher S [ORNL; Sullivan, Blair D [ORNL; Weerapurage, Dinesh P [ORNL
2012-10-01
It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \\emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms we have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.
An Approximate Dynamic Programming Mode for Optimal MEDEVAC Dispatching
2015-03-26
over the myopic policy. This indicates the ADP policy is efficiently managing resources by 28 not immediately sending the nearest available MEDEVAC...DISPATCHING THESIS Presented to the Faculty Department of Operational Sciences Graduate School of Engineering and Management Air Force Institute of Technology...medical evacuation (MEDEVAC) dispatch policies. To solve the MDP, we apply an ap- proximate dynamic programming (ADP) technique. The problem of deciding
Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming.
Wang, Haizhou; Song, Mingzhou
2011-12-01
The heuristic k -means algorithm, widely used for cluster analysis, does not guarantee optimality. We developed a dynamic programming algorithm for optimal one-dimensional clustering. The algorithm is implemented as an R package called Ckmeans.1d.dp . We demonstrate its advantage in optimality and runtime over the standard iterative k -means algorithm.
Optimal blood glucose level control using dynamic programming based on minimal Bergman model
Rettian Anggita Sari, Maria; Hartono
2018-03-01
The purpose of this article is to simulate the glucose dynamic and the insulin kinetic of diabetic patient. The model used in this research is a non-linear Minimal Bergman model. Optimal control theory is then applied to formulate the problem in order to determine the optimal dose of insulin in the treatment of diabetes mellitus such that the glucose level is in the normal range for some specific time range. The optimization problem is solved using dynamic programming. The result shows that dynamic programming is quite reliable to represent the interaction between glucose and insulin levels in diabetes mellitus patient.
Energy Technology Data Exchange (ETDEWEB)
Dall' Anese, Emiliano; Dhople, Sairaj V.; Giannakis, Georgios B.
2015-07-01
This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of pertinent network-wide optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual e-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference signals. Global convergence is guaranteed for diminishing stepsize rules, even when the reference inputs are updated at a faster rate than the dynamical-system settling time. The application of the proposed framework to the control of power-electronic inverters in AC distribution systems is discussed. The objective is to bridge the time-scale separation between real-time inverter control and network-wide optimization. Optimization objectives assume the form of SDP relaxations of prototypical AC optimal power flow problems.
Directory of Open Access Journals (Sweden)
Jingtao Shi
2013-01-01
Full Text Available This paper is concerned with the relationship between maximum principle and dynamic programming for stochastic recursive optimal control problems. Under certain differentiability conditions, relations among the adjoint processes, the generalized Hamiltonian function, and the value function are given. A linear quadratic recursive utility portfolio optimization problem in the financial engineering is discussed as an explicitly illustrated example of the main result.
Dynamic programming for optimization of timber production and grazing in ponderosa pine
Kurt H. Riitters; J. Douglas Brodie; David W. Hann
1982-01-01
Dynamic programming procedures are presented for optimizing thinning and rotation of even-aged ponderosa pine by using the four descriptors: age, basal area, number of trees, and time since thinning. Because both timber yield and grazing yield are functions of stand density, the two outputs-forage and timber-can both be optimized. The soil expectation values for single...
Optimization of fuel-cell tram operation based on two dimension dynamic programming
Zhang, Wenbin; Lu, Xuecheng; Zhao, Jingsong; Li, Jianqiu
2018-02-01
This paper proposes an optimal control strategy based on the two-dimension dynamic programming (2DDP) algorithm targeting at minimizing operation energy consumption for a fuel-cell tram. The energy consumption model with the tram dynamics is firstly deduced. Optimal control problem are analyzed and the 2DDP strategy is applied to solve the problem. The optimal tram speed profiles are obtained for each interstation which consist of three stages: accelerate to the set speed with the maximum traction power, dynamically adjust to maintain a uniform speed and decelerate to zero speed with the maximum braking power at a suitable timing. The optimal control curves of all the interstations are connected with the parking time to form the optimal control method of the whole line. The optimized speed profiles are also simplified for drivers to follow.
An optimal maintenance policy for machine replacement problem using dynamic programming
Mohsen Sadegh Amalnik; Morteza Pourgharibshahi
2017-01-01
In this article, we present an acceptance sampling plan for machine replacement problem based on the backward dynamic programming model. Discount dynamic programming is used to solve a two-state machine replacement problem. We plan to design a model for maintenance by consid-ering the quality of the item produced. The purpose of the proposed model is to determine the optimal threshold policy for maintenance in a finite time horizon. We create a decision tree based on a sequential sampling inc...
Optimization of Algorithms Using Extensions of Dynamic Programming
AbouEisha, Hassan M.
2017-01-01
of the thesis presents a novel model of computation (element partition tree) that represents a class of algorithms for multi-frontal solvers along with cost functions reflecting various complexity measures such as: time and space. It then introduces dynamic
Optimal Charging of Electric Drive Vehicles: A Dynamic Programming Approach
DEFF Research Database (Denmark)
Delikaraoglou, Stefanos; Capion, Karsten Emil; Juul, Nina
2013-01-01
, therefore, we propose an ex ante vehicle aggregation approach. We illustrate the results in a Danish case study and find that, although optimal management of the vehicles does not allow for storage and day-to-day flexibility in the electricity system, the market provides incentive for intra-day flexibility....
Optimal Risk Reduction in the Railway Industry by Using Dynamic Programming
Michael Todinov; Eberechi Weli
2013-01-01
The paper suggests for the first time the use of dynamic programming techniques for optimal risk reduction in the railway industry. It is shown that by using the concept ‘amount of removed risk by a risk reduction option’, the problem related to optimal allocation of a fixed budget to achieve a maximum risk reduction in the railway industry can be reduced to an optimisation problem from dynamic programming. For n risk reduction options and size of the available risk reduction budget B (expres...
Stochastic optimization in insurance a dynamic programming approach
Azcue, Pablo
2014-01-01
The main purpose of the book is to show how a viscosity approach can be used to tackle control problems in insurance. The problems covered are the maximization of survival probability as well as the maximization of dividends in the classical collective risk model. The authors consider the possibility of controlling the risk process by reinsurance as well as by investments. They show that optimal value functions are characterized as either the unique or the smallest viscosity solution of the associated Hamilton-Jacobi-Bellman equation; they also study the structure of the optimal strategies and show how to find them. The viscosity approach was widely used in control problems related to mathematical finance but until quite recently it was not used to solve control problems related to actuarial mathematical science. This book is designed to familiarize the reader on how to use this approach. The intended audience is graduate students as well as researchers in this area.
Optimal Design of Measurement Programs for the Parameter Identification of Dynamic Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune
The design of measurement programs devoted to parameter identification of structural dynamic systems is considered. The design problem is formulated as an optimization problem to minimize the total expected cost that is the cost of failure and the cost of the measurement program. All...... the calculations are based on a priori knowledge and engineering judgement. One of the contribution of the approach is that the optimal number of sensors can be estimated. This is shown in a numerical example where the proposed approach is demonstrated. The example is concerned with design of a measurement program...
Optimal Design of Measurement Programs for the Parameter Identification of Dynamic Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune
The design of a measured program devoted to parameter identification of structural dynamic systems is considered, the design problem is formulated as an optimization problem due to minimize the total expected cost of the measurement program. All the calculations are based on a priori knowledge...... and engineering judgement. One of the contribution of the approach is that the optimal nmber of sensors can be estimated. This is sown in an numerical example where the proposed approach is demonstrated. The example is concerned with design of a measurement program for estimating the modal damping parameters...
International Nuclear Information System (INIS)
Sutrisno; Widowati; Solikhin
2016-01-01
In this paper, we propose a mathematical model in stochastic dynamic optimization form to determine the optimal strategy for an integrated single product inventory control problem and supplier selection problem where the demand and purchasing cost parameters are random. For each time period, by using the proposed model, we decide the optimal supplier and calculate the optimal product volume purchased from the optimal supplier so that the inventory level will be located at some point as close as possible to the reference point with minimal cost. We use stochastic dynamic programming to solve this problem and give several numerical experiments to evaluate the model. From the results, for each time period, the proposed model was generated the optimal supplier and the inventory level was tracked the reference point well. (paper)
Optimal Input Design for Aircraft Parameter Estimation using Dynamic Programming Principles
Morelli, Eugene A.; Klein, Vladislav
1990-01-01
A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.
Lali, Mehdi
2009-03-01
A comprehensive computer program is designed in MATLAB to analyze, design and optimize the propulsion, dynamics, thermodynamics, and kinematics of any serial multi-staging rocket for a set of given data. The program is quite user-friendly. It comprises two main sections: "analysis and design" and "optimization." Each section has a GUI (Graphical User Interface) in which the rocket's data are entered by the user and by which the program is run. The first section analyzes the performance of the rocket that is previously devised by the user. Numerous plots and subplots are provided to display the performance of the rocket. The second section of the program finds the "optimum trajectory" via billions of iterations and computations which are done through sophisticated algorithms using numerical methods and incremental integrations. Innovative techniques are applied to calculate the optimal parameters for the engine and designing the "optimal pitch program." This computer program is stand-alone in such a way that it calculates almost every design parameter in regards to rocket propulsion and dynamics. It is meant to be used for actual launch operations as well as educational and research purposes.
DEFF Research Database (Denmark)
Shuai, Hang; Ai, Xiaomeng; Wen, Jinyu
2017-01-01
This paper proposes a hybrid approximate dynamic programming (ADP) approach for the multiple time-period optimal power flow in integrated gas and power systems. ADP successively solves Bellman's equation to make decisions according to the current state of the system. So, the updated near future...
An optimal maintenance policy for machine replacement problem using dynamic programming
Directory of Open Access Journals (Sweden)
Mohsen Sadegh Amalnik
2017-06-01
Full Text Available In this article, we present an acceptance sampling plan for machine replacement problem based on the backward dynamic programming model. Discount dynamic programming is used to solve a two-state machine replacement problem. We plan to design a model for maintenance by consid-ering the quality of the item produced. The purpose of the proposed model is to determine the optimal threshold policy for maintenance in a finite time horizon. We create a decision tree based on a sequential sampling including renew, repair and do nothing and wish to achieve an optimal threshold for making decisions including renew, repair and continue the production in order to minimize the expected cost. Results show that the optimal policy is sensitive to the data, for the probability of defective machines and parameters defined in the model. This can be clearly demonstrated by a sensitivity analysis technique.
International Nuclear Information System (INIS)
Porteus, E.
1982-01-01
The study of infinite-horizon nonstationary dynamic programs using the operator approach is continued. The point of view here differs slightly from that taken by others, in that Denardo's local income function is not used as a starting point. Infinite-horizon values are defined as limits of finite-horizon values, as the horizons get long. Two important conditions of an earlier paper are weakened, yet the optimality equations, the optimality criterion, and the existence of optimal ''structured'' strategies are still obtained
Optimal Design of Measurement Programs for the Parameter Identification of Dynamic Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune
1993-01-01
The design of a measurement program devoted to parameter identification of structural dynamic systems is considered. The design problem is formulated as an optimization problem to minimize the total expected cost that is the cost of failure and the cost of the measurement program. All...... the calculations are based on a priori knowledge and engineering judgement. One of the contribution of the approach is that the optimal number of sensory can be estimated. This is shown in an numerical example where the proposed approach is demonstrated. The example is concerned with design of a measurement...
Optimal Design of Measurement Programs for the Parameter Identification of Dynamic Systems
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune
1991-01-01
The design of a measurement program devoted to parameter identification of structural dynamic systems is considered. The design problem is formulated as an optimization problem to minimize the total expected cost, i.e. the cost of failure and the cost of the measurement program. All...... the calculations are based on a priori knowledge and engineering judgement. One of the contributions of the approach is that the optimal number of sensors can be estimated. This is shown in a numerical example where the proposed approach is demonstrated. The example is concerned with design of a measurement...
Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai
2013-09-01
In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
Extensions of Dynamic Programming: Decision Trees, Combinatorial Optimization, and Data Mining
Hussain, Shahid
2016-01-01
This thesis is devoted to the development of extensions of dynamic programming to the study of decision trees. The considered extensions allow us to make multi-stage optimization of decision trees relative to a sequence of cost functions, to count the number of optimal trees, and to study relationships: cost vs cost and cost vs uncertainty for decision trees by construction of the set of Pareto-optimal points for the corresponding bi-criteria optimization problem. The applications include study of totally optimal (simultaneously optimal relative to a number of cost functions) decision trees for Boolean functions, improvement of bounds on complexity of decision trees for diagnosis of circuits, study of time and memory trade-off for corner point detection, study of decision rules derived from decision trees, creation of new procedure (multi-pruning) for construction of classifiers, and comparison of heuristics for decision tree construction. Part of these extensions (multi-stage optimization) was generalized to well-known combinatorial optimization problems: matrix chain multiplication, binary search trees, global sequence alignment, and optimal paths in directed graphs.
Extensions of Dynamic Programming: Decision Trees, Combinatorial Optimization, and Data Mining
Hussain, Shahid
2016-07-10
This thesis is devoted to the development of extensions of dynamic programming to the study of decision trees. The considered extensions allow us to make multi-stage optimization of decision trees relative to a sequence of cost functions, to count the number of optimal trees, and to study relationships: cost vs cost and cost vs uncertainty for decision trees by construction of the set of Pareto-optimal points for the corresponding bi-criteria optimization problem. The applications include study of totally optimal (simultaneously optimal relative to a number of cost functions) decision trees for Boolean functions, improvement of bounds on complexity of decision trees for diagnosis of circuits, study of time and memory trade-off for corner point detection, study of decision rules derived from decision trees, creation of new procedure (multi-pruning) for construction of classifiers, and comparison of heuristics for decision tree construction. Part of these extensions (multi-stage optimization) was generalized to well-known combinatorial optimization problems: matrix chain multiplication, binary search trees, global sequence alignment, and optimal paths in directed graphs.
Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing
Ono, Masahiro; Kuwata, Yoshiaki
2013-01-01
A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.
DEFF Research Database (Denmark)
Davidsen, Claus; Liu, Suxia; Mo, Xingguo
2015-01-01
Few studies address water quality in hydro-economic models, which often focus primarily on optimal allocation of water quantities. Water quality and water quantity are closely coupled, and optimal management with focus solely on either quantity or quality may cause large costs in terms of the oth......-er component. In this study, we couple water quality and water quantity in a joint hydro-economic catchment-scale optimization problem. Stochastic dynamic programming (SDP) is used to minimize the basin-wide total costs arising from water allocation, water curtailment and water treatment. The simple water...... quality module can handle conservative pollutants, first order depletion and non-linear reactions. For demonstration purposes, we model pollutant releases as biochemical oxygen demand (BOD) and use the Streeter-Phelps equation for oxygen deficit to compute the resulting min-imum dissolved oxygen...
Stochastic optimal control in infinite dimension dynamic programming and HJB equations
Fabbri, Giorgio; Święch, Andrzej
2017-01-01
Providing an introduction to stochastic optimal control in infinite dimension, this book gives a complete account of the theory of second-order HJB equations in infinite-dimensional Hilbert spaces, focusing on its applicability to associated stochastic optimal control problems. It features a general introduction to optimal stochastic control, including basic results (e.g. the dynamic programming principle) with proofs, and provides examples of applications. A complete and up-to-date exposition of the existing theory of viscosity solutions and regular solutions of second-order HJB equations in Hilbert spaces is given, together with an extensive survey of other methods, with a full bibliography. In particular, Chapter 6, written by M. Fuhrman and G. Tessitore, surveys the theory of regular solutions of HJB equations arising in infinite-dimensional stochastic control, via BSDEs. The book is of interest to both pure and applied researchers working in the control theory of stochastic PDEs, and in PDEs in infinite ...
Directory of Open Access Journals (Sweden)
Zhi-Jun Fu
2017-01-01
Full Text Available In view of the performance requirements (e.g., ride comfort, road holding, and suspension space limitation for vehicle suspension systems, this paper proposes an adaptive optimal control method for quarter-car active suspension system by using the approximate dynamic programming approach (ADP. Online optimal control law is obtained by using a single adaptive critic NN to approximate the solution of the Hamilton-Jacobi-Bellman (HJB equation. Stability of the closed-loop system is proved by Lyapunov theory. Compared with the classic linear quadratic regulator (LQR approach, the proposed ADP-based adaptive optimal control method demonstrates improved performance in the presence of parametric uncertainties (e.g., sprung mass and unknown road displacement. Numerical simulation results of a sedan suspension system are presented to verify the effectiveness of the proposed control strategy.
Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.
Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L
2017-10-01
The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.
AbouEisha, Hassan M.; Moshkov, Mikhail; Calo, Victor M.; Paszynski, Maciej; Goik, Damian; Jopek, Konrad
2014-01-01
In this paper we present a dynamic programming algorithm for finding optimal elimination trees for computational grids refined towards point or edge singularities. The elimination tree is utilized to guide the multi-frontal direct solver algorithm
Energy Technology Data Exchange (ETDEWEB)
Zhang, Xiaodong, E-mail: xiaodong.zhang@beg.utexas.edu [Bureau of Economic Geology, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX 78713 (United States); Huang, Gordon [Institute of Energy, Environment and Sustainable Communities, University of Regina, Regina, Saskatchewan S4S 0A2 (Canada)
2013-02-15
Highlights: ► A dynamic stochastic possibilistic multiobjective programming model is developed. ► Greenhouse gas emission control is considered. ► Three planning scenarios are analyzed and compared. ► Optimal decision schemes under three scenarios and different p{sub i} levels are obtained. ► Tradeoffs between economics and environment are reflected. -- Abstract: Greenhouse gas (GHG) emissions from municipal solid waste (MSW) management facilities have become a serious environmental issue. In MSW management, not only economic objectives but also environmental objectives should be considered simultaneously. In this study, a dynamic stochastic possibilistic multiobjective programming (DSPMP) model is developed for supporting MSW management and associated GHG emission control. The DSPMP model improves upon the existing waste management optimization methods through incorporation of fuzzy possibilistic programming and chance-constrained programming into a general mixed-integer multiobjective linear programming (MOP) framework where various uncertainties expressed as fuzzy possibility distributions and probability distributions can be effectively reflected. Two conflicting objectives are integrally considered, including minimization of total system cost and minimization of total GHG emissions from waste management facilities. Three planning scenarios are analyzed and compared, representing different preferences of the decision makers for economic development and environmental-impact (i.e. GHG-emission) issues in integrated MSW management. Optimal decision schemes under three scenarios and different p{sub i} levels (representing the probability that the constraints would be violated) are generated for planning waste flow allocation and facility capacity expansions as well as GHG emission control. The results indicate that economic and environmental tradeoffs can be effectively reflected through the proposed DSPMP model. The generated decision variables can help
Optimization and analysis of decision trees and rules: Dynamic programming approach
Alkhalid, Abdulaziz
2013-08-01
This paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this systems work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction. © 2013 Taylor and Francis Group, LLC.
Optimization and analysis of decision trees and rules: Dynamic programming approach
Alkhalid, Abdulaziz; Amin, Talha M.; Chikalov, Igor; Hussain, Shahid; Moshkov, Mikhail; Zielosko, Beata
2013-01-01
This paper is devoted to the consideration of software system Dagger created in KAUST. This system is based on extensions of dynamic programming. It allows sequential optimization of decision trees and rules relative to different cost functions, derivation of relationships between two cost functions (in particular, between number of misclassifications and depth of decision trees), and between cost and uncertainty of decision trees. We describe features of Dagger and consider examples of this systems work on decision tables from UCI Machine Learning Repository. We also use Dagger to compare 16 different greedy algorithms for decision tree construction. © 2013 Taylor and Francis Group, LLC.
Dynamic Programming Optimization of Multi-rate Multicast Video-Streaming Services
Directory of Open Access Journals (Sweden)
Nestor Michael Caños Tiglao
2010-06-01
Full Text Available In large scale IP Television (IPTV and Mobile TV distributions, the video signal is typically encoded and transmitted using several quality streams, over IP Multicast channels, to several groups of receivers, which are classified in terms of their reception rate. As the number of video streams is usually constrained by both the number of TV channels and the maximum capacity of the content distribution network, it is necessary to find the selection of video stream transmission rates that maximizes the overall user satisfaction. In order to efficiently solve this problem, this paper proposes the Dynamic Programming Multi-rate Optimization (DPMO algorithm. The latter was comparatively evaluated considering several user distributions, featuring different access rate patterns. The experimental results reveal that DPMO is significantly more efficient than exhaustive search, while presenting slightly higher execution times than the non-optimal Multi-rate Step Search (MSS algorithm.
Optimal Control of Complex Systems Based on Improved Dual Heuristic Dynamic Programming Algorithm
Directory of Open Access Journals (Sweden)
Hui Li
2017-01-01
Full Text Available When applied to solving the data modeling and optimal control problems of complex systems, the dual heuristic dynamic programming (DHP technique, which is based on the BP neural network algorithm (BP-DHP, has difficulty in prediction accuracy, slow convergence speed, poor stability, and so forth. In this paper, a dual DHP technique based on Extreme Learning Machine (ELM algorithm (ELM-DHP was proposed. Through constructing three kinds of network structures, the paper gives the detailed realization process of the DHP technique in the ELM. The controller designed upon the ELM-DHP algorithm controlled a molecular distillation system with complex features, such as multivariability, strong coupling, and nonlinearity. Finally, the effectiveness of the algorithm is verified by the simulation that compares DHP and HDP algorithms based on ELM and BP neural network. The algorithm can also be applied to solve the data modeling and optimal control problems of similar complex systems.
Zhang, Xiaodong; Huang, Gordon
2013-02-15
Greenhouse gas (GHG) emissions from municipal solid waste (MSW) management facilities have become a serious environmental issue. In MSW management, not only economic objectives but also environmental objectives should be considered simultaneously. In this study, a dynamic stochastic possibilistic multiobjective programming (DSPMP) model is developed for supporting MSW management and associated GHG emission control. The DSPMP model improves upon the existing waste management optimization methods through incorporation of fuzzy possibilistic programming and chance-constrained programming into a general mixed-integer multiobjective linear programming (MOP) framework where various uncertainties expressed as fuzzy possibility distributions and probability distributions can be effectively reflected. Two conflicting objectives are integrally considered, including minimization of total system cost and minimization of total GHG emissions from waste management facilities. Three planning scenarios are analyzed and compared, representing different preferences of the decision makers for economic development and environmental-impact (i.e. GHG-emission) issues in integrated MSW management. Optimal decision schemes under three scenarios and different p(i) levels (representing the probability that the constraints would be violated) are generated for planning waste flow allocation and facility capacity expansions as well as GHG emission control. The results indicate that economic and environmental tradeoffs can be effectively reflected through the proposed DSPMP model. The generated decision variables can help the decision makers justify and/or adjust their waste management strategies based on their implicit knowledge and preferences. Copyright © 2012 Elsevier B.V. All rights reserved.
Ibarra, Ignacio L; Melo, Francisco
2010-07-01
Dynamic programming (DP) is a general optimization strategy that is successfully used across various disciplines of science. In bioinformatics, it is widely applied in calculating the optimal alignment between pairs of protein or DNA sequences. These alignments form the basis of new, verifiable biological hypothesis. Despite its importance, there are no interactive tools available for training and education on understanding the DP algorithm. Here, we introduce an interactive computer application with a graphical interface, for the purpose of educating students about DP. The program displays the DP scoring matrix and the resulting optimal alignment(s), while allowing the user to modify key parameters such as the values in the similarity matrix, the sequence alignment algorithm version and the gap opening/extension penalties. We hope that this software will be useful to teachers and students of bioinformatics courses, as well as researchers who implement the DP algorithm for diverse applications. The software is freely available at: http:/melolab.org/sat. The software is written in the Java computer language, thus it runs on all major platforms and operating systems including Windows, Mac OS X and LINUX. All inquiries or comments about this software should be directed to Francisco Melo at fmelo@bio.puc.cl.
International Nuclear Information System (INIS)
Zhang Xiaobing; Fan Ying; Wei Yiming
2009-01-01
China's Strategic Petroleum Reserve (SPR) is currently being prepared. But how large the optimal stockpile size for China should be, what the best acquisition strategies are, how to release the reserve if a disruption occurs, and other related issues still need to be studied in detail. In this paper, we develop a stochastic dynamic programming model based on a total potential cost function of establishing SPRs to evaluate the optimal SPR policy for China. Using this model, empirical results are presented for the optimal size of China's SPR and the best acquisition and drawdown strategies for a few specific cases. The results show that with comprehensive consideration, the optimal SPR size for China is around 320 million barrels. This size is equivalent to about 90 days of net oil import amount in 2006 and should be reached in the year 2017, three years earlier than the national goal, which implies that the need for China to fill the SPR is probably more pressing; the best stockpile release action in a disruption is related to the disruption levels and expected continuation probabilities. The information provided by the results will be useful for decision makers.
International Nuclear Information System (INIS)
Thiriet, L.; Deledicq, A.
1968-09-01
First, the point of applying Dynamic Programming to optimization and Operational Research problems in chemical industries are recalled, as well as the conditions in which a dynamic program is illustrated by a sequential graph. A new algorithm for the determination of sub-optimal politics in a sequential graph is then developed. Finally, the applications of sub-optimality concept is shown when taking into account the indirect effects related to possible strategies, or in the case of stochastic choices and of problems of the siting of plants... application examples are given. (authors) [fr
A Dynamic Programming Solution for Energy-Optimal Video Playback on Mobile Devices
Directory of Open Access Journals (Sweden)
Minseok Song
2016-01-01
Full Text Available Due to the development of mobile technology and wide availability of smartphones, the Internet of Things (IoT starts to handle high volumes of video data to facilitate multimedia-based services, which requires energy-efficient video playback. In video playback, frames have to be decoded and rendered at high playback rate, increasing the computation cost on the CPU. To save the CPU power, dynamic voltage and frequency scaling (DVFS dynamically adjusts the operating voltage of the processor along with frequency, in which appropriate selection of frequency on power could achieve a balance between performance and power. We present a decoding model that allows buffering frames to let the CPU run at low frequency and then propose an algorithm that determines the CPU frequency needed to decode each frame in a video, with the aim of minimizing power consumption while meeting buffer size and deadline constraints, using a dynamic programming technique. We finally extend this algorithm to optimize CPU frequencies over a short sequence of frames, producing a practical method of reducing the energy required for video decoding. Experimental results show a system-wide reduction in energy of 27%, compared with a processor running at full speed.
Optimization of fuel management and control poison of a nuclear power reactor by dynamic programming
International Nuclear Information System (INIS)
Lima, C.A.R. de.
1977-01-01
The distribution of fuel and control poison in a nuclear reactor was optimized by the method of Dynamic Programming. A 620 M We Pressurized Water Reactor similar to Angra-1 was studied. The reactor operation was simulated in a IBM-1130 computer. Two fuel shuffling schemes and three poison management schemes were simultaneously employed in the reactor divided into three regions of equal volume and two consecutive stages were studied in order to determine the influence of poison management on the optimum fuel management policy. When uniform poisoning on all the three regions was permitted the traditional out-in fuel management policy proved to be more economic. On introducing simultaneous poison management, the optimum fuel management sequence was found to be different. The results obtained indicate a stronger interaction between the fuel management and the poison management than anticipated in previous works. (author)
Li, Ji-Qing; Zhang, Yu-Shan; Ji, Chang-Ming; Wang, Ai-Jing; Lund, Jay R
2013-01-01
This paper examines long-term optimal operation using dynamic programming for a large hydropower system of 10 reservoirs in Northeast China. Besides considering flow and hydraulic head, the optimization explicitly includes time-varying electricity market prices to maximize benefit. Two techniques are used to reduce the 'curse of dimensionality' of dynamic programming with many reservoirs. Discrete differential dynamic programming (DDDP) reduces the search space and computer memory needed. Object-oriented programming (OOP) and the ability to dynamically allocate and release memory with the C++ language greatly reduces the cumulative effect of computer memory for solving multi-dimensional dynamic programming models. The case study shows that the model can reduce the 'curse of dimensionality' and achieve satisfactory results.
International Nuclear Information System (INIS)
Cheng Chuntian; Liao Shengli; Tang Zitian; Zhao Mingyan
2009-01-01
Dynamic programming (DP) is one of classic and sophisticated optimization methods that have successfully been applied to solve the problem of hydro unit load dispatch (HULD). However, DP will be faced with the curse of dimensionality with the increase of unit number and installed generating capacity of hydropower station. With the appearance of the huge hydropower station similar to the Three George with 26 generators of 700 MW, it is hard to apply the DP to large scale HULD problem. It is crucial to seek for other optimization techniques in order to improve the operation quality and efficiency. Different with the most of literature about power generation scheduling that focused on the comparisons of novel PSO algorithms with other techniques, the paper will pay emphasis on comparison study of PSO with DP based on a case hydropower station. The objective of study is to seek for an effective and feasible method for the large scale of hydropower station of the current and future in China. This paper first compares the performance of PSO and DP using a sample load curve of the Wujiangdu hydropower plant located in the upper stream of the Yangtze River in China and contained five units with the installed capacity of 1250 MW. Next, the effect of different load interval and unit number on the optimal results and efficiency of two methods has also been implemented. The comparison results show that the PSO is feasible for HULD. Furthermore, we simulated the effect of the magnitude of unit number and load capacity on the optimal results and cost time. The simulation comparisons show that PSO has a great advantage over DP in the efficiency and will be one of effective methods for HULD problem of huge hydropower stations.
Directory of Open Access Journals (Sweden)
Mariana Marselina
2016-08-01
Full Text Available The increasingly growth of population and industry sector have lead to an enhanced demand for electrical energy. One of the electricity providers in the area of Java-Madura Bali (Jamali is Saguling Reservoir. Saguling Reservoir is one of the three reservoirs that stem the flow of Citarum River in advance of to Jatiluhur and Cirata Reservoir. The average electricity production of Saguling Reservoir was 2,334,318.138 MWh/year in the period of 1986-2014. The water intake of Saguling Reservoir is the upstream Citarum Watershed with an area of 2340.88 km2 which also serves as the irrigation, inland fisheries, recreation, and other activities. An effort to improve the function of Saguling Reservoir in producing electrical energy is by optimizing the reservoir management. The optimization of Saguling Reservoir management in this study refers to Government Regulation No. 37/2010 on Dam/Reservoir Article 44 which states that the system of reservoir management consisting of the operation system in dry years, normal years, and wet years. In this research, the determination of the trajectory guideline in Saguling operation was divided in dry, normal and wet years. Trajectory guideline was conducted based on the electricity price of turbine inflow that various in every month. The determination of the trajectory guideline in various electricity price was done by using Program Dynamic Bellman (PD Bellman and “Du Couloir” iterative method which the objective to optimize the gain from electricity production. and “Du Couloir” iterative method was development of PD Bellman that can calculate the value of gain with a smaller discretization until 0,1 juta m3 effectively where PD Bellman just calculate until 10 million m3. Smaller discretization can give maximum benefit from electricity production and the trajectory guideline will be closer to trajectory actual so optimization of Saguling operation will be achieved.
Energy Technology Data Exchange (ETDEWEB)
Cheng Chuntian, E-mail: ctcheng@dlut.edu.c [Department of Civil and Hydraulic Engineering, Dalian University of Technology, 116024 Dalian (China); Liao Shengli; Tang Zitian [Department of Civil and Hydraulic Engineering, Dalian University of Technology, 116024 Dalian (China); Zhao Mingyan [Department of Environmental Science and Engineering, Tsinghua University, 100084 Beijing (China)
2009-12-15
Dynamic programming (DP) is one of classic and sophisticated optimization methods that have successfully been applied to solve the problem of hydro unit load dispatch (HULD). However, DP will be faced with the curse of dimensionality with the increase of unit number and installed generating capacity of hydropower station. With the appearance of the huge hydropower station similar to the Three George with 26 generators of 700 MW, it is hard to apply the DP to large scale HULD problem. It is crucial to seek for other optimization techniques in order to improve the operation quality and efficiency. Different with the most of literature about power generation scheduling that focused on the comparisons of novel PSO algorithms with other techniques, the paper will pay emphasis on comparison study of PSO with DP based on a case hydropower station. The objective of study is to seek for an effective and feasible method for the large scale of hydropower station of the current and future in China. This paper first compares the performance of PSO and DP using a sample load curve of the Wujiangdu hydropower plant located in the upper stream of the Yangtze River in China and contained five units with the installed capacity of 1250 MW. Next, the effect of different load interval and unit number on the optimal results and efficiency of two methods has also been implemented. The comparison results show that the PSO is feasible for HULD. Furthermore, we simulated the effect of the magnitude of unit number and load capacity on the optimal results and cost time. The simulation comparisons show that PSO has a great advantage over DP in the efficiency and will be one of effective methods for HULD problem of huge hydropower stations.
Energy Technology Data Exchange (ETDEWEB)
Chun-tian Cheng; Sheng-li Liao; Zi-Tian Tang [Dept. of Civil and Hydraulic Engineering, Dalian Univ. of Technology, 116024 Dalian (China); Ming-yan Zhao [Dept. of Environmental Science and Engineering, Tsinghua Univ., 100084 Beijing (China)
2009-12-15
Dynamic programming (DP) is one of classic and sophisticated optimization methods that have successfully been applied to solve the problem of hydro unit load dispatch (HULD). However, DP will be faced with the curse of dimensionality with the increase of unit number and installed generating capacity of hydropower station. With the appearance of the huge hydropower station similar to the Three George with 26 generators of 700 MW, it is hard to apply the DP to large scale HULD problem. It is crucial to seek for other optimization techniques in order to improve the operation quality and efficiency. Different with the most of literature about power generation scheduling that focused on the comparisons of novel PSO algorithms with other techniques, the paper will pay emphasis on comparison study of PSO with DP based on a case hydropower station. The objective of study is to seek for an effective and feasible method for the large scale of hydropower station of the current and future in China. This paper first compares the performance of PSO and DP using a sample load curve of the Wujiangdu hydropower plant located in the upper stream of the Yangtze River in China and contained five units with the installed capacity of 1250 MW. Next, the effect of different load interval and unit number on the optimal results and efficiency of two methods has also been implemented. The comparison results show that the PSO is feasible for HULD. Furthermore, we simulated the effect of the magnitude of unit number and load capacity on the optimal results and cost time. The simulation comparisons show that PSO has a great advantage over DP in the efficiency and will be one of effective methods for HULD problem of huge hydropower stations. (author)
Pradanti, Paskalia; Hartono
2018-03-01
Determination of insulin injection dose in diabetes mellitus treatment can be considered as an optimal control problem. This article is aimed to simulate optimal blood glucose control for patient with diabetes mellitus. The blood glucose regulation of diabetic patient is represented by Ackerman’s Linear Model. This problem is then solved using dynamic programming method. The desired blood glucose level is obtained by minimizing the performance index in Lagrange form. The results show that dynamic programming based on Ackerman’s Linear Model is quite good to solve the problem.
Optimization by record dynamics
DEFF Research Database (Denmark)
Barettin, Daniele; Sibani, Paolo
2014-01-01
Large dynamical changes in thermalizing glassy systems are triggered by trajectories crossing record sized barriers, a behavior revealing the presence of a hierarchical structure in configuration space. The observation is here turned into a novel local search optimization algorithm dubbed record...... dynamics optimization,or RDO. RDO uses the Metropolis rule to accept or reject candidate solutions depending on the value of a parameter akin to the temperature and minimizes the cost function of the problem at hand through cycles where its ‘temperature’ is raised and subsequently decreased in order......), is applied to the same problem as a benchmark. RDO and PT turn out to produce solutions of similar quality for similar numerical effort, but RDO is simpler to program and additionally yields geometrical information on the system’s configuration space which is of interest in many applications. In particular...
Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems.
Wei, Qinglai; Liu, Derong; Lin, Hanquan
2016-03-01
In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
Integer 1/0 Knapsack Problem Dynamic Programming Approach in Building Maintenance Optimization
Directory of Open Access Journals (Sweden)
Viska Dewi Fawzy
2017-12-01
Full Text Available The most common problem in urban areas is the high public demand and the limited provision of housing. In meeting the needs of affordable housing for low income communities, the Government of Indonesia implements Rusunawa Project. Object of this research is Pandanarang Rusunawa. Rusunawa Pandanarang is one of the vertical housing in Cilacap that is facing deterioration issue and needs good maintenance management. This study aims at insetting priority and optimizing maintenance plan due to limited funds (limited budget and the amount of damage that must be repaired.This study uses one of the optimization methods of Dynamic Programing on the application of Integer 1/0 Knapsack Problem, to determine an schedule the maintenance activities. The Criteria that are used such as: the level of building components damage and the level of occupants participation. In the first criterion, the benefit (p is the percentage of damage that is fixed with the cost (w. While on the second criterion, the benefit (p is the percentage of occupant participation rate on the maintenance activities with the cost (w. For the budget of Rp 125.000.000, 00, it was obtained from the simulation that the value of the optimum solution on the first criterion at the 7th stage of 71.88% with total cost Rp 106.000.000, 00. At the second criterion, the value of the optimum solution at the 7th stage of 89.29% with total cost Rp 124.000.000, 00.
Dual Dynamic Programming - DDP
International Nuclear Information System (INIS)
Velasquez Bermudez, Jesus M
1998-01-01
Objections are presented to the mathematical formulation of the denominated Dual Dynamic programming-PDD that is the theoretical base of several computational model available for the optimal formulation of interconnected hydrothermal systems
AbouEisha, Hassan M.
2017-07-13
We consider a class of two-and three-dimensional h-refined meshes generated by an adaptive finite element method. We introduce an element partition tree, which controls the execution of the multi-frontal solver algorithm over these refined grids. We propose and study algorithms with polynomial computational cost for the optimization of these element partition trees. The trees provide an ordering for the elimination of unknowns. The algorithms automatically optimize the element partition trees using extensions of dynamic programming. The construction of the trees by the dynamic programming approach is expensive. These generated trees cannot be used in practice, but rather utilized as a learning tool to propose fast heuristic algorithms. In this first part of our paper we focus on the dynamic programming approach, and draw a sketch of the heuristic algorithm. The second part will be devoted to a more detailed analysis of the heuristic algorithm extended for the case of hp-adaptive
AbouEisha, Hassan M.; Calo, Victor Manuel; Jopek, Konrad; Moshkov, Mikhail; Paszyńka, Anna; Paszyński, Maciej; Skotniczny, Marcin
2017-01-01
We consider a class of two-and three-dimensional h-refined meshes generated by an adaptive finite element method. We introduce an element partition tree, which controls the execution of the multi-frontal solver algorithm over these refined grids. We propose and study algorithms with polynomial computational cost for the optimization of these element partition trees. The trees provide an ordering for the elimination of unknowns. The algorithms automatically optimize the element partition trees using extensions of dynamic programming. The construction of the trees by the dynamic programming approach is expensive. These generated trees cannot be used in practice, but rather utilized as a learning tool to propose fast heuristic algorithms. In this first part of our paper we focus on the dynamic programming approach, and draw a sketch of the heuristic algorithm. The second part will be devoted to a more detailed analysis of the heuristic algorithm extended for the case of hp-adaptive
Introduction to dynamic programming
Cooper, Leon; Rodin, E Y
1981-01-01
Introduction to Dynamic Programming provides information pertinent to the fundamental aspects of dynamic programming. This book considers problems that can be quantitatively formulated and deals with mathematical models of situations or phenomena that exists in the real world.Organized into 10 chapters, this book begins with an overview of the fundamental components of any mathematical optimization model. This text then presents the details of the application of dynamic programming to variational problems. Other chapters consider the application of dynamic programming to inventory theory, Mark
DEFF Research Database (Denmark)
Davidsen, Claus; Liu, Suxia; Mo, Xinguo
2014-01-01
. A stochastic dynamic programming (SDP) approach is used to minimize the basin-wide total costs arising from water allocations and water curtailments. Dynamic allocation problems with inclusion of groundwater resources proved to be more complex to solve with SDP than pure surface water allocation problems due...... to head-dependent pumping costs. These dynamic pumping costs strongly affect the total costs and can lead to non-convexity of the future cost function. The water user groups (agriculture, industry, domestic) are characterized by inelastic demands and fixed water allocation and water supply curtailment...
DEFF Research Database (Denmark)
Haahr, Jørgen Thorlund; Pisinger, David; Sabbaghian, Mohammad
2017-01-01
This paper considers a novel solution method for generating improved train speed profiles with reduced energy consumption. The solution method makes use of a time-space graph formulation which can be solved through Dynamic Programming. Instead of using uniform discretization of time and space...
Raso , L.; Malaterre , P.O.; Bader , J.C.
2017-01-01
International audience; This article presents an innovative streamflow process model for use in reservoir operational rule design in stochastic dual dynamic programming (SDDP). Model features, which can be applied independently, are (1) a multiplicative process model for the forward phase and its linearized version for the backward phase; and (2) a nonuniform time-step length that is inversely proportional to seasonal variability. The advantages are (1) guaranteeing positive streamflow values...
International Nuclear Information System (INIS)
Li, Chunlong; Zhou, Jianzhong; Ouyang, Shuo; Ding, Xiaoling; Chen, Lu
2014-01-01
Highlights: • Optimization of large-scale hydropower system in the Yangtze River basin. • Improved decomposition–coordination and discrete differential dynamic programming. • Generating initial solution randomly to reduce generation time. • Proposing relative coefficient for more power generation. • Proposing adaptive bias corridor technology to enhance convergence speed. - Abstract: With the construction of major hydro plants, more and more large-scale hydropower systems are taking shape gradually, which brings up a challenge to optimize these systems. Optimization of large-scale hydropower system (OLHS), which is to determine water discharges or water levels of overall hydro plants for maximizing total power generation when subjecting to lots of constrains, is a high dimensional, nonlinear and coupling complex problem. In order to solve the OLHS problem effectively, an improved decomposition–coordination and discrete differential dynamic programming (IDC–DDDP) method is proposed in this paper. A strategy that initial solution is generated randomly is adopted to reduce generation time. Meanwhile, a relative coefficient based on maximum output capacity is proposed for more power generation. Moreover, an adaptive bias corridor technology is proposed to enhance convergence speed. The proposed method is applied to long-term optimal dispatches of large-scale hydropower system (LHS) in the Yangtze River basin. Compared to other methods, IDC–DDDP has competitive performances in not only total power generation but also convergence speed, which provides a new method to solve the OLHS problem
AbouEisha, Hassan M.
2014-06-06
In this paper we present a dynamic programming algorithm for finding optimal elimination trees for computational grids refined towards point or edge singularities. The elimination tree is utilized to guide the multi-frontal direct solver algorithm. Thus, the criterion for the optimization of the elimination tree is the computational cost associated with the multi-frontal solver algorithm executed over such tree. We illustrate the paper with several examples of optimal trees found for grids with point, isotropic edge and anisotropic edge mixed with point singularity. We show the comparison of the execution time of the multi-frontal solver algorithm with results of MUMPS solver with METIS library, implementing the nested dissection algorithm.
DEFF Research Database (Denmark)
Hu, Rui; Hu, Weihao; Li, Pengfei
2016-01-01
and relatively low cost. Thus, many countries are increasing the wind power penetration in their power system step by step, such as Denmark, Spain and Germany. The incremental wind power penetration brings a lot of new issues in operation and programming. The power system sometimes will operate close to its...... stable limits. Once the blackout happens, a well-designed restoration strategy is significant. This paper focuses on how to ameliorate the power system restoration procedures to adapt the high wind power penetration and how to take full advantages of the wind power plants during the restoration....... In this paper, the possibility to exploit the stochastic wind power during restoration was discussed, and a Dynamic Programming (DP) method was proposed to make wind power contribute in the restoration rationally as far as possible. In this paper, the method is tested and verified by a modified IEEE 30 Buses...
Amin, Talha
2013-01-01
In the paper, we present a comparison of dynamic programming and greedy approaches for construction and optimization of approximate decision rules relative to the number of misclassifications. We use an uncertainty measure that is a difference between the number of rows in a decision table T and the number of rows with the most common decision for T. For a nonnegative real number γ, we consider γ-decision rules that localize rows in subtables of T with uncertainty at most γ. Experimental results with decision tables from the UCI Machine Learning Repository are also presented. © 2013 Springer-Verlag.
Bhattacharjya, D.; Mukerji, T.; Mascarenhas, O.; Weyant, J.
2005-12-01
Designing a cost-effective and reliable monitoring program is crucial to the success of any geological CO2 storage project. Effective design entails determining both, the optimal measurement modality, as well as the frequency of monitoring the site. Time-lapse seismic provides the best spatial coverage and resolution for reservoir monitoring. Initial results from Sleipner (Norway) have demonstrated effective monitoring of CO2 plume movement. However, time-lapse seismic is an expensive monitoring technique especially over the long term life of a storage project and should be used judiciously. We present a mathematical model based on dynamic programming that can be used to estimate site-specific optimal frequency of time-lapse surveys. The dynamics of the CO2 sequestration process are simplified and modeled as a four state Markov process with transition probabilities. The states are M: injected CO2 safely migrating within the target zone; L: leakage from the target zone to the adjacent geosphere; R: safe migration after recovery from leakage state; and S: seepage from geosphere to the biosphere. The states are observed only when a monitoring survey is performed. We assume that the system may go to state S only from state L. We also assume that once observed to be in state L, remedial measures are always taken to bring it back to state R. Remediation benefits are captured by calculating the expected penalty if CO2 seeped into the biosphere. There is a trade-off between the conflicting objectives of minimum discounted costs of performing the next time-lapse survey and minimum risk of seepage and its associated costly consequences. A survey performed earlier would spot the leakage earlier. Remediation methods would have been utilized earlier, resulting in savings in costs attributed to excessive seepage. On the other hand, there are also costs for the survey and remedial measures. The problem is solved numerically using Bellman's optimality principal of dynamic
Bellman, Richard
2003-01-01
An introduction to the mathematical theory of multistage decision processes, this text takes a ""functional equation"" approach to the discovery of optimum policies. Written by a leading developer of such policies, it presents a series of methods, uniqueness and existence theorems, and examples for solving the relevant equations. The text examines existence and uniqueness theorems, the optimal inventory equation, bottleneck problems in multistage production processes, a new formalism in the calculus of variation, strategies behind multistage games, and Markovian decision processes. Each chapte
National Research Council Canada - National Science Library
Khoo, Wai
1999-01-01
.... These problems model stochastic portfolio optimization problems (SPOPs) which assume deterministic unit weight, and normally distributed unit return with known mean and variance for each item type...
Optimal Quadratic Programming Algorithms
Dostal, Zdenek
2009-01-01
Quadratic programming (QP) is one technique that allows for the optimization of a quadratic function in several variables in the presence of linear constraints. This title presents various algorithms for solving large QP problems. It is suitable as an introductory text on quadratic programming for graduate students and researchers
Dynamic Programming Foundations and Principles
Sniedovich, Moshe
2010-01-01
Focusing on the modeling and solution of deterministic multistage decision problems, this book looks at dynamic programming as a problem-solving optimization method. With over 400 useful references, this edition discusses the dynamic programming analysis of a problem, illustrates the rationale behind this analysis, and clarifies the theoretical grounds that justify the rationale. It also explains the meaning and role of the concept of state in dynamic programming, examines the purpose and function of the principle of optimality, and outlines solution strategies for problems defiant of conventi
Stochastic Optimization of Economic Dispatch for Microgrid Based on Approximate Dynamic Programming
DEFF Research Database (Denmark)
Shuai, Hang; Fang, Jiakun; Ai, Xiaomeng
2018-01-01
slope updating strategy is employed for the proposed method. With sufficient information extracted from these scenarios and embedded in the PLF function, the proposed ADPED algorithm can not only be used in day-ahead scheduling but also the intra-day optimization process. The algorithm can make full use......-ahead and intra-day operation under uncertainty....
Optimal City Hierarchy: A Dynamic Programming Approach to Central Place Theory
Wen-Tai Hsu; Thomas J. Holmes
2009-01-01
It is an empirically plausible description of cities and is route to explain empirical regularities in city size distribution and industrial locations. This paper formalizes central place hierarchy by providing a rationale for it via a social planner's problem. The optimal city hierarchy is then compared with the equilibrium city hierarchy in Hsu (2008).
Stochastic integer programming by dynamic programming
Lageweg, B.J.; Lenstra, J.K.; Rinnooy Kan, A.H.G.; Stougie, L.; Ermoliev, Yu.; Wets, R.J.B.
1988-01-01
Stochastic integer programming is a suitable tool for modeling hierarchical decision situations with combinatorial features. In continuation of our work on the design and analysis of heuristics for such problems, we now try to find optimal solutions. Dynamic programming techniques can be used to
Extensions of dynamic programming as a new tool for decision tree optimization
Alkhalid, Abdulaziz
2013-01-01
The chapter is devoted to the consideration of two types of decision trees for a given decision table: α-decision trees (the parameter α controls the accuracy of tree) and decision trees (which allow arbitrary level of accuracy). We study possibilities of sequential optimization of α-decision trees relative to different cost functions such as depth, average depth, and number of nodes. For decision trees, we analyze relationships between depth and number of misclassifications. We also discuss results of computer experiments with some datasets from UCI ML Repository. ©Springer-Verlag Berlin Heidelberg 2013.
Introduction to stochastic dynamic programming
Ross, Sheldon M; Lukacs, E
1983-01-01
Introduction to Stochastic Dynamic Programming presents the basic theory and examines the scope of applications of stochastic dynamic programming. The book begins with a chapter on various finite-stage models, illustrating the wide range of applications of stochastic dynamic programming. Subsequent chapters study infinite-stage models: discounting future returns, minimizing nonnegative costs, maximizing nonnegative returns, and maximizing the long-run average return. Each of these chapters first considers whether an optimal policy need exist-providing counterexamples where appropriate-and the
Optimization with Extremal Dynamics
International Nuclear Information System (INIS)
Boettcher, Stefan; Percus, Allon G.
2001-01-01
We explore a new general-purpose heuristic for finding high-quality solutions to hard discrete optimization problems. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. Extremal optimization successively updates extremely undesirable variables of a single suboptimal solution, assigning them new, random values. Large fluctuations ensue, efficiently exploring many local optima. We use extremal optimization to elucidate the phase transition in the 3-coloring problem, and we provide independent confirmation of previously reported extrapolations for the ground-state energy of ±J spin glasses in d=3 and 4
Dynamic stochastic optimization
Ermoliev, Yuri; Pflug, Georg
2004-01-01
Uncertainties and changes are pervasive characteristics of modern systems involving interactions between humans, economics, nature and technology. These systems are often too complex to allow for precise evaluations and, as a result, the lack of proper management (control) may create significant risks. In order to develop robust strategies we need approaches which explic itly deal with uncertainties, risks and changing conditions. One rather general approach is to characterize (explicitly or implicitly) uncertainties by objec tive or subjective probabilities (measures of confidence or belief). This leads us to stochastic optimization problems which can rarely be solved by using the standard deterministic optimization and optimal control methods. In the stochastic optimization the accent is on problems with a large number of deci sion and random variables, and consequently the focus ofattention is directed to efficient solution procedures rather than to (analytical) closed-form solu tions. Objective an...
Dynamic optimization and differential games
Friesz, Terry L
2010-01-01
Dynamic Optimization and Differential Games has been written to address the increasing number of Operations Research and Management Science problems that involve the explicit consideration of time and of gaming among multiple agents. With end-of-chapter exercises throughout, it is a book that can be used both as a reference and as a textbook. It will be useful as a guide to engineers, operations researchers, applied mathematicians and social scientists whose work involves both the theoretical and computational aspects of dynamic optimization and differential games. Included throughout the text are detailed explanations of several original dynamic and game-theoretic mathematical models which are of particular relevance in today's technologically-driven-global economy: revenue management, oligopoly pricing, production planning, supply chain management, dynamic traffic assignment and dynamic congestion pricing. The book emphasizes deterministic theory, computational tools and applications associated with the stu...
Optimization of multi-response dynamic systems integrating multiple ...
African Journals Online (AJOL)
regression and Taguchi's dynamic signal-to-noise ratio concept ..... algorithm for dynamic multi-response optimization based on goal programming approach. .... problem-solving confirmation, if no grave infringement of model suppositions is ...
Dynamic optimization deterministic and stochastic models
Hinderer, Karl; Stieglitz, Michael
2016-01-01
This book explores discrete-time dynamic optimization and provides a detailed introduction to both deterministic and stochastic models. Covering problems with finite and infinite horizon, as well as Markov renewal programs, Bayesian control models and partially observable processes, the book focuses on the precise modelling of applications in a variety of areas, including operations research, computer science, mathematics, statistics, engineering, economics and finance. Dynamic Optimization is a carefully presented textbook which starts with discrete-time deterministic dynamic optimization problems, providing readers with the tools for sequential decision-making, before proceeding to the more complicated stochastic models. The authors present complete and simple proofs and illustrate the main results with numerous examples and exercises (without solutions). With relevant material covered in four appendices, this book is completely self-contained.
Optimal lag in dynamical investments
Serva, M.
1998-01-01
A portfolio of different stocks and a risk-less security whose composition is dynamically maintained stable by trading shares at any time step leads to a growth of the capital with a nonrandom rate. This is the key for the theory of optimal-growth investment formulated by Kelly. In presence of transaction costs, the optimal composition changes and, more important, it turns out that the frequency of transactions must be reduced. This simple observation leads to the definition of an optimal lag...
Optimal dynamic detection of explosives
Energy Technology Data Exchange (ETDEWEB)
Moore, David Steven [Los Alamos National Laboratory; Mcgrane, Shawn D [Los Alamos National Laboratory; Greenfield, Margo T [Los Alamos National Laboratory; Scharff, R J [Los Alamos National Laboratory; Rabitz, Herschel A [PRINCETON UNIV; Roslund, J [PRINCETON UNIV
2009-01-01
The detection of explosives is a notoriously difficult problem, especially at stand-off distances, due to their (generally) low vapor pressure, environmental and matrix interferences, and packaging. We are exploring optimal dynamic detection to exploit the best capabilities of recent advances in laser technology and recent discoveries in optimal shaping of laser pulses for control of molecular processes to significantly enhance the standoff detection of explosives. The core of the ODD-Ex technique is the introduction of optimally shaped laser pulses to simultaneously enhance sensitivity of explosives signatures while reducing the influence of noise and the signals from background interferents in the field (increase selectivity). These goals are being addressed by operating in an optimal nonlinear fashion, typically with a single shaped laser pulse inherently containing within it coherently locked control and probe sub-pulses. With sufficient bandwidth, the technique is capable of intrinsically providing orthogonal broad spectral information for data fusion, all from a single optimal pulse.
Directory of Open Access Journals (Sweden)
Renxin Xiao
2018-01-01
Full Text Available This paper proposes a comparison study of energy management methods for a parallel plug-in hybrid electric vehicle (PHEV. Based on detailed analysis of the vehicle driveline, quadratic convex functions are presented to describe the nonlinear relationship between engine fuel-rate and battery charging power at different vehicle speed and driveline power demand. The engine-on power threshold is estimated by the simulated annealing (SA algorithm, and the battery power command is achieved by convex optimization with target of improving fuel economy, compared with the dynamic programming (DP based method and the charging depleting–charging sustaining (CD/CS method. In addition, the proposed control methods are discussed at different initial battery state of charge (SOC values to extend the application. Simulation results validate that the proposed strategy based on convex optimization can save the fuel consumption and reduce the computation burden obviously.
Luy, N. T.
2018-04-01
The design of distributed cooperative H∞ optimal controllers for multi-agent systems is a major challenge when the agents' models are uncertain multi-input and multi-output nonlinear systems in strict-feedback form in the presence of external disturbances. In this paper, first, the distributed cooperative H∞ optimal tracking problem is transformed into controlling the cooperative tracking error dynamics in affine form. Second, control schemes and online algorithms are proposed via adaptive dynamic programming (ADP) and the theory of zero-sum differential graphical games. The schemes use only one neural network (NN) for each agent instead of three from ADP to reduce computational complexity as well as avoid choosing initial NN weights for stabilising controllers. It is shown that despite not using knowledge of cooperative internal dynamics, the proposed algorithms not only approximate values to Nash equilibrium but also guarantee all signals, such as the NN weight approximation errors and the cooperative tracking errors in the closed-loop system, to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is shown by simulation results of an application to wheeled mobile multi-robot systems.
Directory of Open Access Journals (Sweden)
Hongwen He
2013-01-01
Full Text Available Energy management strategy influences the power performance and fuel economy of plug-in hybrid electric vehicles greatly. To explore the fuel-saving potential of a plug-in hybrid electric bus (PHEB, this paper searched the global optimal energy management strategy using dynamic programming (DP algorithm. Firstly, the simplified backward model of the PHEB was built which is necessary for DP algorithm. Then the torque and speed of engine and the torque of motor were selected as the control variables, and the battery state of charge (SOC was selected as the state variables. The DP solution procedure was listed, and the way was presented to find all possible control variables at every state of each stage in detail. Finally, the appropriate SOC increment is determined after quantizing the state variables, and then the optimal control of long driving distance of a specific driving cycle is replaced with the optimal control of one driving cycle, which reduces the computational time significantly and keeps the precision at the same time. The simulation results show that the fuel economy of the PEHB with the optimal energy management strategy is improved by 53.7% compared with that of the conventional bus, which can be a benchmark for the assessment of other control strategies.
Dynamic optimization in environmental economics
International Nuclear Information System (INIS)
Moser, Elke; Tragler, Gernot; Veliov, Vladimir M.; Semmler, Willi
2014-01-01
This book contains two chapters with the topics: 1. Chapter: INTERACTIONS BETWEEN ECONOMY AND CLIMATE: (a) Climate Change and Technical Progress: Impact of Informational Constraints. (b) Environmental Policy in a Dynamic Model with Heterogeneous Agents and Voting. (c) Optimal Environmental Policy in the Presence of Multiple Equilibria and Reversible Hysteresis. (d). Modeling the Dynamics of the Transition to a Green Economy. (e) One-Parameter GHG Emission Policy With R and D-Based Growth. (f) Pollution, Public Health Care, and Life Expectancy when Inequality Matters. (g) Uncertain Climate Policy and the Green Paradox. (h) Uniqueness Versus Indeterminacy in the Tragedy of the Commons - A ''Geometric'' Approach. 2. Chapter: OPTIMAL EXTRACTION OF RESOURCES: (j) Dynamic Behavior of Oil Importers and Exporters Under Uncertainty. (k) Robust Control of a Spatially Distributed Commercial Fishery. (l) On the Effect of Resource Exploitation on Growth: Domestic Innovation vs. Technological Diffusion Through Trade. (m) Forest Management and Biodiversity in Size-Structured Forests Under Climate Change. (n) Carbon Taxes and Comparison of Trading Regimes in Fossil Fuels. (o) Landowning, Status and Population Growth. (p) Optimal Harvesting of Size-Structured Biological Populations.
Dynamic optimization in environmental economics
Energy Technology Data Exchange (ETDEWEB)
Moser, Elke; Tragler, Gernot; Veliov, Vladimir M. (eds.) [Vienna Univ. of Technology (Austria). Inst. of Mathematical Methods in Economics; Semmler, Willi [The New School for Social Research, New York, NY (United States). Dept. of Economics
2014-11-01
This book contains two chapters with the topics: 1. Chapter: INTERACTIONS BETWEEN ECONOMY AND CLIMATE: (a) Climate Change and Technical Progress: Impact of Informational Constraints. (b) Environmental Policy in a Dynamic Model with Heterogeneous Agents and Voting. (c) Optimal Environmental Policy in the Presence of Multiple Equilibria and Reversible Hysteresis. (d). Modeling the Dynamics of the Transition to a Green Economy. (e) One-Parameter GHG Emission Policy With R and D-Based Growth. (f) Pollution, Public Health Care, and Life Expectancy when Inequality Matters. (g) Uncertain Climate Policy and the Green Paradox. (h) Uniqueness Versus Indeterminacy in the Tragedy of the Commons - A ''Geometric'' Approach. 2. Chapter: OPTIMAL EXTRACTION OF RESOURCES: (j) Dynamic Behavior of Oil Importers and Exporters Under Uncertainty. (k) Robust Control of a Spatially Distributed Commercial Fishery. (l) On the Effect of Resource Exploitation on Growth: Domestic Innovation vs. Technological Diffusion Through Trade. (m) Forest Management and Biodiversity in Size-Structured Forests Under Climate Change. (n) Carbon Taxes and Comparison of Trading Regimes in Fossil Fuels. (o) Landowning, Status and Population Growth. (p) Optimal Harvesting of Size-Structured Biological Populations.
Optimal Implantable Cardioverter Defibrillator Programming.
Shah, Bindi K
Optimal programming of implantable cardioverter defibrillators (ICDs) is essential to appropriately treat ventricular tachyarrhythmias and to avoid unnecessary and inappropriate shocks. There have been a series of large clinical trials evaluating tailored programming of ICDs. We reviewed the clinical trials evaluating ICD therapies and detection, and the consensus statement on ICD programming. In doing so, we found that prolonged ICD detection times, higher rate cutoffs, and antitachycardia pacing (ATP) programming decreases inappropriate and painful therapies in a primary prevention population. The use of supraventricular tachyarrhythmia discriminators can also decrease inappropriate shocks. Tailored ICD programming using the knowledge gained from recent ICD trials can decrease inappropriate and unnecessary ICD therapies and decrease mortality.
Optimal decisions principles of programming
Lange, Oskar
1971-01-01
Optimal Decisions: Principles of Programming deals with all important problems related to programming.This book provides a general interpretation of the theory of programming based on the application of the Lagrange multipliers, followed by a presentation of the marginal and linear programming as special cases of this general theory. The praxeological interpretation of the method of Lagrange multipliers is also discussed.This text covers the Koopmans' model of transportation, geometric interpretation of the programming problem, and nature of activity analysis. The solution of t
Optimization of biotechnological systems through geometric programming
Directory of Open Access Journals (Sweden)
Torres Nestor V
2007-09-01
Full Text Available Abstract Background In the past, tasks of model based yield optimization in metabolic engineering were either approached with stoichiometric models or with structured nonlinear models such as S-systems or linear-logarithmic representations. These models stand out among most others, because they allow the optimization task to be converted into a linear program, for which efficient solution methods are widely available. For pathway models not in one of these formats, an Indirect Optimization Method (IOM was developed where the original model is sequentially represented as an S-system model, optimized in this format with linear programming methods, reinterpreted in the initial model form, and further optimized as necessary. Results A new method is proposed for this task. We show here that the model format of a Generalized Mass Action (GMA system may be optimized very efficiently with techniques of geometric programming. We briefly review the basics of GMA systems and of geometric programming, demonstrate how the latter may be applied to the former, and illustrate the combined method with a didactic problem and two examples based on models of real systems. The first is a relatively small yet representative model of the anaerobic fermentation pathway in S. cerevisiae, while the second describes the dynamics of the tryptophan operon in E. coli. Both models have previously been used for benchmarking purposes, thus facilitating comparisons with the proposed new method. In these comparisons, the geometric programming method was found to be equal or better than the earlier methods in terms of successful identification of optima and efficiency. Conclusion GMA systems are of importance, because they contain stoichiometric, mass action and S-systems as special cases, along with many other models. Furthermore, it was previously shown that algebraic equivalence transformations of variables are sufficient to convert virtually any types of dynamical models into
Bender, Christian; Gärtner, Christian; Schweizer, Nikolaus
2017-01-01
We present a novel method for deriving tight Monte Carlo confidence intervals for solutions of stochastic dynamic programming equations. Taking some approximate solution to the equation as an input, we construct pathwise recursions with a known bias. Suitably coupling the recursions for lower and
van Hoorn, Jelke J.; Nogueira, Agustín; Ojea, Ignacio; Gromicho Dos Santos, Joaquim Antonio
2017-01-01
In [1] an algorithm is proposed for solving the job-shop scheduling problem optimally using a dynamic programming strategy. This is, according to our knowledge, the first exact algorithm for the Job Shop problem which is not based on integer linear programming and branch and bound. Despite the
Dynamic Optimization of UV Flash Processes
DEFF Research Database (Denmark)
Ritschel, Tobias Kasper Skovborg; Capolei, Andrea; Jørgensen, John Bagterp
2017-01-01
UV ash processes, also referred to as isoenergetic-isochoric ash processes, occur for dynamic simulation and optimization of vapor-liquid equilibrium processes. Dynamic optimization and nonlinear model predictive control of distillation columns, certain two-phase ow problems, as well as oil reser...... that the optimization solver, the compiler, and high-performance linear algebra software are all important for e_cient dynamic optimization of UV ash processes....
Chiavico, Mattia; Raso, Luciano; Dorchies, David; Malaterre, Pierre-Olivier
2015-04-01
Seine river region is an extremely important logistic and economic junction for France and Europe. The hydraulic protection of most part of the region relies on four controlled reservoirs, managed by EPTB Seine-Grands Lacs. Presently, reservoirs operation is not centrally coordinated, and release rules are based on empirical filling curves. In this study, we analyze how a centralized release policy can face flood and drought risks, optimizing water system efficiency. The optimal and centralized decisional problem is solved by Stochastic Dual Dynamic Programming (SDDP) method, minimizing an operational indicator for each planning objective. SDDP allows us to include into the system: 1) the hydrological discharge, specifically a stochastic semi-distributed auto-regressive model, 2) the hydraulic transfer model, represented by a linear lag and route model, and 3) reservoirs and diversions. The novelty of this study lies on the combination of reservoir and hydraulic models in SDDP for flood and drought protection problems. The study case covers the Seine basin until the confluence with Aube River: this system includes two reservoirs, the city of Troyes, and the Nuclear power plant of Nogent-Sur-Seine. The conflict between the interests of flood protection, drought protection, water use and ecology leads to analyze the environmental system in a Multi-Objective perspective.
Programming for Sparse Minimax Optimization
DEFF Research Database (Denmark)
Jonasson, K.; Madsen, Kaj
1994-01-01
We present an algorithm for nonlinear minimax optimization which is well suited for large and sparse problems. The method is based on trust regions and sequential linear programming. On each iteration, a linear minimax problem is solved for a basic step. If necessary, this is followed...... by the determination of a minimum norm corrective step based on a first-order Taylor approximation. No Hessian information needs to be stored. Global convergence is proved. This new method has been extensively tested and compared with other methods, including two well known codes for nonlinear programming...
Design optimization applied in structural dynamics
Akcay-Perdahcioglu, Didem; de Boer, Andries; van der Hoogt, Peter; Tiskarna, T
2007-01-01
This paper introduces the design optimization strategies, especially for structures which have dynamic constraints. Design optimization involves first the modeling and then the optimization of the problem. Utilizing the Finite Element (FE) model of a structure directly in an optimization process
Hybrid Differential Dynamic Programming with Stochastic Search
Aziz, Jonathan; Parker, Jeffrey; Englander, Jacob
2016-01-01
Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASAs Dawn mission. The Dawn trajectory was designed with the DDP-based Static Dynamic Optimal Control algorithm used in the Mystic software. Another recently developed method, Hybrid Differential Dynamic Programming (HDDP) is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.
Constrained Dynamic Optimality and Binomial Terminal Wealth
DEFF Research Database (Denmark)
Pedersen, J. L.; Peskir, G.
2018-01-01
with interest rate $r \\in {R}$). Letting $P_{t,x}$ denote a probability measure under which $X^u$ takes value $x$ at time $t,$ we study the dynamic version of the nonlinear optimal control problem $\\inf_u\\, Var{t,X_t^u}(X_T^u)$ where the infimum is taken over admissible controls $u$ subject to $X_t^u \\ge e...... a martingale method combined with Lagrange multipliers, we derive the dynamically optimal control $u_*^d$ in closed form and prove that the dynamically optimal terminal wealth $X_T^d$ can only take two values $g$ and $\\beta$. This binomial nature of the dynamically optimal strategy stands in sharp contrast...... with other known portfolio selection strategies encountered in the literature. A direct comparison shows that the dynamically optimal (time-consistent) strategy outperforms the statically optimal (time-inconsistent) strategy in the problem....
Energy Technology Data Exchange (ETDEWEB)
Kidoushi, H; Murayama, S; Shiomi, S [Ishikawajima-Harima Heavy Industries Co. Ltd., Tokyo (Japan); Haneda, K; Yamada, Y [Asahi Chemical Industry Co. Ltd., Tokyo (Japan)
1991-05-10
A dynamic programing method was applied for maximizing a production yield in controlling the process of aerobic bacteria fermentation. An optimal control was carried out by air flow rate under a sufficient sugar supply condition, using the bacteria amount as a state variable and the air flow rate as an operation variable, under conditions where the sugar consuming rate is not a limiting factor. The growth and production were modelled, and the relationship of both the specific growth rate and the specific production rate to the specific respiration rate was was expressed as functional tables. A simulation was carried out, which provided an optimum air flow pattern. This relationship between the bacteria amount and the optimum air flow rate was mapped to perform a map control. Conditions other than the air flow pattern were made to agree with the previous empirical cultivation method, and experiments were conducted using a cultivation tank of 0.03 m {sup 3}. A yield increase of 13.6% over the conventional method was attained, thus the reasonability of the modelling was verified. It was found that there are portions where the width of the optimum air flow control is wide and narrow, and it is possible to reduce the number of maps if this this is taken into account. 4 refs., 8 figs., 2 tabs.
Dynamical System Approaches to Combinatorial Optimization
DEFF Research Database (Denmark)
Starke, Jens
2013-01-01
of large times as an asymptotically stable point of the dynamics. The obtained solutions are often not globally optimal but good approximations of it. Dynamical system and neural network approaches are appropriate methods for distributed and parallel processing. Because of the parallelization......Several dynamical system approaches to combinatorial optimization problems are described and compared. These include dynamical systems derived from penalty methods; the approach of Hopfield and Tank; self-organizing maps, that is, Kohonen networks; coupled selection equations; and hybrid methods...... thereof can be used as models for many industrial problems like manufacturing planning and optimization of flexible manufacturing systems. This is illustrated for an example in distributed robotic systems....
Conjugate gradient optimization programs for shuttle reentry
Powers, W. F.; Jacobson, R. A.; Leonard, D. A.
1972-01-01
Two computer programs for shuttle reentry trajectory optimization are listed and described. Both programs use the conjugate gradient method as the optimization procedure. The Phase 1 Program is developed in cartesian coordinates for a rotating spherical earth, and crossrange, downrange, maximum deceleration, total heating, and terminal speed, altitude, and flight path angle are included in the performance index. The programs make extensive use of subroutines so that they may be easily adapted to other atmospheric trajectory optimization problems.
Sensitivity analysis in dynamic optimization
Evers, A.H.
1980-01-01
To find the optimal control of chemical processes, Pontryagin's minimum principle can be used. In practice, however, one is not only interested in the optimal solution, which satisfies the restrictions on the control, the initial and terminal conditions, and the process parameters. It is also
Configuring Airspace Sectors with Approximate Dynamic Programming
Bloem, Michael; Gupta, Pramod
2010-01-01
In response to changing traffic and staffing conditions, supervisors dynamically configure airspace sectors by assigning them to control positions. A finite horizon airspace sector configuration problem models this supervisor decision. The problem is to select an airspace configuration at each time step while considering a workload cost, a reconfiguration cost, and a constraint on the number of control positions at each time step. Three algorithms for this problem are proposed and evaluated: a myopic heuristic, an exact dynamic programming algorithm, and a rollouts approximate dynamic programming algorithm. On problem instances from current operations with only dozens of possible configurations, an exact dynamic programming solution gives the optimal cost value. The rollouts algorithm achieves costs within 2% of optimal for these instances, on average. For larger problem instances that are representative of future operations and have thousands of possible configurations, excessive computation time prohibits the use of exact dynamic programming. On such problem instances, the rollouts algorithm reduces the cost achieved by the heuristic by more than 15% on average with an acceptable computation time.
Joint Chance-Constrained Dynamic Programming
Ono, Masahiro; Kuwata, Yoshiaki; Balaram, J. Bob
2012-01-01
This paper presents a novel dynamic programming algorithm with a joint chance constraint, which explicitly bounds the risk of failure in order to maintain the state within a specified feasible region. A joint chance constraint cannot be handled by existing constrained dynamic programming approaches since their application is limited to constraints in the same form as the cost function, that is, an expectation over a sum of one-stage costs. We overcome this challenge by reformulating the joint chance constraint into a constraint on an expectation over a sum of indicator functions, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the primal variables can be optimized by a standard dynamic programming, while the dual variable is optimized by a root-finding algorithm that converges exponentially. Error bounds on the primal and dual objective values are rigorously derived. We demonstrate the algorithm on a path planning problem, as well as an optimal control problem for Mars entry, descent and landing. The simulations are conducted using a real terrain data of Mars, with four million discrete states at each time step.
Stochastic optimization: beyond mathematical programming
CERN. Geneva
2015-01-01
Stochastic optimization, among which bio-inspired algorithms, is gaining momentum in areas where more classical optimization algorithms fail to deliver satisfactory results, or simply cannot be directly applied. This presentation will introduce baseline stochastic optimization algorithms, and illustrate their efficiency in different domains, from continuous non-convex problems to combinatorial optimization problem, to problems for which a non-parametric formulation can help exploring unforeseen possible solution spaces.
Dynamic programming models and applications
Denardo, Eric V
2003-01-01
Introduction to sequential decision processes covers use of dynamic programming in studying models of resource allocation, methods for approximating solutions of control problems in continuous time, production control, more. 1982 edition.
Dynamic optimization and adaptive controller design
Inamdar, S. R.
2010-10-01
In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.
Energy Technology Data Exchange (ETDEWEB)
Edo, S; Kenmoku, Y; Sakakibara, T [Toyohashi University of Technology, Aichi (Japan); Nakagawa, S [Maizuru College of Technology, Kyoto (Japan); Kawamoto, T [Shizuoka University, Shizuoka (Japan)
1997-11-25
With regard to utilization of a solar/electric hot-water system, a discussion was given by using a dynamic programming method on operation of a system which minimizes power charge. The discussed system is an installation in a welfare facility accommodating 100 persons, where solar heat is stored in a heat storage tank from a heat collector, and utilized for hot water supply. If the solar heat is insufficient for required hot water quantity, the water is heated by using an electric heater. The discussion compared the system operation using the dynamic programming method with the following two systems: the operation method 1, which does not utilize insolation forecast and the operation method 2, in which insolation forecast is utilized and late-night electric power is utilized for heating water in shortage. As a result of the calculation, the operation using the dynamic programming method conducts heat storage by utilizing the late-night power even if insolation is sufficient in winter in order to suppress heating by utilizing late-night power for days with less insolation. Thus, suppression is given on excessive utilization of day-time power and on rise in annual maximum power demand. It was found that the present system reduces power consumption by 37.7% when compared with the operation method 1, and 22.7% when compared even with the operation method 2. 3 refs., 5 figs., 3 tabs.
Mathematical programming methods for large-scale topology optimization problems
DEFF Research Database (Denmark)
Rojas Labanda, Susana
for mechanical problems, but has rapidly extended to many other disciplines, such as fluid dynamics and biomechanical problems. However, the novelty and improvements of optimization methods has been very limited. It is, indeed, necessary to develop of new optimization methods to improve the final designs......, and at the same time, reduce the number of function evaluations. Nonlinear optimization methods, such as sequential quadratic programming and interior point solvers, have almost not been embraced by the topology optimization community. Thus, this work is focused on the introduction of this kind of second...... for the classical minimum compliance problem. Two of the state-of-the-art optimization algorithms are investigated and implemented for this structural topology optimization problem. A Sequential Quadratic Programming (TopSQP) and an interior point method (TopIP) are developed exploiting the specific mathematical...
1978-10-01
8 Track Bushing Research, . . . . . . . . . . . * . . . 8 Advanced frack Concept Development ..... . . . . . 9 TECHNICAL DISCUSSION...machine design effort was conducted. The design which was developed has separate servocontrolled hydraulic actuators to apply radial...back bending-but, in the order and magnitude of the way the torsional stress is incurred in service. This suggests a programable, hydraulically actuated
Stochastic control theory dynamic programming principle
Nisio, Makiko
2015-01-01
This book offers a systematic introduction to the optimal stochastic control theory via the dynamic programming principle, which is a powerful tool to analyze control problems. First we consider completely observable control problems with finite horizons. Using a time discretization we construct a nonlinear semigroup related to the dynamic programming principle (DPP), whose generator provides the Hamilton–Jacobi–Bellman (HJB) equation, and we characterize the value function via the nonlinear semigroup, besides the viscosity solution theory. When we control not only the dynamics of a system but also the terminal time of its evolution, control-stopping problems arise. This problem is treated in the same frameworks, via the nonlinear semigroup. Its results are applicable to the American option price problem. Zero-sum two-player time-homogeneous stochastic differential games and viscosity solutions of the Isaacs equations arising from such games are studied via a nonlinear semigroup related to DPP (the min-ma...
Secure Dynamic Program Repartitioning
DEFF Research Database (Denmark)
Hansen, Rene Rydhoff; Probst, Christian
2005-01-01
Secure program partitioning has been introduced as a language-based technique to allow the distribution of data and computation across mutualy untrusted hosts, while at the same time guaranteeing the protection of confidential data. Programs that have been annotated with security types......, but the partitioning compiler becomes a part of the network and can recompile applications, thus alowing hosts to enter or leave the framework. We contend that this setting is superior to static partitioning, since it allows redistribution of data and computations. This is especialy beneficial if the new host alows...... data and computations to better fulfil the trust requirements of the users. Erasure Policies ensure that the original host of the redistributed data or computation does not store the data any longer....
An Improved Dynamic Programming Decomposition Approach for Network Revenue Management
Dan Zhang
2011-01-01
We consider a nonlinear nonseparable functional approximation to the value function of a dynamic programming formulation for the network revenue management (RM) problem with customer choice. We propose a simultaneous dynamic programming approach to solve the resulting problem, which is a nonlinear optimization problem with nonlinear constraints. We show that our approximation leads to a tighter upper bound on optimal expected revenue than some known bounds in the literature. Our approach can ...
Role of controllability in optimizing quantum dynamics
International Nuclear Information System (INIS)
Wu Rebing; Hsieh, Michael A.; Rabitz, Herschel
2011-01-01
This paper reveals an important role that controllability plays in the complexity of optimizing quantum control dynamics. We show that the loss of controllability generally leads to multiple locally suboptimal controls when gate fidelity in a quantum control system is maximized, which does not happen if the system is controllable. Such local suboptimal controls may attract an optimization algorithm into a local trap when a global optimal solution is sought, even if the target gate can be perfectly realized. This conclusion results from an analysis of the critical topology of the corresponding quantum control landscape, which refers to the gate fidelity objective as a functional of the control fields. For uncontrollable systems, due to SU(2) and SU(3) dynamical symmetries, the control landscape corresponding to an implementable target gate is proven to possess multiple locally optimal critical points, and its ruggedness can be further increased if the target gate is not realizable. These results imply that the optimization of quantum dynamics can be seriously impeded when operating with local search algorithms under these conditions, and thus full controllability is demanded.
Runway Scheduling Using Generalized Dynamic Programming
Montoya, Justin; Wood, Zachary; Rathinam, Sivakumar
2011-01-01
A generalized dynamic programming method for finding a set of pareto optimal solutions for a runway scheduling problem is introduced. The algorithm generates a set of runway fight sequences that are optimal for both runway throughput and delay. Realistic time-based operational constraints are considered, including miles-in-trail separation, runway crossings, and wake vortex separation. The authors also model divergent runway takeoff operations to allow for reduced wake vortex separation. A modeled Dallas/Fort Worth International airport and three baseline heuristics are used to illustrate preliminary benefits of using the generalized dynamic programming method. Simulated traffic levels ranged from 10 aircraft to 30 aircraft with each test case spanning 15 minutes. The optimal solution shows a 40-70 percent decrease in the expected delay per aircraft over the baseline schedulers. Computational results suggest that the algorithm is promising for real-time application with an average computation time of 4.5 seconds. For even faster computation times, two heuristics are developed. As compared to the optimal, the heuristics are within 5% of the expected delay per aircraft and 1% of the expected number of runway operations per hour ad can be 100x faster.
Energy Technology Data Exchange (ETDEWEB)
Thiriet, L; Deledicq, A [Commissariat a l' Energie Atomique, 92 - Fontenay-aux-Roses (France). Centre d' Etudes Nucleaires, section des etudes economiques generales
1968-09-01
First, the point of applying Dynamic Programming to optimization and Operational Research problems in chemical industries are recalled, as well as the conditions in which a dynamic program is illustrated by a sequential graph. A new algorithm for the determination of sub-optimal politics in a sequential graph is then developed. Finally, the applications of sub-optimality concept is shown when taking into account the indirect effects related to possible strategies, or in the case of stochastic choices and of problems of the siting of plants... application examples are given. (authors) [French] On rappelle d'abord l'interet de la Programmation Dynamique dans les problemes d'optimisation et de Recherche Operationnelle dans les industries chimiques, et les conditions de representation d'un programme dynamique par un graphe sequentiel. On expose ensuite un nouvel algorithme de determination de politiques sous-optimales dans un graphe sequentiel. On montre enfin les applications du concept de sous-optimalite a la prise en compte d'effets indirects lies aux politiques possibles, aux choix dans l'aleatoire, a des problemes de localisation optimale d'usines... et on donne des exemples d'utilisation. (auteurs)
Ant colony optimization and constraint programming
Solnon, Christine
2013-01-01
Ant colony optimization is a metaheuristic which has been successfully applied to a wide range of combinatorial optimization problems. The author describes this metaheuristic and studies its efficiency for solving some hard combinatorial problems, with a specific focus on constraint programming. The text is organized into three parts. The first part introduces constraint programming, which provides high level features to declaratively model problems by means of constraints. It describes the main existing approaches for solving constraint satisfaction problems, including complete tree search
A combined stochastic programming and optimal control approach to personal finance and pensions
DEFF Research Database (Denmark)
Konicz, Agnieszka Karolina; Pisinger, David; Rasmussen, Kourosh Marjani
2015-01-01
The paper presents a model that combines a dynamic programming (stochastic optimal control) approach and a multi-stage stochastic linear programming approach (SLP), integrated into one SLP formulation. Stochastic optimal control produces an optimal policy that is easy to understand and implement....
International Nuclear Information System (INIS)
Bieselt, R.
1974-01-01
While making extensive use of the possibilities dynamic programming as an optimization method has to offer, a planning method for the common operation and the extension of a nuclear park and a high-voltage network for a service area of a large size is worked out in a system-analytical manner in this thesis. With the aid of the computer programmes developed, those decisions concerning construction and operation of power plant and network are looked for which result within appr. 10 years of planning in a minimum of overall generating and transmission costs. (orig./LN) [de
Graphic Interface for LCP2 Optimization Program
DEFF Research Database (Denmark)
Nicolae, Taropa Laurentiu; Gaunholt, Hans
1998-01-01
This report provides information about the software interface that is programmed for the Optimization Program LCP2. The first part is about the general description of the program followed by a guide for using the interface. The last chapters contain a discussion about problems or futute extension...... of the project. The program is written in Visual C++5.0 on a Windows NT4.0 operating system.......This report provides information about the software interface that is programmed for the Optimization Program LCP2. The first part is about the general description of the program followed by a guide for using the interface. The last chapters contain a discussion about problems or futute extensions...
Optimal reduction of flexible dynamic system
International Nuclear Information System (INIS)
Jankovic, J.
1994-01-01
Dynamic system reduction is basic procedure in various problems of active control synthesis of flexible structures. In this paper is presented direct method for system reduction by explicit extraction of modes included in reduced model form. Criterion for optimal system discrete approximation in synthesis reduced dynamic model is also presented. Subjected method of system decomposition is discussed in relation to the Schur method of solving matrix algebraic Riccati equation as condition for system reduction. By using exposed method procedure of flexible system reduction in addition with corresponding example is presented. Shown procedure is powerful in problems of active control synthesis of flexible system vibrations
Structural optimization for nonlinear dynamic response
DEFF Research Database (Denmark)
Dou, Suguang; Strachan, B. Scott; Shaw, Steven W.
2015-01-01
by a single vibrating mode, or by a pair of internally resonant modes. The approach combines techniques from nonlinear dynamics, computational mechanics and optimization, and it allows one to relate the geometric and material properties of structural elements to terms in the normal form for a given resonance......Much is known about the nonlinear resonant response of mechanical systems, but methods for the systematic design of structures that optimize aspects of these responses have received little attention. Progress in this area is particularly important in the area of micro-systems, where nonlinear...... resonant behaviour is being used for a variety of applications in sensing and signal conditioning. In this work, we describe a computational method that provides a systematic means for manipulating and optimizing features of nonlinear resonant responses of mechanical structures that are described...
Direct Optimal Control of Duffing Dynamics
Oz, Hayrani; Ramsey, John K.
2002-01-01
The "direct control method" is a novel concept that is an attractive alternative and competitor to the differential-equation-based methods. The direct method is equally well applicable to nonlinear, linear, time-varying, and time-invariant systems. For all such systems, the method yields explicit closed-form control laws based on minimization of a quadratic control performance measure. We present an application of the direct method to the dynamics and optimal control of the Duffing system where the control performance measure is not restricted to a quadratic form and hence may include a quartic energy term. The results we present in this report also constitute further generalizations of our earlier work in "direct optimal control methodology." The approach is demonstrated for the optimal control of the Duffing equation with a softening nonlinear stiffness.
VMAT optimization with dynamic collimator rotation.
Lyu, Qihui; O'Connor, Daniel; Ruan, Dan; Yu, Victoria; Nguyen, Dan; Sheng, Ke
2018-04-16
Although collimator rotation is an optimization variable that can be exploited for dosimetric advantages, existing Volumetric Modulated Arc Therapy (VMAT) optimization uses a fixed collimator angle in each arc and only rotates the collimator between arcs. In this study, we develop a novel integrated optimization method for VMAT, accounting for dynamic collimator angles during the arc motion. Direct Aperture Optimization (DAO) for Dynamic Collimator in VMAT (DC-VMAT) was achieved by adding to the existing dose fidelity objective an anisotropic total variation term for regulating the fluence smoothness, a binary variable for forming simple apertures, and a group sparsity term for controlling collimator rotation. The optimal collimator angle for each beam angle was selected using the Dijkstra's algorithm, where the node costs depend on the estimated fluence map at the current iteration and the edge costs account for the mechanical constraints of multi-leaf collimator (MLC). An alternating optimization strategy was implemented to solve the DAO and collimator angle selection (CAS). Feasibility of DC-VMAT using one full-arc with dynamic collimator rotation was tested on a phantom with two small spherical targets, a brain, a lung and a prostate cancer patient. The plan was compared against a static collimator VMAT (SC-VMAT) plan using three full arcs with 60 degrees of collimator angle separation in patient studies. With the same target coverage, DC-VMAT achieved 20.3% reduction of R50 in the phantom study, and reduced the average max and mean OAR dose by 4.49% and 2.53% of the prescription dose in patient studies, as compared with SC-VMAT. The collimator rotation co-ordinated with the gantry rotation in DC-VMAT plans for deliverability. There were 13 beam angles in the single-arc DC-VMAT plan in patient studies that requires slower gantry rotation to accommodate multiple collimator angles. The novel DC-VMAT approach utilizes the dynamic collimator rotation during arc
Dynamic systems of regional economy management optimization
Trofimov, S.; Kudzh, S.
One of the most actual problems of the Russian economic life is a regional economic systems formation. The hierarchy of economic and branch priorities should follow from the general idea of an industrial policy. The matter is that the concept of an industrial policy is defined by the system of priorities mainly incorporated in it. The problem of priorities is not solved yet neither on federal, nor at a regional level. It is necessary to recognize, that a substantiation of this or that variant of priorities - objectively a challenge. Such substantiation can be received with the help of dynamic structural modeling and management technology. At formation of the regional industrial policy program the special attention is given to creation of modern type commercial structures. In regions there are headquarters and branches of many largest corporations, holdings and banks. Besides it, many regional enterprises already became inter-regional or even the transnational companies. In this connection an assistance of transformation of the industrial enterprises and their groups in vertically integrated companies and modern type holdings can become a prominent aspect of an industrial policy. Regional economic structures should be reconstructed gradually on the general model of the world class competitive companies. Assistance to creation of new corporational control systems, the organization of headquarters and the central services work - all this can be included into the sphere of regional administration industrial policy. The special attention should be turned on necessity of development of own system of the corporate structures, capable to provide to the region an independent participation in use of the natural resources and industrial-technological potential, at the stage of a regional industrial policy program formation. Transformation of the industrial enterprises and their groups into modern type vertically-integrated companies and holdings can become one of the major
Portfolio optimization using fuzzy linear programming
Pandit, Purnima K.
2013-09-01
Portfolio Optimization (PO) is a problem in Finance, in which investor tries to maximize return and minimize risk by carefully choosing different assets. Expected return and risk are the most important parameters with regard to optimal portfolios. In the simple form PO can be modeled as quadratic programming problem which can be put into equivalent linear form. PO problems with the fuzzy parameters can be solved as multi-objective fuzzy linear programming problem. In this paper we give the solution to such problems with an illustrative example.
Optimization of Dynamic Aperture of PEP-X Baseline Design
Energy Technology Data Exchange (ETDEWEB)
Wang, Min-Huey; /SLAC; Cai, Yunhai; /SLAC; Nosochkov, Yuri; /SLAC
2010-08-23
SLAC is developing a long-range plan to transfer the evolving scientific programs at SSRL from the SPEAR3 light source to a much higher performing photon source. Storage ring design is one of the possibilities that would be housed in the 2.2-km PEP-II tunnel. The design goal of PEPX storage ring is to approach an optimal light source design with horizontal emittance less than 100 pm and vertical emittance of 8 pm to reach the diffraction limit of 1-{angstrom} x-ray. The low emittance design requires a lattice with strong focusing leading to high natural chromaticity and therefore to strong sextupoles. The latter caused reduction of dynamic aperture. The dynamic aperture requirement for horizontal injection at injection point is about 10 mm. In order to achieve the desired dynamic aperture the transverse non-linearity of PEP-X is studied. The program LEGO is used to simulate the particle motion. The technique of frequency map is used to analyze the nonlinear behavior. The effect of the non-linearity is tried to minimize at the given constrains of limited space. The details and results of dynamic aperture optimization are discussed in this paper.
Optimization of Dynamic Aperture of PEP-X Baseline Design
International Nuclear Information System (INIS)
Wang, Min-Huey
2010-01-01
SLAC is developing a long-range plan to transfer the evolving scientific programs at SSRL from the SPEAR3 light source to a much higher performing photon source. Storage ring design is one of the possibilities that would be housed in the 2.2-km PEP-II tunnel. The design goal of PEPX storage ring is to approach an optimal light source design with horizontal emittance less than 100 pm and vertical emittance of 8 pm to reach the diffraction limit of 1-(angstrom) x-ray. The low emittance design requires a lattice with strong focusing leading to high natural chromaticity and therefore to strong sextupoles. The latter caused reduction of dynamic aperture. The dynamic aperture requirement for horizontal injection at injection point is about 10 mm. In order to achieve the desired dynamic aperture the transverse non-linearity of PEP-X is studied. The program LEGO is used to simulate the particle motion. The technique of frequency map is used to analyze the nonlinear behavior. The effect of the non-linearity is tried to minimize at the given constrains of limited space. The details and results of dynamic aperture optimization are discussed in this paper.
Optimal installation program for reprocessing plants
International Nuclear Information System (INIS)
Kubokawa, Toshihiko; Kiyose, Ryohei
1976-01-01
Optimization of the program of installation of reprocessing plants is mathematically formulated as problem of mixed integer programming, which is numerically solved by the branch-and-bound method. A new concept of quasi-penalty is used to obviate the difficulties associated with dual degeneracy. The finiteness of the useful life of the plant is also taken into consideration. It is shown that an analogous formulation is possible for the cases in which the demand forecasts and expected plant lives cannot be predicted with certainty. The scale of the problem is found to have kN binary variables, (k+2)N continuous variables, and (k+3)N constraint conditions, where k is the number of intervals used in the piece-wise linear approximation of a nonlinear objective function, and N the overall duration of the period covered by the installation program. Calculations are made for N=24 yr and k=3, with the assumption that the plant life is 15 yr, the plant scale factor 0.5, and the maximum plant capacity 900 (t/yr). The results are calculated and discussed for four different demand forecasts. The difference of net profit between optimal and non-optimal installation programs is found to be in the range of 50 -- 100 M$. The pay-off matrix is calculated, and the optimal choice of action when the demand cannot be forecast with certainty is determined by applying Bayes' theory. The optimal installation program under such conditions of uncertainty is obtained also with a stochastic mixed integer programming model. (auth.)
Optimized Bayesian dynamic advising theory and algorithms
Karny, Miroslav
2006-01-01
Written by one of the world's leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising. Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modelling by dynamic mixture models
Optimal Dynamics of Intermittent Water Supply
Lieb, Anna; Wilkening, Jon; Rycroft, Chris
2014-11-01
In many urban areas of the developing world, piped water is supplied only intermittently, as valves direct water to different parts of the water distribution system at different times. The flow is transient, and may transition between free-surface and pressurized, resulting in complex dynamical features with important consequences for water suppliers and users. These consequences include degradation of distribution system components, compromised water quality, and inequitable water availability. The goal of this work is to model the important dynamics and identify operating conditions that mitigate certain negative effects of intermittent water supply. Specifically, we will look at valve parameters occurring as boundary conditions in a network model of transient, transition flow through closed pipes. Optimization will be used to find boundary values to minimize pressure gradients and ensure equitable water availability.
Propositional Optimal Trajectory Programming for Improving Stability ...
African Journals Online (AJOL)
Propositional Optimal Trajectory Programming for Improving Stability of Hermite Definite Control System. ... PROMOTING ACCESS TO AFRICAN RESEARCH. AFRICAN JOURNALS ONLINE (AJOL) ... Knowledge of systems operation subjected to heat diffusion constraints is required of systems analysts. In an instance that ...
Optimization of Product Instantiation using Integer Programming
van den Broek, P.M.; Botterweck, Goetz; Jarzabek, Stan; Kishi, Tomoji
2010-01-01
We show that Integer Programming (IP) can be used as an optimization technique for the instantiation of products of feature models. This is done by showing that the constraints of feature models can be written in linear form. As particular IP technique, we use Gomory cutting planes. We have applied
Grid-Optimization Program for Photovoltaic Cells
Daniel, R. E.; Lee, T. S.
1986-01-01
CELLOPT program developed to assist in designing grid pattern of current-conducting material on photovoltaic cell. Analyzes parasitic resistance losses and shadow loss associated with metallized grid pattern on both round and rectangular solar cells. Though performs sensitivity studies, used primarily to optimize grid design in terms of bus bar and grid lines by minimizing power loss. CELLOPT written in APL.
Fluid dynamics computer programs for NERVA turbopump
Brunner, J. J.
1972-01-01
During the design of the NERVA turbopump, numerous computer programs were developed for the analyses of fluid dynamic problems within the machine. Program descriptions, example cases, users instructions, and listings for the majority of these programs are presented.
Dynamic programming algorithms for biological sequence comparison.
Pearson, W R; Miller, W
1992-01-01
Efficient dynamic programming algorithms are available for a broad class of protein and DNA sequence comparison problems. These algorithms require computer time proportional to the product of the lengths of the two sequences being compared [O(N2)] but require memory space proportional only to the sum of these lengths [O(N)]. Although the requirement for O(N2) time limits use of the algorithms to the largest computers when searching protein and DNA sequence databases, many other applications of these algorithms, such as calculation of distances for evolutionary trees and comparison of a new sequence to a library of sequence profiles, are well within the capabilities of desktop computers. In particular, the results of library searches with rapid searching programs, such as FASTA or BLAST, should be confirmed by performing a rigorous optimal alignment. Whereas rapid methods do not overlook significant sequence similarities, FASTA limits the number of gaps that can be inserted into an alignment, so that a rigorous alignment may extend the alignment substantially in some cases. BLAST does not allow gaps in the local regions that it reports; a calculation that allows gaps is very likely to extend the alignment substantially. Although a Monte Carlo evaluation of the statistical significance of a similarity score with a rigorous algorithm is much slower than the heuristic approach used by the RDF2 program, the dynamic programming approach should take less than 1 hr on a 386-based PC or desktop Unix workstation. For descriptive purposes, we have limited our discussion to methods for calculating similarity scores and distances that use gap penalties of the form g = rk. Nevertheless, programs for the more general case (g = q+rk) are readily available. Versions of these programs that run either on Unix workstations, IBM-PC class computers, or the Macintosh can be obtained from either of the authors.
COMPARISON OF NONLINEAR DYNAMICS OPTIMIZATION METHODS FOR APS-U
Energy Technology Data Exchange (ETDEWEB)
Sun, Y.; Borland, Michael
2017-06-25
Many different objectives and genetic algorithms have been proposed for storage ring nonlinear dynamics performance optimization. These optimization objectives include nonlinear chromaticities and driving/detuning terms, on-momentum and off-momentum dynamic acceptance, chromatic detuning, local momentum acceptance, variation of transverse invariant, Touschek lifetime, etc. In this paper, the effectiveness of several different optimization methods and objectives are compared for the nonlinear beam dynamics optimization of the Advanced Photon Source upgrade (APS-U) lattice. The optimized solutions from these different methods are preliminarily compared in terms of the dynamic acceptance, local momentum acceptance, chromatic detuning, and other performance measures.
Optimization of the annual construction program solutions
Directory of Open Access Journals (Sweden)
Oleinik Pavel
2017-01-01
Full Text Available The article considers potentially possible optimization solutions in scheduling while forming the annual production programs of the construction complex organizations. The optimization instrument is represented as a two-component system. As a fundamentally new approach in the first block of the annual program solutions, the authors propose to use a scientifically grounded methodology for determining the scope of work permissible for the transfer to a subcontractor without risk of General Contractor’s management control losing over the construction site. For this purpose, a special indicator is introduced that characterizes the activity of the general construction organization - the coefficient of construction production management. In the second block, the principal methods for the formation of calendar plans for the fulfillment of the critical work effort by the leading stream are proposed, depending on the intensity characteristic.
Dynamic portfolio optimization across hidden market regimes
DEFF Research Database (Denmark)
Nystrup, Peter; Madsen, Henrik; Lindström, Erik
2017-01-01
Regime-based asset allocation has been shown to add value over rebalancing to static weights and, in particular, reduce potential drawdowns by reacting to changes in market conditions. The predominant approach in previous studies has been to specify in advance a static decision rule for changing...... the allocation based on the state of financial markets or the economy. In this article, model predictive control (MPC) is used to dynamically optimize a portfolio based on forecasts of the mean and variance of financial returns from a hidden Markov model with time-varying parameters. There are computational...... than a buy-and-hold investment in various major stock market indices. This is after accounting for transaction costs, with a one-day delay in the implementation of allocation changes, and with zero-interest cash as the only alternative to the stock indices. Imposing a trading penalty that reduces...
Probabilistic methods for maintenance program optimization
International Nuclear Information System (INIS)
Liming, J.K.; Smith, M.J.; Gekler, W.C.
1989-01-01
In today's regulatory and economic environments, it is more important than ever that managers, engineers, and plant staff join together in developing and implementing effective management plans for safety and economic risk. This need applied to both power generating stations and other process facilities. One of the most critical parts of these management plans is the development and continuous enhancement of a maintenance program that optimizes plant or facility safety and profitability. The ultimate objective is to maximize the potential for station or facility success, usually measured in terms of projected financial profitability, while meeting or exceeding meaningful and reasonable safety goals, usually measured in terms of projected damage or consequence frequencies. This paper describes the use of the latest concepts in developing and evaluating maintenance programs to achieve maintenance program optimization (MPO). These concepts are based on significant field experience gained through the integration and application of fundamentals developed for industry and Electric Power Research Institute (EPRI)-sponsored projects on preventive maintenance (PM) program development and reliability-centered maintenance (RCM)
Automated Flight Routing Using Stochastic Dynamic Programming
Ng, Hok K.; Morando, Alex; Grabbe, Shon
2010-01-01
Airspace capacity reduction due to convective weather impedes air traffic flows and causes traffic congestion. This study presents an algorithm that reroutes flights in the presence of winds, enroute convective weather, and congested airspace based on stochastic dynamic programming. A stochastic disturbance model incorporates into the reroute design process the capacity uncertainty. A trajectory-based airspace demand model is employed for calculating current and future airspace demand. The optimal routes minimize the total expected traveling time, weather incursion, and induced congestion costs. They are compared to weather-avoidance routes calculated using deterministic dynamic programming. The stochastic reroutes have smaller deviation probability than the deterministic counterpart when both reroutes have similar total flight distance. The stochastic rerouting algorithm takes into account all convective weather fields with all severity levels while the deterministic algorithm only accounts for convective weather systems exceeding a specified level of severity. When the stochastic reroutes are compared to the actual flight routes, they have similar total flight time, and both have about 1% of travel time crossing congested enroute sectors on average. The actual flight routes induce slightly less traffic congestion than the stochastic reroutes but intercept more severe convective weather.
Dynamic Programming Approach for Construction of Association Rule Systems
Alsolami, Fawaz
2016-11-18
In the paper, an application of dynamic programming approach for optimization of association rules from the point of view of knowledge representation is considered. The association rule set is optimized in two stages, first for minimum cardinality and then for minimum length of rules. Experimental results present cardinality of the set of association rules constructed for information system and lower bound on minimum possible cardinality of rule set based on the information obtained during algorithm work as well as obtained results for length.
Dynamic Programming Approach for Construction of Association Rule Systems
Alsolami, Fawaz; Amin, Talha M.; Chikalov, Igor; Moshkov, Mikhail; Zielosko, Beata
2016-01-01
In the paper, an application of dynamic programming approach for optimization of association rules from the point of view of knowledge representation is considered. The association rule set is optimized in two stages, first for minimum cardinality and then for minimum length of rules. Experimental results present cardinality of the set of association rules constructed for information system and lower bound on minimum possible cardinality of rule set based on the information obtained during algorithm work as well as obtained results for length.
Optimized remedial groundwater extraction using linear programming
International Nuclear Information System (INIS)
Quinn, J.J.
1995-01-01
Groundwater extraction systems are typically installed to remediate contaminant plumes or prevent further spread of contamination. These systems are expensive to install and maintain. A traditional approach to designing such a wellfield uses a series of trial-and-error simulations to test the effects of various well locations and pump rates. However, the optimal locations and pump rates of extraction wells are difficult to determine when objectives related to the site hydrogeology and potential pumping scheme are considered. This paper describes a case study of an application of linear programming theory to determine optimal well placement and pump rates. The objectives of the pumping scheme were to contain contaminant migration and reduce contaminant concentrations while minimizing the total amount of water pumped and treated. Past site activities at the area under study included disposal of contaminants in pits. Several groundwater plumes have been identified, and others may be present. The area of concern is bordered on three sides by a wetland, which receives a portion of its input budget as groundwater discharge from the pits. Optimization of the containment pumping scheme was intended to meet three goals: (1) prevent discharge of contaminated groundwater to the wetland, (2) minimize the total water pumped and treated (cost benefit), and (3) avoid dewatering of the wetland (cost and ecological benefits). Possible well locations were placed at known source areas. To constrain the problem, the optimization program was instructed to prevent any flow toward the wetland along a user-specified border. In this manner, the optimization routine selects well locations and pump rates so that a groundwater divide is produced along this boundary
A note on dynamic programming in accounts receivable management
Dirickx, Y.M.I.; Kistner, K.-P.
1982-01-01
The paper considers a dynamic programming formulation of the accounts receivable problem for single outstanding amounts. An optimal collection policy can be computed efficiently by invoking a “planning horizon” result that determines a time period beyond which the decision process cannot extend. The
Exploration of automatic optimization for CUDA programming
Al-Mouhamed, Mayez; Khan, Ayaz ul Hassan
2012-01-01
Graphic processing Units (GPUs) are gaining ground in high-performance computing. CUDA (an extension to C) is most widely used parallel programming framework for general purpose GPU computations. However, the task of writing optimized CUDA program is complex even for experts. We present a method for restructuring loops into an optimized CUDA kernels based on a 3-step algorithm which are loop tiling, coalesced memory access, and resource optimization. We also establish the relationships between the influencing parameters and propose a method for finding possible tiling solutions with coalesced memory access that best meets the identified constraints. We also present a simplified algorithm for restructuring loops and rewrite them as an efficient CUDA Kernel. The execution model of synthesized kernel consists of uniformly distributing the kernel threads to keep all cores busy while transferring a tailored data locality which is accessed using coalesced pattern to amortize the long latency of the secondary memory. In the evaluation, we implement some simple applications using the proposed restructuring strategy and evaluate the performance in terms of execution time and GPU throughput. © 2012 IEEE.
Exploration of automatic optimization for CUDA programming
Al-Mouhamed, Mayez
2012-12-01
Graphic processing Units (GPUs) are gaining ground in high-performance computing. CUDA (an extension to C) is most widely used parallel programming framework for general purpose GPU computations. However, the task of writing optimized CUDA program is complex even for experts. We present a method for restructuring loops into an optimized CUDA kernels based on a 3-step algorithm which are loop tiling, coalesced memory access, and resource optimization. We also establish the relationships between the influencing parameters and propose a method for finding possible tiling solutions with coalesced memory access that best meets the identified constraints. We also present a simplified algorithm for restructuring loops and rewrite them as an efficient CUDA Kernel. The execution model of synthesized kernel consists of uniformly distributing the kernel threads to keep all cores busy while transferring a tailored data locality which is accessed using coalesced pattern to amortize the long latency of the secondary memory. In the evaluation, we implement some simple applications using the proposed restructuring strategy and evaluate the performance in terms of execution time and GPU throughput. © 2012 IEEE.
Approximate dynamic programming solving the curses of dimensionality
Powell, Warren B
2007-01-01
Warren B. Powell, PhD, is Professor of Operations Research and Financial Engineering at Princeton University, where he is founder and Director of CASTLE Laboratory, a research unit that works with industrial partners to test new ideas found in operations research. The recipient of the 2004 INFORMS Fellow Award, Dr. Powell has authored over 100 refereed publications on stochastic optimization, approximate dynamic programming, and dynamic resource management.
Project STOP (Spectral Thermal Optimization Program)
Goldhammer, L. J.; Opjorden, R. W.; Goodelle, G. S.; Powe, J. S.
1977-01-01
The spectral thermal optimization of solar cell configurations for various solar panel applications is considered. The method of optimization depends upon varying the solar cell configuration's optical characteristics to minimize panel temperatures, maximize power output and decrease the power delta from beginning of life to end of life. Four areas of primary investigation are: (1) testing and evaluation of ultraviolet resistant coverslide adhesives, primarily FEP as an adhesive; (2) examination of solar cell absolute spectral response and corresponding cell manufacturing processes that affect it; (3) experimental work with solar cell manufacturing processes that vary cell reflectance (solar absorptance); and (4) experimental and theoretical studies with various coverslide filter designs, mainly a red rejection filter. The Hughes' solar array prediction program has been modified to aid in evaluating the effect of each of the above four areas on the output of a solar panel in orbit.
Chaotic dynamics in optimal monetary policy
Gomes, O.; Mendes, V. M.; Mendes, D. A.; Sousa Ramos, J.
2007-05-01
There is by now a large consensus in modern monetary policy. This consensus has been built upon a dynamic general equilibrium model of optimal monetary policy as developed by, e.g., Goodfriend and King [ NBER Macroeconomics Annual 1997 edited by B. Bernanke and J. Rotemberg (Cambridge, Mass.: MIT Press, 1997), pp. 231 282], Clarida et al. [J. Econ. Lit. 37, 1661 (1999)], Svensson [J. Mon. Econ. 43, 607 (1999)] and Woodford [ Interest and Prices: Foundations of a Theory of Monetary Policy (Princeton, New Jersey, Princeton University Press, 2003)]. In this paper we extend the standard optimal monetary policy model by introducing nonlinearity into the Phillips curve. Under the specific form of nonlinearity proposed in our paper (which allows for convexity and concavity and secures closed form solutions), we show that the introduction of a nonlinear Phillips curve into the structure of the standard model in a discrete time and deterministic framework produces radical changes to the major conclusions regarding stability and the efficiency of monetary policy. We emphasize the following main results: (i) instead of a unique fixed point we end up with multiple equilibria; (ii) instead of saddle-path stability, for different sets of parameter values we may have saddle stability, totally unstable equilibria and chaotic attractors; (iii) for certain degrees of convexity and/or concavity of the Phillips curve, where endogenous fluctuations arise, one is able to encounter various results that seem intuitively correct. Firstly, when the Central Bank pays attention essentially to inflation targeting, the inflation rate has a lower mean and is less volatile; secondly, when the degree of price stickiness is high, the inflation rate displays a larger mean and higher volatility (but this is sensitive to the values given to the parameters of the model); and thirdly, the higher the target value of the output gap chosen by the Central Bank, the higher is the inflation rate and its
Nonlinear dynamic simulation of optimal depletion of crude oil in the lower 48 United States
International Nuclear Information System (INIS)
Ruth, M.; Cleveland, C.J.
1993-01-01
This study combines the economic theory of optimal resource use with econometric estimates of demand and supply parameters to develop a nonlinear dynamic model of crude oil exploration, development, and production in the lower 48 United States. The model is simulated with the graphical programming language STELLA, for the years 1985 to 2020. The procedure encourages use of economic theory and econometrics in combination with nonlinear dynamic simulation to enhance our understanding of complex interactions present in models of optimal resource use. (author)
Dynamic optimization the calculus of variations and optimal control in economics and management
Kamien, Morton I
2012-01-01
Since its initial publication, this text has defined courses in dynamic optimization taught to economics and management science students. The two-part treatment covers the calculus of variations and optimal control. 1998 edition.
Neutrophil programming dynamics and its disease relevance.
Ran, Taojing; Geng, Shuo; Li, Liwu
2017-11-01
Neutrophils are traditionally considered as first responders to infection and provide antimicrobial host defense. However, recent advances indicate that neutrophils are also critically involved in the modulation of host immune environments by dynamically adopting distinct functional states. Functionally diverse neutrophil subsets are increasingly recognized as critical components mediating host pathophysiology. Despite its emerging significance, molecular mechanisms as well as functional relevance of dynamically programmed neutrophils remain to be better defined. The increasing complexity of neutrophil functions may require integrative studies that address programming dynamics of neutrophils and their pathophysiological relevance. This review aims to provide an update on the emerging topics of neutrophil programming dynamics as well as their functional relevance in diseases.
How to Use Linear Programming for Information System Performances Optimization
Directory of Open Access Journals (Sweden)
Hell Marko
2014-09-01
Full Text Available Background: Organisations nowadays operate in a very dynamic environment, and therefore, their ability of continuously adjusting the strategic plan to the new conditions is a must for achieving their strategic objectives. BSC is a well-known methodology for measuring performances enabling organizations to learn how well they are doing. In this paper, “BSC for IS” will be proposed in order to measure the IS impact on the achievement of organizations’ business goals. Objectives: The objective of this paper is to present the original procedure which is used to enhance the BSC methodology in planning the optimal targets of IS performances value in order to maximize the organization's effectiveness. Methods/Approach: The method used in this paper is the quantitative methodology - linear programming. In the case study, linear programming is used for optimizing organization’s strategic performance. Results: Results are shown on the example of a case study national park. An optimal performance value for the strategic objective has been calculated, as well as an optimal performance value for each DO (derived objective. Results are calculated in Excel, using Solver Add-in. Conclusions: The presentation of methodology through the case study of a national park shows that this methodology, though it requires a high level of formalisation, provides a very transparent performance calculation.
Optimal dynamic economic dispatch of generation: A review
International Nuclear Information System (INIS)
Xia, X.; Elaiw, A.M.
2010-01-01
This paper presents a review of the research of the optimal power dynamic dispatch problem. The dynamic dispatch problem differs from the static economic dispatch problem by incorporating generator ramp rate constraints. There are two different formulations of this problem in the literature. The first formulation is the optimal control dynamic dispatch (OCDD) where the power system generation has been modeled as a control system and optimization is done in the optimal control setting with respect to the ramp rates as input variables. The second one is a later formulation known as the dynamic economic dispatch (DED) where optimization is done with respect to the dispatchable powers of the committed generation units. In this paper we first outline the two formulations, then present an overview on the mathematical optimization methods, Artificial Intelligence (AI) techniques and hybrid methods used to solve the problem incorporating extended and complex objective functions or constraints. The DED problem in deregulated electricity markets is also reported. (author)
Dynamic Programming and Graph Algorithms in Computer Vision*
Felzenszwalb, Pedro F.; Zabih, Ramin
2013-01-01
Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting, since by carefully exploiting problem structure they often provide non-trivial guarantees concerning solution quality. In this paper we briefly review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo; the mid-level problem of interactive object segmentation; and the high-level problem of model-based recognition. PMID:20660950
Optimal control of HIV/AIDS dynamic: Education and treatment
Sule, Amiru; Abdullah, Farah Aini
2014-07-01
A mathematical model which describes the transmission dynamics of HIV/AIDS is developed. The optimal control representing education and treatment for this model is explored. The existence of optimal Control is established analytically by the use of optimal control theory. Numerical simulations suggest that education and treatment for the infected has a positive impact on HIV/AIDS control.
Model-based dynamic control and optimization of gas networks
Energy Technology Data Exchange (ETDEWEB)
Hofsten, Kai
2001-07-01
This work contributes to the research on control, optimization and simulation of gas transmission systems to support the dispatch personnel at gas control centres for the decision makings in the daily operation of the natural gas transportation systems. Different control and optimization strategies have been studied. The focus is on the operation of long distance natural gas transportation systems. Stationary optimization in conjunction with linear model predictive control using state space models is proposed for supply security, the control of quality parameters and minimization of transportation costs for networks offering transportation services. The result from the stationary optimization together with a reformulation of a simplified fluid flow model formulates a linear dynamic optimization model. This model is used in a finite time control and state constrained linear model predictive controller. The deviation from the control and the state reference determined from the stationary optimization is penalized quadratically. Because of the time varying status of infrastructure, the control space is also generally time varying. When the average load is expected to change considerably, a new stationary optimization is performed, giving a new state and control reference together with a new dynamic model that is used for both optimization and state estimation. Another proposed control strategy is a control and output constrained nonlinear model predictive controller for the operation of gas transmission systems. Here, the objective is also the security of the supply, quality control and minimization of transportation costs. An output vector is defined, which together with a control vector are both penalized quadratically from their respective references in the objective function. The nonlinear model predictive controller can be combined with a stationary optimization. At each sampling instant, a non convex nonlinear programming problem is solved giving a local minimum
Computer program for optimal BWR congtrol rod programming
International Nuclear Information System (INIS)
Taner, M.S.; Levine, S.H.; Carmody, J.M.
1995-01-01
A fully automated computer program has been developed for designing optimal control rod (CR) patterns for boiling water reactors (BWRs). The new program, called OCTOPUS-3, is based on the OCTOPUS code and employs SIMULATE-3 (Ref. 2) for the analysis. There are three aspects of OCTOPUS-3 that make it successful for use at PECO Energy. It incorporates a new feasibility algorithm that makes the CR design meet all constraints, it has been coupled to a Bourne Shell program 3 to allow the user to run the code interactively without the need for a manual, and it develops a low axial peak to extend the cycle. For PECO Energy Co.'s limericks it increased the energy output by 1 to 2% over the traditional PECO Energy design. The objective of the optimization in OCTOPUS-3 is to approximate a very low axial peaked target power distribution while maintaining criticality, keeping the nodal and assembly peaks below the allowed maximum, and meeting the other constraints. The user-specified input for each exposure point includes: CR groups allowed-to-move, target k eff , and amount of core flow. The OCTOPUS-3 code uses the CR pattern from the previous step as the initial guess unless indicated otherwise
Fast and Cache-Oblivious Dynamic Programming with Local Dependencies
DEFF Research Database (Denmark)
Bille, Philip; Stöckel, Morten
2012-01-01
are widely used in bioinformatics to compare DNA and protein sequences. These problems can all be solved using essentially the same dynamic programming scheme over a two-dimensional matrix, where each entry depends locally on at most 3 neighboring entries. We present a simple, fast, and cache......-oblivious algorithm for this type of local dynamic programming suitable for comparing large-scale strings. Our algorithm outperforms the previous state-of-the-art solutions. Surprisingly, our new simple algorithm is competitive with a complicated, optimized, and tuned implementation of the best cache-aware algorithm...
Optimization Research of Generation Investment Based on Linear Programming Model
Wu, Juan; Ge, Xueqian
Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.
DEGAS: Dynamic Exascale Global Address Space Programming Environments
Energy Technology Data Exchange (ETDEWEB)
Demmel, James [Univ. of California, Berkeley, CA (United States)
2018-02-23
The Dynamic, Exascale Global Address Space programming environment (DEGAS) project will develop the next generation of programming models and runtime systems to meet the challenges of Exascale computing. The Berkeley part of the project concentrated on communication-optimal code generation to optimize speed and energy efficiency by reducing data movement. Our work developed communication lower bounds, and/or communication avoiding algorithms (that either meet the lower bound, or do much less communication than their conventional counterparts) for a variety of algorithms, including linear algebra, machine learning and genomics. The Berkeley part of the project concentrated on communication-optimal code generation to optimize speed and energy efficiency by reducing data movement. Our work developed communication lower bounds, and/or communication avoiding algorithms (that either meet the lower bound, or do much less communication than their conventional counterparts) for a variety of algorithms, including linear algebra, machine learning and genomics.
A program package for solving linear optimization problems
International Nuclear Information System (INIS)
Horikami, Kunihiko; Fujimura, Toichiro; Nakahara, Yasuaki
1980-09-01
Seven computer programs for the solution of linear, integer and quadratic programming (four programs for linear programming, one for integer programming and two for quadratic programming) have been prepared and tested on FACOM M200 computer, and auxiliary programs have been written to make it easy to use the optimization program package. The characteristics of each program are explained and the detailed input/output descriptions are given in order to let users know how to use them. (author)
Dynamic Vehicle Routing Problems with Enhanced Ant Colony Optimization
Directory of Open Access Journals (Sweden)
Haitao Xu
2018-01-01
Full Text Available As we all know, there are a great number of optimization problems in the world. One of the relatively complicated and high-level problems is the vehicle routing problem (VRP. Dynamic vehicle routing problem (DVRP is a major variant of VRP, and it is closer to real logistic scene. In DVRP, the customers’ demands appear with time, and the unserved customers’ points must be updated and rearranged while carrying out the programming paths. Owing to the complexity and significance of the problem, DVRP applications have grabbed the attention of researchers in the past two decades. In this paper, we have two main contributions to solving DVRP. Firstly, DVRP is solved with enhanced Ant Colony Optimization (E-ACO, which is the traditional Ant Colony Optimization (ACO fusing improved K-means and crossover operation. K-means can divide the region with the most reasonable distance, while ACO using crossover is applied to extend search space and avoid falling into local optimum prematurely. Secondly, several new evaluation benchmarks are proposed, which can objectively and comprehensively estimate the proposed method. In the experiment, the results for different scale problems are compared to those of previously published papers. Experimental results show that the algorithm is feasible and efficient.
Parameters control in GAs for dynamic optimization
Directory of Open Access Journals (Sweden)
Khalid Jebari
2013-02-01
Full Text Available The Control of Genetic Algorithms parameters allows to optimize the search process and improves the performance of the algorithm. Moreover it releases the user to dive into a game process of trial and failure to find the optimal parameters.
Exploiting variability for energy optimization of parallel programs
Energy Technology Data Exchange (ETDEWEB)
Lavrijsen, Wim [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Iancu, Costin [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); de Jong, Wibe [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Chen, Xin [Georgia Inst. of Technology, Atlanta, GA (United States); Schwan, Karsten [Georgia Inst. of Technology, Atlanta, GA (United States)
2016-04-18
Here in this paper we present optimizations that use DVFS mechanisms to reduce the total energy usage in scientific applications. Our main insight is that noise is intrinsic to large scale parallel executions and it appears whenever shared resources are contended. The presence of noise allows us to identify and manipulate any program regions amenable to DVFS. When compared to previous energy optimizations that make per core decisions using predictions of the running time, our scheme uses a qualitative approach to recognize the signature of executions amenable to DVFS. By recognizing the "shape of variability" we can optimize codes with highly dynamic behavior, which pose challenges to all existing DVFS techniques. We validate our approach using offline and online analyses for one-sided and two-sided communication paradigms. We have applied our methods to NWChem, and we show best case improvements in energy use of 12% at no loss in performance when using online optimizations running on 720 Haswell cores with one-sided communication. With NWChem on MPI two-sided and offline analysis, capturing the initialization, we find energy savings of up to 20%, with less than 1% performance cost.
Rule of Thumb and Dynamic Programming
Lettau, M.; Uhlig, H.F.H.V.S.
1995-01-01
This paper studies the relationships between learning about rules of thumb (represented by classifier systems) and dynamic programming. Building on a result about Markovian stochastic approximation algorithms, we characterize all decision functions that can be asymptotically obtained through
Dynamic Programming: An Introduction by Example
Zietz, Joachim
2007-01-01
The author introduces some basic dynamic programming techniques, using examples, with the help of the computer algebra system "Maple". The emphasis is on building confidence and intuition for the solution of dynamic problems in economics. To integrate the material better, the same examples are used to introduce different techniques. One covers the…
Liu, Qingshan; Guo, Zhishan; Wang, Jun
2012-02-01
In this paper, a one-layer recurrent neural network is proposed for solving pseudoconvex optimization problems subject to linear equality and bound constraints. Compared with the existing neural networks for optimization (e.g., the projection neural networks), the proposed neural network is capable of solving more general pseudoconvex optimization problems with equality and bound constraints. Moreover, it is capable of solving constrained fractional programming problems as a special case. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed as long as the designed parameters in the model are larger than the derived lower bounds. Numerical examples with simulation results illustrate the effectiveness and characteristics of the proposed neural network. In addition, an application for dynamic portfolio optimization is discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.
Transmission Dynamics and Optimal Control of Malaria in Kenya
Directory of Open Access Journals (Sweden)
Gabriel Otieno
2016-01-01
Full Text Available This paper proposes and analyses a mathematical model for the transmission dynamics of malaria with four-time dependent control measures in Kenya: insecticide treated bed nets (ITNs, treatment, indoor residual spray (IRS, and intermittent preventive treatment of malaria in pregnancy (IPTp. We first considered constant control parameters and calculate the basic reproduction number and investigate existence and stability of equilibria as well as stability analysis. We proved that if R0≤1, the disease-free equilibrium is globally asymptotically stable in D. If R0>1, the unique endemic equilibrium exists and is globally asymptotically stable. The model also exhibits backward bifurcation at R0=1. If R0>1, the model admits a unique endemic equilibrium which is globally asymptotically stable in the interior of feasible region D. The sensitivity results showed that the most sensitive parameters are mosquito death rate and mosquito biting rates. We then consider the time-dependent control case and use Pontryagin’s Maximum Principle to derive the necessary conditions for the optimal control of the disease using the proposed model. The existence of optimal control problem is proved. Numerical simulations of the optimal control problem using a set of reasonable parameter values suggest that the optimal control strategy for malaria control in endemic areas is the combined use of treatment and IRS; for epidemic prone areas is the use of treatment and IRS; for seasonal areas is the use of treatment; and for low risk areas is the use of ITNs and treatment. Control programs that follow these strategies can effectively reduce the spread of malaria disease in different malaria transmission settings in Kenya.
Optimizing the hydraulic program of cementing casing strings
Energy Technology Data Exchange (ETDEWEB)
Novakovic, M
1984-01-01
A technique is described for calculating the optimal parameters of the flow of plugging mud which takes into consideration the geometry of the annular space and the rheological characteristics of the muds. The optimization algorithm was illustrated by a block diagram. Examples are given for practical application of the optimization programs in production conditions. It is stressed that optimizing the hydraulic cementing program is effective if other technical-technological problems in cementing casing strings have been resolved.
Directory of Open Access Journals (Sweden)
Andrey Fyodorovich Shorikov
2013-06-01
Full Text Available This paper reviews a methodical approach to solve multi-step dynamic problem of optimal integrated adaptive management of a product portfolio structure of the enterprise. For the organization of optimal adaptive terminal control of the system the recurrent algorithm, which reduces an initial multistage problem to the realization of the final sequence of problems of optimal program terminal control is offered. In turn, the decision of each problem of optimal program terminal control is reduced to the realization of the final sequence only single-step operations in the form of the problems solving of linear and convex mathematical programming. Thus, the offered approach allows to develop management solutions at current information support, which consider feedback, and which create the optimal structure of an enterprise’s product lines, contributing to optimising of profits, as well as maintenance of the desired level of profit for a long period of time
Guidelines for dynamic international programs
International Nuclear Information System (INIS)
Gold, M.A.
1993-01-01
Matters of global concern-deforestation, global warming, biodiversity loss, sustainable development, fuelwood crises, watershed destruction, and large-scale flooding-frequently involve forests and natural resources. In the future, university students will enter a global setting that more than ever depends on a strong knowledge of international issues. USA land-grant universities are attempting to prepare students for this challenge by improving their international programs including forestry. To improve university programs, several factors will need to be addressed and are discussed, with examples, in this article: commitment of the faculty; program specialization; geographic specialization; reward systems for international contributions; international collaboration; recycled dollars within the university; active teaching programs; research; extention and outreach; language training; international faculty; travel grants; twinning relationships with sister institutions; selective in pursuit of international development assistance; and study centers. 6 refs
Optimized controllers for enhancing dynamic performance of PV interface system
Directory of Open Access Journals (Sweden)
Mahmoud A. Attia
2018-05-01
Full Text Available The dynamic performance of PV interface system can be improved by optimizing the gains of the Proportional–Integral (PI controller. In this work, gravitational search algorithm and harmony search algorithm are utilized to optimal tuning of PI controller gains. Performance comparison between the PV system with optimized PI gains utilizing different techniques are carried out. Finally, the dynamic behavior of the system is studied under hypothetical sudden variations in irradiance. The examination of the proposed techniques for optimal tuning of PI gains is conducted using MATLAB/SIMULINK software package. The main contribution of this work is investigating the dynamic performance of PV interfacing system with application of gravitational search algorithm and harmony search algorithm for optimal PI parameters tuning. Keywords: Photovoltaic power systems, Gravitational search algorithm, Harmony search algorithm, Genetic algorithm, Artificial intelligence
Dynamic Programming Algorithms in Speech Recognition
Directory of Open Access Journals (Sweden)
Titus Felix FURTUNA
2008-01-01
Full Text Available In a system of speech recognition containing words, the recognition requires the comparison between the entry signal of the word and the various words of the dictionary. The problem can be solved efficiently by a dynamic comparison algorithm whose goal is to put in optimal correspondence the temporal scales of the two words. An algorithm of this type is Dynamic Time Warping. This paper presents two alternatives for implementation of the algorithm designed for recognition of the isolated words.
The application of dynamic programming in production planning
Wu, Run
2017-05-01
Nowadays, with the popularity of the computers, various industries and fields are widely applying computer information technology, which brings about huge demand for a variety of application software. In order to develop software meeting various needs with most economical cost and best quality, programmers must design efficient algorithms. A superior algorithm can not only soul up one thing, but also maximize the benefits and generate the smallest overhead. As one of the common algorithms, dynamic programming algorithms are used to solving problems with some sort of optimal properties. When solving problems with a large amount of sub-problems that needs repetitive calculations, the ordinary sub-recursive method requires to consume exponential time, and dynamic programming algorithm can reduce the time complexity of the algorithm to the polynomial level, according to which we can conclude that dynamic programming algorithm is a very efficient compared to other algorithms reducing the computational complexity and enriching the computational results. In this paper, we expound the concept, basic elements, properties, core, solving steps and difficulties of the dynamic programming algorithm besides, establish the dynamic programming model of the production planning problem.
Directory of Open Access Journals (Sweden)
Weixing Su
2017-03-01
Full Text Available There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell’s pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.
Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei
2017-03-01
There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.
Adaptive Dynamic Programming for Control Algorithms and Stability
Zhang, Huaguang; Luo, Yanhong; Wang, Ding
2013-01-01
There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming for Control approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural networks and techniques of value and policy iteration. The text features three main aspects of ADP in which the methods proposed for stabilization and for tracking and games benefit from the incorporation of optimal control methods: • infinite-horizon control for which the difficulty of solving partial differential Hamilton–Jacobi–Bellman equations directly is overcome, and proof provided that the iterative value function updating sequence converges to the infimum of all the value functions obtained by admissible control law sequences; • finite-...
DYNAMIC OPTIMAL BUDGET ALLOCATION FOR INTEGRATED MARKETING CONSIDERING PERSISTENCE
SHIZHONG AI; RONG DU; QIYING HU
2010-01-01
Aiming at forming dynamic optimal integrated marketing policies, we build a budget allocation model considering both current effects and sustained ones. The model includes multiple time periods and multiple marketing tools which interact through a common resource pool as well as through delayed cross influences on each other's sales, reflecting the nature of "integrated marketing" and its dynamics. In our study, marginal analysis is used to illuminate the structure of optimal policy. We deriv...
An Optimization Framework for Dynamic, Distributed Real-Time Systems
Eckert, Klaus; Juedes, David; Welch, Lonnie; Chelberg, David; Bruggerman, Carl; Drews, Frank; Fleeman, David; Parrott, David; Pfarr, Barbara
2003-01-01
Abstract. This paper presents a model that is useful for developing resource allocation algorithms for distributed real-time systems .that operate in dynamic environments. Interesting aspects of the model include dynamic environments, utility and service levels, which provide a means for graceful degradation in resource-constrained situations and support optimization of the allocation of resources. The paper also provides an allocation algorithm that illustrates how to use the model for producing feasible, optimal resource allocations.
Effect of the CTL proliferation program on virus dynamics
DEFF Research Database (Denmark)
Wodarz, Dominik; Thomsen, Allan Randrup
2005-01-01
Experiments have established that CTLs do not require continuous antigenic stimulation for expansion. Instead, responses develop by a process of programmed proliferation which involves approximately 7-10 antigen-independent cell divisions, the generation of effector cells and the differentiation...... virus loads and thus acute symptoms. The reason is that the programmed divisions are independent from antigenic stimulation, and an increase in virus load does not speed up the rate of CTL expansion. We hypothesize that the 7-10 programmed divisions observed in vivo represent an optimal solution...... into memory cells. The effect of this program on the infection dynamics and the advantages gained by the program have, however, not been explored yet. We investigate this with mathematical models. We find that more programmed divisions can make virus clearance more efficient because CTL division continues...
Directory of Open Access Journals (Sweden)
Sundström O.
2009-09-01
Full Text Available In this paper we present issues related to the implementation of dynamic programming for optimal control of a one-dimensional dynamic model, such as the hybrid electric vehicle energy management problem. A study on the resolution of the discretized state space emphasizes the need for careful implementation. A new method is presented to treat numerical issues appropriately. In particular, the method deals with numerical problems that arise due to high gradients in the optimal cost-to-go function. These gradients mainly occur on the border of the feasible state region. The proposed method not only enhances the accuracy of the final global optimum but also allows for a reduction of the state-space resolution with maintained accuracy. The latter substantially reduces the computational effort to calculate the global optimum. This allows for further applications of dynamic programming for hybrid electric vehicles such as extensive parameter studies. Cette publication présente certains problèmes concernant l’implémentation de la programmation dynamique pour le contrôle optimal d’un modèle dynamique scalaire, comme par exemple la gestion énergétique d’un véhicule hybride électrique. Une étude sur la résolution de l’espace d’état discrétisé souligne le besoin d’une implémentation minutieuse. Une nouvelle méthode qui permet de traiter des problèmes numériques d’une façon adéquate est présentée. Cette méthode permet particulièrement de résoudre des problèmes numériques engendrés par de forts gradients dans la fonction coût optimale. Ces gradients se situent surtout aux bornes de l’ensemble d’états atteignables. La méthode proposée améliore non seulement la précision de l’optimum global, mais permet aussi de réduire la résolution de l’espace d’état en conservant la précision. Le nombre de calculs nécessaire pour évaluer l’optimum global est ainsi considérablement réduit. Cela permet des
Evaluating Dynamic Analysis Techniques for Program Comprehension
Cornelissen, S.G.M.
2009-01-01
Program comprehension is an essential part of software development and software maintenance, as software must be sufficiently understood before it can be properly modified. One of the common approaches in getting to understand a program is the study of its execution, also known as dynamic analysis.
DYNAMIC PROGRAMMING – EFFICIENT TOOL FOR POWER SYSTEM EXPANSION PLANNING
Directory of Open Access Journals (Sweden)
SIMO A.
2015-03-01
Full Text Available The paper isfocusing on dynamic programming use for power system expansion planning (EP – transmission network (TNEP and distribution network (DNEP. The EP problem has been approached from the retrospective and prospective point of view. To achieve this goal, the authors are developing two software-tools in Matlab environment. Two techniques have been tackled: particle swarm optimization (PSO and genetic algorithms (GA. The case study refers to Test 25 buses test power system developed within the Power Systems Department.
First principles molecular dynamics without self-consistent field optimization
International Nuclear Information System (INIS)
Souvatzis, Petros; Niklasson, Anders M. N.
2014-01-01
We present a first principles molecular dynamics approach that is based on time-reversible extended Lagrangian Born-Oppenheimer molecular dynamics [A. M. N. Niklasson, Phys. Rev. Lett. 100, 123004 (2008)] in the limit of vanishing self-consistent field optimization. The optimization-free dynamics keeps the computational cost to a minimum and typically provides molecular trajectories that closely follow the exact Born-Oppenheimer potential energy surface. Only one single diagonalization and Hamiltonian (or Fockian) construction are required in each integration time step. The proposed dynamics is derived for a general free-energy potential surface valid at finite electronic temperatures within hybrid density functional theory. Even in the event of irregular functional behavior that may cause a dynamical instability, the optimization-free limit represents a natural starting guess for force calculations that may require a more elaborate iterative electronic ground state optimization. Our optimization-free dynamics thus represents a flexible theoretical framework for a broad and general class of ab initio molecular dynamics simulations
Review of dynamic optimization methods in renewable natural resource management
Williams, B.K.
1989-01-01
In recent years, the applications of dynamic optimization procedures in natural resource management have proliferated. A systematic review of these applications is given in terms of a number of optimization methodologies and natural resource systems. The applicability of the methods to renewable natural resource systems are compared in terms of system complexity, system size, and precision of the optimal solutions. Recommendations are made concerning the appropriate methods for certain kinds of biological resource problems.
Programs To Optimize Spacecraft And Aircraft Trajectories
Brauer, G. L.; Petersen, F. M.; Cornick, D.E.; Stevenson, R.; Olson, D. W.
1994-01-01
POST/6D POST is set of two computer programs providing ability to target and optimize trajectories of powered or unpowered spacecraft or aircraft operating at or near rotating planet. POST treats point-mass, three-degree-of-freedom case. 6D POST treats more-general rigid-body, six-degree-of-freedom (with point masses) case. Used to solve variety of performance, guidance, and flight-control problems for atmospheric and orbital vehicles. Applications include computation of performance or capability of vehicle in ascent, or orbit, and during entry into atmosphere, simulation and analysis of guidance and flight-control systems, dispersion-type analyses and analyses of loads, general-purpose six-degree-of-freedom simulation of controlled and uncontrolled vehicles, and validation of performance in six degrees of freedom. Written in FORTRAN 77 and C language. Two machine versions available: one for SUN-series computers running SunOS(TM) (LAR-14871) and one for Silicon Graphics IRIS computers running IRIX(TM) operating system (LAR-14869).
Optimization of Regional Geodynamic Models for Mantle Dynamics
Knepley, M.; Isaac, T.; Jadamec, M. A.
2016-12-01
The SubductionGenerator program is used to construct high resolution, 3D regional thermal structures for mantle convection simulations using a variety of data sources, including sea floor ages and geographically referenced 3D slab locations based on seismic observations. The initial bulk temperature field is constructed using a half-space cooling model or plate cooling model, and related smoothing functions based on a diffusion length-scale analysis. In this work, we seek to improve the 3D thermal model and test different model geometries and dynamically driven flow fields using constraints from observed seismic velocities and plate motions. Through a formal adjoint analysis, we construct the primal-dual version of the multi-objective PDE-constrained optimization problem for the plate motions and seismic misfit. We have efficient, scalable preconditioners for both the forward and adjoint problems based upon a block preconditioning strategy, and a simple gradient update is used to improve the control residual. The full optimal control problem is formulated on a nested hierarchy of grids, allowing a nonlinear multigrid method to accelerate the solution.
Liu, Ping; Li, Guodong; Liu, Xinggao
2015-09-01
Control vector parameterization (CVP) is an important approach of the engineering optimization for the industrial dynamic processes. However, its major defect, the low optimization efficiency caused by calculating the relevant differential equations in the generated nonlinear programming (NLP) problem repeatedly, limits its wide application in the engineering optimization for the industrial dynamic processes. A novel highly effective control parameterization approach, fast-CVP, is first proposed to improve the optimization efficiency for industrial dynamic processes, where the costate gradient formulae is employed and a fast approximate scheme is presented to solve the differential equations in dynamic process simulation. Three well-known engineering optimization benchmark problems of the industrial dynamic processes are demonstrated as illustration. The research results show that the proposed fast approach achieves a fine performance that at least 90% of the computation time can be saved in contrast to the traditional CVP method, which reveals the effectiveness of the proposed fast engineering optimization approach for the industrial dynamic processes. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Stochastic quasi-gradient based optimization algorithms for dynamic reliability applications
International Nuclear Information System (INIS)
Bourgeois, F.; Labeau, P.E.
2001-01-01
On one hand, PSA results are increasingly used in decision making, system management and optimization of system design. On the other hand, when severe accidental transients are considered, dynamic reliability appears appropriate to account for the complex interaction between the transitions between hardware configurations, the operator behavior and the dynamic evolution of the system. This paper presents an exploratory work in which the estimation of the system unreliability in a dynamic context is coupled with an optimization algorithm to determine the 'best' safety policy. Because some reliability parameters are likely to be distributed, the cost function to be minimized turns out to be a random variable. Stochastic programming techniques are therefore envisioned to determine an optimal strategy. Monte Carlo simulation is used at all stages of the computations, from the estimation of the system unreliability to that of the stochastic quasi-gradient. The optimization algorithm is illustrated on a HNO 3 supply system
Airborne Network Optimization with Dynamic Network Update
2015-03-26
source si and a target ti . For each commodity (si, ki) the commodity specifies a non- negative demand di [5]. The objective of the multi-commodity...queue predictions, and network con- gestion [15]. The implementation of the DRQC uses the Kalman filter to predict the state of the network and optimize
DEFF Research Database (Denmark)
Rong, Aiying; Hakonen, Henri; Lahdelma, Risto
2009-01-01
efficiency of the plants. We introduce in this paper the DRDP-RSC algorithm, which is a dynamic regrouping based dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units, sequential commitment of units in small groups. Relaxed states of the plants are used to reduce...... the dimension of the UC problem and dynamic regrouping is used to improve the solution quality. Numerical results based on real-life data sets show that this algorithm is efficient and optimal or near-optimal solutions with very small optimality gap are obtained....
Bridging developmental systems theory and evolutionary psychology using dynamic optimization.
Frankenhuis, Willem E; Panchanathan, Karthik; Clark Barrett, H
2013-07-01
Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic optimization integrates developmental systems theorists' focus on dynamics and contingency with the 'design stance' of evolutionary psychology. It provides a theoretical framework as well as a set of tools for exploring the properties of developmental systems that natural selection might favor, given particular evolutionary ecologies. We also discuss limitations of the approach. © 2013 Blackwell Publishing Ltd.
Optimizing Technology-Oriented Constructional Paramour's of complex dynamic systems
International Nuclear Information System (INIS)
Novak, S.M.
1998-01-01
Creating optimal vibro systems requires sequential solving of a few problems: selecting the basic pattern of dynamic actions, synthesizing the dynamic active systems, optimizing technological, technical, economic and design parameters. This approach is illustrated by an example of a high-efficiency vibro system synthesized for forming building structure components. When using only one single source to excite oscillations, resonance oscillations are imparted to the product to be formed in the horizontal and vertical planes. In order to obtain versatile and dynamically optimized parameters, a factor is introduced into the differential equations of the motion, accounting for the relationship between the parameters, which determine the frequency characteristics of the system and the parameter variation range. This results in obtaining non-sophisticated mathematical models of the system under investigation, convenient for optimization and for engineering design and calculations as well
Optimal Portfolios Under Dynamic Shortfall Constraints | Akume ...
African Journals Online (AJOL)
industry standard with regulatory authorities enforcing its use in risk measurement and management. Despite its widespread acceptance, VaR is not coherent. Tail Conditional Expectation (TCE), on the other hand, for an underlying continuous distribution, is a coherent risk measures. Our focus in this paper is the dynamic ...
Optimal Portfolios Under Dynamic Shortfall Constraints
African Journals Online (AJOL)
industry standard with regulatory authorities enforcing its use in risk measure- ment and management. Despite its widespread acceptance, VaR is not coherent. Tail Conditional Expectation (TCE), on the other hand, for an underlying con- tinuous distribution, is a coherent risk measures. Our focus in this paper is the dynamic ...
A short note on dynamic programming in a band.
Gibrat, Jean-François
2018-06-15
Third generation sequencing technologies generate long reads that exhibit high error rates, in particular for insertions and deletions which are usually the most difficult errors to cope with. The only exact algorithm capable of aligning sequences with insertions and deletions is a dynamic programming algorithm. In this note, for the sake of efficiency, we consider dynamic programming in a band. We show how to choose the band width in function of the long reads' error rates, thus obtaining an [Formula: see text] algorithm in space and time. We also propose a procedure to decide whether this algorithm, when applied to semi-global alignments, provides the optimal score. We suggest that dynamic programming in a band is well suited to the problem of aligning long reads between themselves and can be used as a core component of methods for obtaining a consensus sequence from the long reads alone. The function implementing the dynamic programming algorithm in a band is available, as a standalone program, at: https://forgemia.inra.fr/jean-francois.gibrat/BAND_DYN_PROG.git.
Practical synchronization on complex dynamical networks via optimal pinning control
Li, Kezan; Sun, Weigang; Small, Michael; Fu, Xinchu
2015-07-01
We consider practical synchronization on complex dynamical networks under linear feedback control designed by optimal control theory. The control goal is to minimize global synchronization error and control strength over a given finite time interval, and synchronization error at terminal time. By utilizing the Pontryagin's minimum principle, and based on a general complex dynamical network, we obtain an optimal system to achieve the control goal. The result is verified by performing some numerical simulations on Star networks, Watts-Strogatz networks, and Barabási-Albert networks. Moreover, by combining optimal control and traditional pinning control, we propose an optimal pinning control strategy which depends on the network's topological structure. Obtained results show that optimal pinning control is very effective for synchronization control in real applications.
Optimization algorithm based on densification and dynamic canonical descent
Bousson, K.; Correia, S. D.
2006-07-01
Stochastic methods have gained some popularity in global optimization in that most of them do not assume the cost functions to be differentiable. They have capabilities to avoid being trapped by local optima, and may converge even faster than gradient-based optimization methods on some problems. The present paper proposes an optimization method, which reduces the search space by means of densification curves, coupled with the dynamic canonical descent algorithm. The performances of the new method are shown on several known problems classically used for testing optimization algorithms, and proved to outperform competitive algorithms such as simulated annealing and genetic algorithms.
Dynamic analysis program for frame structure
International Nuclear Information System (INIS)
Ando, Kozo; Chiba, Toshio
1975-01-01
A general purpose computer program named ISTRAN/FD (Isub(HI) STRucture ANalysis/Frame structure, Dynamic analysis) has been developed for dynamic analysis of three-dimensional frame structures. This program has functions of free vibration analysis, seismic response analysis, graphic display by plotter and CRT, etc. This paper introduces ISTRAN/FD; examples of its application are shown with various problems : idealization of the cantilever, dynamic analysis of the main tower of the suspension bridge, three-dimensional vibration in the plate girder bridge, seismic response in the boiler steel structure, and dynamic properties of the underground LNG tank. In this last example, solid elements, in addition to beam elements, are especially used for the analysis. (auth.)
Optimization of Measurements on Dynamically Sensitive Structures Using a Reliability Approach
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune
Design of a measuring program devoted to parameter identification of structural dynamic systems described by random fields is considered. The design problem is formulated as an optimization problem to minimize the total expected costs due to failure and costs of a measuring program. Design variab...... variables are the numbers of measuring points, the locations of these points and the required number of sample records. An example with a simply supported plane, vibrating beam is considered and tentative results are presented.......Design of a measuring program devoted to parameter identification of structural dynamic systems described by random fields is considered. The design problem is formulated as an optimization problem to minimize the total expected costs due to failure and costs of a measuring program. Design...
Optimization of Measurements on Dynamically Sensitive Structures Using a Reliability Approach
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Sørensen, John Dalsgaard; Brincker, Rune
1990-01-01
Design of measuring program devoted to parameter identification of structural dynamic systems described by random fields is considered. The design problem is formulated as an optimization problem to minimize the total expected costs due to failure and costs of masuring program. Design variables a...... are the numbers of measuring points, the locations of these points and the required number of sample records. An example with a simply supported plane, vibrating beam is considered and tentative results are presented.......Design of measuring program devoted to parameter identification of structural dynamic systems described by random fields is considered. The design problem is formulated as an optimization problem to minimize the total expected costs due to failure and costs of masuring program. Design variables...
Planar multibody dynamics formulation, programming and applications
Nikravesh, Parviz E
2007-01-01
Introduction Multibody Mechanical Systems Types of Analyses Methods of Formulation Computer Programming Application Examples Unit System Remarks Preliminaries Reference Axes Scalars and Vectors Matrices Vector, Array, and Matrix Differentiation Equations and Expressions Remarks Problems Fundamentals of Kinematics A Particle Kinematics of a Rigid Body Definitions Remarks Problems Fundamentals of Dynamics Newton's Laws of Motion Dynamics of a Body Force Elements Applied Forces Reaction Force Remarks Problems Point-Coordinates: Kinematics Multipoint
Gradient-based optimization in nonlinear structural dynamics
DEFF Research Database (Denmark)
Dou, Suguang
The intrinsic nonlinearity of mechanical structures can give rise to rich nonlinear dynamics. Recently, nonlinear dynamics of micro-mechanical structures have contributed to developing new Micro-Electro-Mechanical Systems (MEMS), for example, atomic force microscope, passive frequency divider......, frequency stabilization, and disk resonator gyroscope. For advanced design of these structures, it is of considerable value to extend current optimization in linear structural dynamics into nonlinear structural dynamics. In this thesis, we present a framework for modelling, analysis, characterization......, and optimization of nonlinear structural dynamics. In the modelling, nonlinear finite elements are used. In the analysis, nonlinear frequency response and nonlinear normal modes are calculated based on a harmonic balance method with higher-order harmonics. In the characterization, nonlinear modal coupling...
Directory of Open Access Journals (Sweden)
Farzad Amirkhani
2017-03-01
The proposed method is implemented on classical job-shop problems with objective of makespan and results are compared with mixed integer programming model. Moreover, the appropriate dispatching priorities are achieved for dynamic job-shop problem minimizing a multi-objective criteria. The results show that simulation-based optimization are highly capable to capture the main characteristics of the shop and produce optimal/near-optimal solutions with highly credibility degree.
An algorithm for the solution of dynamic linear programs
Psiaki, Mark L.
1989-01-01
The algorithm's objective is to efficiently solve Dynamic Linear Programs (DLP) by taking advantage of their special staircase structure. This algorithm constitutes a stepping stone to an improved algorithm for solving Dynamic Quadratic Programs, which, in turn, would make the nonlinear programming method of Successive Quadratic Programs more practical for solving trajectory optimization problems. The ultimate goal is to being trajectory optimization solution speeds into the realm of real-time control. The algorithm exploits the staircase nature of the large constraint matrix of the equality-constrained DLPs encountered when solving inequality-constrained DLPs by an active set approach. A numerically-stable, staircase QL factorization of the staircase constraint matrix is carried out starting from its last rows and columns. The resulting recursion is like the time-varying Riccati equation from multi-stage LQR theory. The resulting factorization increases the efficiency of all of the typical LP solution operations over that of a dense matrix LP code. At the same time numerical stability is ensured. The algorithm also takes advantage of dynamic programming ideas about the cost-to-go by relaxing active pseudo constraints in a backwards sweeping process. This further decreases the cost per update of the LP rank-1 updating procedure, although it may result in more changes of the active set that if pseudo constraints were relaxed in a non-stagewise fashion. The usual stability of closed-loop Linear/Quadratic optimally-controlled systems, if it carries over to strictly linear cost functions, implies that the saving due to reduced factor update effort may outweigh the cost of an increased number of updates. An aerospace example is presented in which a ground-to-ground rocket's distance is maximized. This example demonstrates the applicability of this class of algorithms to aerospace guidance. It also sheds light on the efficacy of the proposed pseudo constraint relaxation
Measurement configuration optimization for dynamic metrology using Stokes polarimetry
Liu, Jiamin; Zhang, Chuanwei; Zhong, Zhicheng; Gu, Honggang; Chen, Xiuguo; Jiang, Hao; Liu, Shiyuan
2018-05-01
As dynamic loading experiments such as a shock compression test are usually characterized by short duration, unrepeatability and high costs, high temporal resolution and precise accuracy of the measurements is required. Due to high temporal resolution up to a ten-nanosecond-scale, a Stokes polarimeter with six parallel channels has been developed to capture such instantaneous changes in optical properties in this paper. Since the measurement accuracy heavily depends on the configuration of the probing beam incident angle and the polarizer azimuth angle, it is important to select an optimal combination from the numerous options. In this paper, a systematic error propagation-based measurement configuration optimization method corresponding to the Stokes polarimeter was proposed. The maximal Frobenius norm of the combinatorial matrix of the configuration error propagating matrix and the intrinsic error propagating matrix is introduced to assess the measurement accuracy. The optimal configuration for thickness measurement of a SiO2 thin film deposited on a Si substrate has been achieved by minimizing the merit function. Simulation and experimental results show a good agreement between the optimal measurement configuration achieved experimentally using the polarimeter and the theoretical prediction. In particular, the experimental result shows that the relative error in the thickness measurement can be reduced from 6% to 1% by using the optimal polarizer azimuth angle when the incident angle is 45°. Furthermore, the optimal configuration for the dynamic metrology of a nickel foil under quasi-dynamic loading is investigated using the proposed optimization method.
Evaluation and improvement of dynamic optimality in electrochemical reactors
International Nuclear Information System (INIS)
Vijayasekaran, B.; Basha, C. Ahmed
2005-01-01
A systematic approach for the dynamic optimization problem statement to improve the dynamic optimality in electrochemical reactors is presented in this paper. The formulation takes an account of the diffusion phenomenon in the electrode/electrolyte interface. To demonstrate the present methodology, the optimal time-varying electrode potential for a coupled chemical-electrochemical reaction scheme, that maximizes the production of the desired product in a batch electrochemical reactor with/without recirculation are determined. The dynamic optimization problem statement, based upon this approach, is a nonlinear differential algebraic system, and its solution provides information about the optimal policy. Optimal control policy at different conditions is evaluated using the best-known Pontryagin's maximum principle. The two-point boundary value problem resulting from the application of the maximum principle is then solved using the control vector iteration technique. These optimal time-varying profiles of electrode potential are then compared to the best uniform operation through the relative improvements of the performance index. The application of the proposed approach to two electrochemical systems, described by ordinary differential equations, shows that the existing electrochemical process control strategy could be improved considerably when the proposed method is incorporated
Lean and Efficient Software: Whole-Program Optimization of Executables
2015-09-30
Lean and Efficient Software: Whole-Program Optimization of Executables” Project Summary Report #5 (Report Period: 7/1/2015 to 9/30/2015...TYPE 3. DATES COVERED 00-00-2015 to 00-00-2015 4. TITLE AND SUBTITLE Lean and Efficient Software: Whole-Program Optimization of Executables 5a...unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18 Lean and Efficient Software: Whole-Program
SMART Optimization of a Parenting Program for Active Duty Families
2017-10-01
child and caregiver outcomes over time, based on a sample of 200 military personnel and their co- parents who have recently or will soon separate from...AWARD NUMBER: W81XWH-16-1-0407 TITLE: SMART Optimization of a Parenting Program for Active Duty Families PRINCIPAL INVESTIGATOR: Abigail...Optimization of a Parenting Program for Active Duty 5a. CONTRACT NUMBER Families 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Abigail
A dynamic global and local combined particle swarm optimization algorithm
International Nuclear Information System (INIS)
Jiao Bin; Lian Zhigang; Chen Qunxian
2009-01-01
Particle swarm optimization (PSO) algorithm has been developing rapidly and many results have been reported. PSO algorithm has shown some important advantages by providing high speed of convergence in specific problems, but it has a tendency to get stuck in a near optimal solution and one may find it difficult to improve solution accuracy by fine tuning. This paper presents a dynamic global and local combined particle swarm optimization (DGLCPSO) algorithm to improve the performance of original PSO, in which all particles dynamically share the best information of the local particle, global particle and group particles. It is tested with a set of eight benchmark functions with different dimensions and compared with original PSO. Experimental results indicate that the DGLCPSO algorithm improves the search performance on the benchmark functions significantly, and shows the effectiveness of the algorithm to solve optimization problems.
Conversion Rate Optimization : Visual Neuro Programming Principles
Berezhnaya, Anastasia
2016-01-01
The influence of the world wide web has already spread in every business. Consequently, it has become crucial to develop strong online presence and offer qualified user experience for website visitors. Website optimization undeniably has proved its importance in the recent decade. This research was conducted in order to study the practical application and structure of the stages of the CRO (Conversion Rate Optimization) framework that focuses on the most representative website metric – c...
Directory of Open Access Journals (Sweden)
Jooyoung Park
2015-05-01
Full Text Available Recently, the optimization of power flows in portable hybrid power-supply systems (HPSSs has become an important issue with the advent of a variety of mobile systems and hybrid energy technologies. In this paper, a control strategy is considered for dynamically managing power flows in portable HPSSs employing batteries and supercapacitors. Our dynamic power management strategy utilizes the concept of approximate dynamic programming (ADP. ADP methods are important tools in the fields of stochastic control and machine learning, and the utilization of these tools for practical engineering problems is now an active and promising research field. We propose an ADP-based procedure based on optimization under constraints including the iterated Bellman inequalities, which can be solved by convex optimization carried out offline, to find the optimal power management rules for portable HPSSs. The effectiveness of the proposed procedure is tested through dynamic simulations for smartphone workload scenarios, and simulation results show that the proposed strategy can successfully cope with uncertain workload demands.
Energy Technology Data Exchange (ETDEWEB)
Kim, Jong Woo; Choi, Go Bong; Lee, Jong Min [Seoul National University, Seoul (Korea, Republic of); Suh, Jung Chul [Samchully Corporation, Seoul (Korea, Republic of)
2016-01-15
This paper proposes a Markov decision process (MDP) based approach to derive an optimal schedule of maintenance, rehabilitation and replacement of the water main system. The scheduling problem utilizes auxiliary information of a pipe such as the current state, cost, and deterioration model. The objective function and detailed algorithm of dynamic programming are modified to solve the periodic replacement problem. The optimal policy evaluated by the proposed algorithm is compared to several existing policies via Monte Carlo simulations. The proposed decision framework provides a systematic way to obtain an optimal policy.
The Dynamic Geometrisation of Computer Programming
Sinclair, Nathalie; Patterson, Margaret
2018-01-01
The goal of this paper is to explore dynamic geometry environments (DGE) as a type of computer programming language. Using projects created by secondary students in one particular DGE, we analyse the extent to which the various aspects of computational thinking--including both ways of doing things and particular concepts--were evident in their…
A Dynamic Programming Approach to Constrained Portfolios
DEFF Research Database (Denmark)
Kraft, Holger; Steffensen, Mogens
2013-01-01
This paper studies constrained portfolio problems that may involve constraints on the probability or the expected size of a shortfall of wealth or consumption. Our first contribution is that we solve the problems by dynamic programming, which is in contrast to the existing literature that applies...
Quantum optical device accelerating dynamic programming
Grigoriev, D.; Kazakov, A.; Vakulenko, S.
2005-01-01
In this paper we discuss analogue computers based on quantum optical systems accelerating dynamic programming for some computational problems. These computers, at least in principle, can be realized by actually existing devices. We estimate an acceleration in resolving of some NP-hard problems that can be obtained in such a way versus deterministic computers
Optimality Conditions for Fuzzy Number Quadratic Programming with Fuzzy Coefficients
Directory of Open Access Journals (Sweden)
Xue-Gang Zhou
2014-01-01
Full Text Available The purpose of the present paper is to investigate optimality conditions and duality theory in fuzzy number quadratic programming (FNQP in which the objective function is fuzzy quadratic function with fuzzy number coefficients and the constraint set is fuzzy linear functions with fuzzy number coefficients. Firstly, the equivalent quadratic programming of FNQP is presented by utilizing a linear ranking function and the dual of fuzzy number quadratic programming primal problems is introduced. Secondly, we present optimality conditions for fuzzy number quadratic programming. We then prove several duality results for fuzzy number quadratic programming problems with fuzzy coefficients.
Microsoft Dynamics NAV 7 programming cookbook
Raul, Rakesh
2013-01-01
Written in the style of a cookbook. Microsoft Dynamics NAV 7 Programming Cookbook is full of recipes to help you get the job done.If you are a junior / entry-level NAV developer then the first half of the book is designed primarily for you. You may or may not have any experience programming. It focuses on the basics of NAV programming.If you are a mid-level NAV developer, you will find these chapters explain how to think outside of the NAV box when building solutions. There are also recipes that senior developers will find useful.
Optimal Dynamic Pricing for Perishable Assets with Nonhomogeneous Demand
Wen Zhao; Yu-Sheng Zheng
2000-01-01
We consider a dynamic pricing model for selling a given stock of a perishable product over a finite time horizon. Customers, whose reservation price distribution changes over time, arrive according to a nonhomogeneous Poisson process. We show that at any given time, the optimal price decreases with inventory. We also identify a sufficient condition under which the optimal price decreases over time for a given inventory level. This sufficient condition requires that the willingness of a custom...
Dynamic optimization of a FCC converter unit: numerical analysis
Directory of Open Access Journals (Sweden)
E. Almeida Nt
2011-03-01
Full Text Available Fluidized-bed Catalytic Cracking (FCC is a process subject to frequent variations in the operating conditions (including feed quality and feed rate. The production objectives usually are the maximization of LPG and gasoline production. This fact makes the FCC converter unit an excellent opportunity for real-time optimization. The present work aims to apply a dynamic optimization in an industrial FCC converter unit, using a mechanistic dynamic model, and to carry out a numerical analysis of the solution procedure. A simultaneous approach was used to discretize the system of differential-algebraic equations and the resulting large-scale NLP problem was solved using the IPOPT solver. This study also does a short comparison between the results obtained by a potential dynamic real-time optimization (DRTO against a possible steady-state real-time optimization (RTO application. The results demonstrate that the application of dynamic real-time optimization of a FCC converter unit can bring significant benefits in production.
Optimal Piecewise-Linear Approximation of the Quadratic Chaotic Dynamics
Directory of Open Access Journals (Sweden)
J. Petrzela
2012-04-01
Full Text Available This paper shows the influence of piecewise-linear approximation on the global dynamics associated with autonomous third-order dynamical systems with the quadratic vector fields. The novel method for optimal nonlinear function approximation preserving the system behavior is proposed and experimentally verified. This approach is based on the calculation of the state attractor metric dimension inside a stochastic optimization routine. The approximated systems are compared to the original by means of the numerical integration. Real electronic circuits representing individual dynamical systems are derived using classical as well as integrator-based synthesis and verified by time-domain analysis in Orcad Pspice simulator. The universality of the proposed method is briefly discussed, especially from the viewpoint of the higher-order dynamical systems. Future topics and perspectives are also provided
Numerical methods of mathematical optimization with Algol and Fortran programs
Künzi, Hans P; Zehnder, C A; Rheinboldt, Werner
1971-01-01
Numerical Methods of Mathematical Optimization: With ALGOL and FORTRAN Programs reviews the theory and the practical application of the numerical methods of mathematical optimization. An ALGOL and a FORTRAN program was developed for each one of the algorithms described in the theoretical section. This should result in easy access to the application of the different optimization methods.Comprised of four chapters, this volume begins with a discussion on the theory of linear and nonlinear optimization, with the main stress on an easily understood, mathematically precise presentation. In addition
Directory of Open Access Journals (Sweden)
Wang Qing
2015-04-01
Full Text Available In order to alleviate the dynamic stall effects in helicopter rotor, the sequential quadratic programming (SQP method is employed to optimize the characteristics of airfoil under dynamic stall conditions based on the SC1095 airfoil. The geometry of airfoil is parameterized by the class-shape-transformation (CST method, and the C-topology body-fitted mesh is then automatically generated around the airfoil by solving the Poisson equations. Based on the grid generation technology, the unsteady Reynolds-averaged Navier-Stokes (RANS equations are chosen as the governing equations for predicting airfoil flow field and the highly-efficient implicit scheme of lower–upper symmetric Gauss–Seidel (LU-SGS is adopted for temporal discretization. To capture the dynamic stall phenomenon of the rotor more accurately, the Spalart–Allmaras turbulence model is employed to close the RANS equations. The optimized airfoil with a larger leading edge radius and camber is obtained. The leading edge vortex and trailing edge separation of the optimized airfoil under unsteady conditions are obviously weakened, and the dynamic stall characteristics of optimized airfoil at different Mach numbers, reduced frequencies and angles of attack are also obviously improved compared with the baseline SC1095 airfoil. It is demonstrated that the optimized method is effective and the optimized airfoil is suitable as the helicopter rotor airfoil.
Design and Analysis of Decision Rules via Dynamic Programming
Amin, Talha M.
2017-04-24
The areas of machine learning, data mining, and knowledge representation have many different formats used to represent information. Decision rules, amongst these formats, are the most expressive and easily-understood by humans. In this thesis, we use dynamic programming to design decision rules and analyze them. The use of dynamic programming allows us to work with decision rules in ways that were previously only possible for brute force methods. Our algorithms allow us to describe the set of all rules for a given decision table. Further, we can perform multi-stage optimization by repeatedly reducing this set to only contain rules that are optimal with respect to selected criteria. One way that we apply this study is to generate small systems with short rules by simulating a greedy algorithm for the set cover problem. We also compare maximum path lengths (depth) of deterministic and non-deterministic decision trees (a non-deterministic decision tree is effectively a complete system of decision rules) with regards to Boolean functions. Another area of advancement is the presentation of algorithms for constructing Pareto optimal points for rules and rule systems. This allows us to study the existence of “totally optimal” decision rules (rules that are simultaneously optimal with regards to multiple criteria). We also utilize Pareto optimal points to compare and rate greedy heuristics with regards to two criteria at once. Another application of Pareto optimal points is the study of trade-offs between cost and uncertainty which allows us to find reasonable systems of decision rules that strike a balance between length and accuracy.
Focusing light through dynamical samples using fast continuous wavefront optimization.
Blochet, B; Bourdieu, L; Gigan, S
2017-12-01
We describe a fast continuous optimization wavefront shaping system able to focus light through dynamic scattering media. A micro-electro-mechanical system-based spatial light modulator, a fast photodetector, and field programmable gate array electronics are combined to implement a continuous optimization of a wavefront with a single-mode optimization rate of 4.1 kHz. The system performances are demonstrated by focusing light through colloidal solutions of TiO 2 particles in glycerol with tunable temporal stability.
Optimal interdependence enhances the dynamical robustness of complex systems
Singh, Rishu Kumar; Sinha, Sitabhra
2017-08-01
Although interdependent systems have usually been associated with increased fragility, we show that strengthening the interdependence between dynamical processes on different networks can make them more likely to survive over long times. By coupling the dynamics of networks that in isolation exhibit catastrophic collapse with extinction of nodal activity, we demonstrate system-wide persistence of activity for an optimal range of interdependence between the networks. This is related to the appearance of attractors of the global dynamics comprising disjoint sets ("islands") of stable activity.
Off-road vehicle dynamics analysis, modelling and optimization
Taghavifar, Hamid
2017-01-01
This book deals with the analysis of off-road vehicle dynamics from kinetics and kinematics perspectives and the performance of vehicle traversing over rough and irregular terrain. The authors consider the wheel performance, soil-tire interactions and their interface, tractive performance of the vehicle, ride comfort, stability over maneuvering, transient and steady state conditions of the vehicle traversing, modeling the aforementioned aspects and optimization from energetic and vehicle mobility perspectives. This book brings novel figures for the transient dynamics and original wheel terrain dynamics at on-the-go condition.
New numerical methods for open-loop and feedback solutions to dynamic optimization problems
Ghosh, Pradipto
The topic of the first part of this research is trajectory optimization of dynamical systems via computational swarm intelligence. Particle swarm optimization is a nature-inspired heuristic search method that relies on a group of potential solutions to explore the fitness landscape. Conceptually, each particle in the swarm uses its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on an optimal or near-optimal solution. It is relatively straightforward to implement and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although particle swarm optimization has been successfully employed in solving static optimization problems, its application in dynamic optimization, as posed in optimal control theory, is still relatively new. In the first half of this thesis particle swarm optimization is used to generate near-optimal solutions to several nontrivial trajectory optimization problems including thrust programming for minimum fuel, multi-burn spacecraft orbit transfer, and computing minimum-time rest-to-rest trajectories for a robotic manipulator. A distinct feature of the particle swarm optimization implementation in this work is the runtime selection of the optimal solution structure. Optimal trajectories are generated by solving instances of constrained nonlinear mixed-integer programming problems with the swarming technique. For each solved optimal programming problem, the particle swarm optimization result is compared with a nearly exact solution found via a direct method using nonlinear programming. Numerical experiments indicate that swarm search can locate solutions to very great accuracy. The second half of this research develops a new extremal-field approach for synthesizing nearly optimal feedback controllers for optimal control and two-player pursuit-evasion games described by general nonlinear differential equations. A notable revelation from this development
On the Dynamic Programming Approach for the 3D Navier-Stokes Equations
International Nuclear Information System (INIS)
Manca, Luigi
2008-01-01
The dynamic programming approach for the control of a 3D flow governed by the stochastic Navier-Stokes equations for incompressible fluid in a bounded domain is studied. By a compactness argument, existence of solutions for the associated Hamilton-Jacobi-Bellman equation is proved. Finally, existence of an optimal control through the feedback formula and of an optimal state is discussed
Optimal foraging and predator-prey dynamics III
Czech Academy of Sciences Publication Activity Database
Křivan, Vlastimil; Eisner, Jan
2003-01-01
Roč. 63, - (2003), s. 269-279 ISSN 0040-5809 R&D Projects: GA ČR GA201/03/0091; GA MŠk LA 101 Institutional research plan: CEZ:AV0Z5007907 Keywords : Optimal foraging theory * adaptive behavior * predator-prec population dynamics Subject RIV: EH - Ecology, Behaviour Impact factor: 2.261, year: 2003
Product quality driven design of bakery operations using dynamic optimization
Hadiyanto, M.; Esveld, D.C.; Boom, R.M.; Straten, van G.; Boxtel, van A.J.B.
2008-01-01
Abstract Quality driven design uses specified product qualities as a starting point for process design. By backward reasoning the required process conditions and processing system were found. In this work dynamic optimization was used as a tool to generate processing solutions for baking processes
Optimization of multi-response dynamic systems integrating multiple ...
African Journals Online (AJOL)
It also results in better optimization performance than back-propagation neural network-based approach and data mining-based approach reported by the past researchers. Keywords: multiple responses, multiple regression, weighted dynamic signal-to-noise ratio, performance measure modelling, response function ...
A Thermodynamic Library for Simulation and Optimization of Dynamic Processes
DEFF Research Database (Denmark)
Ritschel, Tobias Kasper Skovborg; Gaspar, Jozsef; Jørgensen, John Bagterp
2017-01-01
Process system tools, such as simulation and optimization of dynamic systems, are widely used in the process industries for development of operational strategies and control for process systems. These tools rely on thermodynamic models and many thermodynamic models have been developed for different...... compounds and mixtures. However, rigorous thermodynamic models are generally computationally intensive and not available as open-source libraries for process simulation and optimization. In this paper, we describe the application of a novel open-source rigorous thermodynamic library, ThermoLib, which...... is designed for dynamic simulation and optimization of vapor-liquid processes. ThermoLib is implemented in Matlab and C and uses cubic equations of state to compute vapor and liquid phase thermodynamic properties. The novelty of ThermoLib is that it provides analytical first and second order derivatives...
Dynamic ADMM for Real-time Optimal Power Flow: Preprint
Energy Technology Data Exchange (ETDEWEB)
Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States)
2018-02-23
This paper considers distribution networks featuring distributed energy resources (DERs), and develops a dynamic optimization method to maximize given operational objectives in real time while adhering to relevant network constraints. The design of the dynamic algorithm is based on suitable linearizations of the AC power flow equations, and it leverages the so-called alternating direction method of multipliers (ADMM). The steps of the ADMM, however, are suitably modified to accommodate appropriate measurements from the distribution network and the DERs. With the aid of these measurements, the resultant algorithm can enforce given operational constraints in spite of inaccuracies in the representation of the AC power flows, and it avoids ubiquitous metering to gather the state of non-controllable resources. Optimality and convergence of the propose algorithm are established in terms of tracking of the solution of a convex surrogate of the AC optimal power flow problem.
Complex fluid network optimization and control integrative design based on nonlinear dynamic model
International Nuclear Information System (INIS)
Sui, Jinxue; Yang, Li; Hu, Yunan
2016-01-01
In view of distribution according to complex fluid network’s needs, this paper proposed one optimization computation method of the nonlinear programming mathematical model based on genetic algorithm. The simulation result shows that the overall energy consumption of the optimized fluid network has a decrease obviously. The control model of the fluid network is established based on nonlinear dynamics. We design the control law based on feedback linearization, take the optimal value by genetic algorithm as the simulation data, can also solve the branch resistance under the optimal value. These resistances can provide technical support and reference for fluid network design and construction, so can realize complex fluid network optimization and control integration design.
Nonlinear Dynamic Analysis and Optimization of Closed-Form Planetary Gear System
Directory of Open Access Journals (Sweden)
Qilin Huang
2013-01-01
Full Text Available A nonlinear purely rotational dynamic model of a multistage closed-form planetary gear set formed by two simple planetary stages is proposed in this study. The model includes time-varying mesh stiffness, excitation fluctuation and gear backlash nonlinearities. The nonlinear differential equations of motion are solved numerically using variable step-size Runge-Kutta. In order to obtain function expression of optimization objective, the nonlinear differential equations of motion are solved analytically using harmonic balance method (HBM. Based on the analytical solution of dynamic equations, the optimization mathematical model which aims at minimizing the vibration displacement of the low-speed carrier and the total mass of the gear transmission system is established. The optimization toolbox in MATLAB program is adopted to obtain the optimal solution. A case is studied to demonstrate the effectiveness of the dynamic model and the optimization method. The results show that the dynamic properties of the closed-form planetary gear transmission system have been improved and the total mass of the gear set has been decreased significantly.
Technical specification optimization program - engineered safety features
International Nuclear Information System (INIS)
Andre, G.R.; Jansen, R.L.
1986-01-01
The Westinghouse Technical Specification Program (TOP) was designed to evaluate on a quantitative basis revisions to Nuclear Power Plant Technical Specifications. The revisions are directed at simplifying plant operation, and reducing unnecessary transients, shutdowns, and manpower requirements. In conjunction with the Westinghouse Owners Group, Westinghouse initiated a program to develop a methodology to justify Technical Specification revisions; particularly revisions related to testing and maintenance requirements on plant operation for instrumentation systems. The methodology was originally developed and applied to the reactor trip features of the reactor protection system (RPS). The current study further refined the methodology and applied it to the engineered safety features of the RPS
Introducing artificial intelligence into structural optimization programs
International Nuclear Information System (INIS)
Jozwiak, S.F.
1987-01-01
Artificial Intelligence /AI/ is defined as the branch of the computer science concerned with the study of the ideas that enable computers to be intelligent. The main purpose of the application of AI in engineering is to develop computer programs which function better as tools for engineers and designers. Many computer programs today have properties which make them inconvenient to their final users and the research carried within the field of AI provides tools and techniques so that these restriction can be removed. The continuous progress in computer technology has lead to developing efficient computer systems which can be applied to more than simple solving sets of equations. (orig.)
Polyhedral and semidefinite programming methods in combinatorial optimization
Tunçel, Levent
2010-01-01
Since the early 1960s, polyhedral methods have played a central role in both the theory and practice of combinatorial optimization. Since the early 1990s, a new technique, semidefinite programming, has been increasingly applied to some combinatorial optimization problems. The semidefinite programming problem is the problem of optimizing a linear function of matrix variables, subject to finitely many linear inequalities and the positive semidefiniteness condition on some of the matrix variables. On certain problems, such as maximum cut, maximum satisfiability, maximum stable set and geometric r
Does programmed CTL proliferation optimize virus control?
DEFF Research Database (Denmark)
Wodarz, Dominik; Thomsen, Allan Randrup
2005-01-01
CD8 T-cell or cytotoxic T-lymphocyte responses develop through an antigen-independent proliferation and differentiation program. This is in contrast to the previous thinking, which was that continuous antigenic stimulation was required. This Opinion discusses why nature has chosen the proliferati...
HOPI: on-line injection optimization program
International Nuclear Information System (INIS)
LeMaire, J.L.
1977-01-01
A method of matching the beam from the 200 MeV linac to the AGS without the necessity of making emittance measurements is presented. An on-line computer program written on the PDP10 computer performs the matching by modifying independently the horizontal and vertical emittance. Experimental results show success with this method, which can be applied to any matching section
Dynamic electricity pricing for electric vehicles using stochastic programming
International Nuclear Information System (INIS)
Soares, João; Ghazvini, Mohammad Ali Fotouhi; Borges, Nuno; Vale, Zita
2017-01-01
Electric Vehicles (EVs) are an important source of uncertainty, due to their variable demand, departure time and location. In smart grids, the electricity demand can be controlled via Demand Response (DR) programs. Smart charging and vehicle-to-grid seem highly promising methods for EVs control. However, high capital costs remain a barrier to implementation. Meanwhile, incentive and price-based schemes that do not require high level of control can be implemented to influence the EVs' demand. Having effective tools to deal with the increasing level of uncertainty is increasingly important for players, such as energy aggregators. This paper formulates a stochastic model for day-ahead energy resource scheduling, integrated with the dynamic electricity pricing for EVs, to address the challenges brought by the demand and renewable sources uncertainty. The two-stage stochastic programming approach is used to obtain the optimal electricity pricing for EVs. A realistic case study projected for 2030 is presented based on Zaragoza network. The results demonstrate that it is more effective than the deterministic model and that the optimal pricing is preferable. This study indicates that adequate DR schemes like the proposed one are promising to increase the customers' satisfaction in addition to improve the profitability of the energy aggregation business. - Highlights: • A stochastic model for energy scheduling tackling several uncertainty sources. • A two-stage stochastic programming is used to tackle the developed model. • Optimal EV electricity pricing seems to improve the profits. • The propose results suggest to increase the customers' satisfaction.
Sandia Dynamic Materials Program Strategic Plan.
Energy Technology Data Exchange (ETDEWEB)
Flicker, Dawn Gustine [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Benage, John F. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Desjarlais, Michael P. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Knudson, Marcus D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Leifeste, Gordon T. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lemke, Raymond W. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Mattsson, Thomas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Wise, Jack L. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2017-05-01
Materials in nuclear and conventional weapons can reach multi-megabar pressures and 1000s of degree temperatures on timescales ranging from microseconds to nanoseconds. Understanding the response of complex materials under these conditions is important for designing and assessing changes to nuclear weapons. In the next few decades, a major concern will be evaluating the behavior of aging materials and remanufactured components. The science to enable the program to underwrite decisions quickly and confidently on use, remanufacturing, and replacement of these materials will be critical to NNSA’s new Stockpile Responsiveness Program. Material response is also important for assessing the risks posed by adversaries or proliferants. Dynamic materials research, which refers to the use of high-speed experiments to produce extreme conditions in matter, is an important part of NNSA’s Stockpile Stewardship Program.
International Nuclear Information System (INIS)
Wu, Xia; Wu, Genhua
2014-01-01
Highlights: • A high efficient method for optimization of atomic clusters is developed. • Its performance is studied by optimizing Lennard-Jones clusters and Ag clusters. • The method is proved to be quite efficient. • A new Ag 61 cluster with stacking-fault face-centered cubic motif is found. - Abstract: Geometrical optimization of atomic clusters is performed by a development of adaptive immune optimization algorithm (AIOA) with dynamic lattice searching (DLS) operation (AIOA-DLS method). By a cycle of construction and searching of the dynamic lattice (DL), DLS algorithm rapidly makes the clusters more regular and greatly reduces the potential energy. DLS can thus be used as an operation acting on the new individuals after mutation operation in AIOA to improve the performance of the AIOA. The AIOA-DLS method combines the merit of evolutionary algorithm and idea of dynamic lattice. The performance of the proposed method is investigated in the optimization of Lennard-Jones clusters within 250 atoms and silver clusters described by many-body Gupta potential within 150 atoms. Results reported in the literature are reproduced, and the motif of Ag 61 cluster is found to be stacking-fault face-centered cubic, whose energy is lower than that of previously obtained icosahedron
A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization
Directory of Open Access Journals (Sweden)
Daqing Wu
2012-01-01
Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.
Directory of Open Access Journals (Sweden)
Meiping Wang
2016-01-01
Full Text Available We developed an effective intelligent model to predict the dynamic heat supply of heat source. A hybrid forecasting method was proposed based on support vector regression (SVR model-optimized particle swarm optimization (PSO algorithms. Due to the interaction of meteorological conditions and the heating parameters of heating system, it is extremely difficult to forecast dynamic heat supply. Firstly, the correlations among heat supply and related influencing factors in the heating system were analyzed through the correlation analysis of statistical theory. Then, the SVR model was employed to forecast dynamic heat supply. In the model, the input variables were selected based on the correlation analysis and three crucial parameters, including the penalties factor, gamma of the kernel RBF, and insensitive loss function, were optimized by PSO algorithms. The optimized SVR model was compared with the basic SVR, optimized genetic algorithm-SVR (GA-SVR, and artificial neural network (ANN through six groups of experiment data from two heat sources. The results of the correlation coefficient analysis revealed the relationship between the influencing factors and the forecasted heat supply and determined the input variables. The performance of the PSO-SVR model is superior to those of the other three models. The PSO-SVR method is statistically robust and can be applied to practical heating system.
A dynamic inertia weight particle swarm optimization algorithm
International Nuclear Information System (INIS)
Jiao Bin; Lian Zhigang; Gu Xingsheng
2008-01-01
Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a set of 6 benchmark functions with 30, 50 and 150 different dimensions and compared with standard PSO. Experimental results indicate that the IPSO improves the search performance on the benchmark functions significantly
Non-linear programming method in optimization of fast reactors
International Nuclear Information System (INIS)
Pavelesku, M.; Dumitresku, Kh.; Adam, S.
1975-01-01
Application of the non-linear programming methods on optimization of nuclear materials distribution in fast reactor is discussed. The programming task composition is made on the basis of the reactor calculation dependent on the fuel distribution strategy. As an illustration of this method application the solution of simple example is given. Solution of the non-linear program is done on the basis of the numerical method SUMT. (I.T.)
Sequential Quadratic Programming Algorithms for Optimization
1989-08-01
quadratic program- ma ng (SQ(2l ) aIiatain.seenis to be relgarded aIs tie( buest choice for the solution of smiall. dlense problema (see S tour L)toS...For the step along d, note that a < nOing + 3 szH + i3.ninA A a K f~Iz,;nd and from Id1 _< ,,, we must have that for some /3 , np , 11P11 < dn"p. 5.2...Nevertheless, many of these problems are considered hard to solve. Moreover, for some of these problems the assumptions made in Chapter 2 to establish the
Directory of Open Access Journals (Sweden)
Hongling Ye
2015-01-01
Full Text Available The dynamic topology optimization of three-dimensional continuum structures subject to frequency constraints is investigated using Independent Continuous Mapping (ICM design variable fields. The composite exponential function (CEF is selected to be a filter function which recognizes the design variables and to implement the changing process of design variables from “discrete” to “continuous” and back to “discrete.” Explicit formulations of frequency constraints are given based on filter functions, first-order Taylor series expansion. And an improved optimal model is formulated using CEF and the explicit frequency constraints. Dual sequential quadratic programming (DSQP algorithm is used to solve the optimal model. The program is developed on the platform of MSC Patran & Nastran. Finally, numerical examples are given to demonstrate the validity and applicability of the proposed method.
Dynamic Optimal Energy Flow in the Integrated Natural Gas and Electrical Power Systems
DEFF Research Database (Denmark)
Fang, Jiakun; Zeng, Qing; Ai, Xiaomeng
2018-01-01
. Simulation on the test case illustrates the success of the modelling and the beneficial roles of the power-to-gas are analyzed. The proposed model can be used in the decision support for both planning and operation of the coordinated natural gas and electrical power systems.......This work focuses on the optimal operation of the integrated gas and electrical power system with bi-directional energy conversion. Considering the different response times of the gas and power systems, the transient gas flow and steady- state power flow are combined to formulate the dynamic...... optimal energy flow in the integrated gas and power systems. With proper assumptions and simplifications, the problem is transformed into a single stage linear programming. And only a single stage linear programming is needed to obtain the optimal operation strategy for both gas and power systems...
Optimal dynamic performance for high-precision actuators/stages
International Nuclear Information System (INIS)
Preissner, C.; Lee, S.-H.; Royston, T. J.; Shu, D.
2002-01-01
System dynamic performance of actuator/stage groups, such as those found in optical instrument positioning systems and other high-precision applications, is dependent upon both individual component behavior and the system configuration. Experimental modal analysis techniques were implemented to determine the six degree of freedom stiffnesses and damping for individual actuator components. These experimental data were then used in a multibody dynamic computer model to investigate the effect of stage group configuration. Running the computer model through the possible stage configurations and observing the predicted vibratory response determined the optimal stage group configuration. Configuration optimization can be performed for any group of stages, provided there is stiffness and damping data available for the constituent pieces
A fast and optimized dynamic economic load dispatch for large scale power systems
International Nuclear Information System (INIS)
Musse Mohamud Ahmed; Mohd Ruddin Ab Ghani; Ismail Hassan
2000-01-01
This paper presents Lagrangian Multipliers (LM) and Linear Programming (LP) based dynamic economic load dispatch (DELD) solution for large-scale power system operations. It is to minimize the operation cost of power generation. units subject to the considered constraints. After individual generator units are economically loaded and periodically dispatched, fast and optimized DELD has been achieved. DELD with period intervals has been taken into consideration The results found from the algorithm based on LM and LP techniques appear to be modest in both optimizing the operation cost and achieving fast computation. (author)
Dynamic optimal foraging theory explains vertical migrations of bigeye tuna
DEFF Research Database (Denmark)
Thygesen, Uffe Høgsbro; Sommer, Lene; Evans, Karen
2016-01-01
Bigeye tuna are known for remarkable daytime vertical migrations between deep water, where food is abundant but the water is cold, and the surface, where water is warm but food is relatively scarce. Here we investigate if these dive patterns can be explained by dynamic optimal foraging theory...... behaves such as to maximize its energy gains. The model therefore provides insight into the processes underlying observed behavioral patterns and allows generating predictions of foraging behavior in unobserved environments...
A Study of Joint Cost Inclusion in Linear Programming Optimization
Directory of Open Access Journals (Sweden)
P. Armaos
2013-08-01
Full Text Available The concept of Structural Optimization has been a topic or research over the past century. Linear Programming Optimization has proved being the most reliable method of structural optimization. Global advances in linear programming optimization have been recently powered by University of Sheffield researchers, to include joint cost, self-weight and buckling considerations. A joint cost inclusion scopes to reduce the number of joints existing in an optimized structural solution, transforming it to a practically viable solution. The topic of the current paper is to investigate the effects of joint cost inclusion, as this is currently implemented in the optimization code. An extended literature review on this subject was conducted prior to familiarization with small scale optimization software. Using IntelliFORM software, a structured series of problems were set and analyzed. The joint cost tests examined benchmark problems and their consequent changes in the member topology, as the design domain was expanding. The findings of the analyses were remarkable and are being commented further on. The distinct topologies of solutions created by optimization processes are also recognized. Finally an alternative strategy of penalizing joints is presented.
Evaluation of Electric Power Procurement Strategies by Stochastic Dynamic Programming
Saisho, Yuichi; Hayashi, Taketo; Fujii, Yasumasa; Yamaji, Kenji
In deregulated electricity markets, the role of a distribution company is to purchase electricity from the wholesale electricity market at randomly fluctuating prices and to provide it to its customers at a given fixed price. Therefore the company has to take risk stemming from the uncertainties of electricity prices and/or demand fluctuation instead of the customers. The way to avoid the risk is to make a bilateral contact with generating companies or install its own power generation facility. This entails the necessity to develop a certain method to make an optimal strategy for electric power procurement. In such a circumstance, this research has the purpose for proposing a mathematical method based on stochastic dynamic programming and additionally considering the characteristics of the start-up cost of electric power generation facility to evaluate strategies of combination of the bilateral contract and power auto-generation with its own facility for procuring electric power in deregulated electricity market. In the beginning we proposed two approaches to solve the stochastic dynamic programming, and they are a Monte Carlo simulation method and a finite difference method to derive the solution of a partial differential equation of the total procurement cost of electric power. Finally we discussed the influences of the price uncertainty on optimal strategies of power procurement.
An energy management for series hybrid electric vehicle using improved dynamic programming
Peng, Hao; Yang, Yaoquan; Liu, Chunyu
2018-02-01
With the increasing numbers of hybrid electric vehicle (HEV), management for two energy sources, engine and battery, is more and more important to achieve the minimum fuel consumption. This paper introduces several working modes of series hybrid electric vehicle (SHEV) firstly and then describes the mathematical model of main relative components in SHEV. On the foundation of this model, dynamic programming is applied to distribute energy of engine and battery on the platform of matlab and acquires less fuel consumption compared with traditional control strategy. Besides, control rule recovering energy in brake profiles is added into dynamic programming, so shorter computing time is realized by improved dynamic programming and optimization on algorithm.
International Nuclear Information System (INIS)
Song Ruizhuo; Wei Qinglai
2017-01-01
We develop an optimal tracking control method for chaotic system with unknown dynamics and disturbances. The method allows the optimal cost function and the corresponding tracking control to update synchronously. According to the tracking error and the reference dynamics, the augmented system is constructed. Then the optimal tracking control problem is defined. The policy iteration (PI) is introduced to solve the min-max optimization problem. The off-policy adaptive dynamic programming (ADP) algorithm is then proposed to find the solution of the tracking Hamilton–Jacobi–Isaacs (HJI) equation online only using measured data and without any knowledge about the system dynamics. Critic neural network (CNN), action neural network (ANN), and disturbance neural network (DNN) are used to approximate the cost function, control, and disturbance. The weights of these networks compose the augmented weight matrix, and the uniformly ultimately bounded (UUB) of which is proven. The convergence of the tracking error system is also proven. Two examples are given to show the effectiveness of the proposed synchronous solution method for the chaotic system tracking problem. (paper)
Confronting dynamics and uncertainty in optimal decision making for conservation
Williams, Byron K.; Johnson, Fred A.
2013-06-01
The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a
Confronting dynamics and uncertainty in optimal decision making for conservation
Williams, Byron K.; Johnson, Fred A.
2013-01-01
The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a
Confronting dynamics and uncertainty in optimal decision making for conservation
International Nuclear Information System (INIS)
Williams, Byron K; Johnson, Fred A
2013-01-01
The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a
Kernel optimization for short-range molecular dynamics
Hu, Changjun; Wang, Xianmeng; Li, Jianjiang; He, Xinfu; Li, Shigang; Feng, Yangde; Yang, Shaofeng; Bai, He
2017-02-01
To optimize short-range force computations in Molecular Dynamics (MD) simulations, multi-threading and SIMD optimizations are presented in this paper. With respect to multi-threading optimization, a Partition-and-Separate-Calculation (PSC) method is designed to avoid write conflicts caused by using Newton's third law. Serial bottlenecks are eliminated with no additional memory usage. The method is implemented by using the OpenMP model. Furthermore, the PSC method is employed on Intel Xeon Phi coprocessors in both native and offload models. We also evaluate the performance of the PSC method under different thread affinities on the MIC architecture. In the SIMD execution, we explain the performance influence in the PSC method, considering the "if-clause" of the cutoff radius check. The experiment results show that our PSC method is relatively more efficient compared to some traditional methods. In double precision, our 256-bit SIMD implementation is about 3 times faster than the scalar version.
Global optimization for quantum dynamics of few-fermion systems
Li, Xikun; Pecak, Daniel; Sowiński, Tomasz; Sherson, Jacob; Nielsen, Anne E. B.
2018-03-01
Quantum state preparation is vital to quantum computation and quantum information processing tasks. In adiabatic state preparation, the target state is theoretically obtained with nearly perfect fidelity if the control parameter is tuned slowly enough. As this, however, leads to slow dynamics, it is often desirable to be able to carry out processes more rapidly. In this work, we employ two global optimization methods to estimate the quantum speed limit for few-fermion systems confined in a one-dimensional harmonic trap. Such systems can be produced experimentally in a well-controlled manner. We determine the optimized control fields and achieve a reduction in the ramping time of more than a factor of four compared to linear ramping. We also investigate how robust the fidelity is to small variations of the control fields away from the optimized shapes.
Energy and ancillary service dispatch through dynamic optimal power flow
International Nuclear Information System (INIS)
Costa, A.L.; Costa, A. Simoes
2007-01-01
This paper presents an approach based on dynamic optimal power flow (DOPF) to clear both energy and spinning reserve day-ahead markets. A competitive environment is assumed, where agents can offer active power for both demand supply and ancillary services. The DOPF jointly determines the optimal solutions for both energy dispatch and reserve allocation. A non-linear representation for the electrical network is employed, which is able to take transmission losses and power flow limits into account. An attractive feature of the proposed approach is that the final optimal solution will automatically meet physical constraints such as generating limits and ramp rate restrictions. In addition, the proposed framework allows the definition of multiple zones in the network for each time interval, in order to ensure a more adequate distribution of reserves throughout the power system. (author)
Optimal protocols for Hamiltonian and Schrödinger dynamics
International Nuclear Information System (INIS)
Schmiedl, Tim; Dieterich, Eckhard; Dieterich, Peter-Simon; Seifert, Udo
2009-01-01
For systems in an externally controllable time dependent potential, the optimal protocol minimizes the mean work spent in a finite time transition between given initial and final values of a control parameter. For an initially thermalized ensemble, we consider both Hamiltonian evolution for classical systems and Schrödinger evolution for quantum systems. In both cases, we show that for harmonic potentials, the optimal work is given by the adiabatic work even in the limit of short transition times. This result is counter-intuitive because the adiabatic work is substantially smaller than the work for an instantaneous jump. We also perform numerical calculations for the optimal protocol for Hamiltonian dynamics in an anharmonic quartic potential. For a two-level spin system, we give examples where the adiabatic work can be reached in either a finite or an arbitrarily short transition time depending on the allowed parameter space
High Dynamic Optimized Carrier Loop Improvement for Tracking Doppler Rates
Directory of Open Access Journals (Sweden)
Amirhossein Fereidountabar
2015-01-01
Full Text Available Mathematical analysis and optimization of a carrier tracking loop are presented. Due to fast changing of the carrier frequency in some satellite systems, such as Low Earth Orbit (LEO or Global Positioning System (GPS, or some planes like Unmanned Aerial Vehicles (UAVs, high dynamic tracking loops play a very important role. In this paper an optimized tracking loop consisting of a third-order Phase Locked Loop (PLL assisted by a second-order Frequency Locked Loop (FLL for UAVs is proposed and discussed. Based on this structure an optimal loop has been designed. The main advantages of this approach are the reduction of the computation complexity and smaller phase error. The paper shows the simulation results, comparing them with a previous work.
Optimally combining dynamical decoupling and quantum error correction.
Paz-Silva, Gerardo A; Lidar, D A
2013-01-01
Quantum control and fault-tolerant quantum computing (FTQC) are two of the cornerstones on which the hope of realizing a large-scale quantum computer is pinned, yet only preliminary steps have been taken towards formalizing the interplay between them. Here we explore this interplay using the powerful strategy of dynamical decoupling (DD), and show how it can be seamlessly and optimally integrated with FTQC. To this end we show how to find the optimal decoupling generator set (DGS) for various subspaces relevant to FTQC, and how to simultaneously decouple them. We focus on stabilizer codes, which represent the largest contribution to the size of the DGS, showing that the intuitive choice comprising the stabilizers and logical operators of the code is in fact optimal, i.e., minimizes a natural cost function associated with the length of DD sequences. Our work brings hybrid DD-FTQC schemes, and their potentially considerable advantages, closer to realization.
Algorithms for optimal sequencing of dynamic multileaf collimators
International Nuclear Information System (INIS)
Kamath, Srijit; Sahni, Sartaj; Palta, Jatinder; Ranka, Sanjay
2004-01-01
Dynamic multileaf collimator (DMLC) intensity modulated radiation therapy (IMRT) is used to deliver intensity modulated beams using a multileaf collimator (MLC), with the leaves in motion. DMLC-IMRT requires the conversion of a radiation intensity map into a leaf sequence file that controls the movement of the MLC while the beam is on. It is imperative that the intensity map delivered using the leaf sequence file be as close as possible to the intensity map generated by the dose optimization algorithm, while satisfying hardware constraints of the delivery system. Optimization of the leaf-sequencing algorithm has been the subject of several recent investigations. In this work, we present a systematic study of the optimization of leaf-sequencing algorithms for dynamic multileaf collimator beam delivery and provide rigorous mathematical proofs of optimized leaf sequence settings in terms of monitor unit (MU) efficiency under the most common leaf movement constraints that include leaf interdigitation constraint. Our analytical analysis shows that leaf sequencing based on unidirectional movement of the MLC leaves is as MU efficient as bi-directional movement of the MLC leaves
Algorithms for optimal sequencing of dynamic multileaf collimators
Energy Technology Data Exchange (ETDEWEB)
Kamath, Srijit [Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL (United States); Sahni, Sartaj [Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL (United States); Palta, Jatinder [Department of Radiation Oncology, University of Florida, Gainesville, FL (United States); Ranka, Sanjay [Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL (United States)
2004-01-07
Dynamic multileaf collimator (DMLC) intensity modulated radiation therapy (IMRT) is used to deliver intensity modulated beams using a multileaf collimator (MLC), with the leaves in motion. DMLC-IMRT requires the conversion of a radiation intensity map into a leaf sequence file that controls the movement of the MLC while the beam is on. It is imperative that the intensity map delivered using the leaf sequence file be as close as possible to the intensity map generated by the dose optimization algorithm, while satisfying hardware constraints of the delivery system. Optimization of the leaf-sequencing algorithm has been the subject of several recent investigations. In this work, we present a systematic study of the optimization of leaf-sequencing algorithms for dynamic multileaf collimator beam delivery and provide rigorous mathematical proofs of optimized leaf sequence settings in terms of monitor unit (MU) efficiency under the most common leaf movement constraints that include leaf interdigitation constraint. Our analytical analysis shows that leaf sequencing based on unidirectional movement of the MLC leaves is as MU efficient as bi-directional movement of the MLC leaves.
Generic Optimization Program User Manual Version 3.0.0
International Nuclear Information System (INIS)
Wetter, Michael
2009-01-01
GenOpt is an optimization program for the minimization of a cost function that is evaluated by an external simulation program. It has been developed for optimization problems where the cost function is computationally expensive and its derivatives are not available or may not even exist. GenOpt can be coupled to any simulation program that reads its input from text files and writes its output to text files. The independent variables can be continuous variables (possibly with lower and upper bounds), discrete variables, or both, continuous and discrete variables. Constraints on dependent variables can be implemented using penalty or barrier functions. GenOpt uses parallel computing to evaluate the simulations. GenOpt has a library with local and global multi-dimensional and one-dimensional optimization algorithms, and algorithms for doing parametric runs. An algorithm interface allows adding new minimization algorithms without knowing the details of the program structure. GenOpt is written in Java so that it is platform independent. The platform independence and the general interface make GenOpt applicable to a wide range of optimization problems. GenOpt has not been designed for linear programming problems, quadratic programming problems, and problems where the gradient of the cost function is available. For such problems, as well as for other problems, special tailored software exists that is more efficient
Generic Optimization Program User Manual Version 3.0.0
Energy Technology Data Exchange (ETDEWEB)
Wetter, Michael
2009-05-11
GenOpt is an optimization program for the minimization of a cost function that is evaluated by an external simulation program. It has been developed for optimization problems where the cost function is computationally expensive and its derivatives are not available or may not even exist. GenOpt can be coupled to any simulation program that reads its input from text files and writes its output to text files. The independent variables can be continuous variables (possibly with lower and upper bounds), discrete variables, or both, continuous and discrete variables. Constraints on dependent variables can be implemented using penalty or barrier functions. GenOpt uses parallel computing to evaluate the simulations. GenOpt has a library with local and global multi-dimensional and one-dimensional optimization algorithms, and algorithms for doing parametric runs. An algorithm interface allows adding new minimization algorithms without knowing the details of the program structure. GenOpt is written in Java so that it is platform independent. The platform independence and the general interface make GenOpt applicable to a wide range of optimization problems. GenOpt has not been designed for linear programming problems, quadratic programming problems, and problems where the gradient of the cost function is available. For such problems, as well as for other problems, special tailored software exists that is more efficient.
Optimal selection for shielding materials by fuzzy linear programming
International Nuclear Information System (INIS)
Kanai, Y.; Miura, N.; Sugasawa, S.
1996-01-01
An application of fuzzy linear programming methods to optimization of a radiation shield is presented. The main purpose of the present study is the choice of materials and the search of the ratio of mixture-component as the first stage of the methodology on optimum shielding design according to individual requirements of nuclear reactor, reprocessing facility, shipping cask installing spent fuel, ect. The characteristic values for the shield optimization may be considered their cost, spatial space, weight and some shielding qualities such as activation rate and total dose rate for neutron and gamma ray (includes secondary gamma ray). This new approach can reduce huge combination calculations for conventional two-valued logic approaches to representative single shielding calculation by group-wised optimization parameters determined in advance. Using the fuzzy linear programming method, possibilities for reducing radiation effects attainable in optimal compositions hydrated, lead- and boron-contained materials are investigated
Directory of Open Access Journals (Sweden)
Xiangyong Chen
2014-01-01
hybrid dynamic systems is established based on Lanchester equation in a (n,1 battle, where a heterogeneous force of n different troop types faces a homogeneous force. This model can be characterized by the interaction of continuous-time models (governed by Lanchester equation, and discrete event systems (described by variable tactics. Furthermore, an expository discussion is presented on an optimal variable tactics control problem for warfare hybrid dynamic system. The optimal control strategies are designed based on dynamic programming and differential game theory. As an example of the consequences of this optimal control problem, we take the (2, 1 case and solve the optimal strategies in a (2, 1 case. Simulation results show the feasibility of warfare hybrid system model and the effectiveness of the optimal control strategies designed.
Optimization of rotor blades for combined structural, dynamic, and aerodynamic properties
He, Cheng-Jian; Peters, David A.
1990-01-01
Optimal helicopter blade design with computer-based mathematical programming has received more and more attention in recent years. Most of the research has focused on optimum dynamic characteristics of rotor blades to reduce vehicle vibration. There is also work on optimization of aerodynamic performance and on composite structural design. This research has greatly increased our understanding of helicopter optimum design in each of these aspects. Helicopter design is an inherently multidisciplinary process involving strong interactions among various disciplines which can appropriately include aerodynamics; dynamics, both flight dynamics and structural dynamics; aeroelasticity: vibrations and stability; and even acoustics. Therefore, the helicopter design process must satisfy manifold requirements related to the aforementioned diverse disciplines. In our present work, we attempt to combine several of these important effects in a unified manner. First, we design a blade with optimum aerodynamic performance by proper layout of blade planform and spanwise twist. Second, the blade is designed to have natural frequencies that are placed away from integer multiples of the rotor speed for a good dynamic characteristics. Third, the structure is made as light as possible with sufficient rotational inertia to allow for autorotational landing, with safe stress margins and flight fatigue life at each cross-section, and with aeroelastical stability and low vibrations. Finally, a unified optimization refines the solution.
Optimal Passive Dynamics for Physical Interaction: Catching a Mass
Directory of Open Access Journals (Sweden)
Kevin Kemper
2013-05-01
Full Text Available For manipulation tasks in uncertain environments, intentionally designed series impedance in mechanical systems can provide significant benefits that cannot be achieved in software. Traditionally, the design of actuated systems revolves around sizing torques, speeds, and control strategies without considering the system’s passive dynamics. However, the passive dynamics of the mechanical system, including inertia, stiffness, and damping along with other parameters such as torque and stroke limits often impose performance limitations that cannot be overcome with software control. In this paper, we develop relationships between an actuator’s passive dynamics and the resulting performance for the purpose of better understanding how to tune the passive dynamics for catching an unexpected object. We use a mathematically optimal controller subject to force limitations to stop the incoming object without breaking contact and bouncing. The use of an optimal controller is important so that our results directly reflect the physical system’s performance. We analytically calculate the maximum velocity that can be caught by a realistic actuator with limitations such as force and stroke limits. The results show that in order to maximize the velocity of an object that can be caught without exceeding the actuator’s torque and stroke limits, a soft spring along with a strong damper will be desired.
Post optimization paradigm in maximum 3-satisfiability logic programming
Mansor, Mohd. Asyraf; Sathasivam, Saratha; Kasihmuddin, Mohd Shareduwan Mohd
2017-08-01
Maximum 3-Satisfiability (MAX-3SAT) is a counterpart of the Boolean satisfiability problem that can be treated as a constraint optimization problem. It deals with a conundrum of searching the maximum number of satisfied clauses in a particular 3-SAT formula. This paper presents the implementation of enhanced Hopfield network in hastening the Maximum 3-Satisfiability (MAX-3SAT) logic programming. Four post optimization techniques are investigated, including the Elliot symmetric activation function, Gaussian activation function, Wavelet activation function and Hyperbolic tangent activation function. The performances of these post optimization techniques in accelerating MAX-3SAT logic programming will be discussed in terms of the ratio of maximum satisfied clauses, Hamming distance and the computation time. Dev-C++ was used as the platform for training, testing and validating our proposed techniques. The results depict the Hyperbolic tangent activation function and Elliot symmetric activation function can be used in doing MAX-3SAT logic programming.
Penempatan Optimal Phasor Measurement Unit (PMU) Dengan Integer Programming
Amrulloh, Yunan Helmy
2013-01-01
Phasor Measurement Unit (PMU) merupakan peralatan yang mampu memberikan pengukuran fasor tegangan dan arus secara real-time. PMU dapat digunakan untuk monitoring, proteksi dan kontrol pada sistem tenaga listrik. Tugas akhir ini membahas penempatan PMU secara optimal berdasarkan topologi jaringan sehingga sistem tenaga listrik dapat diobservasi. Penempatan optimal PMU dirumuskan sebagai masalah Binary Integer Programming (BIP) yang akan memberikan variabel dengan pilihan nilai (0,1) yang menu...
Lewis, F L; Vamvoudakis, Kyriakos G
2011-02-01
Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q -learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.
GPAW optimized for Blue Gene/P using hybrid programming
DEFF Research Database (Denmark)
Kristensen, Mads Ruben Burgdorff; Happe, Hans Henrik; Vinter, Brian
2009-01-01
In this work we present optimizations of a Grid-based projector-augmented wave method software, GPAW for the Blue Gene/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming. The work focuses on optimi......In this work we present optimizations of a Grid-based projector-augmented wave method software, GPAW for the Blue Gene/P architecture. The improvements are achieved by exploring the advantage of shared and distributed memory programming also known as hybrid programming. The work focuses...... on optimizing a very time consuming operation in GPAW, the finite-different stencil operation, and different hybrid programming approaches are evaluated. The work succeeds in demonstrating a hybrid programming model which is clearly beneficial compared to the original flat programming model. In total...... an improvement of 1.94 compared to the original implementation is obtained. The results we demonstrate here are reasonably general and may be applied to other finite difference codes....
Lean and Efficient Software: Whole Program Optimization of Executables
2016-12-31
19b. TELEPHONE NUMBER (Include area code) 12/31/2016 Final Technical Report (Phase I - Base Period) 30-06-2014 - 31-12-2016 Lean and Efficient...Software: Whole-Program Optimization of Executables Final Report Evan Driscoll Tom Johnson GrammaTech, Inc. 531 Esty Street Ithaca, NY 14850 Office of...hardening U U U UU 30 Tom Johnson (607) 273-7340 x.134 Page 1 of 30 “ Lean and Efficient Software: Whole-Program Optimization of Executables
Genetic algorithm enhanced by machine learning in dynamic aperture optimization
Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert
2018-05-01
With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.
Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria
Eckdahl, Todd T.; Campbell, A. Malcolm; Heyer, Laurie J.; Poet, Jeffrey L.; Blauch, David N.; Snyder, Nicole L.; Atchley, Dustin T.; Baker, Erich J.; Brown, Micah; Brunner, Elizabeth C.; Callen, Sean A.; Campbell, Jesse S.; Carr, Caleb J.; Carr, David R.; Chadinha, Spencer A.; Chester, Grace I.; Chester, Josh; Clarkson, Ben R.; Cochran, Kelly E.; Doherty, Shannon E.; Doyle, Catherine; Dwyer, Sarah; Edlin, Linnea M.; Evans, Rebecca A.; Fluharty, Taylor; Frederick, Janna; Galeota-Sprung, Jonah; Gammon, Betsy L.; Grieshaber, Brandon; Gronniger, Jessica; Gutteridge, Katelyn; Henningsen, Joel; Isom, Bradley; Itell, Hannah L.; Keffeler, Erica C.; Lantz, Andrew J.; Lim, Jonathan N.; McGuire, Erin P.; Moore, Alexander K.; Morton, Jerrad; Nakano, Meredith; Pearson, Sara A.; Perkins, Virginia; Parrish, Phoebe; Pierson, Claire E.; Polpityaarachchige, Sachith; Quaney, Michael J.; Slattery, Abagael; Smith, Kathryn E.; Spell, Jackson; Spencer, Morgan; Taye, Telavive; Trueblood, Kamay; Vrana, Caroline J.; Whitesides, E. Tucker
2015-01-01
Current use of microbes for metabolic engineering suffers from loss of metabolic output due to natural selection. Rather than combat the evolution of bacterial populations, we chose to embrace what makes biological engineering unique among engineering fields – evolving materials. We harnessed bacteria to compute solutions to the biological problem of metabolic pathway optimization. Our approach is called Programmed Evolution to capture two concepts. First, a population of cells is programmed with DNA code to enable it to compute solutions to a chosen optimization problem. As analog computers, bacteria process known and unknown inputs and direct the output of their biochemical hardware. Second, the system employs the evolution of bacteria toward an optimal metabolic solution by imposing fitness defined by metabolic output. The current study is a proof-of-concept for Programmed Evolution applied to the optimization of a metabolic pathway for the conversion of caffeine to theophylline in E. coli. Introduced genotype variations included strength of the promoter and ribosome binding site, plasmid copy number, and chaperone proteins. We constructed 24 strains using all combinations of the genetic variables. We used a theophylline riboswitch and a tetracycline resistance gene to link theophylline production to fitness. After subjecting the mixed population to selection, we measured a change in the distribution of genotypes in the population and an increased conversion of caffeine to theophylline among the most fit strains, demonstrating Programmed Evolution. Programmed Evolution inverts the standard paradigm in metabolic engineering by harnessing evolution instead of fighting it. Our modular system enables researchers to program bacteria and use evolution to determine the combination of genetic control elements that optimizes catabolic or anabolic output and to maintain it in a population of cells. Programmed Evolution could be used for applications in energy
Programmed evolution for optimization of orthogonal metabolic output in bacteria.
Directory of Open Access Journals (Sweden)
Todd T Eckdahl
Full Text Available Current use of microbes for metabolic engineering suffers from loss of metabolic output due to natural selection. Rather than combat the evolution of bacterial populations, we chose to embrace what makes biological engineering unique among engineering fields - evolving materials. We harnessed bacteria to compute solutions to the biological problem of metabolic pathway optimization. Our approach is called Programmed Evolution to capture two concepts. First, a population of cells is programmed with DNA code to enable it to compute solutions to a chosen optimization problem. As analog computers, bacteria process known and unknown inputs and direct the output of their biochemical hardware. Second, the system employs the evolution of bacteria toward an optimal metabolic solution by imposing fitness defined by metabolic output. The current study is a proof-of-concept for Programmed Evolution applied to the optimization of a metabolic pathway for the conversion of caffeine to theophylline in E. coli. Introduced genotype variations included strength of the promoter and ribosome binding site, plasmid copy number, and chaperone proteins. We constructed 24 strains using all combinations of the genetic variables. We used a theophylline riboswitch and a tetracycline resistance gene to link theophylline production to fitness. After subjecting the mixed population to selection, we measured a change in the distribution of genotypes in the population and an increased conversion of caffeine to theophylline among the most fit strains, demonstrating Programmed Evolution. Programmed Evolution inverts the standard paradigm in metabolic engineering by harnessing evolution instead of fighting it. Our modular system enables researchers to program bacteria and use evolution to determine the combination of genetic control elements that optimizes catabolic or anabolic output and to maintain it in a population of cells. Programmed Evolution could be used for applications in
Genetic algorithm optimization for dynamic construction site layout planning
Directory of Open Access Journals (Sweden)
Farmakis Panagiotis M.
2018-02-01
Full Text Available The dynamic construction site layout planning (DCSLP problem refers to the efficient placement and relocation of temporary construction facilities within a dynamically changing construction site environment considering the characteristics of facilities and work interrelationships, the shape and topography of the construction site, and the time-varying project needs. A multi-objective dynamic optimization model is developed for this problem that considers construction and relocation costs of facilities, transportation costs of resources moving from one facility to another or to workplaces, as well as safety and environmental considerations resulting from facilities’ operations and interconnections. The latter considerations are taken into account in the form of preferences or constraints regarding the proximity or remoteness of particular facilities to other facilities or work areas. The analysis of multiple project phases and the dynamic facility relocation from phase to phase highly increases the problem size, which, even in its static form, falls within the NP (for Nondeterministic Polynomial time- hard class of combinatorial optimization problems. For this reason, a genetic algorithm has been implemented for the solution due to its capability to robustly search within a large solution space. Several case studies and operational scenarios have been implemented through the Palisade’s Evolver software for model testing and evaluation. The results indicate satisfactory model response to time-varying input data in terms of solution quality and computation time. The model can provide decision support to site managers, allowing them to examine alternative scenarios and fine-tune optimal solutions according to their experience by introducing desirable preferences or constraints in the decision process.
Sutrisno; Widowati; Heru Tjahjana, R.
2017-01-01
In this paper, we propose a mathematical model in the form of dynamic/multi-stage optimization to solve an integrated supplier selection problem and tracking control problem of single product inventory system with product discount. The product discount will be stated as a piece-wise linear function. We use dynamic programming to solve this proposed optimization to determine the optimal supplier and the optimal product volume that will be purchased from the optimal supplier for each time period so that the inventory level tracks a reference trajectory given by decision maker with minimal total cost. We give a numerical experiment to evaluate the proposed model. From the result, the optimal supplier was determined for each time period and the inventory level follows the given reference well.
Matzke, Orville R.
The purpose of this study was to formulate a linear programming model to simulate a foundation type support program and to apply this model to a state support program for the public elementary and secondary school districts in the State of Iowa. The model was successful in producing optimal solutions to five objective functions proposed for…
TRU Waste Management Program. Cost/schedule optimization analysis
International Nuclear Information System (INIS)
Detamore, J.A.; Raudenbush, M.H.; Wolaver, R.W.; Hastings, G.A.
1985-10-01
This Current Year Work Plan presents in detail a description of the activities to be performed by the Joint Integration Office Rockwell International (JIO/RI) during FY86. It breaks down the activities into two major work areas: Program Management and Program Analysis. Program Management is performed by the JIO/RI by providing technical planning and guidance for the development of advanced TRU waste management capabilities. This includes equipment/facility design, engineering, construction, and operations. These functions are integrated to allow transition from interim storage to final disposition. JIO/RI tasks include program requirements identification, long-range technical planning, budget development, program planning document preparation, task guidance development, task monitoring, task progress information gathering and reporting to DOE, interfacing with other agencies and DOE lead programs, integrating public involvement with program efforts, and preparation of reports for DOE detailing program status. Program Analysis is performed by the JIO/RI to support identification and assessment of alternatives, and development of long-term TRU waste program capabilities. These analyses include short-term analyses in response to DOE information requests, along with performing an RH Cost/Schedule Optimization report. Systems models will be developed, updated, and upgraded as needed to enhance JIO/RI's capability to evaluate the adequacy of program efforts in various fields. A TRU program data base will be maintained and updated to provide DOE with timely responses to inventory related questions
Optimized maritime emergency resource allocation under dynamic demand.
Directory of Open Access Journals (Sweden)
Wenfen Zhang
Full Text Available Emergency resource is important for people evacuation and property rescue when accident occurs. The relief efforts could be promoted by a reasonable emergency resource allocation schedule in advance. As the marine environment is complicated and changeful, the place, type, severity of maritime accident is uncertain and stochastic, bringing about dynamic demand of emergency resource. Considering dynamic demand, how to make a reasonable emergency resource allocation schedule is challenging. The key problem is to determine the optimal stock of emergency resource for supplier centers to improve relief efforts. This paper studies the dynamic demand, and which is defined as a set. Then a maritime emergency resource allocation model with uncertain data is presented. Afterwards, a robust approach is developed and used to make sure that the resource allocation schedule performs well with dynamic demand. Finally, a case study shows that the proposed methodology is feasible in maritime emergency resource allocation. The findings could help emergency manager to schedule the emergency resource allocation more flexibly in terms of dynamic demand.
Optimizing spread dynamics on graphs by message passing
International Nuclear Information System (INIS)
Altarelli, F; Braunstein, A; Dall’Asta, L; Zecchina, R
2013-01-01
Cascade processes are responsible for many important phenomena in natural and social sciences. Simple models of irreversible dynamics on graphs, in which nodes activate depending on the state of their neighbors, have been successfully applied to describe cascades in a large variety of contexts. Over the past decades, much effort has been devoted to understanding the typical behavior of the cascades arising from initial conditions extracted at random from some given ensemble. However, the problem of optimizing the trajectory of the system, i.e. of identifying appropriate initial conditions to maximize (or minimize) the final number of active nodes, is still considered to be practically intractable, with the only exception being models that satisfy a sort of diminishing returns property called submodularity. Submodular models can be approximately solved by means of greedy strategies, but by definition they lack cooperative characteristics which are fundamental in many real systems. Here we introduce an efficient algorithm based on statistical physics for the optimization of trajectories in cascade processes on graphs. We show that for a wide class of irreversible dynamics, even in the absence of submodularity, the spread optimization problem can be solved efficiently on large networks. Analytic and algorithmic results on random graphs are complemented by the solution of the spread maximization problem on a real-world network (the Epinions consumer reviews network). (paper)
Optimizing spread dynamics on graphs by message passing
Altarelli, F.; Braunstein, A.; Dall'Asta, L.; Zecchina, R.
2013-09-01
Cascade processes are responsible for many important phenomena in natural and social sciences. Simple models of irreversible dynamics on graphs, in which nodes activate depending on the state of their neighbors, have been successfully applied to describe cascades in a large variety of contexts. Over the past decades, much effort has been devoted to understanding the typical behavior of the cascades arising from initial conditions extracted at random from some given ensemble. However, the problem of optimizing the trajectory of the system, i.e. of identifying appropriate initial conditions to maximize (or minimize) the final number of active nodes, is still considered to be practically intractable, with the only exception being models that satisfy a sort of diminishing returns property called submodularity. Submodular models can be approximately solved by means of greedy strategies, but by definition they lack cooperative characteristics which are fundamental in many real systems. Here we introduce an efficient algorithm based on statistical physics for the optimization of trajectories in cascade processes on graphs. We show that for a wide class of irreversible dynamics, even in the absence of submodularity, the spread optimization problem can be solved efficiently on large networks. Analytic and algorithmic results on random graphs are complemented by the solution of the spread maximization problem on a real-world network (the Epinions consumer reviews network).
Research on Dynamic Optimization for Road-friendly Vehicle Suspension
Directory of Open Access Journals (Sweden)
Lu Yongjie
2014-10-01
Full Text Available The heavy vehicle brings large dynamic loads to the road surface, which would reduce vehicle ride comfort and shorten road service life. The structure characteristic of heavy vehicle suspension has a significant impact on vehicle performance. Based on the D'Alembert principle, the dynamics models of independent and integral balanced suspension are proposed considering mass and inertia of balancing rod. The sprung mass acceleration and the tire dynamic force for two kinds of balanced suspension and the traditional quarter vehicle model are compared in frequency-domain and time-domain respectively. It is concluded that a quarter vehicle model simplified for balanced suspension could be used to evaluate the ride comfort of vehicle well, but it has some limitations in assessing the vehicle road-friendliness. Then, the sprung mass acceleration and the road damage coefficients are also analyzed under different vehicle design and running parameters at detail. Some conclusions are obtained: low suspension stiffness, high suspension damping and low tire stiffness are all favorable to improve vehicle performance; there is a saturation range of suspension damping enhancing vehicle performance; improving the road surface roughness and avoiding the no-load running are two effective methods to accomplish the better ride comfort and road-friendliness. The suspension stiffness and damping parameters are chosen for optimal parameters matching of road friendliness based on the approximation optimization method.
Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu
2016-01-01
An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.
Hamiltonian-Driven Adaptive Dynamic Programming for Continuous Nonlinear Dynamical Systems.
Yang, Yongliang; Wunsch, Donald; Yin, Yixin
2017-08-01
This paper presents a Hamiltonian-driven framework of adaptive dynamic programming (ADP) for continuous time nonlinear systems, which consists of evaluation of an admissible control, comparison between two different admissible policies with respect to the corresponding the performance function, and the performance improvement of an admissible control. It is showed that the Hamiltonian can serve as the temporal difference for continuous-time systems. In the Hamiltonian-driven ADP, the critic network is trained to output the value gradient. Then, the inner product between the critic and the system dynamics produces the value derivative. Under some conditions, the minimization of the Hamiltonian functional is equivalent to the value function approximation. An iterative algorithm starting from an arbitrary admissible control is presented for the optimal control approximation with its convergence proof. The implementation is accomplished by a neural network approximation. Two simulation studies demonstrate the effectiveness of Hamiltonian-driven ADP.
Optimal traffic control in highway transportation networks using linear programming
Li, Yanning; Canepa, Edward S.; Claudel, Christian G.
2014-01-01
of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can
BILGO: Bilateral greedy optimization for large scale semidefinite programming
Hao, Zhifeng
2013-10-03
Many machine learning tasks (e.g. metric and manifold learning problems) can be formulated as convex semidefinite programs. To enable the application of these tasks on a large-scale, scalability and computational efficiency are considered as desirable properties for a practical semidefinite programming algorithm. In this paper, we theoretically analyze a new bilateral greedy optimization (denoted BILGO) strategy in solving general semidefinite programs on large-scale datasets. As compared to existing methods, BILGO employs a bilateral search strategy during each optimization iteration. In such an iteration, the current semidefinite matrix solution is updated as a bilateral linear combination of the previous solution and a suitable rank-1 matrix, which can be efficiently computed from the leading eigenvector of the descent direction at this iteration. By optimizing for the coefficients of the bilateral combination, BILGO reduces the cost function in every iteration until the KKT conditions are fully satisfied, thus, it tends to converge to a global optimum. In fact, we prove that BILGO converges to the global optimal solution at a rate of O(1/k), where k is the iteration counter. The algorithm thus successfully combines the efficiency of conventional rank-1 update algorithms and the effectiveness of gradient descent. Moreover, BILGO can be easily extended to handle low rank constraints. To validate the effectiveness and efficiency of BILGO, we apply it to two important machine learning tasks, namely Mahalanobis metric learning and maximum variance unfolding. Extensive experimental results clearly demonstrate that BILGO can solve large-scale semidefinite programs efficiently.
Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization
Gelman, Andrew; Lee, Daniel; Guo, Jiqiang
2015-01-01
Stan is a free and open-source C++ program that performs Bayesian inference or optimization for arbitrary user-specified models and can be called from the command line, R, Python, Matlab, or Julia and has great promise for fitting large and complex statistical models in many areas of application. We discuss Stan from users' and developers'…
BILGO: Bilateral greedy optimization for large scale semidefinite programming
Hao, Zhifeng; Yuan, Ganzhao; Ghanem, Bernard
2013-01-01
Many machine learning tasks (e.g. metric and manifold learning problems) can be formulated as convex semidefinite programs. To enable the application of these tasks on a large-scale, scalability and computational efficiency are considered as desirable properties for a practical semidefinite programming algorithm. In this paper, we theoretically analyze a new bilateral greedy optimization (denoted BILGO) strategy in solving general semidefinite programs on large-scale datasets. As compared to existing methods, BILGO employs a bilateral search strategy during each optimization iteration. In such an iteration, the current semidefinite matrix solution is updated as a bilateral linear combination of the previous solution and a suitable rank-1 matrix, which can be efficiently computed from the leading eigenvector of the descent direction at this iteration. By optimizing for the coefficients of the bilateral combination, BILGO reduces the cost function in every iteration until the KKT conditions are fully satisfied, thus, it tends to converge to a global optimum. In fact, we prove that BILGO converges to the global optimal solution at a rate of O(1/k), where k is the iteration counter. The algorithm thus successfully combines the efficiency of conventional rank-1 update algorithms and the effectiveness of gradient descent. Moreover, BILGO can be easily extended to handle low rank constraints. To validate the effectiveness and efficiency of BILGO, we apply it to two important machine learning tasks, namely Mahalanobis metric learning and maximum variance unfolding. Extensive experimental results clearly demonstrate that BILGO can solve large-scale semidefinite programs efficiently.
ROTAX: a nonlinear optimization program by axes rotation method
International Nuclear Information System (INIS)
Suzuki, Tadakazu
1977-09-01
A nonlinear optimization program employing the axes rotation method has been developed for solving nonlinear problems subject to nonlinear inequality constraints and its stability and convergence efficiency were examined. The axes rotation method is a direct search of the optimum point by rotating the orthogonal coordinate system in a direction giving the minimum objective. The searching direction is rotated freely in multi-dimensional space, so the method is effective for the problems represented with the contours having deep curved valleys. In application of the axes rotation method to the optimization problems subject to nonlinear inequality constraints, an improved version of R.R. Allran and S.E.J. Johnsen's method is used, which deals with a new objective function composed of the original objective and a penalty term to consider the inequality constraints. The program is incorporated in optimization code system SCOOP. (auth.)
Optimal diabatic dynamics of Majorana-based quantum gates
Rahmani, Armin; Seradjeh, Babak; Franz, Marcel
2017-08-01
In topological quantum computing, unitary operations on qubits are performed by adiabatic braiding of non-Abelian quasiparticles, such as Majorana zero modes, and are protected from local environmental perturbations. In the adiabatic regime, with timescales set by the inverse gap of the system, the errors can be made arbitrarily small by performing the process more slowly. To enhance the performance of quantum information processing with Majorana zero modes, we apply the theory of optimal control to the diabatic dynamics of Majorana-based qubits. While we sacrifice complete topological protection, we impose constraints on the optimal protocol to take advantage of the nonlocal nature of topological information and increase the robustness of our gates. By using the Pontryagin's maximum principle, we show that robust equivalent gates to perfect adiabatic braiding can be implemented in finite times through optimal pulses. In our implementation, modifications to the device Hamiltonian are avoided. Focusing on thermally isolated systems, we study the effects of calibration errors and external white and 1 /f (pink) noise on Majorana-based gates. While a noise-induced antiadiabatic behavior, where a slower process creates more diabatic excitations, prohibits indefinite enhancement of the robustness of the adiabatic scheme, our fast optimal protocols exhibit remarkable stability to noise and have the potential to significantly enhance the practical performance of Majorana-based information processing.
Numerical Optimization Design of Dynamic Quantizer via Matrix Uncertainty Approach
Directory of Open Access Journals (Sweden)
Kenji Sawada
2013-01-01
Full Text Available In networked control systems, continuous-valued signals are compressed to discrete-valued signals via quantizers and then transmitted/received through communication channels. Such quantization often degrades the control performance; a quantizer must be designed that minimizes the output difference between before and after the quantizer is inserted. In terms of the broadbandization and the robustness of the networked control systems, we consider the continuous-time quantizer design problem. In particular, this paper describes a numerical optimization method for a continuous-time dynamic quantizer considering the switching speed. Using a matrix uncertainty approach of sampled-data control, we clarify that both the temporal and spatial resolution constraints can be considered in analysis and synthesis, simultaneously. Finally, for the slow switching, we compare the proposed and the existing methods through numerical examples. From the examples, a new insight is presented for the two-step design of the existing continuous-time optimal quantizer.
Using linear programming to analyze and optimize stochastic flow lines
DEFF Research Database (Denmark)
Helber, Stefan; Schimmelpfeng, Katja; Stolletz, Raik
2011-01-01
This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time...... programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines....
A dynamic model of optimal reduction of marine oil pollution
Energy Technology Data Exchange (ETDEWEB)
Deissenberg, C. [CEFI-CNRS, Les Milles (France); Gottinger, H.W. [International Inst. for Environmental Economics and Management, Bad Waldsee (Germany); Gurman, V. [RAS, Program Systems Inst., Pereslavl-Zalessky (Russian Federation); Marinushkin, D. [Pereslavl Univ., Pereslavl-Zalessky (Russian Federation)
2001-07-01
This paper proposes a system of dynamic models to describe the interactive behaviour of different agents (polluters, inspectors, and a principal pollution control agency) involved in the processes of marine oil pollution and of its prevention and purification, under some realistic assumptions, In particular, short- and long-term economic responses of polluters to monitoring efforts, as well as possible collusions between polluters and inspectors, are taken into account. A numerical example is considered using the results of Deissenberg et al., (2001), which show the existence of optimal fines and inspector wage rates that minimize (along with other variables) a simple and visual 'social damage' criterion. (Author)
Morphing-Based Shape Optimization in Computational Fluid Dynamics
Rousseau, Yannick; Men'Shov, Igor; Nakamura, Yoshiaki
In this paper, a Morphing-based Shape Optimization (MbSO) technique is presented for solving Optimum-Shape Design (OSD) problems in Computational Fluid Dynamics (CFD). The proposed method couples Free-Form Deformation (FFD) and Evolutionary Computation, and, as its name suggests, relies on the morphing of shape and computational domain, rather than direct shape parameterization. Advantages of the FFD approach compared to traditional parameterization are first discussed. Then, examples of shape and grid deformations by FFD are presented. Finally, the MbSO approach is illustrated and applied through an example: the design of an airfoil for a future Mars exploration airplane.
Dynamic mesh optimization based on the spring analogy
Directory of Open Access Journals (Sweden)
Schmidt Jonas
2014-01-01
Full Text Available We present an implementation of the spring analogy for three dimensional meshes in OpenFOAM. All parameters of the spring system are treated as fields that can either be pre-defined by the user, or updated at each time step according to specified geometrical regions or diffusion equations. The purpose of the method is to provide a pre-processing tool for mesh optimization. We study three simple test cases, a deformed block, an airfoil and a hill, and we analyze the evolution of skewness, non-orthogonality and aspect ratio during the approach of dynamic equilibrium.
A Monarch Butterfly Optimization for the Dynamic Vehicle Routing Problem
Directory of Open Access Journals (Sweden)
Shifeng Chen
2017-09-01
Full Text Available The dynamic vehicle routing problem (DVRP is a variant of the Vehicle Routing Problem (VRP in which customers appear dynamically. The objective is to determine a set of routes that minimizes the total travel distance. In this paper, we propose a monarch butterfly optimization (MBO algorithm to solve DVRPs, utilizing a greedy strategy. Both migration operation and the butterfly adjusting operator only accept the offspring of butterfly individuals that have better fitness than their parents. To improve performance, a later perturbation procedure is implemented, to maintain a balance between global diversification and local intensification. The computational results indicate that the proposed technique outperforms the existing approaches in the literature for average performance by at least 9.38%. In addition, 12 new best solutions were found. This shows that this proposed technique consistently produces high-quality solutions and outperforms other published heuristics for the DVRP.
Optimal environmental policy and the dynamic property in LDCs
Directory of Open Access Journals (Sweden)
Masahiro Yabuta
2002-01-01
Full Text Available This paper has provided a model framework of foreign assistance policy in the context of dynamic optimal control and investigated the environmental policies in LDCs that received some financial support from abroad. The model framework features a specific behavior of the social planner who determines the level of voluntary expenditure for preservation of natural environment. Because more financial needs for natural environmental protection means less allowance of growth-oriented investment, the social planner confronts a trade-off problem between economic growth and environmental preservation. To tackle with this clearly, we have built a dynamic model with two control variables: per-capita consumption and voluntary expenditure for natural environment.
4500 V SPT+ IGBT optimization on static and dynamic losses
International Nuclear Information System (INIS)
Dai Qingyun; Tian Xiaoli; Zhang Wenliang; Lu Shuojin; Zhu Yangjun
2015-01-01
This paper concerns the need for improving the static and dynamic performance of the high voltage insulated gate bipolar transistor (HV IGBTs). A novel structure with a carrier stored layer on the cathode side, known as an enhanced planar IGBT of the 4500 V voltage class is investigated. With the adoption of a soft punch through (SPT) concept as the vertical structure and an enhanced planar concept as the top structure, signed as SPT + IGBT, the simulation results indicate the turn-off switching waveform of the 4500 V SPT + IGBT is soft and also realizes an improved trade-off relationship between on-state voltage drop (V on ) and turn-off loss (E off ) in comparison with the SPT IGBT. Attention is also paid to the influences caused by different carrier stored layer doping dose on static and dynamic performances, to optimize on-state and switching losses of SPT + IGBT. (paper)
Optimal dynamic pricing and replenishment policies for deteriorating items
Directory of Open Access Journals (Sweden)
Masoud Rabbani
2014-08-01
Full Text Available Marketing strategies and proper inventory replenishment policies are often incorporated by enterprises to stimulate demand and maximize profit. The aim of this paper is to represent an integrated model for dynamic pricing and inventory control of deteriorating items. To reflect the dynamic characteristic of the problem, the selling price is defined as a time-dependent function of the initial selling price and the discount rate. In this regard, the price is exponentially discounted to compensate negative impact of the deterioration. The planning horizon is assumed to be infinite and the deterioration rate is time-dependent. In addition to price, the demand rate is dependent on advertisement as a powerful marketing tool. Several theoretical results and an iterative solution algorithm are developed to provide the optimal solution. Finally, to show validity of the model and illustrate the solution procedure, numerical results are presented.
Dynamic Simulation and Optimization of Nuclear Hydrogen Production Systems
Energy Technology Data Exchange (ETDEWEB)
Paul I. Barton; Mujid S. Kaximi; Georgios Bollas; Patricio Ramirez Munoz
2009-07-31
This project is part of a research effort to design a hydrogen plant and its interface with a nuclear reactor. This project developed a dynamic modeling, simulation and optimization environment for nuclear hydrogen production systems. A hybrid discrete/continuous model captures both the continuous dynamics of the nuclear plant, the hydrogen plant, and their interface, along with discrete events such as major upsets. This hybrid model makes us of accurate thermodynamic sub-models for the description of phase and reaction equilibria in the thermochemical reactor. Use of the detailed thermodynamic models will allow researchers to examine the process in detail and have confidence in the accurary of the property package they use.
Energy Technology Data Exchange (ETDEWEB)
Xu, J.P.; Li, X.F. [Sichuan University, Chengdu (China)
2011-09-15
In this paper, we have developed a model that integrates system dynamics with fuzzy multiple objective programming (SD-FMOP). This model can be used to study the complex interactions in a industry system. In the process of confirming sensitive parameters and fuzzy variables of the SD model, we made use of fuzzy multi-objective programming to help yield the solution. We adopted the chance-constraint programming model to convert the fuzzy variables into precise values. We use genetic algorithm to solve FMOP model, and obtain the Pareto solution through the programming models. It is evident that FMOP is effective in optimizing the given system to obtain the decision objectives of the SD model. The results recorded from the SD model are in our option, reasonable and credible. These results may help governments to establish more effective policy related to the coal industry development.
Industrial cogeneration optimization program. Final report, September 1979
Energy Technology Data Exchange (ETDEWEB)
Davis, Jerry; McWhinney, Jr., Robert T.
1980-01-01
This study program is part of the DOE Integrated Industry Cogeneration Program to optimize, evaluate, and demonstrate cogeneration systems, with direct participation of the industries most affected. One objective is to characterize five major energy-intensive industries with respect to their energy-use profiles. The industries are: petroleum refining and related industries, textile mill products, paper and allied products, chemicals and allied products, and food and kindred products. Another objective is to select optimum cogeneration systems for site-specific reference case plants in terms of maximum energy savings subject to given return on investment hurdle rates. Analyses were made that define the range of optimal cogeneration systems for each reference-case plant considering technology applicability, economic factors, and energy savings by type of fuel. This study also provides guidance to other parts of the program through information developed with regard to component development requirements, institutional and regulatory barriers, as well as fuel use and environmental considerations. (MCW)
Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments
Directory of Open Access Journals (Sweden)
Renata De Paris
2015-01-01
Full Text Available Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR model containing a 20 ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand.
Implementing a Dynamic Street-Children's Program: Successes and ...
African Journals Online (AJOL)
dynamic street children's program in Mzuzu Malawi – using a developmental ... dynamics of parentchild, parent-parent and child-parent-environment; life-events; ... of child and adolescent development, and how they can influence the child's ...
Mortgage Loan Portfolio Optimization Using Multi-Stage Stochastic Programming
DEFF Research Database (Denmark)
Rasmussen, Kourosh Marjani; Clausen, Jens
2007-01-01
We consider the dynamics of the Danish mortgage loan system and propose several models to reflect the choices of a mortgagor as well as his attitude towards risk. The models are formulated as multi stage stochastic integer programs, which are difficult to solve for more than 10 stages. Scenario...
The optimization of demand response programs in smart grids
International Nuclear Information System (INIS)
Derakhshan, Ghasem; Shayanfar, Heidar Ali; Kazemi, Ahad
2016-01-01
The potential to schedule portion of the electricity demand in smart energy systems is clear as a significant opportunity to enhance the efficiency of the grids. Demand response is one of the new developments in the field of electricity which is meant to engage consumers in improving the energy consumption pattern. We used Teaching & Learning based Optimization (TLBO) and Shuffled Frog Leaping (SFL) algorithms to propose an optimization model for consumption scheduling in smart grid when payment costs of different periods are reduced. This study conducted on four types residential consumers obtained in the summer for some residential houses located in the centre of Tehran city in Iran: first with time of use pricing, second with real-time pricing, third one with critical peak pricing, and the last consumer had no tariff for pricing. The results demonstrate that the adoption of demand response programs can reduce total payment costs and determine a more efficient use of optimization techniques. - Highlights: •An optimization model for the demand response program is made. •TLBO and SFL algorithms are applied to reduce payment costs in smart grid. •The optimal condition is provided for the maximization of the social welfare problem. •An application to some residential houses located in the centre of Tehran city in Iran is demonstrated.
TH-EF-BRB-02: Feasibility of Optimization for Dynamic Trajectory Radiotherapy
Energy Technology Data Exchange (ETDEWEB)
Fix, MK; Frei, D; Volken, W; Terribilini, D; Aebersold, DM; Manser, P [Division of Medical Radiation Physics and Department of Radiation Oncology, Inselspital, Bern University Hospital, and University of Bern, Berne (Switzerland)
2016-06-15
Purpose: Over the last years, volumetric modulated arc therapy (VMAT) has been widely introduced into clinical routine using a coplanar delivery technique. However, VMAT might be improved by including dynamic couch and collimator rotations, leading to dynamic trajectory radiotherapy (DTRT). In this work the feasibility and the potential benefit of DTRT was investigated. Methods: A general framework for the optimization was developed using the Eclipse Scripting Research Application Programming Interface (ESRAPI). Based on contoured target and organs at risk (OARs), the structures are extracted using the ESRAPI. Sampling potential beam directions, regularly distributed on a sphere using a Fibanocci-lattice, the fractional volume-overlap of each OAR and the target is determined and used to establish dynamic gantry-couch movements. Then, for each gantry-couch track the most suitable collimator angle is determined for each control point by optimizing the area between the MLC leaves and the target contour. The resulting dynamic trajectories are used as input to perform the optimization using a research version of the VMAT optimization algorithm and the ESRAPI. The feasibility of this procedure was tested for a clinically motivated head and neck case. Resulting dose distributions for the VMAT plan and for the dynamic trajectory treatment plan were compared based on DVH-parameters. Results: While the DVH for the target is virtually preserved, improvements in maximum dose for the DTRT plan were achieved for all OARs except for the inner-ear, where maximum dose remains the same. The major improvements in maximum dose were 6.5% of the prescribed dose (66 Gy) for the parotid and 5.5% for the myelon and the eye. Conclusion: The result of this work suggests that DTRT has a great potential to reduce dose to OARs with similar target coverage when compared to conventional VMAT treatment plans. This work was supported by Varian Medical Systems. This work was supported by Varian
Snijders, H.M.J.; Janssen, J.G.M.; Cramers, C.A.M.G.; Sandra, P; Bertsch, W.; Sandra, P.; Devos, G.
1996-01-01
In this work a method is described which allows off-line optimization of temperature programmed GC separations. Recently, we described a new numerical method to predict off-line retention times and peak widths of a mixture containing components with known identities in capillary GC. In the present
Aircraft path planning for optimal imaging using dynamic cost functions
Christie, Gordon; Chaudhry, Haseeb; Kochersberger, Kevin
2015-05-01
Unmanned aircraft development has accelerated with recent technological improvements in sensing and communications, which has resulted in an "applications lag" for how these aircraft can best be utilized. The aircraft are becoming smaller, more maneuverable and have longer endurance to perform sensing and sampling missions, but operating them aggressively to exploit these capabilities has not been a primary focus in unmanned systems development. This paper addresses a means of aerial vehicle path planning to provide a realistic optimal path in acquiring imagery for structure from motion (SfM) reconstructions and performing radiation surveys. This method will allow SfM reconstructions to occur accurately and with minimal flight time so that the reconstructions can be executed efficiently. An assumption is made that we have 3D point cloud data available prior to the flight. A discrete set of scan lines are proposed for the given area that are scored based on visibility of the scene. Our approach finds a time-efficient path and calculates trajectories between scan lines and over obstacles encountered along those scan lines. Aircraft dynamics are incorporated into the path planning algorithm as dynamic cost functions to create optimal imaging paths in minimum time. Simulations of the path planning algorithm are shown for an urban environment. We also present our approach for image-based terrain mapping, which is able to efficiently perform a 3D reconstruction of a large area without the use of GPS data.
Doubly Robust Estimation of Optimal Dynamic Treatment Regimes
DEFF Research Database (Denmark)
Barrett, Jessica K; Henderson, Robin; Rosthøj, Susanne
2014-01-01
We compare methods for estimating optimal dynamic decision rules from observational data, with particular focus on estimating the regret functions defined by Murphy (in J. R. Stat. Soc., Ser. B, Stat. Methodol. 65:331-355, 2003). We formulate a doubly robust version of the regret-regression appro......We compare methods for estimating optimal dynamic decision rules from observational data, with particular focus on estimating the regret functions defined by Murphy (in J. R. Stat. Soc., Ser. B, Stat. Methodol. 65:331-355, 2003). We formulate a doubly robust version of the regret......-regression approach of Almirall et al. (in Biometrics 66:131-139, 2010) and Henderson et al. (in Biometrics 66:1192-1201, 2010) and demonstrate that it is equivalent to a reduced form of Robins' efficient g-estimation procedure (Robins, in Proceedings of the Second Symposium on Biostatistics. Springer, New York, pp....... 189-326, 2004). Simulation studies suggest that while the regret-regression approach is most efficient when there is no model misspecification, in the presence of misspecification the efficient g-estimation procedure is more robust. The g-estimation method can be difficult to apply in complex...
Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization
Directory of Open Access Journals (Sweden)
Weishang Gao
2013-01-01
Full Text Available Evolutionary algorithms (EAs were shown to be effective for complex constrained optimization problems. However, inflexible exploration-exploitation and improper penalty in EAs with penalty function would lead to losing the global optimum nearby or on the constrained boundary. To determine an appropriate penalty coefficient is also difficult in most studies. In this paper, we propose a bidirectional dynamic diversity evolutionary algorithm (Bi-DDEA with multiagents guiding exploration-exploitation through local extrema to the global optimum in suitable steps. In Bi-DDEA potential advantage is detected by three kinds of agents. The scale and the density of agents will change dynamically according to the emerging of potential optimal area, which play an important role of flexible exploration-exploitation. Meanwhile, a novel double optimum estimation strategy with objective fitness and penalty fitness is suggested to compute, respectively, the dominance trend of agents in feasible region and forbidden region. This bidirectional evolving with multiagents can not only effectively avoid the problem of determining penalty coefficient but also quickly converge to the global optimum nearby or on the constrained boundary. By examining the rapidity and veracity of Bi-DDEA across benchmark functions, the proposed method is shown to be effective.
Near-Optimal Tracking Control of Mobile Robots Via Receding-Horizon Dual Heuristic Programming.
Lian, Chuanqiang; Xu, Xin; Chen, Hong; He, Haibo
2016-11-01
Trajectory tracking control of wheeled mobile robots (WMRs) has been an important research topic in control theory and robotics. Although various tracking control methods with stability have been developed for WMRs, it is still difficult to design optimal or near-optimal tracking controller under uncertainties and disturbances. In this paper, a near-optimal tracking control method is presented for WMRs based on receding-horizon dual heuristic programming (RHDHP). In the proposed method, a backstepping kinematic controller is designed to generate desired velocity profiles and the receding horizon strategy is used to decompose the infinite-horizon optimal control problem into a series of finite-horizon optimal control problems. In each horizon, a closed-loop tracking control policy is successively updated using a class of approximate dynamic programming algorithms called finite-horizon dual heuristic programming (DHP). The convergence property of the proposed method is analyzed and it is shown that the tracking control system based on RHDHP is asymptotically stable by using the Lyapunov approach. Simulation results on three tracking control problems demonstrate that the proposed method has improved control performance when compared with conventional model predictive control (MPC) and DHP. It is also illustrated that the proposed method has lower computational burden than conventional MPC, which is very beneficial for real-time tracking control.
Approximate dynamic programming approaches for appointment scheduling with patient preferences.
Li, Xin; Wang, Jin; Fung, Richard Y K
2018-04-01
During the appointment booking process in out-patient departments, the level of patient satisfaction can be affected by whether or not their preferences can be met, including the choice of physicians and preferred time slot. In addition, because the appointments are sequential, considering future possible requests is also necessary for a successful appointment system. This paper proposes a Markov decision process model for optimizing the scheduling of sequential appointments with patient preferences. In contrast to existing models, the evaluation of a booking decision in this model focuses on the extent to which preferences are satisfied. Characteristics of the model are analysed to develop a system for formulating booking policies. Based on these characteristics, two types of approximate dynamic programming algorithms are developed to avoid the curse of dimensionality. Experimental results suggest directions for further fine-tuning of the model, as well as improving the efficiency of the two proposed algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.
Optimal Dynamic Strategies for Index Tracking and Algorithmic Trading
Ward, Brian
In this thesis we study dynamic strategies for index tracking and algorithmic trading. Tracking problems have become ever more important in Financial Engineering as investors seek to precisely control their portfolio risks and exposures over different time horizons. This thesis analyzes various tracking problems and elucidates the tracking errors and strategies one can employ to minimize those errors and maximize profit. In Chapters 2 and 3, we study the empirical tracking properties of exchange traded funds (ETFs), leveraged ETFs (LETFs), and futures products related to spot gold and the Chicago Board Option Exchange (CBOE) Volatility Index (VIX), respectively. These two markets provide interesting and differing examples for understanding index tracking. We find that static strategies work well in the nonleveraged case for gold, but fail to track well in the corresponding leveraged case. For VIX, tracking via neither ETFs, nor futures\\ portfolios succeeds, even in the nonleveraged case. This motivates the need for dynamic strategies, some of which we construct in these two chapters and further expand on in Chapter 4. There, we analyze a framework for index tracking and risk exposure control through financial derivatives. We derive a tracking condition that restricts our exposure choices and also define a slippage process that characterizes the deviations from the index over longer horizons. The framework is applied to a number of models, for example, Black Scholes model and Heston model for equity index tracking, as well as the Square Root (SQR) model and the Concatenated Square Root (CSQR) model for VIX tracking. By specifying how each of these models fall into our framework, we are able to understand the tracking errors in each of these models. Finally, Chapter 5 analyzes a tracking problem of a different kind that arises in algorithmic trading: schedule following for optimal execution. We formulate and solve a stochastic control problem to obtain the optimal
METAHEURISTIC OPTIMIZATION METHODS FOR PARAMETERS ESTIMATION OF DYNAMIC SYSTEMS
Directory of Open Access Journals (Sweden)
V. Panteleev Andrei
2017-01-01
Full Text Available The article considers the usage of metaheuristic methods of constrained global optimization: “Big Bang - Big Crunch”, “Fireworks Algorithm”, “Grenade Explosion Method” in parameters of dynamic systems estimation, described with algebraic-differential equations. Parameters estimation is based upon the observation results from mathematical model behavior. Their values are derived after criterion minimization, which describes the total squared error of state vector coordinates from the deduced ones with precise values observation at different periods of time. Paral- lelepiped type restriction is imposed on the parameters values. Used for solving problems, metaheuristic methods of constrained global extremum don’t guarantee the result, but allow to get a solution of a rather good quality in accepta- ble amount of time. The algorithm of using metaheuristic methods is given. Alongside with the obvious methods for solving algebraic-differential equation systems, it is convenient to use implicit methods for solving ordinary differen- tial equation systems. Two ways of solving the problem of parameters evaluation are given, those parameters differ in their mathematical model. In the first example, a linear mathematical model describes the chemical action parameters change, and in the second one, a nonlinear mathematical model describes predator-prey dynamics, which characterize the changes in both kinds’ population. For each of the observed examples there are calculation results from all the three methods of optimization, there are also some recommendations for how to choose methods parameters. The obtained numerical results have demonstrated the efficiency of the proposed approach. The deduced parameters ap- proximate points slightly differ from the best known solutions, which were deduced differently. To refine the results one should apply hybrid schemes that combine classical methods of optimization of zero, first and second orders and
Energy Technology Data Exchange (ETDEWEB)
Tanabe, R.; Yasuda, K.; Yokoyama, R. (Tokyo Metropolitan Univ., Tokyo (Japan))
1992-05-20
To supply cheap, high-reliable and a planty of the electricity is an important task of the electric supply system because the requirement for the electricity is rapidly increased in Japan. In order to solve this problem, the authors of the paper are developing a most suitable practical method based on algorithm, according to which the generation expansion planning is divided into two problems: the optimal generation mix and the optimal generation construction process and the two problems are solved respectively. But there are some bad points in the method, for example, there are only approximative practical restriction of the capacity of single machine and the existing electric supply etc., because the optimal generation mix is determined on the basis of non-linear planning. So, in the present paper, the electric supply support system is practically constructed while proposing an unified generation expansion planning based on the dynamic programming that is possible to consider these restrictions strictly and the usefullness of the method is inspected. 12 refs., 7 figs., 5 tabs.
DEFF Research Database (Denmark)
Davidsen, Claus; Cardenal, Silvio Javier Pereira; Liu, Suxia
2015-01-01
of stochastic dynamic programming, to optimize water resources management in the Ziya River basin. Natural runoff from the upper basin was estimated with a rainfall-runoff model autocalibrated using in situ measured discharge. The runoff serial correlation was described by a Markov chain and used as input...
Optimizing Biorefinery Design and Operations via Linear Programming Models
Energy Technology Data Exchange (ETDEWEB)
Talmadge, Michael; Batan, Liaw; Lamers, Patrick; Hartley, Damon; Biddy, Mary; Tao, Ling; Tan, Eric
2017-03-28
The ability to assess and optimize economics of biomass resource utilization for the production of fuels, chemicals and power is essential for the ultimate success of a bioenergy industry. The team of authors, consisting of members from the National Renewable Energy Laboratory (NREL) and the Idaho National Laboratory (INL), has developed simple biorefinery linear programming (LP) models to enable the optimization of theoretical or existing biorefineries. The goal of this analysis is to demonstrate how such models can benefit the developing biorefining industry. It focuses on a theoretical multi-pathway, thermochemical biorefinery configuration and demonstrates how the biorefinery can use LP models for operations planning and optimization in comparable ways to the petroleum refining industry. Using LP modeling tools developed under U.S. Department of Energy's Bioenergy Technologies Office (DOE-BETO) funded efforts, the authors investigate optimization challenges for the theoretical biorefineries such as (1) optimal feedstock slate based on available biomass and prices, (2) breakeven price analysis for available feedstocks, (3) impact analysis for changes in feedstock costs and product prices, (4) optimal biorefinery operations during unit shutdowns / turnarounds, and (5) incentives for increased processing capacity. These biorefinery examples are comparable to crude oil purchasing and operational optimization studies that petroleum refiners perform routinely using LPs and other optimization models. It is important to note that the analyses presented in this article are strictly theoretical and they are not based on current energy market prices. The pricing structure assigned for this demonstrative analysis is consistent with $4 per gallon gasoline, which clearly assumes an economic environment that would favor the construction and operation of biorefineries. The analysis approach and examples provide valuable insights into the usefulness of analysis tools for
An optimal strategy for functional mapping of dynamic trait loci.
Jin, Tianbo; Li, Jiahan; Guo, Ying; Zhou, Xiaojing; Yang, Runqing; Wu, Rongling
2010-02-01
As an emerging powerful approach for mapping quantitative trait loci (QTLs) responsible for dynamic traits, functional mapping models the time-dependent mean vector with biologically meaningful equations and are likely to generate biologically relevant and interpretable results. Given the autocorrelation nature of a dynamic trait, functional mapping needs the implementation of the models for the structure of the covariance matrix. In this article, we have provided a comprehensive set of approaches for modelling the covariance structure and incorporated each of these approaches into the framework of functional mapping. The Bayesian information criterion (BIC) values are used as a model selection criterion to choose the optimal combination of the submodels for the mean vector and covariance structure. In an example for leaf age growth from a rice molecular genetic project, the best submodel combination was found between the Gaussian model for the correlation structure, power equation of order 1 for the variance and the power curve for the mean vector. Under this combination, several significant QTLs for leaf age growth trajectories were detected on different chromosomes. Our model can be well used to study the genetic architecture of dynamic traits of agricultural values.
Penempatan Optimal Phasor Measurement Unit (PMU dengan Integer Programming
Directory of Open Access Journals (Sweden)
Yunan Helmy Amrulloh
2013-09-01
Full Text Available Phasor Measurement Unit (PMU merupakan peralatan yang mampu memberikan pengukuran fasor tegangan dan arus secara real-time. PMU dapat digunakan untuk monitoring, proteksi dan kontrol pada sistem tenaga listrik. Tugas akhir ini membahas penempatan PMU secara optimal berdasarkan topologi jaringan sehingga sistem tenaga listrik dapat diobservasi. Penempatan optimal PMU dirumuskan sebagai masalah Binary Integer Programming (BIP yang akan memberikan variabel dengan pilihan nilai (0,1 yang menunjukkan tempat yang harus dipasang PMU. Dalam tugas akhir ini, BIP diterapkan untuk menyelesaikan masalah penempatan PMU secara optimal pada sistem tenaga listrik Jawa-Bali 500 KV yang selanjutnya diterapkan dengan penambahan konsep incomplete observability. Hasil simulasi menunjukkan bahwa penerapan BIP pada sistem dengan incomplete observability memberikan jumlah PMU yang lebih sedikit dibandingkan dengan sistem tanpa konsep incomplete observability.
Optimal traffic control in highway transportation networks using linear programming
Li, Yanning
2014-06-01
This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.
Optimal parameters for the FFA-Beddoes dynamic stall model
Energy Technology Data Exchange (ETDEWEB)
Bjoerck, A; Mert, M [FFA, The Aeronautical Research Institute of Sweden, Bromma (Sweden); Madsen, H A [Risoe National Lab., Roskilde (Denmark)
1999-03-01
Unsteady aerodynamic effects, like dynamic stall, must be considered in calculation of dynamic forces for wind turbines. Models incorporated in aero-elastic programs are of semi-empirical nature. Resulting aerodynamic forces therefore depend on values used for the semi-empiricial parameters. In this paper a study of finding appropriate parameters to use with the Beddoes-Leishman model is discussed. Minimisation of the `tracking error` between results from 2D wind tunnel tests and simulation with the model is used to find optimum values for the parameters. The resulting optimum parameters show a large variation from case to case. Using these different sets of optimum parameters in the calculation of blade vibrations, give rise to quite different predictions of aerodynamic damping which is discussed. (au)
Spatial cluster detection using dynamic programming
Directory of Open Access Journals (Sweden)
Sverchkov Yuriy
2012-03-01
Full Text Available Abstract Background The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. Methods We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. Results When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. Conclusions We conclude that the dynamic
Grafton, R. Quentin; Chu, Hoang Long; Stewardson, Michael; Kompas, Tom
2011-12-01
A key challenge in managing semiarid basins, such as in the Murray-Darling in Australia, is to balance the trade-offs between the net benefits of allocating water for irrigated agriculture, and other uses, versus the costs of reduced surface flows for the environment. Typically, water planners do not have the tools to optimally and dynamically allocate water among competing uses. We address this problem by developing a general stochastic, dynamic programming model with four state variables (the drought status, the current weather, weather correlation, and current storage) and two controls (environmental release and irrigation allocation) to optimally allocate water between extractions and in situ uses. The model is calibrated to Australia's Murray River that generates: (1) a robust qualitative result that "pulse" or artificial flood events are an optimal way to deliver environmental flows over and above conveyance of base flows; (2) from 2001 to 2009 a water reallocation that would have given less to irrigated agriculture and more to environmental flows would have generated between half a billion and over 3 billion U.S. dollars in overall economic benefits; and (3) water markets increase optimal environmental releases by reducing the losses associated with reduced water diversions.
Numerical optimization of piezolaminated beams under static and dynamic excitations
Directory of Open Access Journals (Sweden)
Rajan L. Wankhade
2017-06-01
Full Text Available Shape and vibration controls of smart structures in structural applications have gained much attraction due to their ability of actuation and sensing. The response of structure to bending, vibration, and buckling can be controlled by the use of this ability of a piezoelectric material. In the present work, the static and dynamic control of smart piezolaminated beams is presented. The optimal locations of piezoelectric patches are found out and then a detailed analysis is performed using finite element modeling considering the higher order shear deformation theory. In the first part, for an extension mode, the piezolaminated beam with stacking sequence PZT5/Al/PZT5 is considered. The length of the beam is 100 mm, whereas the thickness of an aluminum core is 16 mm and that of the piezo layer is of 1 mm. The PZT actuators are positioned with an identical poling direction along the thickness and are excited by a direct current voltage of 10 V. For the shear mode, the stacking sequence Al/PZT5/Al is adopted. The length of the beam is kept the same as the extension mechanism i.e. 100 mm, whereas the thickness of the aluminum core is 8 mm and that of the piezo layer is of 2 mm. The actuator is excited by a direct current voltage of 20 V. In the second part, the control of the piezolaminated beam with an optimal location of the actuator is investigated under a dynamic excitation. Electromechanical loading is considered in the finite element formulation for the analysis purpose. Results are provided for beams with different boundary conditions and loading for future references. Both the extension and shear actuation mechanisms are employed for the piezolaminated beam. These results may be used to identify the response of a beam under static and dynamic excitations. From the present work, the optimal location of a piezoelectric patch can be easily identified for the corresponding boundary condition of the beam.
Portfolio optimization in enhanced index tracking with goal programming approach
Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin
2014-09-01
Enhanced index tracking is a popular form of passive fund management in stock market. Enhanced index tracking aims to generate excess return over the return achieved by the market index without purchasing all of the stocks that make up the index. This can be done by establishing an optimal portfolio to maximize the mean return and minimize the risk. The objective of this paper is to determine the portfolio composition and performance using goal programming approach in enhanced index tracking and comparing it to the market index. Goal programming is a branch of multi-objective optimization which can handle decision problems that involve two different goals in enhanced index tracking, a trade-off between maximizing the mean return and minimizing the risk. The results of this study show that the optimal portfolio with goal programming approach is able to outperform the Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index because of higher mean return and lower risk without purchasing all the stocks in the market index.
Dynamic Optimization Design of Cranes Based on Human–Crane–Rail System Dynamics and Annoyance Rate
Directory of Open Access Journals (Sweden)
Yunsheng Xin
2017-01-01
Full Text Available The operators of overhead traveling cranes experience discomfort as a result of the vibrations of crane structures. These vibrations are produced by defects in the rails on which the cranes move. To improve the comfort of operators, a nine-degree-of-freedom (nine-DOF mathematical model of a “human–crane–rail” system was constructed. Based on the theoretical guidance provided in ISO 2631-1, an annoyance rate model was established, and quantization results were determined. A dynamic optimization design method for overhead traveling cranes is proposed. A particle swarm optimization (PSO algorithm was used to optimize the crane structural design, with the structure parameters as the basic variables, the annoyance rate model as the objective function, and the acceleration amplitude and displacement amplitude of the crane as the constraint conditions. The proposed model and method were used to optimize the design of a double-girder 100 t–28.5 m casting crane, and the optimal parameters are obtained. The results show that optimization decreases the human annoyance rate from 28.3% to 9.8% and the root mean square of the weighted acceleration of human vibration from 0.59 m/s2 to 0.38 m/s2. These results demonstrate the effectiveness and practical applicability of the models and method proposed in this paper.
On Revenue-Optimal Dynamic Auctions for Bidders with Interdependent Values
Constantin, Florin; Parkes, David C.
In a dynamic market, being able to update one's value based on information available to other bidders currently in the market can be critical to having profitable transactions. This is nicely captured by the model of interdependent values (IDV): a bidder's value can explicitly depend on the private information of other bidders. In this paper we present preliminary results about the revenue properties of dynamic auctions for IDV bidders. We adopt a computational approach to design single-item revenue-optimal dynamic auctions with known arrivals and departures but (private) signals that arrive online. In leveraging a characterization of truthful auctions, we present a mixed-integer programming formulation of the design problem. Although a discretization is imposed on bidder signals the solution is a mechanism applicable to continuous signals. The formulation size grows exponentially in the dependence of bidders' values on other bidders' signals. We highlight general properties of revenue-optimal dynamic auctions in a simple parametrized example and study the sensitivity of prices and revenue to model parameters.
Optimal investment in a portfolio of HIV prevention programs.
Zaric, G S; Brandeau, M L
2001-01-01
In this article, the authors determine the optimal allocation of HIV prevention funds and investigate the impact of different allocation methods on health outcomes. The authors present a resource allocation model that can be used to determine the allocation of HIV prevention funds that maximizes quality-adjusted life years (or life years) gained or HIV infections averted in a population over a specified time horizon. They apply the model to determine the allocation of a limited budget among 3 types of HIV prevention programs in a population of injection drug users and nonusers: needle exchange programs, methadone maintenance treatment, and condom availability programs. For each prevention program, the authors estimate a production function that relates the amount invested to the associated change in risky behavior. The authors determine the optimal allocation of funds for both objective functions for a high-prevalence population and a low-prevalence population. They also consider the allocation of funds under several common rules of thumb that are used to allocate HIV prevention resources. It is shown that simpler allocation methods (e.g., allocation based on HIV incidence or notions of equity among population groups) may lead to alloctions that do not yield the maximum health benefit. The optimal allocation of HIV prevention funds in a population depends on HIV prevalence and incidence, the objective function, the production functions for the prevention programs, and other factors. Consideration of cost, equity, and social and political norms may be important when allocating HIV prevention funds. The model presented in this article can help decision makers determine the health consequences of different allocations of funds.
Framework for optimal power flow incorporating dynamic system security
International Nuclear Information System (INIS)
El-Kady, M.A.; Owayedh, M.S.
2006-01-01
This paper introduces a novel framework and methodologies which are capable of tackling the complex issue of power system economy versus security in a practical and effective manner. At heart of achieving such a challenging and far-reaching objective is the incorporation of the Dyanamic Security Assessment (DSA) into production optimization techniques using the Transient Energy Function (TEF) method. In addition, and in parallel with the already well established concept of the system security, two new concepts pertaining to power system performance will be introduced in this paper, namely the concept of system dynamic susceptibility, which measures the level of systems weakness to a particular contingency and the concept of system consequent restorability, which measures the extent of contingency severity in terms of the required subsequent system restoration work should a particular contingency occur. (author)
Optimal growth entails risky localization in population dynamics
Gueudré, Thomas; Martin, David G.
2018-03-01
Essential to each other, growth and exploration are jointly observed in alive and inanimate entities, such as animals, cells or goods. But how the environment's structural and temporal properties weights in this balance remains elusive. We analyze a model of stochastic growth with time correlations and diffusive dynamics that sheds light on the way populations grow and spread over general networks. This model suggests natural explanations of empirical facts in econo-physics or ecology, such as the risk-return trade-off and the Zipf law. We conclude that optimal growth leads to a localized population distribution, but such risky position can be mitigated through the space geometry. These results have broad applicability and are subsequently illustrated over an empirical study of financial data.
Looking for the optimal rate of recombination for evolutionary dynamics
Saakian, David B.
2018-01-01
We consider many-site mutation-recombination models of evolution with selection. We are looking for situations where the recombination increases the mean fitness of the population, and there is an optimal recombination rate. We found two fitness landscapes supporting such nonmonotonic behavior of the mean fitness versus the recombination rate. The first case is related to the evolution near the error threshold on a neutral-network-like fitness landscape, for moderate genome lengths and large population. The more realistic case is the second one, in which we consider the evolutionary dynamics of a finite population on a rugged fitness landscape (the smooth fitness landscape plus some random contributions to the fitness). We also give the solution to the horizontal gene transfer model in the case of asymmetric mutations. To obtain nonmonotonic behavior for both mutation and recombination, we need a specially designed (ideal) fitness landscape.
Dynamic control of biped locomotion robot using optimal regulator
International Nuclear Information System (INIS)
Sano, Akihito; Furusho, Junji
1988-01-01
For moving in indoor space, it is generally recognized that biped locomotion is suitable. This paper proposes a hierarchical control strategy for the lower level where the position control or the force control at each joint is implemented. In the upper level control, the robot motion is divided into a sagittal plane and a lateral plane. We applied the optimal control algorithm to the motion control in the lateral plane in order to improve the robustness of the control system. The effects of these control schemes are shown by the experiments using the new walking robot BLR-G 1 and the parallel calculation system. BLR-G 1 has 9 degrees of freedom and equips the foot-pressure-sensors and a rate gyroscope. Complete dynamic walking is realized, in which the cycle for each step is about 1.0 second. (author)
Optimal dispatch in dynamic security constrained open power market
International Nuclear Information System (INIS)
Singh, S.N.; David, A.K.
2002-01-01
Power system security is a new concern in the competitive power market operation, because the integration of the system controller and the generation owner has been broken. This paper presents an approach for dynamic security constrained optimal dispatch in restructured power market environment. The transient energy margin using transient energy function (TEF) approach has been used to calculate the stability margin of the system and a hybrid method is applied to calculate the approximate unstable equilibrium point (UEP) that is used to calculate the exact UEP and thus, the energy margin using TEF. The case study results illustrated on two systems shows that the operating mechanisms are compatible with the new business environment. (author)
A man in the loop trajectory optimization program (MILTOP)
Reinfields, J.
1974-01-01
An interactive trajectory optimization program is developed for use in initial fixing of launch configurations. The program is called MILTOP for Man-In-the-Loop-Trajectory Optimization-Program. The program is designed to facilitate quick look studies using man-machine decision combinations to reduce the time required to solve a given problem. MILTOP integrates the equations of motion of a point-mass in 3-Dimensions with drag as the only aerodynamic force present. Any point in time at which an integration step terminates, may be used as a decision-break-point, with complete user control over all variables and routines at this point. Automatic phases are provided for different modes of control: vertical rise, pitch-over, gravity turn, chi-freeze and control turn. Stage parameters are initialized from a separate routine so the user may fly as many stages as his problem demands. The MILTOP system uses both interactively on storage scope consoles, or in batch mode with numerical output on the live printer.
International Nuclear Information System (INIS)
Culver, T.B.
1991-01-01
Several modifications of the linear-quadratic regulator (LQR) optimization algorithm are developed, and the computational efficiency of each algorithm with respect to groundwater remediation is evaluated. In each case, the optimization model is combined with a finite element groundwater flow and transport simulation model to determine the optimal time-varying pump-and-treat policy. The first modification of the LQR algorithm incorporated management periods, which are groups of simulation time steps during which the pumping policy remains constant. Management periods reduced the total computational demand, as measured by the CPU time, by as much as 85% compared to the time needed for the LQR solution without management periods. Complexity analysis revealed that computational savings of equal or greater magnitude can be expected in general for groundwater remediation applications and for many other applications of dynamic control. The LQR algorithm with management periods was further modified by assuming steady-state hydraulics within a management period (SSLQR), which simplifies the derivatives of the transition equation. A quasi-Newton differential dynamic programming (QNDDP) was formulated by approximating the complicated second derivatives of the transition equation using a Broyden rank-one approximation. QNDDP converged to the optimal policy for the test problem significantly faster than the LQR algorithm, requiring approximately half the computational time. With the test problem expanded to include the capacity of the treatment facility as a state variable, QNDDP with management periods can determine the optimal treatment facility capacity. With many management periods, the addition of the capital costs of the treatment facility changed the optimal policy so that the required treatment facility capacity was reduced
Role of optimization in the human dynamics of task execution
Cajueiro, Daniel O.; Maldonado, Wilfredo L.
2008-03-01
In order to explain the empirical evidence that the dynamics of human activity may not be well modeled by Poisson processes, a model based on queuing processes was built in the literature [A. L. Barabasi, Nature (London) 435, 207 (2005)]. The main assumption behind that model is that people execute their tasks based on a protocol that first executes the high priority item. In this context, the purpose of this paper is to analyze the validity of that hypothesis assuming that people are rational agents that make their decisions in order to minimize the cost of keeping nonexecuted tasks on the list. Therefore, we build and analytically solve a dynamic programming model with two priority types of tasks and show that the validity of this hypothesis depends strongly on the structure of the instantaneous costs that a person has to face if a given task is kept on the list for more than one period. Moreover, one interesting finding is that in one of the situations the protocol used to execute the tasks generates complex one-dimensional dynamics.
Optimal Diet Planning for Eczema Patient Using Integer Programming
Zhen Sheng, Low; Sufahani, Suliadi
2018-04-01
Human diet planning is conducted by choosing appropriate food items that fulfill the nutritional requirements into the diet formulation. This paper discusses the application of integer programming to build the mathematical model of diet planning for eczema patients. The model developed is used to solve the diet problem of eczema patients from young age group. The integer programming is a scientific approach to select suitable food items, which seeks to minimize the costs, under conditions of meeting desired nutrient quantities, avoiding food allergens and getting certain foods into the diet that brings relief to the eczema conditions. This paper illustrates that the integer programming approach able to produce the optimal and feasible solution to deal with the diet problem of eczema patient.
Keren, Baruch; Pliskin, Joseph S
2011-12-01
The optimal timing for performing radical medical procedures as joint (e.g., hip) replacement must be seriously considered. In this paper we show that under deterministic assumptions the optimal timing for joint replacement is a solution of a mathematical programming problem, and under stochastic assumptions the optimal timing can be formulated as a stochastic programming problem. We formulate deterministic and stochastic models that can serve as decision support tools. The results show that the benefit from joint replacement surgery is heavily dependent on timing. Moreover, for a special case where the patient's remaining life is normally distributed along with a normally distributed survival of the new joint, the expected benefit function from surgery is completely solved. This enables practitioners to draw the expected benefit graph, to find the optimal timing, to evaluate the benefit for each patient, to set priorities among patients and to decide if joint replacement should be performed and when.
The optimal filtering of a class of dynamic multiscale systems
Institute of Scientific and Technical Information of China (English)
PAN Quan; ZHANG Lei; CUI Peiling; ZHANG Hongcai
2004-01-01
This paper discusses the optimal filtering of a class of dynamic multiscale systems (DMS), which are observed independently by several sensors distributed at different resolution spaces. The system is subject to known dynamic system model. The resolution and sampling frequencies of the sensors are supposed to decrease by a factor of two. By using the Haar wavelet transform to link the state nodes at each of the scales within a time block, a discrete-time model of this class of multiscale systems is given, and the conditions for applying Kalman filtering are proven. Based on the linear time-invariant system, the controllability and observability of the system and the stability of the Kalman filtering is studied, and a theorem is given. It is proved that the Kalman filter is stable if only the system is controllable and observable at the finest scale. Finally, a constant-velocity process is used to obtain insight into the efficiencies offered by our model and algorithm.
A nonlinear optimal control approach for chaotic finance dynamics
Rigatos, G.; Siano, P.; Loia, V.; Tommasetti, A.; Troisi, O.
2017-11-01
A new nonlinear optimal control approach is proposed for stabilization of the dynamics of a chaotic finance model. The dynamic model of the financial system, which expresses interaction between the interest rate, the investment demand, the price exponent and the profit margin, undergoes approximate linearization round local operating points. These local equilibria are defined at each iteration of the control algorithm and consist of the present value of the systems state vector and the last value of the control inputs vector that was exerted on it. The approximate linearization makes use of Taylor series expansion and of the computation of the associated Jacobian matrices. The truncation of higher order terms in the Taylor series expansion is considered to be a modelling error that is compensated by the robustness of the control loop. As the control algorithm runs, the temporary equilibrium is shifted towards the reference trajectory and finally converges to it. The control method needs to compute an H-infinity feedback control law at each iteration, and requires the repetitive solution of an algebraic Riccati equation. Through Lyapunov stability analysis it is shown that an H-infinity tracking performance criterion holds for the control loop. This implies elevated robustness against model approximations and external perturbations. Moreover, under moderate conditions the global asymptotic stability of the control loop is proven.
Dynamic modeling and optimal joint torque coordination of advanced robotic systems
Kang, Hee-Jun
The development is documented of an efficient dynamic modeling algorithm and the subsequent optimal joint input load coordination of advanced robotic systems for industrial application. A closed-form dynamic modeling algorithm for the general closed-chain robotic linkage systems is presented. The algorithm is based on the transfer of system dependence from a set of open chain Lagrangian coordinates to any desired system generalized coordinate set of the closed-chain. Three different techniques for evaluation of the kinematic closed chain constraints allow the representation of the dynamic modeling parameters in terms of system generalized coordinates and have no restriction with regard to kinematic redundancy. The total computational requirement of the closed-chain system model is largely dependent on the computation required for the dynamic model of an open kinematic chain. In order to improve computational efficiency, modification of an existing open-chain KIC based dynamic formulation is made by the introduction of the generalized augmented body concept. This algorithm allows a 44 pct. computational saving over the current optimized one (O(N4), 5995 when N = 6). As means of resolving redundancies in advanced robotic systems, local joint torque optimization is applied for effectively using actuator power while avoiding joint torque limits. The stability problem in local joint torque optimization schemes is eliminated by using fictitious dissipating forces which act in the necessary null space. The performance index representing the global torque norm is shown to be satisfactory. In addition, the resulting joint motion trajectory becomes conservative, after a transient stage, for repetitive cyclic end-effector trajectories. The effectiveness of the null space damping method is shown. The modular robot, which is built of well defined structural modules from a finite-size inventory and is controlled by one general computer system, is another class of evolving
Real-Time Reactive Power Distribution in Microgrids by Dynamic Programing
DEFF Research Database (Denmark)
Levron, Yoash; Beck, Yuval; Katzir, Liran
2017-01-01
In this paper a new real-time optimization method for reactive power distribution in microgrids is proposed. The method enables location of a globally optimal distribution of reactive power under normal operating conditions. The method exploits the typical compact structure of microgrids to obtain...... combination of reactive powers, by means of dynamic programming. Since every single step involves a one-dimensional problem, the complexity of the solution is only linear with the number of clusters, and as a result, a globally optimal solution may be obtained in real time. The paper includes the results...
Nguyen, Howard; Willacy, Karen; Allen, Mark
2012-01-01
KINETICS is a coupled dynamics and chemistry atmosphere model that is data intensive and computationally demanding. The potential performance gain from using a supercomputer motivates the adaptation from a serial version to a parallelized one. Although the initial parallelization had been done, bottlenecks caused by an abundance of communication calls between processors led to an unfavorable drop in performance. Before starting on the parallel optimization process, a partial overhaul was required because a large emphasis was placed on streamlining the code for user convenience and revising the program to accommodate the new supercomputers at Caltech and JPL. After the first round of optimizations, the partial runtime was reduced by a factor of 23; however, performance gains are dependent on the size of the data, the number of processors requested, and the computer used.
The Adjoint Method for Gradient-based Dynamic Optimization of UV Flash Processes
DEFF Research Database (Denmark)
Ritschel, Tobias Kasper Skovborg; Capolei, Andrea; Jørgensen, John Bagterp
2017-01-01
This paper presents a novel single-shooting algorithm for gradient-based solution of optimal control problems with vapor-liquid equilibrium constraints. Dynamic optimization of UV flash processes is relevant in nonlinear model predictive control of distillation columns, certain two-phase flow pro......-component flash process which demonstrate the importance of the optimization solver, the compiler, and the linear algebra software for the efficiency of dynamic optimization of UV flash processes....
OPTIMIZING ANTIMICROBIAL PHARMACODYNAMICS: A GUIDE FOR YOUR STEWARDSHIP PROGRAM
Directory of Open Access Journals (Sweden)
Joseph L. Kuti, PharmD
2016-09-01
Full Text Available Pharmacodynamic concepts should be applied to optimize antibiotic dosing regimens, particularly in the face of some multidrug resistant bacterial infections. Although the pharmacodynamics of most antibiotic classes used in the hospital setting are well described, guidance on how to select regimens and implement them into an antimicrobial stewardship program in one's institution are more limited. The role of the antibiotic MIC is paramount in understanding which regimens might benefit from implementation as a protocol or use in individual patients. This review article outlines the pharmacodynamics of aminoglycosides, beta-lactams, fluoroquinolones, tigecycline, vancomycin, and polymyxins with the goal of providing a basis for strategy to select an optimized antibiotic regimen in your hospital setting.
Local beam angle optimization with linear programming and gradient search
International Nuclear Information System (INIS)
Craft, David
2007-01-01
The optimization of beam angles in IMRT planning is still an open problem, with literature focusing on heuristic strategies and exhaustive searches on discrete angle grids. We show how a beam angle set can be locally refined in a continuous manner using gradient-based optimization in the beam angle space. The gradient is derived using linear programming duality theory. Applying this local search to 100 random initial angle sets of a phantom pancreatic case demonstrates the method, and highlights the many-local-minima aspect of the BAO problem. Due to this function structure, we recommend a search strategy of a thorough global search followed by local refinement at promising beam angle sets. Extensions to nonlinear IMRT formulations are discussed. (note)
Particle swarm optimization for programming deep brain stimulation arrays.
Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D
2017-02-01
Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (⩽9.2%) and ROA (⩽1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n = 3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations showed discrepancies
Particle Swarm Optimization for Programming Deep Brain Stimulation Arrays
Peña, Edgar; Zhang, Simeng; Deyo, Steve; Xiao, YiZi; Johnson, Matthew D.
2017-01-01
Objective Deep brain stimulation (DBS) therapy relies on both precise neurosurgical targeting and systematic optimization of stimulation settings to achieve beneficial clinical outcomes. One recent advance to improve targeting is the development of DBS arrays (DBSAs) with electrodes segmented both along and around the DBS lead. However, increasing the number of independent electrodes creates the logistical challenge of optimizing stimulation parameters efficiently. Approach Solving such complex problems with multiple solutions and objectives is well known to occur in biology, in which complex collective behaviors emerge out of swarms of individual organisms engaged in learning through social interactions. Here, we developed a particle swarm optimization (PSO) algorithm to program DBSAs using a swarm of individual particles representing electrode configurations and stimulation amplitudes. Using a finite element model of motor thalamic DBS, we demonstrate how the PSO algorithm can efficiently optimize a multi-objective function that maximizes predictions of axonal activation in regions of interest (ROI, cerebellar-receiving area of motor thalamus), minimizes predictions of axonal activation in regions of avoidance (ROA, somatosensory thalamus), and minimizes power consumption. Main Results The algorithm solved the multi-objective problem by producing a Pareto front. ROI and ROA activation predictions were consistent across swarms (<1% median discrepancy in axon activation). The algorithm was able to accommodate for (1) lead displacement (1 mm) with relatively small ROI (≤9.2%) and ROA (≤1%) activation changes, irrespective of shift direction; (2) reduction in maximum per-electrode current (by 50% and 80%) with ROI activation decreasing by 5.6% and 16%, respectively; and (3) disabling electrodes (n=3 and 12) with ROI activation reduction by 1.8% and 14%, respectively. Additionally, comparison between PSO predictions and multi-compartment axon model simulations
Distributed Optimal Consensus Control for Nonlinear Multiagent System With Unknown Dynamic.
Zhang, Jilie; Zhang, Huaguang; Feng, Tao
2017-08-01
This paper focuses on the distributed optimal cooperative control for continuous-time nonlinear multiagent systems (MASs) with completely unknown dynamics via adaptive dynamic programming (ADP) technology. By introducing predesigned extra compensators, the augmented neighborhood error systems are derived, which successfully circumvents the system knowledge requirement for ADP. It is revealed that the optimal consensus protocols actually work as the solutions of the MAS differential game. Policy iteration algorithm is adopted, and it is theoretically proved that the iterative value function sequence strictly converges to the solution of the coupled Hamilton-Jacobi-Bellman equation. Based on this point, a novel online iterative scheme is proposed, which runs based on the data sampled from the augmented system and the gradient of the value function. Neural networks are employed to implement the algorithm and the weights are updated, in the least-square sense, to the ideal value, which yields approximated optimal consensus protocols. Finally, a numerical example is given to illustrate the effectiveness of the proposed scheme.
A Dynamic Optimization Strategy for the Operation of Large Scale Seawater Reverses Osmosis System
Directory of Open Access Journals (Sweden)
Aipeng Jiang
2014-01-01
Full Text Available In this work, an efficient strategy was proposed for efficient solution of the dynamic model of SWRO system. Since the dynamic model is formulated by a set of differential-algebraic equations, simultaneous strategies based on collocations on finite element were used to transform the DAOP into large scale nonlinear programming problem named Opt2. Then, simulation of RO process and storage tanks was carried element by element and step by step with fixed control variables. All the obtained values of these variables then were used as the initial value for the optimal solution of SWRO system. Finally, in order to accelerate the computing efficiency and at the same time to keep enough accuracy for the solution of Opt2, a simple but efficient finite element refinement rule was used to reduce the scale of Opt2. The proposed strategy was applied to a large scale SWRO system with 8 RO plants and 4 storage tanks as case study. Computing result shows that the proposed strategy is quite effective for optimal operation of the large scale SWRO system; the optimal problem can be successfully solved within decades of iterations and several minutes when load and other operating parameters fluctuate.
Dynamic Allocation of SPM Based on Time-Slotted Cache Conflict Graph for System Optimization
Wu, Jianping; Ling, Ming; Zhang, Yang; Mei, Chen; Wang, Huan
This paper proposes a novel dynamic Scratch-pad Memory allocation strategy to optimize the energy consumption of the memory sub-system. Firstly, the whole program execution process is sliced into several time slots according to the temporal dimension; thereafter, a Time-Slotted Cache Conflict Graph (TSCCG) is introduced to model the behavior of Data Cache (D-Cache) conflicts within each time slot. Then, Integer Nonlinear Programming (INP) is implemented, which can avoid time-consuming linearization process, to select the most profitable data pages. Virtual Memory System (VMS) is adopted to remap those data pages, which will cause severe Cache conflicts within a time slot, to SPM. In order to minimize the swapping overhead of dynamic SPM allocation, a novel SPM controller with a tightly coupled DMA is introduced to issue the swapping operations without CPU's intervention. Last but not the least, this paper discusses the fluctuation of system energy profit based on different MMU page size as well as the Time Slot duration quantitatively. According to our design space exploration, the proposed method can optimize all of the data segments, including global data, heap and stack data in general, and reduce the total energy consumption by 27.28% on average, up to 55.22% with a marginal performance promotion. And comparing to the conventional static CCG (Cache Conflicts Graph), our approach can obtain 24.7% energy profit on average, up to 30.5% with a sight boost in performance.
The Programming Optimization of Capacitorless 1T DRAM Based on the Dual-Gate TFET.
Li, Wei; Liu, Hongxia; Wang, Shulong; Chen, Shupeng; Wang, Qianqiong
2017-09-06
The larger volume of capacitor and higher leakage current of transistor have become the inherent disadvantages for the traditional one transistor (1T)-one capacitor (1C) dynamic random access memory (DRAM). Recently, the tunneling FET (TFET) is applied in DRAM cell due to the low off-state current and high switching ratio. The dual-gate TFET (DG-TFET) DRAM cell with the capacitorless structure has the superior performance-higher retention time (RT) and weak temperature dependence. But the performance of TFET DRAM cell is sensitive to programming condition. In this paper, the guideline of programming optimization is discussed in detail by using simulation tool-Silvaco Atlas. Both the writing and reading operations of DG-TFET DRAM depend on the band-to-band tunneling (BTBT). During the writing operation, the holes coming from BTBT governed by Gate2 are stored in potential well under Gate2. A small negative voltage is applied at Gate2 to retain holes for a long time during holding "1". The BTBT governed by Gate1 mainly influences the reading current. Using the optimized programming condition, the DG-TFET DRAM obtains the higher current ratio of reading "1" to reading "0" (10 7 ) and RT of more than 2 s. The higher RT reduces the refresh rate and dynamic power consumption of DRAM.
The Programming Optimization of Capacitorless 1T DRAM Based on the Dual-Gate TFET
Li, Wei; Liu, Hongxia; Wang, Shulong; Chen, Shupeng; Wang, Qianqiong
2017-09-01
The larger volume of capacitor and higher leakage current of transistor have become the inherent disadvantages for the traditional one transistor (1T)-one capacitor (1C) dynamic random access memory (DRAM). Recently, the tunneling FET (TFET) is applied in DRAM cell due to the low off-state current and high switching ratio. The dual-gate TFET (DG-TFET) DRAM cell with the capacitorless structure has the superior performance-higher retention time (RT) and weak temperature dependence. But the performance of TFET DRAM cell is sensitive to programming condition. In this paper, the guideline of programming optimization is discussed in detail by using simulation tool—Silvaco Atlas. Both the writing and reading operations of DG-TFET DRAM depend on the band-to-band tunneling (BTBT). During the writing operation, the holes coming from BTBT governed by Gate2 are stored in potential well under Gate2. A small negative voltage is applied at Gate2 to retain holes for a long time during holding "1". The BTBT governed by Gate1 mainly influences the reading current. Using the optimized programming condition, the DG-TFET DRAM obtains the higher current ratio of reading "1" to reading "0" (107) and RT of more than 2 s. The higher RT reduces the refresh rate and dynamic power consumption of DRAM.
An Optimization Method for Virtual Globe Ocean Surface Dynamic Visualization
Directory of Open Access Journals (Sweden)
HUANG Wumeng
2016-12-01
Full Text Available The existing visualization method in the virtual globe mainly uses the projection grid to organize the ocean grid. This special grid organization has the defects in reflecting the difference characteristics of different ocean areas. The method of global ocean visualization based on global discrete grid can make up the defect of the projection grid method by matching with the discrete space of the virtual globe, so it is more suitable for the virtual ocean surface simulation application.But the available global discrete grids method has many problems which limiting its application such as the low efficiency of rendering and loading, the need of repairing grid crevices. To this point, we propose an optimization for the global discrete grids method. At first, a GPU-oriented multi-scale grid model of ocean surface which develops on the foundation of global discrete grids was designed to organize and manage the ocean surface grids. Then, in order to achieve the wind-drive wave dynamic rendering, this paper proposes a dynamic wave rendering method based on the multi-scale ocean surface grid model to support real-time wind field updating. At the same time, considering the effect of repairing grid crevices on the system efficiency, this paper presents an efficient method for repairing ocean surface grid crevices based on the characteristics of ocean grid and GPU technology. At last, the feasibility and validity of the method are verified by the comparison experiment. The experimental results show that the proposed method is efficient, stable and fast, and can compensate for the lack of function of the existing methods, so the application range is more extensive.
Directory of Open Access Journals (Sweden)
Luman Zhao
2015-01-01
Full Text Available A thrust allocation method was proposed based on a hybrid optimization algorithm to efficiently and dynamically position a semisubmersible drilling rig. That is, the thrust allocation was optimized to produce the generalized forces and moment required while at the same time minimizing the total power consumption under the premise that forbidden zones should be taken into account. An optimization problem was mathematically formulated to provide the optimal thrust allocation by introducing the corresponding design variables, objective function, and constraints. A hybrid optimization algorithm consisting of a genetic algorithm and a sequential quadratic programming (SQP algorithm was selected and used to solve this problem. The proposed method was evaluated by applying it to a thrust allocation problem for a semisubmersible drilling rig. The results indicate that the proposed method can be used as part of a cost-effective strategy for thrust allocation of the rig.
Approximate Dynamic Programming Solving the Curses of Dimensionality
Powell, Warren B
2011-01-01
Praise for the First Edition "Finally, a book devoted to dynamic programming and written using the language of operations research (OR)! This beautiful book fills a gap in the libraries of OR specialists and practitioners."-Computing Reviews This new edition showcases a focus on modeling and computation for complex classes of approximate dynamic programming problems Understanding approximate dynamic programming (ADP) is vital in order to develop practical and high-quality solutions to complex industrial problems, particularly when those problems involve making decisions in the presence of unce
Feedback optimal control of dynamic stochastic two-machine flowshop with a finite buffer
Directory of Open Access Journals (Sweden)
Thang Diep
2010-06-01
Full Text Available This paper examines the optimization of production involving a tandem two-machine system producing a single part type, with each machine being subject to random breakdowns and repairs. An analytical model is formulated with a view to solving an optimal stochastic production problem of the system with machines having up-downtime non-exponential distributions. The model developed is obtained by using a dynamic programming approach and a semi-Markov process. The control problem aims to find the production rates needed by the machines to meet the demand rate, through a minimization of the inventory/shortage cost. Using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation, which depends on time and system states, and ultimately, leads to a feedback control. Consequently, the new model enables us to improve the coefficient of variation (CVup/down to be less than one while it is equal to one in Markov model. Heuristics methods are used to involve the problem because of the difficulty of the analytical model using several states, and to show what control law should be used in each system state (i.e., including Kanban, feedback and CONWIP control. Numerical methods are used to solve the optimality conditions and to show how a machine should produce.
Granular contact dynamics using mathematical programming methods
DEFF Research Database (Denmark)
Krabbenhoft, K.; Lyamin, A. V.; Huang, J.
2012-01-01
granular contact dynamics formulation uses an implicit time discretization, thus allowing for large time steps. Moreover, in the limit of an infinite time step, the general dynamic formulation reduces to a static formulation that is useful in simulating common quasi-static problems such as triaxial tests...... is developed and it is concluded that the associated sliding rule, in the context of granular contact dynamics, may be viewed as an artifact of the time discretization and that the use of an associated flow rule at the particle scale level generally is physically acceptable. (C) 2012 Elsevier Ltd. All rights...
An Algebraic Programming Style for Numerical Software and Its Optimization
Directory of Open Access Journals (Sweden)
T.B. Dinesh
2000-01-01
Full Text Available The abstract mathematical theory of partial differential equations (PDEs is formulated in terms of manifolds, scalar fields, tensors, and the like, but these algebraic structures are hardly recognizable in actual PDE solvers. The general aim of the Sophus programming style is to bridge the gap between theory and practice in the domain of PDE solvers. Its main ingredients are a library of abstract datatypes corresponding to the algebraic structures used in the mathematical theory and an algebraic expression style similar to the expression style used in the mathematical theory. Because of its emphasis on abstract datatypes, Sophus is most naturally combined with object-oriented languages or other languages supporting abstract datatypes. The resulting source code patterns are beyond the scope of current compiler optimizations, but are sufficiently specific for a dedicated source-to-source optimizer. The limited, domain-specific, character of Sophus is the key to success here. This kind of optimization has been tested on computationally intensive Sophus style code with promising results. The general approach may be useful for other styles and in other application domains as well.
Optimization programs of radiation protection applied to post-graduation and encouraging research
International Nuclear Information System (INIS)
Levy, Denise S.; Sordi, Gian Maria A.A.
2013-01-01
In 2011 we started the automation and integration of radiological protection optimization programs, in order to offer unified programs and inter-related information in Portuguese, providing Brazilian radioactive facilities a complete repository for research, consultation and information. The authors of this project extended it to postgraduate education, in order to encourage postgraduate students researches, expanding methods for enhancing student learning through the use of different combined resources, such as educational technology, information technology and group dynamics. This new methodology was applied in a postgraduate discipline at Instituto de Pesquisas Energeticas e Nucleares (IPEN), Brazil, in the postgraduate discipline entitled Fundamental Elements of Radiological Protection (TNA-5732). Students have six weeks to assimilate a complex content of optimization, considering national and international standards, guidelines and recommendations published by different organizations over the past decades. Unlike traditional classes, in which students receive prompt responses, this new methodology stimulates discussion, encouraging collective thinking processes and promoting ongoing personal reflection and researches. Case-oriented problem-solving permitted students to play different roles, promoting whole-group discussions and cooperative learning, approaching theory and practical applications. Students discussed different papers, published in international conferences, and their implications according to current standards. The automation of optimization programs was essential as a research tool during the course. The results of this experience were evaluated in two consecutive years. We had excellent results compared to the previous 14 years. The methodology has exceeded expectations and will be also applied in 2013 to ionizing radiation monitoring postgraduate classes. (author)
Optimizing Crawler4j using MapReduce Programming Model
Siddesh, G. M.; Suresh, Kavya; Madhuri, K. Y.; Nijagal, Madhushree; Rakshitha, B. R.; Srinivasa, K. G.
2017-06-01
World wide web is a decentralized system that consists of a repository of information on the basis of web pages. These web pages act as a source of information or data in the present analytics world. Web crawlers are used for extracting useful information from web pages for different purposes. Firstly, it is used in web search engines where the web pages are indexed to form a corpus of information and allows the users to query on the web pages. Secondly, it is used for web archiving where the web pages are stored for later analysis phases. Thirdly, it can be used for web mining where the web pages are monitored for copyright purposes. The amount of information processed by the web crawler needs to be improved by using the capabilities of modern parallel processing technologies. In order to solve the problem of parallelism and the throughput of crawling this work proposes to optimize the Crawler4j using the Hadoop MapReduce programming model by parallelizing the processing of large input data. Crawler4j is a web crawler that retrieves useful information about the pages that it visits. The crawler Crawler4j coupled with data and computational parallelism of Hadoop MapReduce programming model improves the throughput and accuracy of web crawling. The experimental results demonstrate that the proposed solution achieves significant improvements with respect to performance and throughput. Hence the proposed approach intends to carve out a new methodology towards optimizing web crawling by achieving significant performance gain.
Photocathode Optimization for a Dynamic Transmission Electron Microscope: Final Report
Energy Technology Data Exchange (ETDEWEB)
Ellis, P; Flom, Z; Heinselman, K; Nguyen, T; Tung, S; Haskell, R; Reed, B W; LaGrange, T
2011-08-04
The Dynamic Transmission Electron Microscope (DTEM) team at Harvey Mudd College has been sponsored by LLNL to design and build a test setup for optimizing the performance of the DTEM's electron source. Unlike a traditional TEM, the DTEM achieves much faster exposure times by using photoemission from a photocathode to produce electrons for imaging. The DTEM team's work is motivated by the need to improve the coherence and current density of the electron cloud produced by the electron gun in order to increase the image resolution and contrast achievable by DTEM. The photoemission test setup is nearly complete and the team will soon complete baseline tests of electron gun performance. The photoemission laser and high voltage power supply have been repaired; the optics path for relaying the laser to the photocathode has been finalized, assembled, and aligned; the internal setup of the vacuum chamber has been finalized and mostly implemented; and system control, synchronization, and data acquisition has been implemented in LabVIEW. Immediate future work includes determining a consistent alignment procedure to place the laser waist on the photocathode, and taking baseline performance measurements of the tantalum photocathode. Future research will examine the performance of the electron gun as a function of the photoemission laser profile, the photocathode material, and the geometry and voltages of the accelerating and focusing components in the electron gun. This report presents the team's progress and outlines the work that remains.
Optimal spectral tracking--adapting to dynamic regime change.
Brittain, John-Stuart; Halliday, David M
2011-01-30
Real world data do not always obey the statistical restraints imposed upon them by sophisticated analysis techniques. In spectral analysis for instance, an ergodic process--the interchangeability of temporal for spatial averaging--is assumed for a repeat-trial design. Many evolutionary scenarios, such as learning and motor consolidation, do not conform to such linear behaviour and should be approached from a more flexible perspective. To this end we previously introduced the method of optimal spectral tracking (OST) in the study of trial-varying parameters. In this extension to our work we modify the OST routines to provide an adaptive implementation capable of reacting to dynamic transitions in the underlying system state. In so doing, we generalise our approach to characterise both slow-varying and rapid fluctuations in time-series, simultaneously providing a metric of system stability. The approach is first applied to a surrogate dataset and compared to both our original non-adaptive solution and spectrogram approaches. The adaptive OST is seen to display fast convergence and desirable statistical properties. All three approaches are then applied to a neurophysiological recording obtained during a study on anaesthetic monitoring. Local field potentials acquired from the posterior hypothalamic region of a deep brain stimulation patient undergoing anaesthesia were analysed. The characterisation of features such as response delay, time-to-peak and modulation brevity are considered. Copyright © 2010 Elsevier B.V. All rights reserved.
Conceptualizing a tool to optimize therapy based on dynamic heterogeneity
International Nuclear Information System (INIS)
Liao, David; Estévez-Salmerón, Luis; Tlsty, Thea D
2012-01-01
Complex biological systems often display a randomness paralleled in processes studied in fundamental physics. This simple stochasticity emerges owing to the complexity of the system and underlies a fundamental aspect of biology called phenotypic stochasticity. Ongoing stochastic fluctuations in phenotype at the single-unit level can contribute to two emergent population phenotypes. Phenotypic stochasticity not only generates heterogeneity within a cell population, but also allows reversible transitions back and forth between multiple states. This phenotypic interconversion tends to restore a population to a previous composition after that population has been depleted of specific members. We call this tendency homeostatic heterogeneity. These concepts of dynamic heterogeneity can be applied to populations composed of molecules, cells, individuals, etc. Here we discuss the concept that phenotypic stochasticity both underlies the generation of heterogeneity within a cell population and can be used to control population composition, contributing, in particular, to both the ongoing emergence of drug resistance and an opportunity for depleting drug-resistant cells. Using notions of both ‘large’ and ‘small’ numbers of biomolecular components, we rationalize our use of Markov processes to model the generation and eradication of drug-resistant cells. Using these insights, we have developed a graphical tool, called a metronomogram, that we propose will allow us to optimize dosing frequencies and total course durations for clinical benefit. (paper)
Optimal design and dynamic impact tests of removable bollards
Chen, Suwen; Liu, Tianyi; Li, Guoqiang; Liu, Qing; Sun, Jianyun
2017-10-01
Anti-ram bollard systems, which are installed around buildings and infrastructure, can prevent unauthorized vehicles from entering, maintain distance from vehicle-borne improvised explosive devices (VBIED) and reduce the corresponding damage. Compared with a fixed bollard system, a removable bollard system provides more flexibility as it can be removed when needed. This paper first proposes a new type of K4-rated removable anti-ram bollard system. To simulate the collision of a vehicle hitting the bollard system, a finite element model was then built and verified through comparison of numerical simulation results and existing experimental results. Based on the orthogonal design method, the factors influencing the safety and economy of this proposed system were examined and sorted according to their importance. An optimal design scheme was then produced. Finally, to validate the effectiveness of the proposed design scheme, four dynamic impact tests, including two front impact tests and two side impact tests, have been conducted according to BSI Specifications. The residual rotation angles of the specimen are smaller than 30º and satisfy the requirements of the BSI Specification.
Application of genetic programming in shape optimization of concrete gravity dams by metaheuristics
Directory of Open Access Journals (Sweden)
Abdolhossein Baghlani
2014-12-01
Full Text Available A gravity dam maintains its stability against the external loads by its massive size. Hence, minimization of the weight of the dam can remarkably reduce the construction costs. In this paper, a procedure for finding optimal shape of concrete gravity dams with a computationally efficient approach is introduced. Genetic programming (GP in conjunction with metaheuristics is used for this purpose. As a case study, shape optimization of the Bluestone dam is presented. Pseudo-dynamic analysis is carried out on a total number of 322 models in order to establish a database of the results. This database is then used to find appropriate relations based on GP for design criteria of the dam. This procedure eliminates the necessity of the time-consuming process of structural analyses in evolutionary optimization methods. The method is hybridized with three different metaheuristics, including particle swarm optimization, firefly algorithm (FA, and teaching–learning-based optimization, and a comparison is made. The results show that although all algorithms are very suitable, FA is slightly superior to other two algorithms in finding a lighter structure in less number of iterations. The proposed method reduces the weight of dam up to 14.6% with very low computational effort.
International Nuclear Information System (INIS)
Hunter, J.A.
1984-01-01
Equipment qualification research is being conducted to investigate acceptable criteria, requirements, and methodologies for the dynamic (including seismic) and environmental qualification of mechanical equipment and for the dynamic (including seismic) qualification of electrical equipment. The program is organized into three elements: (1) General Research, (2) Environmental Research, and (3) Dynamic Research. This paper presents the highlights of the results to date in these three elements of the program
Warehouse stocking optimization based on dynamic ant colony genetic algorithm
Xiao, Xiaoxu
2018-04-01
In view of the various orders of FAW (First Automotive Works) International Logistics Co., Ltd., the SLP method is used to optimize the layout of the warehousing units in the enterprise, thus the warehouse logistics is optimized and the external processing speed of the order is improved. In addition, the relevant intelligent algorithms for optimizing the stocking route problem are analyzed. The ant colony algorithm and genetic algorithm which have good applicability are emphatically studied. The parameters of ant colony algorithm are optimized by genetic algorithm, which improves the performance of ant colony algorithm. A typical path optimization problem model is taken as an example to prove the effectiveness of parameter optimization.
Bulgakov, V. K.; Strigunov, V. V.
2009-05-01
The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.
Dynamic Programming Approaches for the Traveling Salesman Problem with Drone
Bouman, Paul; Agatz, Niels; Schmidt, Marie
2017-01-01
markdownabstractA promising new delivery model involves the use of a delivery truck that collaborates with a drone to make deliveries. Effectively combining a drone and a truck gives rise to a new planning problem that is known as the Traveling Salesman Problem with Drone (TSP-D). This paper presents an exact solution approach for the TSP-D based on dynamic programming and present experimental results of different dynamic programming based heuristics. Our numerical experiments show that our a...
Dynamic Line Rating Oncor Electric Delivery Smart Grid Program
Energy Technology Data Exchange (ETDEWEB)
Johnson, Justin; Smith, Cale; Young, Mike; Donohoo, Ken; Owen, Ross; Clark, Eddit; Espejo, Raul; Aivaliotis, Sandy; Stelmak, Ron; Mohr, Ron; Barba, Cristian; Gonzalez, Guillermo; Malkin, Stuart; Dimitrova, Vessela; Ragsdale, Gary; Mitchem, Sean; Jeirath, Nakul; Loomis, Joe; Trevino, Gerardo; Syracuse, Steve; Hurst, Neil; Mereness, Matt; Johnson, Chad; Bivens, Carrie
2013-05-04
Electric transmission lines are the lifeline of the electric utility industry, delivering its product from source to consumer. This critical infrastructure is often constrained such that there is inadequate capacity on existing transmission lines to efficiently deliver the power to meet demand in certain areas or to transport energy from high-generation areas to high-consumption regions. When this happens, the cost of the energy rises; more costly sources of power are used to meet the demand or the system operates less reliably. These economic impacts are known as congestion, and they can amount to substantial dollars for any time frame of reference: hour, day or year. There are several solutions to the transmission constraint problem, including: construction of new generation, construction of new transmission facilities, rebuilding and reconductoring of existing transmission assets, and Dynamic Line Rating (DLR). All of these options except DLR are capital intensive, have long lead times and often experience strong public and regulatory opposition. The Smart Grid Demonstration Program (SGDP) project co-funded by the Department of Energy (DOE) and Oncor Electric Delivery Company developed and deployed the most extensive and advanced DLR installation to demonstrate that DLR technology is capable of resolving many transmission capacity constraint problems with a system that is reliable, safe and very cost competitive. The SGDP DLR deployment is the first application of DLR technology to feed transmission line real-time dynamic ratings directly into the system operation’s State Estimator and load dispatch program, which optimizes the matching of generation with load demand on a security, reliability and economic basis. The integrated Dynamic Line Rating (iDLR)1 collects transmission line parameters at remote locations on the lines, calculates the real-time line rating based on the equivalent conductor temperature, ambient temperature and influence of wind and solar
Pythran: enabling static optimization of scientific Python programs
Guelton, Serge; Brunet, Pierrick; Amini, Mehdi; Merlini, Adrien; Corbillon, Xavier; Raynaud, Alan
2015-01-01
Pythran is an open source static compiler that turns modules written in a subset of Python language into native ones. Assuming that scientific modules do not rely much on the dynamic features of the language, it trades them for powerful, possibly inter-procedural, optimizations. These optimizations include detection of pure functions, temporary allocation removal, constant folding, Numpy ufunc fusion and parallelization, explicit thread-level parallelism through OpenMP annotations, false variable polymorphism pruning, and automatic vector instruction generation such as AVX or SSE. In addition to these compilation steps, Pythran provides a C++ runtime library that leverages the C++ STL to provide generic containers, and the Numeric Template Toolbox for Numpy support. It takes advantage of modern C++11 features such as variadic templates, type inference, move semantics and perfect forwarding, as well as classical idioms such as expression templates. Unlike the Cython approach, Pythran input code remains compatible with the Python interpreter. Output code is generally as efficient as the annotated Cython equivalent, if not more, but without the backward compatibility loss.
Dynamic Performance Tuning Supported by Program Specification
Directory of Open Access Journals (Sweden)
Eduardo César
2002-01-01
Full Text Available Performance analysis and tuning of parallel/distributed applications are very difficult tasks for non-expert programmers. It is necessary to provide tools that automatically carry out these tasks. These can be static tools that carry out the analysis on a post-mortem phase or can tune the application on the fly. Both kind of tools have their target applications. Static automatic analysis tools are suitable for stable application while dynamic tuning tools are more appropriate to applications with dynamic behaviour. In this paper, we describe KappaPi as an example of a static automatic performance analysis tool, and also a general environment based on parallel patterns for developing and dynamically tuning parallel/distributed applications.
Zhu, Yuanheng; Zhao, Dongbin; Yang, Xiong; Zhang, Qichao
2018-02-01
Sum of squares (SOS) polynomials have provided a computationally tractable way to deal with inequality constraints appearing in many control problems. It can also act as an approximator in the framework of adaptive dynamic programming. In this paper, an approximate solution to the optimal control of polynomial nonlinear systems is proposed. Under a given attenuation coefficient, the Hamilton-Jacobi-Isaacs equation is relaxed to an optimization problem with a set of inequalities. After applying the policy iteration technique and constraining inequalities to SOS, the optimization problem is divided into a sequence of feasible semidefinite programming problems. With the converged solution, the attenuation coefficient is further minimized to a lower value. After iterations, approximate solutions to the smallest -gain and the associated optimal controller are obtained. Four examples are employed to verify the effectiveness of the proposed algorithm.
Optimal management with hybrid dynamics : The shallow lake problem
Reddy, P.V.; Schumacher, Hans; Engwerda, Jacob; Camlibel, M.K.; Julius, A.A.; Pasumarthy, R.
2015-01-01
In this article we analyze an optimal management problem that arises in ecological economics using hybrid systems modeling. First, we introduce a discounted autonomous infinite horizon hybrid optimal control problem and develop few tools to analyze the necessary conditions for optimality. Next,
International Nuclear Information System (INIS)
Elsays, Mostafa A.; Naguib Aly, M.; Badawi, Alya A.
2009-01-01
In this paper, the Particle Swarm Optimization (PSO) algorithm is modified to deal with Multiobjective Optimization Problems (MOPs). A mathematical model for predicting the dynamic response of the H. B. Robinson nuclear power plant, which represents an Initial Value Problem (IVP) of Ordinary Differential Equations (ODEs), is solved using Runge-Kutta formula. The resulted data values are represented as a system of nonlinear algebraic equations by interpolation schemes for data fitting. This system of fitted nonlinear algebraic equations represents a nonlinear multiobjective optimization problem. A Multiobjective Particle Swarm Optimizer (MOPSO) which is based on the Pareto optimality concept is developed and applied to maximize the above mentioned problem. Results show that MOPSO efficiently cope with the problem and finds multiple Pareto optimal solutions. (orig.)
Replacement model of city bus: A dynamic programming approach
Arifin, Dadang; Yusuf, Edhi
2017-06-01
This paper aims to develop a replacement model of city bus vehicles operated in Bandung City. This study is driven from real cases encountered by the Damri Company in the efforts to improve services to the public. The replacement model propounds two policy alternatives: First, to maintain or keep the vehicles, and second is to replace them with new ones taking into account operating costs, revenue, salvage value, and acquisition cost of a new vehicle. A deterministic dynamic programming approach is used to solve the model. The optimization process was heuristically executed using empirical data of Perum Damri. The output of the model is to determine the replacement schedule and the best policy if the vehicle has passed the economic life. Based on the results, the technical life of the bus is approximately 20 years old, while the economic life is an average of 9 (nine) years. It means that after the bus is operated for 9 (nine) years, managers should consider the policy of rejuvenation.
Approximate Dynamic Programming for Military Resource Allocation
2014-12-26
To my wife and children ; Mom, Poppa, and Grammy. v Acknowledgements I would like to express my sincere appreciation to my research advisor Dr. Darryl...the first stage and using weapons in the second stage. Because the second stage uti - lizes a single resource class, the optimality of the MMR...generates two children , so the size of the next generation remains constant. Based on the recommendations of Chu and Beasley [27], these operators
Lan, C. Edward; Ge, Fuying
1989-01-01
Control system design for general nonlinear flight dynamic models is considered through numerical simulation. The design is accomplished through a numerical optimizer coupled with analysis of flight dynamic equations. The general flight dynamic equations are numerically integrated and dynamic characteristics are then identified from the dynamic response. The design variables are determined iteratively by the optimizer to optimize a prescribed objective function which is related to desired dynamic characteristics. Generality of the method allows nonlinear effects to aerodynamics and dynamic coupling to be considered in the design process. To demonstrate the method, nonlinear simulation models for an F-5A and an F-16 configurations are used to design dampers to satisfy specifications on flying qualities and control systems to prevent departure. The results indicate that the present method is simple in formulation and effective in satisfying the design objectives.
Optimal dynamic voltage scaling for wireless sensor nodes with real-time constraints
Cassandras, Christos G.; Zhuang, Shixin
2005-11-01
Sensors are increasingly embedded in manufacturing systems and wirelessly networked to monitor and manage operations ranging from process and inventory control to tracking equipment and even post-manufacturing product monitoring. In building such sensor networks, a critical issue is the limited and hard to replenish energy in the devices involved. Dynamic voltage scaling is a technique that controls the operating voltage of a processor to provide desired performance while conserving energy and prolonging the overall network's lifetime. We consider such power-limited devices processing time-critical tasks which are non-preemptive, aperiodic and have uncertain arrival times. We treat voltage scaling as a dynamic optimization problem whose objective is to minimize energy consumption subject to hard or soft real-time execution constraints. In the case of hard constraints, we build on prior work (which engages a voltage scaling controller at task completion times) by developing an intra-task controller that acts at all arrival times of incoming tasks. We show that this optimization problem can be decomposed into two simpler ones whose solution leads to an algorithm that does not actually require solving any nonlinear programming problems. In the case of soft constraints, this decomposition must be partly relaxed, but it still leads to a scalable (linear in the number of tasks) algorithm. Simulation results are provided to illustrate performance improvements in systems with intra-task controllers compared to uncontrolled systems or those using inter-task control.
Aether: leveraging linear programming for optimal cloud computing in genomics.
Luber, Jacob M; Tierney, Braden T; Cofer, Evan M; Patel, Chirag J; Kostic, Aleksandar D
2018-05-01
Across biology, we are seeing rapid developments in scale of data production without a corresponding increase in data analysis capabilities. Here, we present Aether (http://aether.kosticlab.org), an intuitive, easy-to-use, cost-effective and scalable framework that uses linear programming to optimally bid on and deploy combinations of underutilized cloud computing resources. Our approach simultaneously minimizes the cost of data analysis and provides an easy transition from users' existing HPC pipelines. Data utilized are available at https://pubs.broadinstitute.org/diabimmune and with EBI SRA accession ERP005989. Source code is available at (https://github.com/kosticlab/aether). Examples, documentation and a tutorial are available at http://aether.kosticlab.org. chirag_patel@hms.harvard.edu or aleksandar.kostic@joslin.harvard.edu. Supplementary data are available at Bioinformatics online.
ARSTEC, Nonlinear Optimization Program Using Random Search Method
International Nuclear Information System (INIS)
Rasmuson, D. M.; Marshall, N. H.
1979-01-01
1 - Description of problem or function: The ARSTEC program was written to solve nonlinear, mixed integer, optimization problems. An example of such a problem in the nuclear industry is the allocation of redundant parts in the design of a nuclear power plant to minimize plant unavailability. 2 - Method of solution: The technique used in ARSTEC is the adaptive random search method. The search is started from an arbitrary point in the search region and every time a point that improves the objective function is found, the search region is centered at that new point. 3 - Restrictions on the complexity of the problem: Presently, the maximum number of independent variables allowed is 10. This can be changed by increasing the dimension of the arrays
Designing, programming, and optimizing a (small) quantum computer
Svore, Krysta
In 1982, Richard Feynman proposed to use a computer founded on the laws of quantum physics to simulate physical systems. In the more than thirty years since, quantum computers have shown promise to solve problems in number theory, chemistry, and materials science that would otherwise take longer than the lifetime of the universe to solve on an exascale classical machine. The practical realization of a quantum computer requires understanding and manipulating subtle quantum states while experimentally controlling quantum interference. It also requires an end-to-end software architecture for programming, optimizing, and implementing a quantum algorithm on the quantum device hardware. In this talk, we will introduce recent advances in connecting abstract theory to present-day real-world applications through software. We will highlight recent advancement of quantum algorithms and the challenges in ultimately performing a scalable solution on a quantum device.
Developing optimal nurses work schedule using integer programming
Shahidin, Ainon Mardhiyah; Said, Mohd Syazwan Md; Said, Noor Hizwan Mohamad; Sazali, Noor Izatie Amaliena
2017-08-01
Time management is the art of arranging, organizing and scheduling one's time for the purpose of generating more effective work and productivity. Scheduling is the process of deciding how to commit resources between varieties of possible tasks. Thus, it is crucial for every organization to have a good work schedule for their staffs. The job of Ward nurses at hospitals runs for 24 hours every day. Therefore, nurses will be working using shift scheduling. This study is aimed to solve the nurse scheduling problem at an emergency ward of a private hospital. A 7-day work schedule for 7 consecutive weeks satisfying all the constraints set by the hospital will be developed using Integer Programming. The work schedule for the nurses obtained gives an optimal solution where all the constraints are being satisfied successfully.
Dynamic Learning Objects to Teach Java Programming Language
Narasimhamurthy, Uma; Al Shawkani, Khuloud
2010-01-01
This article describes a model for teaching Java Programming Language through Dynamic Learning Objects. The design of the learning objects was based on effective learning design principles to help students learn the complex topic of Java Programming. Visualization was also used to facilitate the learning of the concepts. (Contains 1 figure and 2…
Proving deadlock freedom of logic programs with dynamic scheduling
E. Marchiori; F. Teusink (Frank)
1996-01-01
textabstractIn increasingly many logic programming systems, the Prolog left to right selection rule has been replaced with dynamic selection rules, that select an atom of a query among those satisfying suitable conditions. These conditions describe the form of the arguments of every program
Optimization of refinery product blending by using linear programming
International Nuclear Information System (INIS)
Ristikj, Julija; Tripcheva-Trajkovska, Loreta; Rikaloski, Ice; Markovska, Liljana
1999-01-01
The product slate of a simple refinery consists mainly of liquefied petroleum gas, leaded and unleaded gasoline, jet fuel, diesel fuel, extra light heating oil and fuel oil. The quality of the oil products (fuels) for sale has to comply with the adopted standards for liquid fuels, and the produced quantities have to be comply with the market needs. The oil products are manufactured by blending two or more different fractions which quantities and physical-chemical properties depend on the crude oil type, the way and conditions of processing, and at the same time the fractions are used to blend one or more products. It is in producer's interest to do the blending in an optimal way, namely, to satisfy the requirements for the oil products quality and quantity with a maximal usage of the available fractions and, of course, with a maximal profit out of the sold products. This could be accomplished by applying linear programming, that is by using a linear model for oil products blending optimization. (Author)
Discrete Time McKean–Vlasov Control Problem: A Dynamic Programming Approach
Energy Technology Data Exchange (ETDEWEB)
Pham, Huyên, E-mail: pham@math.univ-paris-diderot.fr; Wei, Xiaoli, E-mail: tyswxl@gmail.com [Laboratoire de Probabilités et Modèles Aléatoires, CNRS, UMR 7599, Université Paris Diderot (France)
2016-12-15
We consider the stochastic optimal control problem of nonlinear mean-field systems in discrete time. We reformulate the problem into a deterministic control problem with marginal distribution as controlled state variable, and prove that dynamic programming principle holds in its general form. We apply our method for solving explicitly the mean-variance portfolio selection and the multivariate linear-quadratic McKean–Vlasov control problem.
Discrete Time McKean–Vlasov Control Problem: A Dynamic Programming Approach
International Nuclear Information System (INIS)
Pham, Huyên; Wei, Xiaoli
2016-01-01
We consider the stochastic optimal control problem of nonlinear mean-field systems in discrete time. We reformulate the problem into a deterministic control problem with marginal distribution as controlled state variable, and prove that dynamic programming principle holds in its general form. We apply our method for solving explicitly the mean-variance portfolio selection and the multivariate linear-quadratic McKean–Vlasov control problem.
International Nuclear Information System (INIS)
Merwin, S.E.; Martin, J.B.; Selby, J.M.; Vallario, E.J.
1986-01-01
Six numerical examples of optimization of radiation protection are provided in the appendices of ICRP Publication 37. In each case, the calculations are based on fairly well defined parameters and assumptions that were well understood. In this paper, we have examined two numerical examples that are based on empirical data and less certain assumptions. These examples may represent typical applications of optimization principles to the evaluation of specific elements of a radiation protection program. In the first example, the optimum bioassay frequency for tritium workers was found to be once every 95 days, which compared well with ICRP Publication 10 recommendations. However, this result depended heavily on the assumption that the value of a potential undetected rem was US $1000. The second example showed that the optimum frequency for recalibrating Cutie Pie (CP) type ionization chamber survey instruments was once every 102 days, which compared well with the Hanford standard frequency of once every 90 days. This result depended largely on the assumption that an improperly operating CP instrument could lead to a serious overexposure. These examples have led us to conclude that optimization of radiation protection programs must be a very dynamic process. Examples must be recalculated as empirical data expand and improve and as the uncertainties surrounding assumptions are reduced
Nonlinear beam dynamics experimental program at SPEAR
International Nuclear Information System (INIS)
Tran, P.; Pellegrini, C.; Cornacchia, M.; Lee, M.; Corbett, W.
1995-01-01
Since nonlinear effects can impose strict performance limitations on modern colliders and storage rings, future performance improvements depend on further understanding of nonlinear beam dynamics. Experimental studies of nonlinear beam motion in three-dimensional space have begun in SPEAR using turn-by-turn transverse and longitudinal phase-space monitors. This paper presents preliminary results from an on-going experiment in SPEAR
Modelling of windmill induction generators in dynamic simulation programs
DEFF Research Database (Denmark)
Akhmatov, Vladislav; Knudsen, Hans
1999-01-01
with and without a model of the mechanical shaft. The reason for the discrepancies are explained, and it is shown that the phenomenon is due partly to the presence of DC offset currents in the induction machine stator, and partly to the mechanical shaft system of the wind turbine and the generator rotor......For AC networks with large amounts of induction generators-in case of e.g. windmills-the paper demonstrates a significant discrepancy in the simulated voltage recovery after faults in weak networks, when comparing result obtained with dynamic stability programs and transient programs, respectively....... It is shown that it is possible to include a transient model in dynamic stability programs and thus obtain correct results also in dynamic stability programs. A mechanical model of the shaft system has also been included in the generator model...
Optimal Control and Forecasting of Complex Dynamical Systems
Grigorenko, Ilya
2006-01-01
This important book reviews applications of optimization and optimal control theory to modern problems in physics, nano-science and finance. The theory presented here can be efficiently applied to various problems, such as the determination of the optimal shape of a laser pulse to induce certain excitations in quantum systems, the optimal design of nanostructured materials and devices, or the control of chaotic systems and minimization of the forecast error for a given forecasting model (for example, artificial neural networks). Starting from a brief review of the history of variational calcul
C-program LINOP for the evaluation of film dosemeters by linear optimization. User manual
International Nuclear Information System (INIS)
Kragh, P.
1995-11-01
Linear programming results in an optimal measuring value for film dosemeters. The Linop program was developed to be used for linear programming. The program permits the evaluation and control of film dosemeters and of all other multi-component dosemeters. This user manual for the Linop program contains the source program, a description of the program and installation and use instructions. The data sets with programs and examples are available upon request. (orig.) [de
Program packages for dynamics systems analysis and design
International Nuclear Information System (INIS)
Athani, V.V.
1976-01-01
The development of computer program packages for dynamic system analysis and design are reported. The purpose of developing these program packages is to take the burden of writing computer programs off the mind of the system engineer and to enable him to concentrate on his main system analysis and design work. Towards this end, four standard computer program packages have been prepared : (1) TFANA - starting from system transfer function this program computes transient response, frequency response, root locus and stability by Routh Hurwitz criterion, (2) TFSYN - classical synthesis using algebraic method of Shipley, (3) MODANA - starting from state equations of the system this program computes solution of state equations, controllability, observability and stability, (4) OPTCON - This program obtains solutions of (i) linear regulator problem, (ii) servomechanism problems and (iii) problem of pole placement. The paper describes these program packages with the help of flowcharts and illustrates their use with the help of examples. (author)
Dynamic Modeling, Model-Based Control, and Optimization of Solid Oxide Fuel Cells
Spivey, Benjamin James
2011-07-01
Solid oxide fuel cells are a promising option for distributed stationary power generation that offers efficiencies ranging from 50% in stand-alone applications to greater than 80% in cogeneration. To advance SOFC technology for widespread market penetration, the SOFC should demonstrate improved cell lifetime and load-following capability. This work seeks to improve lifetime through dynamic analysis of critical lifetime variables and advanced control algorithms that permit load-following while remaining in a safe operating zone based on stress analysis. Control algorithms typically have addressed SOFC lifetime operability objectives using unconstrained, single-input-single-output control algorithms that minimize thermal transients. Existing SOFC controls research has not considered maximum radial thermal gradients or limits on absolute temperatures in the SOFC. In particular, as stress analysis demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs. This dissertation presents a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop control simulations. A constrained, MIMO model predictive control algorithm is developed and applied to control the SOFC. Closed-loop control simulation results demonstrate effective load-following, constraint satisfaction for critical lifetime variables, and disturbance rejection. Nonlinear programming is applied to find the optimal SOFC size and steady-state operating conditions to minimize total system costs.
The optimal dynamic immunization under a controlled heterogeneous node-based SIRS model
Yang, Lu-Xing; Draief, Moez; Yang, Xiaofan
2016-05-01
Dynamic immunizations, under which the state of the propagation network of electronic viruses can be changed by adjusting the control measures, are regarded as an alternative to static immunizations. This paper addresses the optimal dynamical immunization under the widely accepted SIRS assumption. First, based on a controlled heterogeneous node-based SIRS model, an optimal control problem capturing the optimal dynamical immunization is formulated. Second, the existence of an optimal dynamical immunization scheme is shown, and the corresponding optimality system is derived. Next, some numerical examples are given to show that an optimal immunization strategy can be worked out by numerically solving the optimality system, from which it is found that the network topology has a complex impact on the optimal immunization strategy. Finally, the difference between a payoff and the minimum payoff is estimated in terms of the deviation of the corresponding immunization strategy from the optimal immunization strategy. The proposed optimal immunization scheme is justified, because it can achieve a low level of infections at a low cost.
A dynamic approach for the optimal electricity dispatch in the deregulated market
International Nuclear Information System (INIS)
Carraretto, Cristian; Lazzaretto, Andrea
2004-01-01
The electricity market has been experiencing the deregulation process in many countries. Effective approaches to the management of single power plants or groups of plants are therefore becoming crucial for the competitiveness of energy utilities. A dynamic programming approach is presented in this paper for the optimal plant management in the new Italian deregulated market. A thorough description of the method is given in cases of free or fixed production over time (e.g. when the overall production is limited by bilateral contracts or cogeneration). Analysis of market characteristics, detailed thermodynamic models of plant operation and reliable price forecasts over the time period of interest are required. The suggested approach is useful for both long-term scheduling and planning daily offers in the market
Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo
2017-07-01
This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.
Dynamic optimization of dead-end membrane filtration
Blankert, B.; Betlem, Bernardus H.L.; Roffel, B.; Marquardt, Wolfgang; Pantelides, Costas
2006-01-01
An operating strategy aimed at minimizing the energy consumption during the filtration phase of dead-end membrane filtration has been formulated. A method allowing fast calculation of trajectories is used to allow incorporation in a hierarchical optimization scheme. The optimal trajectory can be
Optimal Dynamic Advertising Strategy Under Age-Specific Market Segmentation
Krastev, Vladimir
2011-12-01
We consider the model proposed by Faggian and Grosset for determining the advertising efforts and goodwill in the long run of a company under age segmentation of consumers. Reducing this model to optimal control sub problems we find the optimal advertising strategy and goodwill.
Macroscopic reality and the dynamical reduction program
International Nuclear Information System (INIS)
Ghirardi, G.C.
1995-10-01
With reference to recently proposed theoretical models accounting for reduction in terms of a unified dynamics governing all physical processes, we analyze the problem of working out a worldview accommodating our knowledge about natural phenomena. We stress the relevant conceptual differences between the considered models and standard quantum mechanics. In spite of the fact that both theories describe individual physical systems within a genuine Hilbert space framework, the nice features of spontaneous reduction theories drastically limit the class of states which are dynamically stable. This allows one to work out a description of the world in terms of a mass density function in ordinary configuration space. A topology based on this function and differing radically from the one characterizing the Hilbert space is introduced and in terms of it the idea of similarity of macroscopic situations is made precise. Finally it is shown how the formalism and the proposed interpretation yield a natural criterion for establishing the psychophysical parallelism. The conclusion is that, within the considered theoretical models and at the nonrelativistic level, one can satisfy all sensible requirements for a consistent, unified, and objective description of reality at the macroscopic level. (author). 16 refs
Macroscopic reality and the dynamical reduction program
Energy Technology Data Exchange (ETDEWEB)
Ghirardi, G C
1995-10-01
With reference to recently proposed theoretical models accounting for reduction in terms of a unified dynamics governing all physical processes, we analyze the problem of working out a worldview accommodating our knowledge about natural phenomena. We stress the relevant conceptual differences between the considered models and standard quantum mechanics. In spite of the fact that both theories describe individual physical systems within a genuine Hilbert space framework, the nice features of spontaneous reduction theories drastically limit the class of states which are dynamically stable. This allows one to work out a description of the world in terms of a mass density function in ordinary configuration space. A topology based on this function and differing radically from the one characterizing the Hilbert space is introduced and in terms of it the idea of similarity of macroscopic situations is made precise. Finally it is shown how the formalism and the proposed interpretation yield a natural criterion for establishing the psychophysical parallelism. The conclusion is that, within the considered theoretical models and at the nonrelativistic level, one can satisfy all sensible requirements for a consistent, unified, and objective description of reality at the macroscopic level. (author). 16 refs.
Computing the optimal path in stochastic dynamical systems
International Nuclear Information System (INIS)
Bauver, Martha; Forgoston, Eric; Billings, Lora
2016-01-01
In stochastic systems, one is often interested in finding the optimal path that maximizes the probability of escape from a metastable state or of switching between metastable states. Even for simple systems, it may be impossible to find an analytic form of the optimal path, and in high-dimensional systems, this is almost always the case. In this article, we formulate a constructive methodology that is used to compute the optimal path numerically. The method utilizes finite-time Lyapunov exponents, statistical selection criteria, and a Newton-based iterative minimizing scheme. The method is applied to four examples. The first example is a two-dimensional system that describes a single population with internal noise. This model has an analytical solution for the optimal path. The numerical solution found using our computational method agrees well with the analytical result. The second example is a more complicated four-dimensional system where our numerical method must be used to find the optimal path. The third example, although a seemingly simple two-dimensional system, demonstrates the success of our method in finding the optimal path where other numerical methods are known to fail. In the fourth example, the optimal path lies in six-dimensional space and demonstrates the power of our method in computing paths in higher-dimensional spaces.
International Nuclear Information System (INIS)
El-Gohary, Awad
2005-01-01
This paper considers the problem of optimal controlling of a programmed motion of a rigid spacecraft. Given a cost of the spacecraft as a quadratic function of state and control variables we seek for optimal control laws as functions of the state variables and the angle of programmed rotation that minimize this cost and asymptotically stabilize the required programmed motion. The stabilizing properties of the proposed controllers are proved using the optimal Liapunov techniques. Numerical simulation study is presented
An optimal dynamic interval stabbing-max data structure?
DEFF Research Database (Denmark)
Agarwal, Pankaj Kumar; Arge, Lars; Yi, Ke
2005-01-01
In this paper we consider the dynamic stabbing-max problem, that is, the problem of dynamically maintaining a set S of n axis-parallel hyper-rectangles in Rd, where each rectangle s ∈ S has a weight w(s) ∈ R, so that the rectangle with the maximum weight containing a query point can be determined...
Optimal investment for enhancing social concern about biodiversity conservation: a dynamic approach.
Lee, Joung Hun; Iwasa, Yoh
2012-11-01
To maintain biodiversity conservation areas, we need to invest in activities, such as monitoring the condition of the ecosystem, preventing illegal exploitation, and removing harmful alien species. These require a constant supply of resources, the level of which is determined by the concern of the society about biodiversity conservation. In this paper, we study the optimal fraction of the resources to invest in activities for enhancing the social concern y(t) by environmental education, museum displays, publications, and media exposure. We search for the strategy that maximizes the time-integral of the quality of the conservation area x(t) with temporal discounting. Analyses based on dynamic programming and Pontryagin's maximum principle show that the optimal control consists of two phases: (1) in the first phase, the social concern level approaches to the final optimal value y(∗), (2) in the second phase, resources are allocated to both activities, and the social concern level is kept constant y(t) = y(∗). If the social concern starts from a low initial level, the optimal path includes a period in which the quality of the conservation area declines temporarily, because all the resources are invested to enhance the social concern. When the support rate increases with the quality of the conservation area itself x(t) as well as with the level of social concern y(t), both variables may increase simultaneously in the second phase. We discuss the implication of the results to good management of biodiversity conservation areas. 2012 Elsevier Inc. All rights reserved
Dynamic electricity pricing—Which programs do consumers prefer?
International Nuclear Information System (INIS)
Dütschke, Elisabeth; Paetz, Alexandra-Gwyn
2013-01-01
Dynamic pricing is being discussed as one method of demand side management (DSM) which could be crucial for integrating more renewable energy sources into the electricity system. At the same time, there have been very few analyses of consumer preferences in this regard: Which type of pricing program are consumers most likely to choose and why? This paper sheds some light on these issues based on two empirical studies from Germany: (1) A questionnaire study including a conjoint analysis-design and (2) A field experiment with test-residents of a smart home laboratory. The results show that consumers are open to dynamic pricing, but prefer simple programs to complex and highly dynamic ones; smart home technologies including demand automation are seen as a prerequisite for DSM. The study provides some indications that consumers might be more willing to accept more dynamic pricing programs if they have the chance to experience in practice how these can be managed in everyday life. At the same time, the individual and societal advantages of such programs are not obvious to consumers. For this reason, any market roll-out will need to be accompanied by convincing communication and information campaigns to ensure that these advantages are perceived. - Highlights: • Little is known about consumer preferences on dynamic pricing. • Two studies are conducted to analyze this topic. • A survey shows that consumers without experience prefer conventional programs. • Test residents of a smart home were more open to dynamic pricing. • They also prefer well-structured programs
Weather and Climate Manipulation as an Optimal Control for Adaptive Dynamical Systems
Directory of Open Access Journals (Sweden)
Sergei A. Soldatenko
2017-01-01
Full Text Available The weather and climate manipulation is examined as an optimal control problem for the earth climate system, which is considered as a complex adaptive dynamical system. Weather and climate manipulations are actually amorphous operations. Since their objectives are usually formulated vaguely, the expected results are fairly unpredictable and uncertain. However, weather and climate modification is a purposeful process and, therefore, we can formulate operations to manipulate weather and climate as the optimization problem within the framework of the optimal control theory. The complexity of the earth’s climate system is discussed and illustrated using the simplified low-order coupled chaotic dynamical system. The necessary conditions of optimality are derived for the large-scale atmospheric dynamics. This confirms that even a relatively simplified control problem for the atmospheric dynamics requires significant efforts to obtain the solution.
2015-06-01
This Technical Report on Prototype Intelligent Network Flow Optimization (INFLO) Dynamic Speed Harmonization and : Queue Warning is the final report for the project. It describes the prototyping, acceptance testing and small-scale : demonstration of ...
Optimal dynamic control of resources in a distributed system
Shin, Kang G.; Krishna, C. M.; Lee, Yann-Hang
1989-01-01
The authors quantitatively formulate the problem of controlling resources in a distributed system so as to optimize a reward function and derive optimal control strategies using Markov decision theory. The control variables treated are quite general; they could be control decisions related to system configuration, repair, diagnostics, files, or data. Two algorithms for resource control in distributed systems are derived for time-invariant and periodic environments, respectively. A detailed example to demonstrate the power and usefulness of the approach is provided.
Optimal Pricing Strategies for New Products in Dynamic Oligopolies
Engelbert Dockner; Steffen Jørgensen
1988-01-01
This paper deals with the determination of optimal pricing policies for firms in oligopolistic markets. The problem is studied as a differential game and optimal pricing policies are established as Nash open-loop controls. Cost learning effects are assumed such that unit costs are decreasing with cumulative output. Discounting of future profits is also taken into consideration. Initially, the problem is addressed in a general framework, and we proceed to study some specific cases that are rel...
Miró, Anton; Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Egea, Jose A; Jiménez, Laureano
2012-05-10
The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens. This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by BARON.
Step by step parallel programming method for molecular dynamics code
International Nuclear Information System (INIS)
Orii, Shigeo; Ohta, Toshio
1996-07-01
Parallel programming for a numerical simulation program of molecular dynamics is carried out with a step-by-step programming technique using the two phase method. As a result, within the range of a certain computing parameters, it is found to obtain parallel performance by using the level of parallel programming which decomposes the calculation according to indices of do-loops into each processor on the vector parallel computer VPP500 and the scalar parallel computer Paragon. It is also found that VPP500 shows parallel performance in wider range computing parameters. The reason is that the time cost of the program parts, which can not be reduced by the do-loop level of the parallel programming, can be reduced to the negligible level by the vectorization. After that, the time consuming parts of the program are concentrated on less parts that can be accelerated by the do-loop level of the parallel programming. This report shows the step-by-step parallel programming method and the parallel performance of the molecular dynamics code on VPP500 and Paragon. (author)
Crashworthiness design optimization using multipoint sequential linear programming
Etman, L.F.P.; Adriaens, J.M.T.A.; Slagmaat, van M.T.P.; Schoofs, A.J.G.
1996-01-01
A design optimization tool has been developed for the crash victim simulation software MADYMO. The crash worthiness optimization problem is characterized by a noisy behaviour of objective function and constraints. Additionally, objective function and constraint values follow from a computationally
Dynamic optimization and robust explicit model predictive control of hydrogen storage tank
Panos, C.
2010-09-01
We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.
Dynamic optimization and robust explicit model predictive control of hydrogen storage tank
Panos, C.; Kouramas, K.I.; Georgiadis, M.C.; Pistikopoulos, E.N.
2010-01-01
We present a general framework for the optimal design and control of a metal-hydride bed under hydrogen desorption operation. The framework features: (i) a detailed two-dimension dynamic process model, (ii) a design and operational dynamic optimization step, and (iii) an explicit/multi-parametric model predictive controller design step. For the controller design, a reduced order approximate model is obtained, based on which nominal and robust multi-parametric controllers are designed. © 2010 Elsevier Ltd.
Optimizing Dynamic Class Composition in a Statically Typed Language
DEFF Research Database (Denmark)
Nielsen, Anders Bach; Ernst, Erik
2008-01-01
-enable a type safe treatment of classifiers and their associated types and instances, even in the case where classifiers are created dynamically. This opens the opportunity to make dynamic class computations available as an integrated part of the language semantics. The language gbeta is an example where...... this is achieved based on mixins and linearization. In this paper we focus on the virtual machine related challenges of supporting dynamic class composition. In particular we present some core algorithms used for creating new classes, as well as some performance enhancements in these algorithms....
Portfolio optimization by using linear programing models based on genetic algorithm
Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.
2018-01-01
In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.
Optimal Route Searching with Multiple Dynamical Constraints—A Geometric Algebra Approach
Directory of Open Access Journals (Sweden)
Dongshuang Li
2018-05-01
Full Text Available The process of searching for a dynamic constrained optimal path has received increasing attention in traffic planning, evacuation, and personalized or collaborative traffic service. As most existing multiple constrained optimal path (MCOP methods cannot search for a path given various types of constraints that dynamically change during the search, few approaches for dynamic multiple constrained optimal path (DMCOP with type II dynamics are available for practical use. In this study, we develop a method to solve the DMCOP problem with type II dynamics based on the unification of various types of constraints under a geometric algebra (GA framework. In our method, the network topology and three different types of constraints are represented by using algebraic base coding. With a parameterized optimization of the MCOP algorithm based on a greedy search strategy under the generation-refinement paradigm, this algorithm is found to accurately support the discovery of optimal paths as the constraints of numerical values, nodes, and route structure types are dynamically added to the network. The algorithm was tested with simulated cases of optimal tourism route searches in China’s road networks with various combinations of constraints. The case study indicates that our algorithm can not only solve the DMCOP with different types of constraints but also use constraints to speed up the route filtering.
Blood platelet production : optimization by dynamic programming and simulation
Haijema, R.; Wal, van der J.; Dijk, van N.M.
2007-01-01
Blood platelets are precious, as voluntarily supplied by donors, and highly perishable, with limited lifetimes of 5–7 days. Demand is highly variable and uncertain. A practical production and inventory rule is strived for that minimizes shortages and spill. The demand and production are periodic, as
Blood Platelet Production: Optimization by Dynamic Programming and Simulation
Haijema, R.; Wal, van der J.; Dijk, van N.M.
2007-01-01
Blood platelets are precious, as voluntarily supplied by donors, and highly perishable, with limited lifetimes of 5¿7 days. Demand is highly variable and uncertain. A practical production and inventory rule is strived for that minimizes shortages and spill. The demand and production are periodic, as
Stockpile strategy for China's emergency oil reserve: A dynamic programming approach
International Nuclear Information System (INIS)
Bai, Y.; Dahl, C.A.; Zhou, D.Q.; Zhou, P.
2014-01-01
China is currently accelerating construction of its strategic petroleum reserves. How should China fill the SPR in a cost-effective manner in the short-run? How might this affect world oil prices? Using a dynamic programming model to answer these questions, the objective of this paper is to minimize the stockpiling costs, including consumer surplus as well as crude acquisition and holding costs. The crude oil acquisition price in the model is determined by global equilibrium between supply and demand. Demand, in turn, depends on world market conditions including China's stockpile filling rate. Our empirical study under different market conditions shows that China's optimal stockpile acquisition rate varies from 9 to 19 million barrels per month, and the optimal stockpiling drives up the world oil price by 3–7%. The endogenous price increase accounts for 52% of total stockpiling costs in the base case. When the market is tighter or the demand function is more inelastic, the stockpiling affects the market more significantly and pushes prices even higher. Alternatively, in a disruption, drawdown from the stockpile can effectively dampen soaring prices, though the shortage is likely to leave the price higher than before the disruption. - Highlights: • China's SPR policies are examined by dynamic programming. • The optimal stockpile acquisition rate varies from 9 to 19 million barrels per month. • The optimal stockpiling drives up world oil price by 3–7%
A Dynamic Programming Algorithm for the k-Haplotyping Problem
Institute of Scientific and Technical Information of China (English)
Zhen-ping Li; Ling-yun Wu; Yu-ying Zhao; Xiang-sun Zhang
2006-01-01
The Minimum Fragments Removal (MFR) problem is one of the haplotyping problems: given a set of fragments, remove the minimum number of fragments so that the resulting fragments can be partitioned into k classes of non-conflicting subsets. In this paper, we formulate the k-MFR problem as an integer linear programming problem, and develop a dynamic programming approach to solve the k-MFR problem for both the gapless and gap cases.
Evaluating dynamic covariance matrix forecasting and portfolio optimization
Sendstad, Lars Hegnes; Holten, Dag Martin
2012-01-01
In this thesis we have evaluated the covariance forecasting ability of the simple moving average, the exponential moving average and the dynamic conditional correlation models. Overall we found that a dynamic portfolio can gain significant improvements by implementing a multivariate GARCH forecast. We further divided the global investment universe into sectors and regions in order to investigate the relative portfolio performance of several asset allocation strategies with both variance and c...
Optimization with PDE constraints ESF networking program 'OPTPDE'
2014-01-01
This book on PDE Constrained Optimization contains contributions on the mathematical analysis and numerical solution of constrained optimal control and optimization problems where a partial differential equation (PDE) or a system of PDEs appears as an essential part of the constraints. The appropriate treatment of such problems requires a fundamental understanding of the subtle interplay between optimization in function spaces and numerical discretization techniques and relies on advanced methodologies from the theory of PDEs and numerical analysis as well as scientific computing. The contributions reflect the work of the European Science Foundation Networking Programme ’Optimization with PDEs’ (OPTPDE).
Tunjo Perić; Željko Mandić
2017-01-01
This paper presents the production plan optimization in the metal industry considered as a multi-criteria programming problem. We first provided the definition of the multi-criteria programming problem and classification of the multicriteria programming methods. Then we applied two multi-criteria programming methods (the STEM method and the PROMETHEE method) in solving a problem of multi-criteria optimization production plan in a company from the metal industry. The obtained resul...
Boundary Control of Linear Uncertain 1-D Parabolic PDE Using Approximate Dynamic Programming.
Talaei, Behzad; Jagannathan, Sarangapani; Singler, John
2018-04-01
This paper develops a near optimal boundary control method for distributed parameter systems governed by uncertain linear 1-D parabolic partial differential equations (PDE) by using approximate dynamic programming. A quadratic surface integral is proposed to express the optimal cost functional for the infinite-dimensional state space. Accordingly, the Hamilton-Jacobi-Bellman (HJB) equation is formulated in the infinite-dimensional domain without using any model reduction. Subsequently, a neural network identifier is developed to estimate the unknown spatially varying coefficient in PDE dynamics. Novel tuning law is proposed to guarantee the boundedness of identifier approximation error in the PDE domain. A radial basis network (RBN) is subsequently proposed to generate an approximate solution for the optimal surface kernel function online. The tuning law for near optimal RBN weights is created, such that the HJB equation error is minimized while the dynamics are identified and closed-loop system remains stable. Ultimate boundedness (UB) of the closed-loop system is verified by using the Lyapunov theory. The performance of the proposed controller is successfully confirmed by simulation on an unstable diffusion-reaction process.
An Adaptive Genetic Algorithm with Dynamic Population Size for Optimizing Join Queries
Vellev, Stoyan
2008-01-01
The problem of finding the optimal join ordering executing a query to a relational database management system is a combinatorial optimization problem, which makes deterministic exhaustive solution search unacceptable for queries with a great number of joined relations. In this work an adaptive genetic algorithm with dynamic population size is proposed for optimizing large join queries. The performance of the algorithm is compared with that of several classical non-determinis...
A Hybrid Dynamic Programming for Solving Fixed Cost Transportation with Discounted Mechanism
Directory of Open Access Journals (Sweden)
Farhad Ghassemi Tari
2016-01-01
Full Text Available The problem of allocating different types of vehicles for transporting a set of products from a manufacturer to its depots/cross docks, in an existing transportation network, to minimize the total transportation costs, is considered. The distribution network involves a heterogeneous fleet of vehicles, with a variable transportation cost and a fixed cost in which a discount mechanism is applied on the fixed part of the transportation costs. It is assumed that the number of available vehicles is limited for some types. A mathematical programming model in the form of the discrete nonlinear optimization model is proposed. A hybrid dynamic programming algorithm is developed for finding the optimal solution. To increase the computational efficiency of the solution algorithm, several concepts and routines, such as the imbedded state routine, surrogate constraint concept, and bounding schemes, are incorporated in the dynamic programming algorithm. A real world case problem is selected and solved by the proposed solution algorithm, and the optimal solution is obtained.
Optimal control of an invasive species using a reaction-diffusion model and linear programming
Bonneau, Mathieu; Johnson, Fred A.; Smith, Brian J.; Romagosa, Christina M.; Martin, Julien; Mazzotti, Frank J.
2017-01-01
Managing an invasive species is particularly challenging as little is generally known about the species’ biological characteristics in its new habitat. In practice, removal of individuals often starts before the species is studied to provide the information that will later improve control. Therefore, the locations and the amount of control have to be determined in the face of great uncertainty about the species characteristics and with a limited amount of resources. We propose framing spatial control as a linear programming optimization problem. This formulation, paired with a discrete reaction-diffusion model, permits calculation of an optimal control strategy that minimizes the remaining number of invaders for a fixed cost or that minimizes the control cost for containment or protecting specific areas from invasion. We propose computing the optimal strategy for a range of possible model parameters, representing current uncertainty on the possible invasion scenarios. Then, a best strategy can be identified depending on the risk attitude of the decision-maker. We use this framework to study the spatial control of the Argentine black and white tegus (Salvator merianae) in South Florida. There is uncertainty about tegu demography and we considered several combinations of model parameters, exhibiting various dynamics of invasion. For a fixed one-year budget, we show that the risk-averse strategy, which optimizes the worst-case scenario of tegus’ dynamics, and the risk-neutral strategy, which optimizes the expected scenario, both concentrated control close to the point of introduction. A risk-seeking strategy, which optimizes the best-case scenario, focuses more on models where eradication of the species in a cell is possible and consists of spreading control as much as possible. For the establishment of a containment area, assuming an exponential growth we show that with current control methods it might not be possible to implement such a strategy for some of the
Modelling of Rabies Transmission Dynamics Using Optimal Control Analysis
Directory of Open Access Journals (Sweden)
Joshua Kiddy K. Asamoah
2017-01-01
Full Text Available We examine an optimal way of eradicating rabies transmission from dogs into the human population, using preexposure prophylaxis (vaccination and postexposure prophylaxis (treatment due to public education. We obtain the disease-free equilibrium, the endemic equilibrium, the stability, and the sensitivity analysis of the optimal control model. Using the Latin hypercube sampling (LHS, the forward-backward sweep scheme and the fourth-order Range-Kutta numerical method predict that the global alliance for rabies control’s aim of working to eliminate deaths from canine rabies by 2030 is attainable through mass vaccination of susceptible dogs and continuous use of pre- and postexposure prophylaxis in humans.
The Value of Optimization in Dynamic Ride-Sharing: a Simulation Study in Metro Atlanta
N.A.H. Agatz (Niels); A. Erera (Alan); M.W.P. Savelsbergh (Martin); X. Wang (Xing)
2010-01-01
textabstractSmartphone technology enables dynamic ride-sharing systems that bring together people with similar itineraries and time schedules to share rides on short-notice. This paper considers the problem of matching drivers and riders in this dynamic setting. We develop optimization-based
Kleijnen, J.P.C.
1995-01-01
This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for
Energy-Aware Routing Optimization in Dynamic GMPLS Controlled Optical Networks
DEFF Research Database (Denmark)
Wang, Jiayuan; Ricciardi, Sergio; Fagertun, Anna Manolova
2012-01-01
In this paper, routing optimizations based on energy sources are proposed in dynamic GMPLS controlled optical networks. The influences of re-routing and load balancing factors on the algorithm are evaluated, with a focus on different re-routing thresholds. Results from dynamic network simulations...
Tao, Ye; Xu, Lijia; Zhang, Zhen; Chen, Runfeng; Li, Huanhuan; Xu, Hui; Zheng, Chao; Huang, Wei
2016-08-03
Current static-state explorations of organic semiconductors for optimal material properties and device performance are hindered by limited insights into the dynamically changed molecular states and charge transport and energy transfer processes upon device operation. Here, we propose a simple yet successful strategy, resonance variation-based dynamic adaptation (RVDA), to realize optimized self-adaptive properties in donor-resonance-acceptor molecules by engineering the resonance variation for dynamic tuning of organic semiconductors. Organic light-emitting diodes hosted by these RVDA materials exhibit remarkably high performance, with external quantum efficiencies up to 21.7% and favorable device stability. Our approach, which supports simultaneous realization of dynamically adapted and selectively enhanced properties via resonance engineering, illustrates a feasible design map for the preparation of smart organic semiconductors capable of dynamic structure and property modulations, promoting the studies of organic electronics from static to dynamic.
PACE: A dynamic programming algorithm for hardware/software partitioning
DEFF Research Database (Denmark)
Knudsen, Peter Voigt; Madsen, Jan
1996-01-01
This paper presents the PACE partitioning algorithm which is used in the LYCOS co-synthesis system for partitioning control/dataflow graphs into hardware and software parts. The algorithm is a dynamic programming algorithm which solves both the problem of minimizing system execution time...
Dynamic Frames Based Verification Method for Concurrent Java Programs
Mostowski, Wojciech
2016-01-01
In this paper we discuss a verification method for concurrent Java programs based on the concept of dynamic frames. We build on our earlier work that proposes a new, symbolic permission system for concurrent reasoning and we provide the following new contributions. First, we describe our approach
Optimum workforce-size model using dynamic programming approach
African Journals Online (AJOL)
This paper presents an optimum workforce-size model which determines the minimum number of excess workers (overstaffing) as well as the minimum total recruitment cost during a specified planning horizon. The model is an extension of other existing dynamic programming models for manpower planning in the sense ...
The Functional Programming Language R and the Paradigm of Dynamic Scientific Programming
Trancón y Widemann, B.; Bolz, C.F.; Grelck, C.; Loidl, H.-W.; Peña, R.
2013-01-01
R is an environment and functional programming language for statistical data analysis and visualization. Largely unknown to the functional programming community, it is popular and influential in many empirical sciences. Due to its integrated combination of dynamic and reflective scripting on one
Multiple-Optimizing Dynamic Sensor Networks with MIMO Technology (PREPRINT)
2010-06-01
cluster edge is always d and never changes. A backbone is a rooted tree formed by cluster heads. The transmission distance for a backbone-edge...optimization considerations and algorithms,” IEEE Transactions on Mobile Computing, 2004. 15. T. Tang, M. Park, R. W. Heath, Jr. and Scott M. Nettles , “A
An Optimization Approach to the Dynamic Allocation of Economic Capital
Laeven, R.J.A.; Goovaerts, M.J.
2004-01-01
We propose an optimization approach to allocating economic capital, distinguishing between an allocation or raising principle and a measure for the risk residual. The approach is applied both at the aggregate (conglomerate) level and at the individual (subsidiary) level and yields an integrated
Was Your Glass Left Half Full? Family Dynamics and Optimism
Buri, John R.; Gunty, Amy
2008-01-01
Students' levels of a frequently studied adaptive schema (optimism) as a function of parenting variables (parental authority, family intrusiveness, parental overprotection, parentification, parental psychological control, and parental nurturance) were investigated. Results revealed that positive parenting styles were positively related to the…
International Nuclear Information System (INIS)
Huang, Xiaobiao; Safranek, James
2014-01-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications
Energy Technology Data Exchange (ETDEWEB)
Huang, Xiaobiao, E-mail: xiahuang@slac.stanford.edu; Safranek, James
2014-09-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
International Nuclear Information System (INIS)
Gentle, Jake Paul
2016-01-01
One primary goal of rendering today's transmission grid 'smarter' is to optimize and better manage its power transfer capacity in real time. Power transfer capacity is affected by three main elements: stability, voltage limits, and thermal ratings. All three are critical, but thermal ratings represent the greatest opportunity to quickly, reliably and economically utilize the grid's true capacity. With the 'Smarter Grid', new solutions have been sought to give operators a better grasp on real time conditions, allowing them to manage and extend the usefulness of existing transmission infrastructure in a safe and reliable manner. The objective of the INL Wind Program is to provide industry a Dynamic Line Rating (DLR) solution that is state of the art as measured by cost, accuracy and dependability, to enable human operators to make informed decisions and take appropriate actions without human or system overloading and impacting the reliability of the grid. In addition to mitigating transmission line congestion to better integrate wind, DLR also offers the opportunity to improve the grid with optimized utilization of transmission lines to relieve congestion in general. As wind-generated energy has become a bigger part of the nation's energy portfolio, researchers have learned that wind not only turns turbine blades to generate electricity, but can cool transmission lines and increase transfer capabilities significantly, sometimes up to 60 percent. INL's DLR development supports EERE and The Wind Energy Technology Office's goals by informing system planners and grid operators of available transmission capacity, beyond typical Static Line Ratings (SLR). SLRs are based on a fixed set of conservative environmental conditions to establish a limit on the amount of current lines can safely carry without overheating. Using commercially available weather monitors mounted on industry informed custom brackets developed by INL in combination with Computational Fluid Dynamics (CFD
Dynamic Aperture Optimization for Low Emittance Light Sources
Kramer, Stephen L
2005-01-01
State of the art low emittance light source lattices, require small bend angle dipole magnets and strong quadrupoles. This in turn creates large chromaticity and small value of dispersion in the lattice. To counter the high chromaticity strong sextupoles are required which limit the dynamic aperture. Traditional methods for expanding the dynamic aperture use harmonic sextupoles to counter the tune shift with amplitude. This has been successful up to now, but is non-deterministic and limited as the sextupole strength increases, driving higher order nonlinearities. We have taken a different approach that makes use of the tune flexibility of a TBA lattice to minimize the lowest order nonlinearities, freeing the harmonic sextupoles to counter the higher order nonlinearities. This procedure is being used to improve the nonlinear dynamics of the NSLS-II lattice.
de Klerk, E.; Laurent, M.
2011-01-01
The Lasserre hierarchy of semidefinite programming approximations to convex polynomial optimization problems is known to converge finitely under some assumptions. [J. B. Lasserre, Convexity in semialgebraic geometry and polynomial optimization, SIAM J. Optim., 19 (2009), pp. 1995–2014]. We give a
Intensive Research Program on Advances in Nonsmooth Dynamics 2016
Jeffrey, Mike; Lázaro, J; Olm, Josep
2017-01-01
This volume contains extended abstracts outlining selected talks and other selected presentations given by participants throughout the "Intensive Research Program on Advances in Nonsmooth Dynamics 2016", held at the Centre de Recerca Matemàtica (CRM) in Barcelona from February 1st to April 29th, 2016. They include brief research articles reporting new results, descriptions of preliminary work or open problems, and outlines of prominent discussion sessions. The articles are all the result of direct collaborations initiated during the research program. The topic is the theory and applications of Nonsmooth Dynamics. This includes systems involving elements of: impacting, switching, on/off control, hybrid discrete-continuous dynamics, jumps in physical properties, and many others. Applications include: electronics, climate modeling, life sciences, mechanics, ecology, and more. Numerous new results are reported concerning the dimensionality and robustness of nonsmooth models, shadowing variables, numbers of limit...
Hybrid Semantics of Stochastic Programs with Dynamic Reconfiguration
Directory of Open Access Journals (Sweden)
Alberto Policriti
2009-10-01
Full Text Available We begin by reviewing a technique to approximate the dynamics of stochastic programs --written in a stochastic process algebra-- by a hybrid system, suitable to capture a mixed discrete/continuous evolution. In a nutshell, the discrete dynamics is kept stochastic while the continuous evolution is given in terms of ODEs, and the overall technique, therefore, naturally associates a Piecewise Deterministic Markov Process with a stochastic program. The speciﬁc contribution in this work consists in an increase of the ﬂexibility of the translation scheme, obtained by allowing a dynamic reconﬁguration of the degree of discreteness/continuity of the semantics. We also discuss the relationships of this approach with other hybrid simulation strategies for biochemical systems.
An optimal dynamic interval preventive maintenance scheduling for series systems
International Nuclear Information System (INIS)
Gao, Yicong; Feng, Yixiong; Zhang, Zixian; Tan, Jianrong
2015-01-01
This paper studies preventive maintenance (PM) with dynamic interval for a multi-component system. Instead of equal interval, the time of PM period in the proposed dynamic interval model is not a fixed constant, which varies from interval-down to interval-up. It is helpful to reduce the outage loss on frequent repair parts and avoid lack of maintenance of the equipment by controlling the equipment maintenance frequency, when compared to a periodic PM scheme. According to the definition of dynamic interval, the reliability of system is analyzed from the failure mechanisms of its components and the different effects of non-periodic PM actions on the reliability of the components. Following the proposed model of reliability, a novel framework for solving the non-periodical PM schedule with dynamic interval based on the multi-objective genetic algorithm is proposed. The framework denotes the strategies include updating strategy, deleting strategy, inserting strategy and moving strategy, which is set to correct the invalid population individuals of the algorithm. The values of the dynamic interval and the selections of PM action for the components on every PM stage are determined by achieving a certain level of system availability with the minimum total PM-related cost. Finally, a typical rotary table system of NC machine tool is used as an example to describe the proposed method. - Highlights: • A non-periodic preventive maintenance scheduling model is proposed. • A framework for solving the non-periodical PM schedule problem is developed. • The interval of non-periodic PM is flexible and schedule can be better adjusted. • Dynamic interval leads to more efficient solutions than fixed interval does
DYNAMIC PROGRAMMING APPROACH TO TESTING RESOURCE ALLOCATION PROBLEM FOR MODULAR SOFTWARE
Directory of Open Access Journals (Sweden)
P.K. Kapur
2003-02-01
Full Text Available Testing phase of a software begins with module testing. During this period modules are tested independently to remove maximum possible number of faults within a specified time limit or testing resource budget. This gives rise to some interesting optimization problems, which are discussed in this paper. Two Optimization models are proposed for optimal allocation of testing resources among the modules of a Software. In the first model, we maximize the total fault removal, subject to budgetary Constraint. In the second model, additional constraint representing aspiration level for fault removals for each module of the software is added. These models are solved using dynamic programming technique. The methods have been illustrated through numerical examples.
Memory-efficient dynamic programming backtrace and pairwise local sequence alignment.
Newberg, Lee A
2008-08-15
A backtrace through a dynamic programming algorithm's intermediate results in search of an optimal path, or to sample paths according to an implied probability distribution, or as the second stage of a forward-backward algorithm, is a task of fundamental importance in computational biology. When there is insufficient space to store all intermediate results in high-speed memory (e.g. cache) existing approaches store selected stages of the computation, and recompute missing values from these checkpoints on an as-needed basis. Here we present an optimal checkpointing strategy, and demonstrate its utility with pairwise local sequence alignment of sequences of length 10,000. Sample C++-code for optimal backtrace is available in the Supplementary Materials. Supplementary data is available at Bioinformatics online.
Large Portfolio Risk Management and Optimal Portfolio Allocation with Dynamic Copulas
Thorsten Lehnert; Xisong Jin
2011-01-01
Previous research focuses on the importance of modeling the multivariate distribution for optimal portfolio allocation and active risk management. However, available dynamic models are not easily applied for high-dimensional problems due to the curse of dimensionality. In this paper, we extend the framework of the Dynamic Conditional Correlation/Equicorrelation and an extreme value approach into a series of Dynamic Conditional Elliptical Copulas. We investigate risk measures like Value at Ris...
Wihartiko, F. D.; Wijayanti, H.; Virgantari, F.
2018-03-01
Genetic Algorithm (GA) is a common algorithm used to solve optimization problems with artificial intelligence approach. Similarly, the Particle Swarm Optimization (PSO) algorithm. Both algorithms have different advantages and disadvantages when applied to the case of optimization of the Model Integer Programming for Bus Timetabling Problem (MIPBTP), where in the case of MIPBTP will be found the optimal number of trips confronted with various constraints. The comparison results show that the PSO algorithm is superior in terms of complexity, accuracy, iteration and program simplicity in finding the optimal solution.
Economic optimization and evolutionary programming when using remote sensing data
Shamin Roman; Alberto Gabriel Enrike; Uryngaliyeva Ayzhana; Semenov Aleksandr
2018-01-01
The article considers the issues of optimizing the use of remote sensing data. Built a mathematical model to describe the economic effect of the use of remote sensing data. It is shown that this model is incorrect optimisation task. Given a numerical method of solving this problem. Also discusses how to optimize organizational structure by using genetic algorithm based on remote sensing. The methods considered allow the use of remote sensing data in an optimal way. The proposed mathematical m...
DEFF Research Database (Denmark)
Le, T.H.A.; Pham, D. T.; Canh, Nam Nguyen
2010-01-01
Both the efficient and weakly efficient sets of an affine fractional vector optimization problem, in general, are neither convex nor given explicitly. Optimization problems over one of these sets are thus nonconvex. We propose two methods for optimizing a real-valued function over the efficient...... and weakly efficient sets of an affine fractional vector optimization problem. The first method is a local one. By using a regularization function, we reformulate the problem into a standard smooth mathematical programming problem that allows applying available methods for smooth programming. In case...... the objective function is linear, we have investigated a global algorithm based upon a branch-and-bound procedure. The algorithm uses Lagrangian bound coupling with a simplicial bisection in the criteria space. Preliminary computational results show that the global algorithm is promising....
Topology optimization of continuum structure with dynamic constraints using mode identification
International Nuclear Information System (INIS)
Li, Jianhongyu; Chen, Shenyan; Huang, Hai
2015-01-01
For the problems such as mode exchange and localized modes in topology optimization of continuum structure with dynamic constraints, it is difficult to apply the traditional optimization model which considers fixed order mode frequencies as constraints in optimization calculation. A new optimization model is established, in which the dynamical constraints are changed as frequencies of structural principal vibrations. The order of the principal vibrations is recognized through modal identification in the optimization process, and the constraints are updated to make the optimization calculation execute smoothly. Localized mode elimination techniques are introduced to reduce the localized modes induced by the low density elements, which could improve the optimization efficiency. A new optimization process is designed, which achieves the purpose of overcoming mode exchange problem and localized mode problem at the cost of increasing several structural analyses. Optimization system is developed by using Nastran to perform structural analysis and sensitivity analysis and two-level multipoint approximation algorithm as optimizer. Numerical results verified that the presented method is effective and reasonable.
An Optimized Grey Dynamic Model for Forecasting the Output of High-Tech Industry in China
Directory of Open Access Journals (Sweden)
Zheng-Xin Wang
2014-01-01
Full Text Available The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data and n series related, abbreviated as GDMC(1,n, performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC(1,n, n interpolation coefficients (taken as unknown parameters are introduced into the background values of the n variables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic output of high-tech industry in China. Comparisons of the obtained modelling results from the optimized GDMC(1,n model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC(1,n model. The modelling results can assist the government in developing future policies regarding high-tech industry management.
Dynamic thread assignment in web server performance optimization
van der Weij, W.; Bhulai, S.; van der Mei, R.D.
2009-01-01
Popular web sites are expected to handle huge number of requests concurrently within a reasonable time frame. The performance of these web sites is largely dependent on effective thread management of their web servers. Although the implementation of static and dynamic thread policies is common
Worst-Case-Optimal Dynamic Reinsurance for Large Claims
DEFF Research Database (Denmark)
Korn, Ralf; Menkens, Olaf; Steffensen, Mogens
2012-01-01
We control the surplus process of a non-life insurance company by dynamic proportional reinsurance. The objective is to maximize expected (utility of the) surplus under the worst-case claim development. In the large claim case with a worst-case upper limit on claim numbers and claim sizes, we fin...
Dynamic Modeling, Optimization, and Advanced Control for Large Scale Biorefineries
DEFF Research Database (Denmark)
Prunescu, Remus Mihail
with a complex conversion route. Computational fluid dynamics is used to model transport phenomena in large reactors capturing tank profiles, and delays due to plug flows. This work publishes for the first time demonstration scale real data for validation showing that the model library is suitable...
Dynamic Memory Model for Non-Stationary Optimization
DEFF Research Database (Denmark)
Bendtsen, Claus Nørgaard; Krink, Thiemo
2002-01-01
Real-world problems are often nonstationary and can cause cyclic, repetitive patterns in the search landscape. For this class of problems, we introduce a new GA with dynamic explicit memory, which showed superior performance compared to a classic GA and a previously introduced memory-based GA for...
Optimal control of peridinin excited-state dynamics
Czech Academy of Sciences Publication Activity Database
Dietzek, B.; Chábera, P.; Hanf, R.; Tschierlei, S.; Popp, J.; Pascher, T.; Yartsev, A.; Polívka, Tomáš
2010-01-01
Roč. 373, 1-2 (2010), s. 129-136 ISSN 0301-0104 Institutional research plan: CEZ:AV0Z50510513 Keywords : peridin * excited-state dynamics * coherent control Subject RIV: BO - Biophysics Impact factor: 2.017, year: 2010
A Stereo Dual-Channel Dynamic Programming Algorithm for UAV Image Stitching.
Li, Ming; Chen, Ruizhi; Zhang, Weilong; Li, Deren; Liao, Xuan; Wang, Lei; Pan, Yuanjin; Zhang, Peng
2017-09-08
Dislocation is one of the major challenges in unmanned aerial vehicle (UAV) image stitching. In this paper, we propose a new algorithm for seamlessly stitching UAV images based on a dynamic programming approach. Our solution consists of two steps: Firstly, an image matching algorithm is used to correct the images so that they are in the same coordinate system. Secondly, a new dynamic programming algorithm is developed based on the concept of a stereo dual-channel energy accumulation. A new energy aggregation and traversal strategy is adopted in our solution, which can find a more optimal seam line for image stitching. Our algorithm overcomes the theoretical limitation of the classical Duplaquet algorithm. Experiments show that the algorithm can effectively solve the dislocation problem in UAV image stitching, especially for the cases in dense urban areas. Our solution is also direction-independent, which has better adaptability and robustness for stitching images.
Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.
Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin
2018-04-03
Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.
Dynamics of underactuated multibody systems modeling, control and optimal design
Seifried, Robert
2014-01-01
Underactuated multibody systems are intriguing mechatronic systems, as they possess fewer control inputs than degrees of freedom. Some examples are modern light-weight flexible robots and articulated manipulators with passive joints. This book investigates such underactuated multibody systems from an integrated perspective. This includes all major steps from the modeling of rigid and flexible multibody systems, through nonlinear control theory, to optimal system design. The underlying theories and techniques from these different fields are presented using a self-contained and unified approach and notation system. Subsequently, the book focuses on applications to large multibody systems with multiple degrees of freedom, which require a combination of symbolical and numerical procedures. Finally, an integrated, optimization-based design procedure is proposed, whereby both structural and control design are considered concurrently. Each chapter is supplemented by illustrated examples.
Optimization of CW Fiber Lasers With Strong Nonlinear Cavity Dynamics
Shtyrina, O. V.; Efremov, S. A.; Yarutkina, I. A.; Skidin, A. S.; Fedoruk, M. P.
2018-04-01
In present work the equation for the saturated gain is derived from one-level gain equations describing the energy evolution inside the laser cavity. It is shown how to derive the parameters of the mathematical model from the experimental results. The numerically-estimated energy and spectrum of the signal are in good agreement with the experiment. Also, the optimization of the output energy is performed for a given set of model parameters.
Large portfolio risk management and optimal portfolio allocation with dynamic elliptical copulas
Directory of Open Access Journals (Sweden)
Jin Xisong
2018-02-01
Full Text Available Previous research has focused on the importance of modeling the multivariate distribution for optimal portfolio allocation and active risk management. However, existing dynamic models are not easily applied to high-dimensional problems due to the curse of dimensionality. In this paper, we extend the framework of the Dynamic Conditional Correlation/Equicorrelation and an extreme value approach into a series of Dynamic Conditional Elliptical Copulas. We investigate risk measures such as Value at Risk (VaR and Expected Shortfall (ES for passive portfolios and dynamic optimal portfolios using Mean-Variance and ES criteria for a sample of US stocks over a period of 10 years. Our results suggest that (1 Modeling the marginal distribution is important for dynamic high-dimensional multivariate models. (2 Neglecting the dynamic dependence in the copula causes over-aggressive risk management. (3 The DCC/DECO Gaussian copula and t-copula work very well for both VaR and ES. (4 Grouped t-copulas and t-copulas with dynamic degrees of freedom further match the fat tail. (5 Correctly modeling the dependence structure makes an improvement in portfolio optimization with respect to tail risk. (6 Models driven by multivariate t innovations with exogenously given degrees of freedom provide a flexible and applicable alternative for optimal portfolio risk management.
Sub-optimal control of fuzzy linear dynamical systems under granular differentiability concept.
Mazandarani, Mehran; Pariz, Naser
2018-05-01
This paper deals with sub-optimal control of a fuzzy linear dynamical system. The aim is to keep the state variables of the fuzzy linear dynamical system close to zero in an optimal manner. In the fuzzy dynamical system, the fuzzy derivative is considered as the granular derivative; and all the coefficients and initial conditions can be uncertain. The criterion for assessing the optimality is regarded as a granular integral whose integrand is a quadratic function of the state variables and control inputs. Using the relative-distance-measure (RDM) fuzzy interval arithmetic and calculus of variations, the optimal control law is presented as the fuzzy state variables feedback. Since the optimal feedback gains are obtained as fuzzy functions, they need to be defuzzified. This will result in the sub-optimal control law. This paper also sheds light on the restrictions imposed by the approaches which are based on fuzzy standard interval arithmetic (FSIA), and use strongly generalized Hukuhara and generalized Hukuhara differentiability concepts for obtaining the optimal control law. The granular eigenvalues notion is also defined. Using an RLC circuit mathematical model, it is shown that, due to their unnatural behavior in the modeling phenomenon, the FSIA-based approaches may obtain some eigenvalues sets that might be different from the inherent eigenvalues set of the fuzzy dynamical system. This is, however, not the case with the approach proposed in this study. The notions of granular controllability and granular stabilizability of the fuzzy linear dynamical system are also presented in this paper. Moreover, a sub-optimal control for regulating a Boeing 747 in longitudinal direction with uncertain initial conditions and parameters is gained. In addition, an uncertain suspension system of one of the four wheels of a bus is regulated using the sub-optimal control introduced in this paper. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.
Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.
Xu, Dongpo; Xia, Yili; Mandic, Danilo P
2016-02-01
The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel generalized Hamilton-real (GHR) calculus, thus making a possible efficient derivation of general optimization algorithms directly in the quaternion field, rather than using the isomorphism with the real domain, as is current practice. In addition, unlike the existing quaternion gradients, the GHR calculus allows for the product and chain rule, and for a one-to-one correspondence of the novel quaternion gradient and Hessian with their real counterparts. Properties of the quaternion gradient and Hessian relevant to numerical applications are also introduced, opening a new avenue of research in quaternion optimization and greatly simplified the derivations of learning algorithms. The proposed GHR calculus is shown to yield the same generic algorithm forms as the corresponding real- and complex-valued algorithms. Advantages of the proposed framework are illuminated over illustrative simulations in quaternion signal processing and neural networks.