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.

Ant colony optimization and constraint programming

CERN Document Server

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

Portfolio optimization using fuzzy linear programming

Science.gov (United States)

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 Research of Generation Investment Based on Linear Programming Model

Science.gov (United States)

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.

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

High effectiveness of self-help programs after drug addiction therapy

Directory of Open Access Journals (Sweden)

Kristensen Øistein

2006-08-01

Full Text Available Abstract Background The self-help groups Alcoholics Anonymous (AA and Narcotics Anonymous (NA are very well established. AA and NA employ a 12-step program and are found in most large cities around the world. Although many have argued that these organizations are valuable, substantial scepticism remains as to whether they are actually effective. Few treatment facilities give clear recommendations to facilitate participation, and the use of these groups has been disputed. The purpose of this study was to examine whether the use of self-help groups after addiction treatment is associated with higher rates of abstinence. Methods One hundred and fourteen patients, 59 with alcohol dependency and 55 with multiple drug dependency, who started in self-help groups after addiction treatment, were examined two years later using a questionnaire. Return rate was 66%. Six (5% of the patients were dead. Results Intention-to-treat-analysis showed that 38% still participated in self-help programs two years after treatment. Among the regular participants, 81% had been abstinent over the previous 6 months, compared with only 26% of the non-participants. Logistic regression analysis showed OR = 12.6, 95% CI (4.1–38.3, p Conclusion The study has several methodological problems; in particular, correlation does not necessarily indicate causality. These problems are discussed and we conclude that the probability of a positive effect is sufficient to recommend participation in self-help groups as a supplement to drug addiction treatment. Previous publication This article is based on a study originally published in Norwegian: Kristensen O, Vederhus JK: Self-help programs in drug addiction therapy. Tidsskr Nor Laegeforen 2005, 125:2798–2801.

Optimization of environmental management strategies through a dynamic stochastic possibilistic multiobjective program

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

Optimal Implantable Cardioverter Defibrillator Programming.

Science.gov (United States)

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 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...

Polyhedral and semidefinite programming methods in combinatorial optimization

CERN Document Server

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

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....

Portfolio optimization by using linear programing models based on genetic algorithm

Science.gov (United States)

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.

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)

Optimization of environmental management strategies through a dynamic stochastic possibilistic multiobjective program.

Science.gov (United States)

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.

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

Lean and Efficient Software: Whole-Program Optimization of Executables

Science.gov (United States)

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

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...

Pareto optimization in algebraic dynamic programming.

Science.gov (United States)

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.

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)

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.

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...

[A program for optimizing the use of antimicrobials (PROA): experience in a regional hospital].

Science.gov (United States)

Ugalde-Espiñeira, J; Bilbao-Aguirregomezcorta, J; Sanjuan-López, A Z; Floristán-Imízcoz, C; Elorduy-Otazua, L; Viciola-García, M

2016-08-01

Programs for optimizing the use of antibiotics (PROA) or antimicrobial stewardship programs are multidisciplinary programs developed in response to the increase of antibiotic resistant bacteria, the objective of which are to improve clinical results, to minimize adverse events and to reduce costs associated with the use of antimicrobials. The implementation of a PROA program in a 128-bed general hospital and the results obtained at 6 months are here reported. An intervention quasi-experimental study with historical control group was designed with the objective of assessing the impact of a PROA program with a non-restrictive intervention model to help prescription, with a direct and bidirectional intervention. The basis of the program is an optimization audit of the use of antimicrobials with not imposed personalized recommendations and the use of information technologies applied to this setting. The impact on the pharmaceutical consumption and costs, cost per process, mean hospital stay, percentage of readmissions to the hospital are described. A total of 307 audits were performed. In 65.8% of cases, treatment was discontinued between the 7th and the 10th day. The main reasons of treatment discontinuation were completeness of treatment (43.6%) and lack of indication (14.7%). The reduction of pharmaceutical expenditure was 8.59% (P = 0.049) and 5.61% of the consumption in DDD/100 stays (P=0.180). The costs by processes in general surgery showed a 3.14% decrease (p=0.000). The results obtained support the efficiency of these programs in small size hospitals with limited resources.

Optimal timing of joint replacement using mathematical programming and stochastic programming models.

Science.gov (United States)

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.

A multiobjective interval programming model for wind-hydrothermal power system dispatching using 2-step optimization algorithm.

Science.gov (United States)

Ren, Kun; Jihong, Qu

2014-01-01

Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision.

A Multiobjective Interval Programming Model for Wind-Hydrothermal Power System Dispatching Using 2-Step Optimization Algorithm

Science.gov (United States)

Jihong, Qu

2014-01-01

Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision. PMID:24895663

Promoting Awareness of a High School Peer Helping Program.

Science.gov (United States)

Fielding, Sarah; Pili, Chris; Chambliss, Catherine

Peer helping has recently been adopted by many schools, but use of these services remains mixed. The different ways in which peer helpers can be selected are described and examples of effective programs already in place are offered. The two types of cognitive processes used to evaluate advertising campaigns--automatic and strategic--are discussed…

Helping Students with Difficult First Year Subjects through the PASS Program

Science.gov (United States)

Sultan, Fauziah K. P. D.; Narayansany, Kannaki S.; Kee, Hooi Ling; Kuan, Chin Hoay; Palaniappa Manickam, M. Kamala; Tee, Meng Yew

2013-01-01

The purpose of this action research was to find out if participants of a pilot PASS program found it to be helpful. The program was implemented for the first time in an institute of higher learning in Malaysia. An action research design guided the study, with surveys, documents, and reflections as primary data sources. The findings were largely…

Numerical methods of mathematical optimization with Algol and Fortran programs

CERN Document Server

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

Needs assessment for developing a program to help train advanced-practice pharmacists for research.

Science.gov (United States)

Bulkley, Christina F; Miller, Michael J; Bush, Colleen G; Nussbaum, Barbara B; Draugalis, JoLaine R

2017-12-01

Results of a needs assessment to determine priority topics and preferred formats for research training in pharmacy residency programs are reported. For pharmacists seeking advanced-practice positions in academia, the ability to conduct practice-based research is expected. Pharmacy residency programs are a primary recruitment source for these positions, but research training varies by residency site and available expertise. To help define the optimal content and format of resident research training, ASHP and the ASHP Research and Education Foundation conducted a needs assessment targeting postgraduate year 1 (PGY1) pharmacy residency directors (RPDs). The response rate was 36.5% (271 of 743 invitees); the information obtained was used to guide development of a Web-based training series. Only 12% of the RPDs who participated in the survey indicated that currently available research training resources within their residency programs were sufficient. Sixty-seven percent of surveyed RPDs agreed that a Web-based training program would be a useful resource, and 81% agreed that the target audience should be pharmacy residents. Training topics of greatest interest to RPDs included (1) components of a resident research plan, (2) identifying research questions, (3) study design and sample selection, (4) project management, (5) data acquisition, cleaning, management, and analysis, and (6) presenting and publishing project results. This needs assessment clearly identified opportunities for improving the infrastructure and content of PGY1 residency research training. At a minimum, training programs should focus on practice-based research concepts using readily accessible health-system data systems and provide universal accessibility and sufficient flexibility to allow residency programs to integrate the training in a manner that works best for the program. Copyright © 2017 by the American Society of Health-System Pharmacists, Inc. All rights reserved.

Mehar Methods for Fuzzy Optimal Solution and Sensitivity Analysis of Fuzzy Linear Programming with Symmetric Trapezoidal Fuzzy Numbers

Directory of Open Access Journals (Sweden)

Sukhpreet Kaur Sidhu

2014-01-01

Full Text Available The drawbacks of the existing methods to obtain the fuzzy optimal solution of such linear programming problems, in which coefficients of the constraints are represented by real numbers and all the other parameters as well as variables are represented by symmetric trapezoidal fuzzy numbers, are pointed out, and to resolve these drawbacks, a new method (named as Mehar method is proposed for the same linear programming problems. Also, with the help of proposed Mehar method, a new method, much easy as compared to the existing methods, is proposed to deal with the sensitivity analysis of the same type of linear programming problems.

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.

Optimal Airport Surface Traffic Planning Using Mixed-Integer Linear Programming

Directory of Open Access Journals (Sweden)

P. C. Roling

2008-01-01

Full Text Available We describe an ongoing research effort pertaining to the development of a surface traffic automation system that will help controllers to better coordinate surface traffic movements related to arrival and departure traffic. More specifically, we describe the concept for a taxi-planning support tool that aims to optimize the routing and scheduling of airport surface traffic in such a way as to deconflict the taxi plans while optimizing delay, total taxi-time, or some other airport efficiency metric. Certain input parameters related to resource demand, such as the expected landing times and the expected pushback times, are rather difficult to predict accurately. Due to uncertainty in the input data driving the taxi-planning process, the taxi-planning tool is designed such that it produces solutions that are robust to uncertainty. The taxi-planning concept presented herein, which is based on mixed-integer linear programming, is designed such that it is able to adapt to perturbations in these input conditions, as well as to account for failure in the actual execution of surface trajectories. The capabilities of the tool are illustrated in a simple hypothetical airport.

Empowering Adult Learners. NIF Literacy Program Helps ABE Accomplish Human Development Mission.

Science.gov (United States)

Hurley, Mary E.

1991-01-01

The National Issues Forum's Literacy Program uses study circles and group discussion to promote empowerment and enhance adult literacy through civic education. The program has helped the Westonka (Minnesota) Adult Basic Education project accomplish its mission and has expanded the staff's view of adult learning. (SK)

A Web-Disseminated Self-Help and Peer Support Program Could Fill Gaps in Mental Health Care: Lessons From a Consumer Survey

Science.gov (United States)

Banschback, Kaitlin; Santorelli, Gennarina D; Constantino, Michael J

2017-01-01

Background Self-guided mental health interventions that are disseminated via the Web have the potential to circumvent barriers to treatment and improve public mental health. However, self-guided interventions often fail to attract consumers and suffer from user nonadherence. Uptake of novel interventions could be improved by consulting consumers from the beginning of the development process in order to assess their interest and their preferences. Interventions can then be tailored using this feedback to optimize appeal. Objective The aim of our study was to determine the level of public interest in a new mental health intervention that incorporates elements of self-help and peer counseling and that is disseminated via a Web-based training course; to identify predictors of interest in the program; and to identify consumer preferences for features of Web-based courses and peer support programs. Methods We surveyed consumers via Amazon’s Mechanical Turk to estimate interest in the self-help and peer support program. We assessed associations between demographic and clinical characteristics and interest in the program, and we obtained feedback on desired features of the program. Results Overall, 63.9% (378/592) of respondents said that they would try the program; interest was lower but still substantial among those who were not willing or able to access traditional mental health services. Female gender, lower income, and openness to using psychotherapy were the most consistent predictors of interest in the program. The majority of respondents, although not all, preferred romantic partners or close friends as peer counselors and would be most likely to access the program if the training course were accessed on a stand-alone website. In general, respondents valued training in active listening skills. Conclusions In light of the apparent public interest in this program, Web-disseminated self-help and peer support interventions have enormous potential to fill gaps in

SMART Optimization of a Parenting Program for Active Duty Families

Science.gov (United States)

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

Diabetes Awareness of Low-Income Middle School Students Participating in the Help a Friend, Help Yourself Youth Diabetes Awareness Education Program

Science.gov (United States)

Wroten, Kathryn; Reames, Elizabeth S.; Tuuri, Georgianna

2012-01-01

The study reported here investigated the effectiveness of the LSU AgCenter Help a Friend, Help Yourself youth diabetes education curriculum to increase knowledge and awareness of diabetes and its symptoms in low-income middle school students participating in the Boys and Girls Club after-school program. The curriculum includes four lessons with…

Military Personnel: Performance Measures Needed to Determine How Well DOD’s Credentialing Program Helps Servicemembers

Science.gov (United States)

2016-10-01

MILITARY PERSONNEL Performance Measures Needed to Determine How Well DOD’s Credentialing Program Helps Servicemembers...Measures Needed to Determine How Well DOD’s Credentialing Program Helps Servicemembers What GAO Found The Department of Defense (DOD) has taken steps to...establish the statutorily required credentialing program, but it has not developed performance measures to gauge the program’s effectiveness

From Harassment to Helping with Antisocial Youth: The EQUIP Program.

Science.gov (United States)

Gibbs, John C.; Potter, Granville Bud; Goldstein, Arnold P.; Brendtro, Larry K.

1996-01-01

Describes a new psychoeducational treatment model for antisocial youth. Discusses the principles and methods for cultivating a positive caring culture in the schools and how the program equips youth for effective peer helping by integrating positive peer culture with training in moral development, anger management, social skills, and correcting…

Lean and Efficient Software: Whole Program Optimization of Executables

Science.gov (United States)

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

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

Programmed Evolution for Optimization of Orthogonal Metabolic Output in Bacteria

Science.gov (United States)

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

Exploration of automatic optimization for CUDA programming

KAUST Repository

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

KAUST Repository

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.

Quasiconvex optimization and location theory

CERN Document Server

Santos Gromicho, Jaoquim António

1998-01-01

grams of which the objective is given by the ratio of a convex by a positive (over a convex domain) concave function. As observed by Sniedovich (Ref. [102, 103]) most of the properties of fractional pro­ grams could be found in other programs, given that the objective function could be written as a particular composition of functions. He called this new field C­ programming, standing for composite concave programming. In his seminal book on dynamic programming (Ref. [104]), Sniedovich shows how the study of such com­ positions can help tackling non-separable dynamic programs that otherwise would defeat solution. Barros and Frenk (Ref. [9]) developed a cutting plane algorithm capable of optimizing C-programs. More recently, this algorithm has been used by Carrizosa and Plastria to solve a global optimization problem in facility location (Ref. [16]). The distinction between global optimization problems (Ref. [54]) and generalized convex problems can sometimes be hard to establish. That is exactly the reason ...

Uncertain and multi-objective programming models for crop planting structure optimization

Directory of Open Access Journals (Sweden)

Mo LI,Ping GUO,Liudong ZHANG,Chenglong ZHANG

2016-03-01

Full Text Available Crop planting structure optimization is a significant way to increase agricultural economic benefits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic profits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study, three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming (IFCCP model and an inexact fuzzy linear programming (IFLP model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimization-theory-based fuzzy linear multi-objective programming model was developed, which is capable of reflecting both uncertainties and multi-objective. In addition, a multi-objective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic benefits and the denominator representing minimum crop planting area allocation. These models better reflect actual situations, considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in Minqin County, north-west China. The advantages, the applicable conditions and the solution methods

[Drug Addiction Self-Help Recovery scale (DASH-scale): an approach to the measurement of recovery from drug addiction in self-help program among drug addicts].

Science.gov (United States)

Shimane, Takuya; Misago, Chizuru

2004-12-01

The purpose of the study was to develop a scale for measuring the recovery in self-help program for drug addicts. Our study sites were fourteen self-help groups for drug addicts called "DARC: Drug Addiction Rehabilitation Center". DARC activities were based on Narcotics Anonymous types of self-help program. The 25-items DASH-scale questionnaire was developed using data, which were obtained through in-depth interview among DARC staff. A cross-sectional study among recovering addicts participating in "DARC" activities was implemented from Jan 2004 to Feb 2004. 164 subjects were responded to our questionnaire. Factor analysis was carried out and items with weaker or split loadings were removed. Factor analysis of DASH-scale results produced a surprisingly clean four-factor solution. 19-items were left to form the final DASH-scale; regular life-style (6 items), acceptance of drug addiction (5 items), sympathy with member (5 items), reborn (3 items). The internal consistency (Cronbach's Alpha) of these scales was very high (0.87). Low but significant concurrent correlations were observed between the DASH-scale and the Rosenberg Self-Esteem Scale (0.22), Purpose in Life Test (0.35). Discriminant validity of the DASH-scale was supported by significant increase with exposed period of self-help program. Evidence supports the DASH-scale was possible to measure recovery in self-help program.

Post optimization paradigm in maximum 3-satisfiability logic programming

Science.gov (United States)

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.

A Fuzzy Max–Min Decision Bi-Level Fuzzy Programming Model for Water Resources Optimization Allocation under Uncertainty

Directory of Open Access Journals (Sweden)

Chongfeng Ren

2018-04-01

Full Text Available Water competing conflict among water competing sectors from different levels should be taken under consideration during the optimization allocation of water resources. Furthermore, uncertainties are inevitable in the optimization allocation of water resources. In order to deal with the above problems, this study developed a fuzzy max–min decision bi-level fuzzy programming model. The developed model was then applied to a case study in Wuwei, Gansu Province, China. In this study, the net benefit and yield were regarded as the upper-level and lower-level objectives, respectively. Optimal water resource plans were obtained under different possibility levels of fuzzy parameters, which could deal with water competing conflict between the upper level and the lower level effectively. The obtained results are expected to make great contribution in helping local decision-makers to make decisions on dealing with the water competing conflict between the upper and lower level and the optimal use of water resources under uncertainty.

Optimizing the Regional Industrial Structure Based on the Environmental Carrying Capacity: An Inexact Fuzzy Multi-Objective Programming Model

Directory of Open Access Journals (Sweden)

Wenyi Wang

2013-12-01

Full Text Available An inexact fuzzy multi-objective programming model (IFMOP based on the environmental carrying capacity is provided for industrial structure optimization problems. In the IFMOP model, both fuzzy linear programming (FLP and inexact linear programming (ILP methods are introduced into a multi-objective programming framework. It allows uncertainties to be directly communicated into the problem solving processing, and it can effectively reflect the complexity and uncertainty of an industrial system without impractical simplification. The two objective functions utilized in the optimization study are the maximum total output value and population size, and the constraints include water environmental capacity, water resource supply, atmospheric environmental capacity and energy supply. The model is subsequently employed in a realistic case for industrial development in the Tongzhou district, Beijing, China. The results demonstrate that the model can help to analyze whether the environmental carrying capacity of Tongzhou can meet the needs of the social economic objectives in the new town plan in the two scenarios and can assist decision makers in generating stable and balanced industrial structure patterns with consideration of the resources, energy and environmental constraints to meet the maximum social economic efficiency.

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

Optimizing Grid Patterns on Photovoltaic Cells

Science.gov (United States)

Burger, D. R.

1984-01-01

CELCAL computer program helps in optimizing grid patterns for different photovoltaic cell geometries and metalization processes. Five different powerloss phenomena associated with front-surface metal grid pattern on photovoltaic cells.

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....

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.

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.

77 FR 71609 - Self-Help Homeownership Opportunity Program (SHOP) Grant Monitoring

Science.gov (United States)

2012-12-03

... DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT [Docket No. FR-5603-N-89] Self-Help Homeownership Opportunity Program (SHOP) Grant Monitoring AGENCY: Office of the Chief Information Officer, HUD. ACTION... electronic submission of responses. This notice also lists the following information: Title of Proposed: Self...

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 ...

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

BILGO: Bilateral greedy optimization for large scale semidefinite programming

KAUST Repository

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.

BILGO: Bilateral greedy optimization for large scale semidefinite programming

KAUST Repository

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.

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)

Methods for optimizing over the efficient and weakly efficient sets of an affine fractional vector optimization program

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....

Optimization of Algorithms Using Extensions of Dynamic Programming

KAUST Repository

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

Help Increase the Peace, A Youth-Focused Program in Peace Education

Science.gov (United States)

Morrison, Mary Lee; Austad, Carol Shaw; Cota, Kate

2011-01-01

This study investigated specific attitudes and beliefs, related to the concepts of peace education, of participants in an "Introductory, basic help increase the peace program" (HIPP) workshop. Pre- and post-workshop ratings showed significant differences on two important attitudinal variables: first, the importance of being familiar with the…

Grid-Optimization Program for Photovoltaic Cells

Science.gov (United States)

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.

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.)

An Improved Particle Swarm Optimization for Solving Bilevel Multiobjective Programming Problem

Directory of Open Access Journals (Sweden)

Tao Zhang

2012-01-01

Full Text Available An improved particle swarm optimization (PSO algorithm is proposed for solving bilevel multiobjective programming problem (BLMPP. For such problems, the proposed algorithm directly simulates the decision process of bilevel programming, which is different from most traditional algorithms designed for specific versions or based on specific assumptions. The BLMPP is transformed to solve multiobjective optimization problems in the upper level and the lower level interactively by an improved PSO. And a set of approximate Pareto optimal solutions for BLMPP is obtained using the elite strategy. This interactive procedure is repeated until the accurate Pareto optimal solutions of the original problem are found. Finally, some numerical examples are given to illustrate the feasibility of the proposed algorithm.

A man in the loop trajectory optimization program (MILTOP)

Science.gov (United States)

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.

Optimization of fractionated radiotherapy of tumors

International Nuclear Information System (INIS)

Ivanov, V.K.

1984-01-01

Underlying modern conceptions of clinical radiobiology and mathematic methods in system theory a model of radiation therapy for tumors is developed. To obtain optimal fractionating conditions the principle of gradual optimization is used. A optimal therapeutic method permits to minimize the survival of a tumor cell population with localized lesions of the intact tissue. An analytic research is carried out for the simplest variant of the model. By help of a SORT-program unit the conditions are ascertained for gradual optimization of radiotherapy. (author)

Invention Development Program Helps Nurture NCI at Frederick Technologies | Poster

Science.gov (United States)

The Invention Development Fund (IDF) was piloted by the Technology Transfer Center (TTC) in 2014 to facilitate the commercial development of NCI technologies. The IDF received a second round of funding from the NCI Office of the Director and the Office of Budget and Management to establish the Invention Development Program (IDP) for fiscal year 2016. The IDP is using these funds to help advance a second set of inventions.

MULTI-CRITERIA PROGRAMMING METHODS AND PRODUCTION PLAN OPTIMIZATION PROBLEM SOLVING IN METAL INDUSTRY

OpenAIRE

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...

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.)

Optimization of Product Instantiation using Integer Programming

NARCIS (Netherlands)

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

How to implement the Science Fair Self-Help Development Program in schools

Energy Technology Data Exchange (ETDEWEB)

Menicucci, D.

1994-01-01

This manual is intended to act as a working guide for setting up a Science Fair Volunteer Support Committee at your school. The Science Fair Volunteer Support Committee, or SFVSC, is the key component of the Science Fair Self-Help program, which was developed by Sandia National Laboratories and is designed to support a school`s science activities. The SFVSC is a team of parents and community volunteers who work in concert with a school`s teaching staff to assist and manage all areas of a school Science and Engineering Fair. The main advantage of creating such a committee is that it frees the science teachers from the organizational aspects of the fair and lets them concentrate on their job of teaching science. This manual is based on information gained through a Self-Help Development pilot program that was developed by Sandia National Laboratories during the 1991--92 school year at three Albuquerque, NM, middle schools. The manual describes the techniques that were successful in the pilot program and discusses how these techniques might be implemented in other schools. This manual also discusses problems that may be encountered, including suggestions for how they might be resolved.

A comparison of online versus workbook delivery of a self-help positive parenting program.

Science.gov (United States)

Sanders, Matthew R; Dittman, Cassandra K; Farruggia, Susan P; Keown, Louise J

2014-06-01

A noninferiority randomized trial design compared the efficacy of two self-help variants of the Triple P-Positive Parenting Program: an online version and a self-help workbook. We randomly assigned families of 193 children displaying early onset disruptive behavior difficulties to the online (N = 97) or workbook (N = 96) interventions. Parents completed questionnaire measures of child behavior, parenting, child maltreatment risk, personal adjustment and relationship quality at pre- and post-intervention and again at 6-month follow up. The short-term intervention effects of the Triple P Online program were not inferior to the workbook on the primary outcomes of disruptive child behavior and dysfunctional parenting as reported by both mothers and fathers. Both interventions were associated with significant and clinically meaningful declines from pre- to post-intervention in levels of disruptive child behavior, dysfunctional parenting styles, risk of child maltreatment, and inter-parental conflict on both mother and father report measures. Intervention effects were largely maintained at 6-month follow up, thus supporting the use of self-help parenting programs within a comprehensive population-based system of parenting support to reduce child maltreatment and behavioral problems in children.

Dynamic Programming Approach for Exact Decision Rule Optimization

KAUST Repository

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

A Sequential Convex Semidefinite Programming Algorithm for Multiple-Load Free Material Optimization

Czech Academy of Sciences Publication Activity Database

Stingl, M.; Kočvara, Michal; Leugering, G.

2009-01-01

Roč. 20, č. 1 (2009), s. 130-155 ISSN 1052-6234 R&D Projects: GA AV ČR IAA1075402 Grant - others:commision EU(XE) EU-FP6-30717 Institutional research plan: CEZ:AV0Z10750506 Keywords : structural optimization * material optimization * semidefinite programming * sequential convex programming Subject RIV: BA - General Mathematics Impact factor: 1.429, year: 2009

Penempatan Optimal Phasor Measurement Unit (PMU) Dengan Integer Programming

OpenAIRE

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...

Portfolio optimization in enhanced index tracking with goal programming approach

Science.gov (United States)

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.

Optimal control of a programmed motion of a rigid spacecraft using redundant kinematics parameterizations

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

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.

Artificial intelligence programming with LabVIEW: genetic algorithms for instrumentation control and optimization.

Science.gov (United States)

Moore, J H

1995-06-01

A genetic algorithm for instrumentation control and optimization was developed using the LabVIEW graphical programming environment. The usefulness of this methodology for the optimization of a closed loop control instrument is demonstrated with minimal complexity and the programming is presented in detail to facilitate its adaptation to other LabVIEW applications. Closed loop control instruments have variety of applications in the biomedical sciences including the regulation of physiological processes such as blood pressure. The program presented here should provide a useful starting point for those wishing to incorporate genetic algorithm approaches to LabVIEW mediated optimization of closed loop control instruments.

Perceived helpfulness of the individual components of a behavioural weight loss program: results from the Hopkins POWER Trial

OpenAIRE

Dalcin, A. T.; Jerome, G. J.; Fitzpatrick, S. L.; Louis, T. A.; Wang, N?Y.; Bennett, W. L.; Durkin, N.; Clark, J. M.; Daumit, G. L.; Appel, L. J.; Coughlin, J. W.

2015-01-01

Summary Background Behavioural weight loss programs are effective first?line treatments for obesity and are recommended by the US Preventive Services Task Force. Gaining an understanding of intervention components that are found helpful by different demographic groups can improve tailoring of weight loss programs. This paper examined the perceived helpfulness of different weight loss program components. Methods Participants (n?=?236) from the active intervention conditions of the Practice?bas...

Optimal Implementation of Prescription Drug Monitoring Programs in the Emergency Department

Directory of Open Access Journals (Sweden)

Garrett DePalma

2018-02-01

Full Text Available The opioid epidemic is the most significant modern-day, public health crisis. Physicians and lawmakers have developed methods and practices to curb opioid use. This article describes one method, prescription drug monitoring programs (PDMP, through the lens of how to optimize use for emergency departments (ED. EDs have rapidly become a central location to combat opioid abuse and drug diversion. PDMPs can provide emergency physicians with comprehensive prescribing information to improve clinical decisions around opioids. However, PDMPs vary tremendously in their accessibility and usability in the ED, which limits their effectiveness at the point of care. Problems are complicated by varying state-to-state requirements for data availability and accessibility. Several potential solutions to improving the utility of PDMPs in EDs include integrating PDMPs with electronic health records, implementing unsolicited reporting and prescription context, improving PDMP accessibility, data analytics, and expanding the scope of PDMPs. These improvements may help improve clinical decision-making for emergency physicians through better data, data presentation, and accessibility.

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....

A Linear Programming Model to Optimize Various Objective Functions of a Foundation Type State Support Program.

Science.gov (United States)

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…

MULTI-CRITERIA PROGRAMMING METHODS AND PRODUCTION PLAN OPTIMIZATION PROBLEM SOLVING IN METAL INDUSTRY

Directory of Open Access Journals (Sweden)

Tunjo Perić

2017-09-01

Full Text Available 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 results indicate a high efficiency of the applied methods in solving the problem.

Emotionally troubled teens' help-seeking behaviors: an evaluation of surviving the Teens® suicide prevention and depression awareness program.

Science.gov (United States)

Strunk, Catherine M; Sorter, Michael T; Ossege, Julianne; King, Keith A

2014-10-01

Many school-based suicide prevention programs do not show a positive impact on help-seeking behaviors among emotionally troubled teens despite their being at high risk for suicide. This study is a secondary analysis of the Surviving the Teens(®) program evaluation to determine its effect on help-seeking behaviors among troubled youth. Results showed significant increases in mean scores of the Behavioral Intent to Communicate with Important Others Regarding Emotional Health Issues subscale (p Teens program has a positive effect on help-seeking behaviors in troubled youth. © The Author(s) 2013.

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...

Optimal Risk Reduction in the Railway Industry by Using Dynamic Programming

OpenAIRE

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...

Adaptive dynamic programming with applications in optimal control

CERN Document Server

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...

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.

Project STOP (Spectral Thermal Optimization Program)

Science.gov (United States)

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.

Optimization programs for reactor core fuel loading exhibiting reduced neutron leakage

International Nuclear Information System (INIS)

Darilek, P.

1991-01-01

The program MAXIM was developed for the optimization of the fuel loading of WWER-440 reactors. It enables the reactor core reactivity to be maximized by modifying the arrangement of the fuel assemblies. The procedure is divided into three steps. The first step includes the passage from the three-dimensional model of the reactor core to the two-dimensional model. In the second step, the solution to the problem is sought assuming that the multiplying properties, or the reactivity in the zones of the core, vary continuously. In the third step, parameters of actual fuel assemblies are inserted in the ''continuous'' solution obtained. Combined with the program PROPAL for a detailed refinement of the loading, the program MAXIM forms a basis for the development of programs for the optimization of fuel loading with burnable poisons. (Z.M.). 16 refs

Dynamic Programming Approach for Exact Decision Rule Optimization

KAUST Repository

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.

Optimization of temperature-programmed GC separations. II. Off-line simplex optimization and column selection

NARCIS (Netherlands)

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

Asymptotic Normality of the Optimal Solution in Multiresponse Surface Mathematical Programming

OpenAIRE

Díaz-García, José A.; Caro-Lopera, Francisco J.

2015-01-01

An explicit form for the perturbation effect on the matrix of regression coeffi- cients on the optimal solution in multiresponse surface methodology is obtained in this paper. Then, the sensitivity analysis of the optimal solution is studied and the critical point characterisation of the convex program, associated with the optimum of a multiresponse surface, is also analysed. Finally, the asymptotic normality of the optimal solution is derived by the standard methods.

POBE: A Computer Program for Optimal Design of Multi-Subject Blocked fMRI Experiments

Directory of Open Access Journals (Sweden)

Bärbel Maus

2014-01-01

Full Text Available For functional magnetic resonance imaging (fMRI studies, researchers can use multi-subject blocked designs to identify active brain regions for a certain stimulus type of interest. Before performing such an experiment, careful planning is necessary to obtain efficient stimulus effect estimators within the available financial resources. The optimal number of subjects and the optimal scanning time for a multi-subject blocked design with fixed experimental costs can be determined using optimal design methods. In this paper, the user-friendly computer program POBE 1.2 (program for optimal design of blocked experiments, version 1.2 is presented. POBE provides a graphical user interface for fMRI researchers to easily and efficiently design their experiments. The computer program POBE calculates the optimal number of subjects and the optimal scanning time for user specified experimental factors and model parameters so that the statistical efficiency is maximised for a given study budget. POBE can also be used to determine the minimum budget for a given power. Furthermore, a maximin design can be determined as efficient design for a possible range of values for the unknown model parameters. In this paper, the computer program is described and illustrated with typical experimental factors for a blocked fMRI experiment.

Nutritionally Optimized, Culturally Acceptable, Cost-Minimized Diets for Low Income Ghanaian Families Using Linear Programming.

Science.gov (United States)

Nykänen, Esa-Pekka A; Dunning, Hanna E; Aryeetey, Richmond N O; Robertson, Aileen; Parlesak, Alexandr

2018-04-07

The Ghanaian population suffers from a double burden of malnutrition. Cost of food is considered a barrier to achieving a health-promoting diet. Food prices were collected in major cities and in rural areas in southern Ghana. Linear programming (LP) was used to calculate nutritionally optimized diets (food baskets (FBs)) for a low-income Ghanaian family of four that fulfilled energy and nutrient recommendations in both rural and urban settings. Calculations included implementing cultural acceptability for families living in extreme and moderate poverty (food budget under USD 1.9 and 3.1 per day respectively). Energy-appropriate FBs minimized for cost, following Food Balance Sheets (FBS), lacked key micronutrients such as iodine, vitamin B12 and iron for the mothers. Nutritionally adequate FBs were achieved in all settings when optimizing for a diet cheaper than USD 3.1. However, when delimiting cost to USD 1.9 in rural areas, wild foods had to be included in order to meet nutritional adequacy. Optimization suggested to reduce roots, tubers and fruits and to increase cereals, vegetables and oil-bearing crops compared with FBS. LP is a useful tool to design culturally acceptable diets at minimum cost for low-income Ghanaian families to help advise national authorities how to overcome the double burden of malnutrition.

Signal Timing Optimization Based on Fuzzy Compromise Programming for Isolated Signalized Intersection

Directory of Open Access Journals (Sweden)

Dexin Yu

2016-01-01

Full Text Available In order to optimize the signal timing for isolated intersection, a new method based on fuzzy programming approach is proposed in this paper. Considering the whole operation efficiency of the intersection comprehensively, traffic capacity, vehicle cycle delay, cycle stops, and exhaust emission are chosen as optimization goals to establish a multiobjective function first. Then fuzzy compromise programming approach is employed to give different weight coefficients to various optimization objectives for different traffic flow ratios states. And then the multiobjective function is converted to a single objective function. By using genetic algorithm, the optimized signal cycle and effective green time can be obtained. Finally, the performance of the traditional method and new method proposed in this paper is compared and analyzed through VISSIM software. It can be concluded that the signal timing optimized in this paper can effectively reduce vehicle delays and stops, which can improve traffic capacity of the intersection as well.

On the Lasserre hierarchy of semidefinite programming relaxations of convex polynomial optimization problems

NARCIS (Netherlands)

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

Multiphase Return Trajectory Optimization Based on Hybrid Algorithm

Directory of Open Access Journals (Sweden)

Yi Yang

2016-01-01

Full Text Available A hybrid trajectory optimization method consisting of Gauss pseudospectral method (GPM and natural computation algorithm has been developed and utilized to solve multiphase return trajectory optimization problem, where a phase is defined as a subinterval in which the right-hand side of the differential equation is continuous. GPM converts the optimal control problem to a nonlinear programming problem (NLP, which helps to improve calculation accuracy and speed of natural computation algorithm. Through numerical simulations, it is found that the multiphase optimal control problem could be solved perfectly.

Dynamic programming approach to optimization of approximate decision rules

KAUST Repository

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

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

Science.gov (United States)

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.

Large-scale hydropower system optimization using dynamic programming and object-oriented programming: the case of the Northeast China Power Grid.

Science.gov (United States)

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.

76 FR 67759 - Announcement of Funding Awards for the Self-Help Homeownership Opportunity Program (SHOP) for...

Science.gov (United States)

2011-11-02

... required. The SHOP funds together with the sweat equity and volunteer labor contributions significantly... Awards for the Self-Help Homeownership Opportunity Program (SHOP) for Fiscal Year 2011 AGENCY: Office of... Opportunity Program (SHOP). This announcement contains the consolidated names and addresses of this year's...

Optimization of axial enrichment and gadolinia distributions for BWR fuel under control rod programming, (2)

International Nuclear Information System (INIS)

Hida, Kazuki; Yoshioka, Ritsuo

1992-01-01

A method has been developed for optimizing the axial enrichment and gadolinia distributions for the reload BWR fuel under control rod programming. The problem was to minimize the enrichment requirement subject to the criticality and axial power peaking constraints. The optimization technique was based on the successive linear programming method, each linear programming problem being solved by a goal programming algorithm. A rapid and practically accurate core neutronics model, named the modified one-dimensional core model, was developed to describe the batch-averaged burnup behavior of the reload fuel. A core burnup simulation algorithm, employing a burnup-power-void iteration, was also developed to calculate the rigorous equilibrium cycle performance. This method was applied to the optimization of axial two- and 24-region fuels for demonstrative purposes. The optimal solutions for both fuels have proved the optimality of what is called burnup shape optimization spectral shift. For the two-region fuel with a practical power peaking of 1.4, the enrichment distribution was nearly uniform, because a bottom-peaked burnup shape flattens the axial power shape. Optimization of the 24-region fuel has shown a potential improvement in BWR fuel cycle economics, which will guide future advancement in BWR fuel designs. (author)

Emotionally Troubled Teens' Help-Seeking Behaviors: An Evaluation of Surviving the Teens® Suicide Prevention and Depression Awareness Program

Science.gov (United States)

Strunk, Catherine M.; Sorter, Michael T.; Ossege, Julianne; King, Keith A.

2014-01-01

Many school-based suicide prevention programs do not show a positive impact on help-seeking behaviors among emotionally troubled teens despite their being at high risk for suicide. This study is a secondary analysis of the Surviving the Teens® program evaluation to determine its effect on help-seeking behaviors among troubled youth. Results showed…

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

Optimal blood glucose level control using dynamic programming based on minimal Bergman model

Science.gov (United States)

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.

76 FR 48876 - Announcement of Funding Awards for the Self-Help Homeownership Opportunity Program (SHOP) for...

Science.gov (United States)

2011-08-09

... labor is also required. The SHOP funds together with the sweat equity and volunteer labor contributions... Awards for the Self-Help Homeownership Opportunity Program (SHOP) for Fiscal Year 2010 AGENCY: Office of... Opportunity Program (SHOP). This announcement contains the consolidated names and addresses of this year's...

Computer Program for Analysis, Design and Optimization of Propulsion, Dynamics, and Kinematics of Multistage Rockets

Science.gov (United States)

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.

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....

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.

APPLYING ROBUST RANKING METHOD IN TWO PHASE FUZZY OPTIMIZATION LINEAR PROGRAMMING PROBLEMS (FOLPP

Directory of Open Access Journals (Sweden)

Monalisha Pattnaik

2014-12-01

Full Text Available Background: This paper explores the solutions to the fuzzy optimization linear program problems (FOLPP where some parameters are fuzzy numbers. In practice, there are many problems in which all decision parameters are fuzzy numbers, and such problems are usually solved by either probabilistic programming or multi-objective programming methods. Methods: In this paper, using the concept of comparison of fuzzy numbers, a very effective method is introduced for solving these problems. This paper extends linear programming based problem in fuzzy environment. With the problem assumptions, the optimal solution can still be theoretically solved using the two phase simplex based method in fuzzy environment. To handle the fuzzy decision variables can be initially generated and then solved and improved sequentially using the fuzzy decision approach by introducing robust ranking technique. Results and conclusions: The model is illustrated with an application and a post optimal analysis approach is obtained. The proposed procedure was programmed with MATLAB (R2009a version software for plotting the four dimensional slice diagram to the application. Finally, numerical example is presented to illustrate the effectiveness of the theoretical results, and to gain additional managerial insights. 

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...

Power Grid Construction Project Portfolio Optimization Based on Bi-level programming model

Science.gov (United States)

Zhao, Erdong; Li, Shangqi

2017-08-01

As the main body of power grid operation, county-level power supply enterprises undertake an important emission to guarantee the security of power grid operation and safeguard social power using order. The optimization of grid construction projects has been a key issue of power supply capacity and service level of grid enterprises. According to the actual situation of power grid construction project optimization of county-level power enterprises, on the basis of qualitative analysis of the projects, this paper builds a Bi-level programming model based on quantitative analysis. The upper layer of the model is the target restriction of the optimal portfolio; the lower layer of the model is enterprises’ financial restrictions on the size of the enterprise project portfolio. Finally, using a real example to illustrate operation proceeding and the optimization result of the model. Through qualitative analysis and quantitative analysis, the bi-level programming model improves the accuracy and normative standardization of power grid enterprises projects.

Optimizing the creation of base populations for aquaculture breeding programs using phenotypic and genomic data and its consequences on genetic progress.

Science.gov (United States)

Fernández, Jesús; Toro, Miguel Á; Sonesson, Anna K; Villanueva, Beatriz

2014-01-01

The success of an aquaculture breeding program critically depends on the way in which the base population of breeders is constructed since all the genetic variability for the traits included originally in the breeding goal as well as those to be included in the future is contained in the initial founders. Traditionally, base populations were created from a number of wild strains by sampling equal numbers from each strain. However, for some aquaculture species improved strains are already available and, therefore, mean phenotypic values for economically important traits can be used as a criterion to optimize the sampling when creating base populations. Also, the increasing availability of genome-wide genotype information in aquaculture species could help to refine the estimation of relationships within and between candidate strains and, thus, to optimize the percentage of individuals to be sampled from each strain. This study explores the advantages of using phenotypic and genome-wide information when constructing base populations for aquaculture breeding programs in terms of initial and subsequent trait performance and genetic diversity level. Results show that a compromise solution between diversity and performance can be found when creating base populations. Up to 6% higher levels of phenotypic performance can be achieved at the same level of global diversity in the base population by optimizing the selection of breeders instead of sampling equal numbers from each strain. The higher performance observed in the base population persisted during 10 generations of phenotypic selection applied in the subsequent breeding program.

Near-Optimal Tracking Control of Mobile Robots Via Receding-Horizon Dual Heuristic Programming.

Science.gov (United States)

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.

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.

Performance comparison of genetic algorithms and particle swarm optimization for model integer programming bus timetabling problem

Science.gov (United States)

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.

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.

Fuzzy preference based interactive fuzzy physical programming and its application in multi-objective optimization

International Nuclear Information System (INIS)

Zhang, Xu; Huang, Hong Zhong; Yu, Lanfeng

2006-01-01

Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer's preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer

Reverse Osmosis Optimization

Energy Technology Data Exchange (ETDEWEB)

McMordie Stoughton, Kate; Duan, Xiaoli; Wendel, Emily M.

2013-08-26

This technology evaluation was prepared by Pacific Northwest National Laboratory on behalf of the U.S. Department of Energy’s Federal Energy Management Program (FEMP). ¬The technology evaluation assesses techniques for optimizing reverse osmosis (RO) systems to increase RO system performance and water efficiency. This evaluation provides a general description of RO systems, the influence of RO systems on water use, and key areas where RO systems can be optimized to reduce water and energy consumption. The evaluation is intended to help facility managers at Federal sites understand the basic concepts of the RO process and system optimization options, enabling them to make informed decisions during the system design process for either new projects or recommissioning of existing equipment. This evaluation is focused on commercial-sized RO systems generally treating more than 80 gallons per hour.¬

Reverse Osmosis Optimization

Energy Technology Data Exchange (ETDEWEB)

None

2013-08-01

This technology evaluation was prepared by Pacific Northwest National Laboratory on behalf of the U.S. Department of Energy’s Federal Energy Management Program (FEMP). The technology evaluation assesses techniques for optimizing reverse osmosis (RO) systems to increase RO system performance and water efficiency. This evaluation provides a general description of RO systems, the influence of RO systems on water use, and key areas where RO systems can be optimized to reduce water and energy consumption. The evaluation is intended to help facility managers at Federal sites understand the basic concepts of the RO process and system optimization options, enabling them to make informed decisions during the system design process for either new projects or recommissioning of existing equipment. This evaluation is focused on commercial-sized RO systems generally treating more than 80 gallons per hour.

Fire-tube immersion heater optimization program and field heater audit program

Energy Technology Data Exchange (ETDEWEB)

Croteau, P. [Petro-Canada, Calgary, AB (Canada)

2007-07-01

This presentation provided an overview of the top 5 priorities for emission reduction and eco-efficiency by the Petroleum Technology Alliance of Canada (PTAC). These included venting of methane emissions; fuel consumption in reciprocating engines; fuel consumption in fired heaters; flaring and incineration; and fugitive emissions. It described the common concern for many upstream operating companies as being energy consumption associated with immersion heaters. PTAC fire-tube heater and line heater studies were presented. Combustion efficiency was discussed in terms of excess air, fire-tube selection, heat flux rate, and reliability guidelines. Other topics included heat transfer and fire-tube design; burner selection; burner duty cycle; heater tune up inspection procedure; and insulation. Two other programs were also discussed, notably a Petro-Canada fire-tube immersion heater optimization program and the field audit program run by Natural Resources Canada. It was concluded that improved efficiency involves training; managing excess air in combustion; managing the burner duty cycle; striving for 82 per cent combustion efficiency; and providing adequate insulation to reduce energy demand. tabs., figs.

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.

Extensions of Dynamic Programming: Decision Trees, Combinatorial Optimization, and Data Mining

KAUST Repository

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

KAUST Repository

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.

Optimizing diffusion of an online computer tailored lifestyle program: a study protocol

Directory of Open Access Journals (Sweden)

Schulz Daniela N

2011-06-01

Full Text Available Abstract Background Although the Internet is a promising medium to offer lifestyle interventions to large amounts of people at relatively low costs and effort, actual exposure rates of these interventions fail to meet the high expectations. Since public health impact of interventions is determined by intervention efficacy and level of exposure to the intervention, it is imperative to put effort in optimal dissemination. The present project attempts to optimize the dissemination process of a new online computer tailored generic lifestyle program by carefully studying the adoption process and developing a strategy to achieve sustained use of the program. Methods/Design A prospective study will be conducted to yield relevant information concerning the adoption process by studying the level of adoption of the program, determinants involved in adoption and characteristics of adopters and non-adopters as well as satisfied and unsatisfied users. Furthermore, a randomized control trial will be conducted to the test the effectiveness of a proactive strategy using periodic e-mail prompts in optimizing sustained use of the new program. Discussion Closely mapping the adoption process will gain insight in characteristics of adopters and non-adopters and satisfied and unsatisfied users. This insight can be used to further optimize the program by making it more suitable for a wider range of users, or to develop adjusted interventions to attract subgroups of users that are not reached or satisfied with the initial intervention. Furthermore, by studying the effect of a proactive strategy using period prompts compared to a reactive strategy to stimulate sustained use of the intervention and, possibly, behaviour change, specific recommendations on the use and the application of prompts in online lifestyle interventions can be developed. Trial registration Dutch Trial Register NTR1786 and Medical Ethics Committee of Maastricht University and the University Hospital

Can Parents Treat their Anxious Child using CBT? A Brief Report of a Self-Help Program

DEFF Research Database (Denmark)

Esbjørn, Barbara Hoff; Christiansen, Bianca Munkebo; Walczak, Monika Anna

2016-01-01

Objective: We developed and tested a self-help program with minimal therapist involvement for parents of anxious children. Method: The program focused on transfer of control from therapist to parents of children with moderate anxiety, and consisted of two therapist-led workshops, a Facebook group......, and Cool Kids manuals for parents and children. The sample consisted of 20 families, and 17 completed treatment. Results: After treatment, intent-to-treat analyses indicated that 65% of the children were free of all anxiety disorders. The corresponding figure for completers was 76.5%. Conclusion: Our...... results suggest that parent-based self-help groups focusing on transfer of control may be a cost-effective way of providing treatment to children with moderate anxiety...

Research on numerical method for multiple pollution source discharge and optimal reduction program

Science.gov (United States)

Li, Mingchang; Dai, Mingxin; Zhou, Bin; Zou, Bin

2018-03-01

In this paper, the optimal method for reduction program is proposed by the nonlinear optimal algorithms named that genetic algorithm. The four main rivers in Jiangsu province, China are selected for reducing the environmental pollution in nearshore district. Dissolved inorganic nitrogen (DIN) is studied as the only pollutant. The environmental status and standard in the nearshore district is used to reduce the discharge of multiple river pollutant. The research results of reduction program are the basis of marine environmental management.

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.

The Tobacco-Free Village Program: Helping Rural Areas Implement and Achieve Goals of Tobacco Control Policies in India.

Science.gov (United States)

Chatterjee, Nilesh; Patil, Deepak; Kadam, Rajashree; Fernandes, Genevie

2017-09-27

India has 274 million tobacco users and a tobacco use prevalence of 38% in rural areas. Tobacco consumption causes 1 million deaths and costs the health system nearly US$23 billion annually. Tobacco control policies exist but lack proper implementation. In this article, we review the Tobacco-free Village (TfV) program conducted in Maharashtra state in India and describe its process to help villages in rural India achieve "tobacco-free" status (i.e., the sale and use of tobacco are prohibited by law). We reviewed program documents and conducted 22 qualitative interviews with program staff and village-level stakeholders. From 2008 to 2014, Salaam Mumbai Foundation implemented the TfV program in 60 villages in Maharashtra state. The program used a number of strategies to help villages become tobacco free, including collaborating with a community-based organization, leveraging existing health workers, conducting a situation analysis, training health workers, engaging stakeholders, developing TfV assessment criteria, mobilizing the community, conducting health education, imposing sanctions, and offering incentives. By 2014, 4 villages had achieved tobacco-free status according to 11 assessment criteria. Successful villages demonstrated strong local leader involvement, ownership of the program, and commitment to the cause by residents. The TfV program faced barriers including poor motivation of health workers, difficulty in changing social norms of tobacco use, and refusal of local vendors to stop tobacco sales due to financial losses. This low-cost, community-driven program holds promise for helping public health practitioners and governments implement and achieve the goals of tobacco control policies, especially in resource-scarce settings. © Chatterjee et al.

Learners Programming Language a Helping System for Introductory Programming Courses

Directory of Open Access Journals (Sweden)

MUHAMMAD SHUMAIL NAVEED

2016-07-01

Full Text Available Programming is the core of computer science and due to this momentousness a special care is taken in designing the curriculum of programming courses. A substantial work has been conducted on the definition of programming courses, yet the introductory programming courses are still facing high attrition, low retention and lack of motivation. This paper introduced a tiny pre-programming language called LPL (Learners Programming Language as a ZPL (Zeroth Programming Language to illuminate novice students about elementary concepts of introductory programming before introducing the first imperative programming course. The overall objective and design philosophy of LPL is based on a hypothesis that the soft introduction of a simple and paradigm specific textual programming can increase the motivation level of novice students and reduce the congenital complexities and hardness of the first programming course and eventually improve the retention rate and may be fruitful in reducing the dropout/failure level. LPL also generates the equivalent high level programs from user source program and eventually very fruitful in understanding the syntax of introductory programming languages. To overcome the inherent complexities of unusual and rigid syntax of introductory programming languages, the LPL provide elementary programming concepts in the form of algorithmic and plain natural language based computational statements. The initial results obtained after the introduction of LPL are very encouraging in motivating novice students and improving the retention rate.

Investigating the Optimal Management Strategy for a Healthcare Facility Maintenance Program

National Research Council Canada - National Science Library

Gaillard, Daria

2004-01-01

...: strategic partnering with an equipment management firm. The objective of this study is to create a decision-model for selecting the optimal management strategy for a healthcare organization's facility maintenance program...

Dynamic programming for optimization of timber production and grazing in ponderosa pine

Science.gov (United States)

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...

Optimal Diet Planning for Eczema Patient Using Integer Programming

Science.gov (United States)

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.

Impact of Demand Response Programs on Optimal Operation of Multi-Microgrid System

Directory of Open Access Journals (Sweden)

Anh-Duc Nguyen

2018-06-01

Full Text Available The increased penetration of renewables is beneficial for power systems but it poses several challenges, i.e., uncertainty in power supply, power quality issues, and other technical problems. Backup generators or storage system have been proposed to solve this problem but there are limitations remaining due to high installation and maintenance cost. Furthermore, peak load is also an issue in the power distribution system. Due to the adjustable characteristics of loads, strategies on demand side such as demand response (DR are more appropriate in order to deal with these challenges. Therefore, this paper studies how DR programs influence the operation of the multi-microgrid (MMG. The implementation is executed based on a hierarchical energy management system (HiEMS including microgrid EMSs (MG-EMSs responsible for local optimization in each MG and community EMS (C-EMS responsible for community optimization in the MMG. Mixed integer linear programming (MILP-based mathematical models are built for MMG optimal operation. Five scenarios consisting of single DR programs and DR groups are tested in an MMG test system to evaluate their impact on MMG operation. Among the five scenarios, some DR programs apply curtailing strategies, resulting in a study about the influence of base load value and curtailable load percentage on the amount of curtailed load and shifted load as well as the operation cost of the MMG. Furthermore, the impact of DR programs on the amount of external and internal trading power in the MMG is also examined. In summary, each individual DR program or group could be handy in certain situations depending on the interest of the MMG such as external trading, self-sufficiency or operation cost minimization.

Ckmeans.1d.dp: Optimal k-means Clustering in One Dimension by Dynamic Programming.

Science.gov (United States)

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.

A Constraint Programming Model for Fast Optimal Stowage of Container Vessel Bays

DEFF Research Database (Denmark)

Delgado-Ortegon, Alberto; Jensen, Rune Møller; Janstrup, Kira

2012-01-01

Container vessel stowage planning is a hard combinatorial optimization problem with both high economic and environmental impact. We have developed an approach that often is able to generate near-optimal plans for large container vessels within a few minutes. It decomposes the problem into a master...... planning phase that distributes the containers to bay sections and a slot planning phase that assigns containers of each bay section to slots. In this paper, we focus on the slot planning phase of this approach and present a constraint programming and integer programming model for stowing a set...... of containers in a single bay section. This so-called slot planning problem is NP-hard and often involves stowing several hundred containers. Using state-of-the-art constraint solvers and modeling techniques, however, we were able to solve 90% of 236 real instances from our industrial collaborator to optimality...

Optimal traffic control in highway transportation networks using linear programming

KAUST Repository

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

A Quantitative Optimization Framework for Market-Driven Academic Program Portfolios

NARCIS (Netherlands)

Burgher, Joshua; Hamers, Herbert

2017-01-01

We introduce a quantitative model that can be used for decision support for planning and optimizing the composition of portfolios of market-driven academic programs within the context of higher education. This model is intended to enable leaders in colleges and universities to maximize financial

Designing optimal food intake patterns to achieve nutritional goals for Japanese adults through the use of linear programming optimization models.

Science.gov (United States)

Okubo, Hitomi; Sasaki, Satoshi; Murakami, Kentaro; Yokoyama, Tetsuji; Hirota, Naoko; Notsu, Akiko; Fukui, Mitsuru; Date, Chigusa

2015-06-06

Simultaneous dietary achievement of a full set of nutritional recommendations is difficult. Diet optimization model using linear programming is a useful mathematical means of translating nutrient-based recommendations into realistic nutritionally-optimal food combinations incorporating local and culture-specific foods. We used this approach to explore optimal food intake patterns that meet the nutrient recommendations of the Dietary Reference Intakes (DRIs) while incorporating typical Japanese food selections. As observed intake values, we used the food and nutrient intake data of 92 women aged 31-69 years and 82 men aged 32-69 years living in three regions of Japan. Dietary data were collected with semi-weighed dietary record on four non-consecutive days in each season of the year (16 days total). The linear programming models were constructed to minimize the differences between observed and optimized food intake patterns while also meeting the DRIs for a set of 28 nutrients, setting energy equal to estimated requirements, and not exceeding typical quantities of each food consumed by each age (30-49 or 50-69 years) and gender group. We successfully developed mathematically optimized food intake patterns that met the DRIs for all 28 nutrients studied in each sex and age group. Achieving nutritional goals required minor modifications of existing diets in older groups, particularly women, while major modifications were required to increase intake of fruit and vegetables in younger groups of both sexes. Across all sex and age groups, optimized food intake patterns demanded greatly increased intake of whole grains and reduced-fat dairy products in place of intake of refined grains and full-fat dairy products. Salt intake goals were the most difficult to achieve, requiring marked reduction of salt-containing seasoning (65-80%) in all sex and age groups. Using a linear programming model, we identified optimal food intake patterns providing practical food choices and

Cost Conscious: Incentive and Discount Programs Help Students Meet the Rising Cost of a Community College Education

Science.gov (United States)

Ullman, Ellen

2013-01-01

Aware that rising costs could force some community colleges to compromise their long-standing open-door policies, administrators have put in place programs and incentives to offset the higher price of the average community college education. This article features ideas and programs to help struggling community colleges cope with rising costs such…

Regulation of Dynamical Systems to Optimal Solutions of Semidefinite Programs: Algorithms and Applications to AC Optimal Power Flow

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.

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

A Constraint programming-based genetic algorithm for capacity output optimization

Directory of Open Access Journals (Sweden)

Kate Ean Nee Goh

2014-10-01

Full Text Available Purpose: The manuscript presents an investigation into a constraint programming-based genetic algorithm for capacity output optimization in a back-end semiconductor manufacturing company.Design/methodology/approach: In the first stage, constraint programming defining the relationships between variables was formulated into the objective function. A genetic algorithm model was created in the second stage to optimize capacity output. Three demand scenarios were applied to test the robustness of the proposed algorithm.Findings: CPGA improved both the machine utilization and capacity output once the minimum requirements of a demand scenario were fulfilled. Capacity outputs of the three scenarios were improved by 157%, 7%, and 69%, respectively.Research limitations/implications: The work relates to aggregate planning of machine capacity in a single case study. The constraints and constructed scenarios were therefore industry-specific.Practical implications: Capacity planning in a semiconductor manufacturing facility need to consider multiple mutually influenced constraints in resource availability, process flow and product demand. The findings prove that CPGA is a practical and an efficient alternative to optimize the capacity output and to allow the company to review its capacity with quick feedback.Originality/value: The work integrates two contemporary computational methods for a real industry application conventionally reliant on human judgement.

Optimization of Thermal Object Nonlinear Control Systems by Energy Efficiency Criterion.

Science.gov (United States)

Velichkin, Vladimir A.; Zavyalov, Vladimir A.

2018-03-01

This article presents the results of thermal object functioning control analysis (heat exchanger, dryer, heat treatment chamber, etc.). The results were used to determine a mathematical model of the generalized thermal control object. The appropriate optimality criterion was chosen to make the control more energy-efficient. The mathematical programming task was formulated based on the chosen optimality criterion, control object mathematical model and technological constraints. The “maximum energy efficiency” criterion helped avoid solving a system of nonlinear differential equations and solve the formulated problem of mathematical programming in an analytical way. It should be noted that in the case under review the search for optimal control and optimal trajectory reduces to solving an algebraic system of equations. In addition, it is shown that the optimal trajectory does not depend on the dynamic characteristics of the control object.

Optimizing a mobile robot control system using GPU acceleration

Science.gov (United States)

Tuck, Nat; McGuinness, Michael; Martin, Fred

2012-01-01

This paper describes our attempt to optimize a robot control program for the Intelligent Ground Vehicle Competition (IGVC) by running computationally intensive portions of the system on a commodity graphics processing unit (GPU). The IGVC Autonomous Challenge requires a control program that performs a number of different computationally intensive tasks ranging from computer vision to path planning. For the 2011 competition our Robot Operating System (ROS) based control system would not run comfortably on the multicore CPU on our custom robot platform. The process of profiling the ROS control program and selecting appropriate modules for porting to run on a GPU is described. A GPU-targeting compiler, Bacon, is used to speed up development and help optimize the ported modules. The impact of the ported modules on overall performance is discussed. We conclude that GPU optimization can free a significant amount of CPU resources with minimal effort for expensive user-written code, but that replacing heavily-optimized library functions is more difficult, and a much less efficient use of time.

Particle swarm optimization for programming deep brain stimulation arrays.

Science.gov (United States)

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

Science.gov (United States)

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

An overview of the Douglas Aircraft Company Aeroelastic Design Optimization Program (ADOP)

Science.gov (United States)

Dodd, Alan J.

1989-01-01

From a program manager's viewpoint, the history, scope and architecture of a major structural design program at Douglas Aircraft Company called Aeroelastic Design Optimization Program (ADOP) are described. ADOP was originally intended for the rapid, accurate, cost-effective evaluation of relatively small structural models at the advanced design level, resulting in improved proposal competitiveness and avoiding many costly changes later in the design cycle. Before release of the initial version in November 1987, however, the program was expanded to handle very large production-type analyses.

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.

Detection of imminent vein graft occlusion: what is the optimal surveillance program?

Science.gov (United States)

Tinder, Chelsey N; Bandyk, Dennis F

2009-12-01

The prediction of infrainguinal vein bypass failure remains an inexact judgment. Patient demographics, technical factors, and vascular laboratory graft surveillance testing are helpful in identifying a high-risk graft cohort. The optimal surveillance program to detect the bypass at risk for imminent occlusion continues to be developed, but required elements are known and include clinical assessment for new or changes in limb ischemia symptoms, measurement of ankle and/or toe systolic pressure, and duplex ultrasound imaging of the bypass graft. Duplex ultrasound assessment of bypass hemodynamics may be the most accurate method to detect imminent vein graft occlusion. The finding of low graft flow during intraoperative assessment or at a scheduled surveillance study predicts failure; and if associated with an occlusive lesion, a graft revision can prolong patency. The most common abnormality producing graft failure is conduit stenosis caused by myointimal hyperplasia; and the majority can be repaired by an endovascular intervention. Frequency of testing to detect the failing bypass should be individualized to the patient, the type of arterial bypass, and prior duplex ultrasound scan findings. The focus of surveillance is on identification of the low-flow arterial bypass and timely repair of detected critical stenosis defined by duplex velocity spectra criteria of a peak systolic velocity 300 cm/s and peak systolic velocity ratio across the stenosis >3.5-correlating with >70% diameter-reducing stenosis. When conducted appropriately, a graft surveillance program should result in an unexpected graft failure rate of <3% per year.

Optimization of fuel-cell tram operation based on two dimension dynamic programming

Science.gov (United States)

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.

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

Science.gov (United States)

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.

Learning From Our Past: How a Vietnam-Era Pacification Program Can Help Us Win in Afghanistan

Science.gov (United States)

2009-09-01

it as an operational and management problem—it was everybody’s business and nobody’s. It fell between the cracks . The reason I began zeroing in on...kind of help from us.”264 Farming programs can also help revive the economy by growing what other nations want to import: pomegranates , almonds...pistachios, raisins, and fruits such as apricots that can be dried or turned into juice.265 The Agribusiness Development Teams manned by state- based

Application of linear programming and perturbation theory in optimization of fuel utilization in a nuclear reactor

International Nuclear Information System (INIS)

Zavaljevski, N.

1985-01-01

Proposed optimization procedure is fast due to application of linear programming. Non-linear constraints which demand iterative application of linear programming are slowing down the calculation. Linearization can be done by different procedures starting from simple empirical rules for fuel in-core management to complicated general perturbation theory with higher order of corrections. A mathematical model was formulated for optimization of improved fuel cycle. A detailed algorithm for determining minimum of fresh fuel at the beginning of each fuel cycle is shown and the problem is linearized by first order perturbation theory and it is optimized by linear programming. Numerical illustration of the proposed method was done for the experimental reactor mostly for saving computer time

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

Optimization and industry new frontiers

CERN Document Server

Korotkikh, Victor

2003-01-01

Optimization from Human Genes to Cutting Edge Technologies The challenges faced by industry today are so complex that they can only be solved through the help and participation of optimization ex­ perts. For example, many industries in e-commerce, finance, medicine, and engineering, face several computational challenges due to the mas­ sive data sets that arise in their applications. Some of the challenges include, extended memory algorithms and data structures, new program­ ming environments, software systems, cryptographic protocols, storage devices, data compression, mathematical and statistical methods for knowledge mining, and information visualization. With advances in computer and information systems technologies, and many interdisci­ plinary efforts, many of the "data avalanche challenges" are beginning to be addressed. Optimization is the most crucial component in these efforts. Nowadays, the main task of optimization is to investigate the cutting edge frontiers of these technologies and systems ...

What We Know about Guided Pathways: Helping Students to Complete Programs Faster. Research Overview

Science.gov (United States)

Bailey, Thomas; Jaggars, Shanna Smith; Jenkins, Davis

2015-01-01

The idea behind guided pathways is straightforward. College students are more likely to complete a degree in a timely fashion if they choose a program and develop an academic plan early on, have a clear road map of the courses they need to take to complete a credential, and receive guidance and support to help them stay on plan. However, most…

A Hybrid Programming Framework for Modeling and Solving Constraint Satisfaction and Optimization Problems

OpenAIRE

Sitek, Paweł; Wikarek, Jarosław

2016-01-01

This paper proposes a hybrid programming framework for modeling and solving of constraint satisfaction problems (CSPs) and constraint optimization problems (COPs). Two paradigms, CLP (constraint logic programming) and MP (mathematical programming), are integrated in the framework. The integration is supplemented with the original method of problem transformation, used in the framework as a presolving method. The transformation substantially reduces the feasible solution space. The framework a...

Optimization of in-vivo monitoring program for radiation emergency response

Energy Technology Data Exchange (ETDEWEB)

Ha, Wi Ho; Kim, Jong Kyung [Dept. of Nuclear Engineering, Hanyang University, Seoul (Korea, Republic of)

2016-12-15

In case of radiation emergencies, internal exposure monitoring for the members of public will be required to confirm internal contamination of each individual. In-vivo monitoring technique using portable gamma spectrometer can be easily applied for internal exposure monitoring in the vicinity of the on-site area. In this study, minimum detectable doses (MDDs) for '1'3'4Cs, {sup 137}Cs, and {sup 131}I were calculated adjusting minimum detectable activities (MDAs) from 50 to 1,000 Bq to find out the optimal in-vivo counting condition. DCAL software was used to derive retention fraction of Cs and I isotopes in the whole body and thyroid, respectively. A minimum detectable level was determined to set committed effective dose of 0.1 mSv for emergency response. We found that MDDs at each MDA increased along with the elapsed time. 1,000 Bq for {sup 134}Cs and {sup 137}Cs, and 100 Bq for {sup 131}I were suggested as optimal MDAs to provide in-vivo monitoring service in case of radiation emergencies. In-vivo monitoring program for emergency response should be designed to achieve the optimal MDA suggested from the present work. We expect that a reduction of counting time compared with routine monitoring program can achieve the high throughput system in case of radiation emergencies.

Development of a Positive Youth Development Program: Helping Parents to Improve Their Parenting Skills

Directory of Open Access Journals (Sweden)

Daniel T.L. Shek

2006-01-01

Full Text Available The Project P.A.T.H.S. (Positive Adolescent Training through Holistic Social Programs is a positive youth development program that attempts to promote holistic development in adolescents in Hong Kong. In the Tier 2 Program of this project, social workers are expected to develop positive youth development programs for adolescents having greater psychosocial needs. They are required to submit proposals that will be evaluated in terms of whether the proposals are evidence based, and appropriate evaluation mechanisms are included. With reference to the literature on parental control processes that Chinese parents may be loose in their behavioral control and they tend to overemphasize academic excellence, it is argued that improvement of the parenting skills of parents of Chinese adolescents is an important area to be addressed. To facilitate social workers to prepare the related proposals, a sample proposal on how to improve the parenting skills of Chinese parents is described, including its conceptual framework, proposed program, and evaluation plan. It is argued that this supportive approach (i.e., preparation of a sample proposal can help social workers to develop quality proposals on positive youth development programs in Hong Kong.

Top Textbooks on Reserve: Creating, Promoting, and Assessing a Program to Help Meet Students' Need for Affordable Textbooks

Science.gov (United States)

Thompson, Hilary H.; Cotton, Jennifer E. M.

2017-01-01

In Fall 2014 the University of Maryland Libraries launched a textbook reserves program to help relieve the burden of high textbook costs on students. Although its initial performance was lackluster, workflow refinements and expanded promotion greatly improved usage, resulting in a tenfold increase in circulation and expansion of the program. This…

Mass Optimization of Battery/Supercapacitors Hybrid Systems Based on a Linear Programming Approach

Science.gov (United States)

Fleury, Benoit; Labbe, Julien

2014-08-01

The objective of this paper is to show that, on a specific launcher-type mission profile, a 40% gain of mass is expected using a battery/supercapacitors active hybridization instead of a single battery solution. This result is based on the use of a linear programming optimization approach to perform the mass optimization of the hybrid power supply solution.

A Promising Tool for Helping Vulnerable Workers? An Exploration of the Use of Employee Assistance Programs (EAPs) to Help Low-Wage Workers on College Campuses

Science.gov (United States)

Hahn, Andrew B.

2005-01-01

Employee assistance programs, or EAPs, are an employee benefit designed to help workers meet their work and family needs. However, questions have been raised about the design, utilization, and scale of services that EAPs make possible for low-wage workers. This article explores whether on college campuses an EAP benefit can simultaneously meet the…

Applications of sub-optimality in dynamic programming to location and construction of nuclear fuel processing plant

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

Conditions for characterizing the structure of optimal strategies in infinite-horizon dynamic programs

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 Input Design for Aircraft Parameter Estimation using Dynamic Programming Principles

Science.gov (United States)

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.

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

Science.gov (United States)

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

An efficient inverse radiotherapy planning method for VMAT using quadratic programming optimization.

Science.gov (United States)

Hoegele, W; Loeschel, R; Merkle, N; Zygmanski, P

2012-01-01

The purpose of this study is to investigate the feasibility of an inverse planning optimization approach for the Volumetric Modulated Arc Therapy (VMAT) based on quadratic programming and the projection method. The performance of this method is evaluated against a reference commercial planning system (eclipse(TM) for rapidarc(TM)) for clinically relevant cases. The inverse problem is posed in terms of a linear combination of basis functions representing arclet dose contributions and their respective linear coefficients as degrees of freedom. MLC motion is decomposed into basic motion patterns in an intuitive manner leading to a system of equations with a relatively small number of equations and unknowns. These equations are solved using quadratic programming under certain limiting physical conditions for the solution, such as the avoidance of negative dose during optimization and Monitor Unit reduction. The modeling by the projection method assures a unique treatment plan with beneficial properties, such as the explicit relation between organ weightings and the final dose distribution. Clinical cases studied include prostate and spine treatments. The optimized plans are evaluated by comparing isodose lines, DVH profiles for target and normal organs, and Monitor Units to those obtained by the clinical treatment planning system eclipse(TM). The resulting dose distributions for a prostate (with rectum and bladder as organs at risk), and for a spine case (with kidneys, liver, lung and heart as organs at risk) are presented. Overall, the results indicate that similar plan qualities for quadratic programming (QP) and rapidarc(TM) could be achieved at significantly more efficient computational and planning effort using QP. Additionally, results for the quasimodo phantom [Bohsung et al., "IMRT treatment planning: A comparative inter-system and inter-centre planning exercise of the estro quasimodo group," Radiother. Oncol. 76(3), 354-361 (2005)] are presented as an example

Stan: A Probabilistic Programming Language for Bayesian Inference and Optimization

Science.gov (United States)

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'…

Optimal traffic control in highway transportation networks using linear programming

KAUST Repository

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.

Mathematical programming model for heat exchanger design through optimization of partial objectives

International Nuclear Information System (INIS)

Onishi, Viviani C.; Ravagnani, Mauro A.S.S.; Caballero, José A.

2013-01-01

Highlights: • Rigorous design of shell-and-tube heat exchangers according to TEMA standards. • Division of the problem into sets of equations that are easier to solve. • Selected heuristic objective functions based on the physical behavior of the problem. • Sequential optimization approach to avoid solutions stuck in local minimum. • The results obtained with this model improved the values reported in the literature. - Abstract: Mathematical programming can be used for the optimal design of shell-and-tube heat exchangers (STHEs). This paper proposes a mixed integer non-linear programming (MINLP) model for the design of STHEs, following rigorously the standards of the Tubular Exchanger Manufacturers Association (TEMA). Bell–Delaware Method is used for the shell-side calculations. This approach produces a large and non-convex model that cannot be solved to global optimality with the current state of the art solvers. Notwithstanding, it is proposed to perform a sequential optimization approach of partial objective targets through the division of the problem into sets of related equations that are easier to solve. For each one of these problems a heuristic objective function is selected based on the physical behavior of the problem. The global optimal solution of the original problem cannot be ensured even in the case in which each of the sub-problems is solved to global optimality, but at least a very good solution is always guaranteed. Three cases extracted from the literature were studied. The results showed that in all cases the values obtained using the proposed MINLP model containing multiple objective functions improved the values presented in the literature

An optimal maintenance policy for machine replacement problem using dynamic programming

OpenAIRE

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...

Cost-benefit study of consumer product take-back programs using IBM's WIT reverse logistics optimization tool

Science.gov (United States)

Veerakamolmal, Pitipong; Lee, Yung-Joon; Fasano, J. P.; Hale, Rhea; Jacques, Mary

2002-02-01

In recent years, there has been increased focus by regulators, manufacturers, and consumers on the issue of product end of life management for electronics. This paper presents an overview of a conceptual study designed to examine the costs and benefits of several different Product Take Back (PTB) scenarios for used electronics equipment. The study utilized a reverse logistics supply chain model to examine the effects of several different factors in PTB programs. The model was done using the IBM supply chain optimization tool known as WIT (Watson Implosion Technology). Using the WIT tool, we were able to determine a theoretical optimal cost scenario for PTB programs. The study was designed to assist IBM internally in determining theoretical optimal Product Take Back program models and determining potential incentives for increasing participation rates.

A first formal link between the price equation and an optimization program.

Science.gov (United States)

Grafen, Alan

2002-07-07

The Darwin unification project is pursued. A meta-model encompassing an important class of population genetic models is formed by adding an abstract model of the number of successful gametes to the Price equation under uncertainty. A class of optimization programs are defined to represent the "individual-as-maximizing-agent analogy" in a general way. It is then shown that for each population genetic model there is a corresponding optimization program with which formal links can be established. These links provide a secure logical foundation for the commonplace biological principle that natural selection leads organisms to act as if maximizing their "fitness", provides a definition of "fitness", and clarifies the limitations of that principle. The situations covered do not include frequency dependence or social behaviour, but the approach is capable of extension.

Enhanced index tracking modeling in portfolio optimization with mixed-integer programming z approach

Science.gov (United States)

Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin

2014-09-01

Enhanced index tracking is a popular form of portfolio management in stock market investment. Enhanced index tracking aims to construct an optimal portfolio to generate excess return over the return achieved by the stock market index without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using mixed-integer programming model which adopts regression approach in order to generate higher portfolio mean return than stock market index return. In this study, the data consists of 24 component stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2012. The results of this study show that the optimal portfolio of mixed-integer programming model is able to generate higher mean return than FTSE Bursa Malaysia Kuala Lumpur Composite Index return with only selecting 30% out of the total stock market index components.

Creating a comprehensive customer service program to help convey critical and acute results of radiology studies.

Science.gov (United States)

Towbin, Alexander J; Hall, Seth; Moskovitz, Jay; Johnson, Neil D; Donnelly, Lane F

2011-01-01

Communication of acute or critical results between the radiology department and referring clinicians has been a deficiency of many radiology departments. The failure to perform or document these communications can lead to poor patient care, patient safety issues, medical-legal issues, and complaints from referring clinicians. To mitigate these factors, a communication and documentation tool was created and incorporated into our departmental customer service program. This article will describe the implementation of a comprehensive customer service program in a hospital-based radiology department. A comprehensive customer service program was created in the radiology department. Customer service representatives were hired to answer the telephone calls to the radiology reading rooms and to help convey radiology results. The radiologists, referring clinicians, and customer service representatives were then linked via a novel workflow management system. This workflow management system provided tools to help facilitate the communication needs of each group. The number of studies with results conveyed was recorded from the implementation of the workflow management system. Between the implementation of the workflow management system on August 1, 2005, and June 1, 2009, 116,844 radiology results were conveyed to the referring clinicians and documented in the system. This accounts for more than 14% of the 828,516 radiology cases performed in this time frame. We have been successful in creating a comprehensive customer service program to convey and document communication of radiology results. This program has been widely used by the ordering clinicians as well as radiologists since its inception.

Fuzzy linear programming based optimal fuel scheduling incorporating blending/transloading facilities

Energy Technology Data Exchange (ETDEWEB)

Djukanovic, M.; Babic, B.; Milosevic, B. [Electrical Engineering Inst. Nikola Tesla, Belgrade (Yugoslavia); Sobajic, D.J. [EPRI, Palo Alto, CA (United States). Power System Control; Pao, Y.H. [Case Western Reserve Univ., Cleveland, OH (United States)]|[AI WARE, Inc., Cleveland, OH (United States)

1996-05-01

In this paper the blending/transloading facilities are modeled using an interactive fuzzy linear programming (FLP), in order to allow the decision-maker to solve the problem of uncertainty of input information within the fuel scheduling optimization. An interactive decision-making process is formulated in which decision-maker can learn to recognize good solutions by considering all possibilities of fuzziness. The application of the fuzzy formulation is accompanied by a careful examination of the definition of fuzziness, appropriateness of the membership function and interpretation of results. The proposed concept provides a decision support system with integration-oriented features, whereby the decision-maker can learn to recognize the relative importance of factors in the specific domain of optimal fuel scheduling (OFS) problem. The formulation of a fuzzy linear programming problem to obtain a reasonable nonfuzzy solution under consideration of the ambiguity of parameters, represented by fuzzy numbers, is introduced. An additional advantage of the FLP formulation is its ability to deal with multi-objective problems.

Specific attitudes which predict psychology students' intentions to seek help for psychological distress.

Science.gov (United States)

Thomas, Susan J; Caputi, Peter; Wilson, Coralie J

2014-03-01

Although many postgraduate psychology programs address students' mental health, there are compelling indications that earlier, undergraduate, interventions may be optimal. We investigated specific attitudes that predict students' intentions to seek treatment for psychological distress to inform targeted interventions. Psychology students (N = 289; mean age = 19.75 years) were surveyed about attitudes and intentions to seek treatment for stress, anxiety, or depression. Less than one quarter of students reported that they would be likely to seek treatment should they develop psychological distress. Attitudes that predicted help-seeking intentions related to recognition of symptoms and the benefits of professional help, and openness to treatment for emotional problems. The current study identified specific attitudes which predict help-seeking intentions in psychology students. These attitudes could be strengthened in undergraduate educational interventions promoting well-being and appropriate treatment uptake among psychology students. © 2013 Wiley Periodicals, Inc.

Optimality Conditions in Vector Optimization

CERN Document Server

Jiménez, Manuel Arana; Lizana, Antonio Rufián

2011-01-01

Vector optimization is continuously needed in several science fields, particularly in economy, business, engineering, physics and mathematics. The evolution of these fields depends, in part, on the improvements in vector optimization in mathematical programming. The aim of this Ebook is to present the latest developments in vector optimization. The contributions have been written by some of the most eminent researchers in this field of mathematical programming. The Ebook is considered essential for researchers and students in this field.

The use of linear programming in optimization of HDR implant dose distributions

International Nuclear Information System (INIS)

Jozsef, Gabor; Streeter, Oscar E.; Astrahan, Melvin A.

2003-01-01

The introduction of high dose rate brachytherapy enabled optimization of dose distributions to be used on a routine basis. The objective of optimization is to homogenize the dose distribution within the implant while simultaneously satisfying dose constraints on certain points. This is accomplished by varying the time the source dwells at different locations. As the dose at any point is a linear function of the dwell times, a linear programming approach seems to be a natural choice. The dose constraints are inherently linear inequalities. Homogeneity requirements are linearized by minimizing the maximum deviation of the doses at points inside the implant from a prescribed dose. The revised simplex method was applied for the solution of this linear programming problem. In the homogenization process the possible source locations were chosen as optimization points. To avoid the problem of the singular value of the dose at a source location from the source itself we define the 'self-contribution' as the dose at a small distance from the source. The effect of varying this distance is discussed. Test cases were optimized for planar, biplanar and cylindrical implants. A semi-irregular, fan-like implant with diverging needles was also investigated. Mean central dose calculation based on 3D Delaunay-triangulation of the source locations was used to evaluate the dose distributions. The optimization method resulted in homogeneous distributions (for brachytherapy). Additional dose constraints--when applied--were satisfied. The method is flexible enough to include other linear constraints such as the inclusion of the centroids of the Delaunay-triangulation for homogenization, or limiting the maximum allowable dwell time

Optimal local dimming for LED-backlit LCD displays via linear programming

DEFF Research Database (Denmark)

Shu, Xiao; Wu, Xiaolin; Forchhammer, Søren

2012-01-01

and the attenuations of LCD pixels. The objective is to minimize the distortion in luminance reproduction due to the leakage of LCD and the coarse granularity of the LED lights. The optimization problem is formulated as one of linear programming, and both exact and approximate algorithms are proposed. Simulation...

CALIBRATION, OPTIMIZATION, AND SENSITIVITY AND UNCERTAINTY ALGORITHMS APPLICATION PROGRAMMING INTERFACE (COSU-API)

Science.gov (United States)

The Application Programming Interface (API) for Uncertainty Analysis, Sensitivity Analysis, and Parameter Estimation (UA/SA/PE API) tool development, here fore referred to as the Calibration, Optimization, and Sensitivity and Uncertainty Algorithms API (COSU-API), was initially d...

Automated design and optimization of flexible booster autopilots via linear programming. Volume 2: User's manual

Science.gov (United States)

Hauser, F. D.; Szollosi, G. D.; Lakin, W. S.

1972-01-01

COEBRA, the Computerized Optimization of Elastic Booster Autopilots, is an autopilot design program. The bulk of the design criteria is presented in the form of minimum allowed gain/phase stability margins. COEBRA has two optimization phases: (1) a phase to maximize stability margins; and (2) a phase to optimize structural bending moment load relief capability in the presence of minimum requirements on gain/phase stability margins.

Parameter identification using optimization techniques in the continuous simulation programs FORSIM and MACKSIM

International Nuclear Information System (INIS)

Carver, M.B.; Austin, C.F.; Ross, N.E.

1980-02-01

This report discusses the mechanics of automated parameter identification in simulation packages, and reviews available integration and optimization algorithms and their interaction within the recently developed optimization options in the FORSIM and MACKSIM simulation packages. In the MACKSIM mass-action chemical kinetics simulation package, the form and structure of the ordinary differential equations involved is known, so the implementation of an optimizing option is relatively straightforward. FORSIM, however, is designed to integrate ordinary and partial differential equations of abritrary definition. As the form of the equations is not known in advance, the design of the optimizing option is more intricate, but the philosophy could be applied to most simulation packages. In either case, however, the invocation of the optimizing interface is simple and user-oriented. Full details for the use of the optimizing mode for each program are given; specific applications are used as examples. (O.T.)

Worst-Case Execution Time Based Optimization of Real-Time Java Programs

DEFF Research Database (Denmark)

Hepp, Stefan; Schoeberl, Martin

2012-01-01

optimization is method in lining. It is especially important for languages, like Java, where small setter and getter methods are considered good programming style. In this paper we present and explore WCET driven in lining of Java methods. We use the WCET analysis tool for the Java processor JOP to guide...

Optimization of hot water transport and distribution networks by analytical method: OPTAL program

International Nuclear Information System (INIS)

Barreau, Alain; Caizergues, Robert; Moret-Bailly, Jean

1977-06-01

This report presents optimization studies of hot water transport and distribution network by minimizing operating cost. Analytical optimization is used: Lagrange's method of undetermined multipliers. Optimum diameter of each pipe is calculated for minimum network operating cost. The characteristics of the computer program used for calculations, OPTAL, are given in this report. An example of network is calculated and described: 52 branches and 27 customers. Results are discussed [fr

Optimization of the Hockey Fans in Training (Hockey FIT) weight loss and healthy lifestyle program for male hockey fans.

Science.gov (United States)

Blunt, Wendy; Gill, Dawn P; Sibbald, Shannon L; Riggin, Brendan; Pulford, Roseanne W; Scott, Ryan; Danylchuk, Karen; Gray, Cindy M; Wyke, Sally; Bunn, Christopher; Petrella, Robert J

2017-11-28

The health outcomes of men continue to be poorer than women globally. Challenges in addressing this problem include difficulties engaging men in weight loss programs as they tend to view these programs as contrary to the masculine narrative of independence and self-reliance. Researchers have been turning towards sports fans to engage men in health promotion programs as sports fans are typically male, and tend to have poor health habits. Developed from the highly successful gender-sensitized Football Fans in Training program, Hockey Fans in Training (Hockey FIT) recruited 80 male hockey fans of the London Knights and Sarnia Sting who were overweight or obese into a weekly, 90-minute classroom education and group exercise program held over 12 weeks; a 40-week minimally-supported phase followed. A process evaluation of the Hockey FIT program was completed alongside a pragmatic randomized controlled trial and outcome evaluation in order to fully explore the acceptability of the Hockey FIT program from the perspectives of coaches delivering and participants engaged in the program. Data sources included attendance records, participant focus groups, coach interviews, assessment of fidelity (program observations and post-session coach reflections), and 12-month participant interviews. Coaches enjoyed delivering the program and found it simple to deliver. Men valued being among others of similar body shape and similar weight loss goals, and found the knowledge they gained through the program helped them to make and maintain health behaviour changes. Suggested improvements include having more hockey-related information and activities, greater flexibility with timing of program delivery, and greater promotion of technology support tools. We confirmed Hockey FIT was an acceptable "gender-sensitized" health promotion program for male hockey fans who were overweight or obese. Minor changes were required for optimization, which will be evaluated in a future definitive trial

Meeting the challenges with the Douglas Aircraft Company Aeroelastic Design Optimization Program (ADOP)

Science.gov (United States)

Rommel, Bruce A.

1989-01-01

An overview of the Aeroelastic Design Optimization Program (ADOP) at the Douglas Aircraft Company is given. A pilot test program involving the animation of mode shapes with solid rendering as well as wire frame displays, a complete aircraft model of a high-altitude hypersonic aircraft to test ADOP procedures, a flap model, and an aero-mesh modeler for doublet lattice aerodynamics are discussed.

NEPTUNE Helping Program Managers Understand Their Program Customers

National Research Council Canada - National Science Library

Uriell, Zannette

2004-01-01

.... This annotated brief outlines some of these studies and discusses in greater detail a recent project that assessed a number of dissimilar programs, leading to the creation of the NEPTUNE System...

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)

Visualising Pareto-optimal trade-offs helps move beyond monetary-only criteria for water management decisions

Science.gov (United States)

Hurford, Anthony; Harou, Julien

2014-05-01

Water related eco-system services are important to the livelihoods of the poorest sectors of society in developing countries. Degradation or loss of these services can increase the vulnerability of people decreasing their capacity to support themselves. New approaches to help guide water resources management decisions are needed which account for the non-market value of ecosystem goods and services. In case studies from Brazil and Kenya we demonstrate the capability of many objective Pareto-optimal trade-off analysis to help decision makers balance economic and non-market benefits from the management of existing multi-reservoir systems. A multi-criteria search algorithm is coupled to a water resources management simulator of each basin to generate a set of Pareto-approximate trade-offs representing the best case management decisions. In both cases, volume dependent reservoir release rules are the management decisions being optimised. In the Kenyan case we further assess the impacts of proposed irrigation investments, and how the possibility of new investments impacts the system's trade-offs. During the multi-criteria search (optimisation), performance of different sets of management decisions (policies) is assessed against case-specific objective functions representing provision of water supply and irrigation, hydropower generation and maintenance of ecosystem services. Results are visualised as trade-off surfaces to help decision makers understand the impacts of different policies on a broad range of stakeholders and to assist in decision-making. These case studies show how the approach can reveal unexpected opportunities for win-win solutions, and quantify the trade-offs between investing to increase agricultural revenue and negative impacts on protected ecosystems which support rural livelihoods.

Dynamic programming approach for partial decision rule optimization

KAUST Repository

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.

Dynamic programming approach for partial decision rule optimization

KAUST Repository

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.

Bandgap optimization of two-dimensional photonic crystals using semidefinite programming and subspace methods

International Nuclear Information System (INIS)

Men, H.; Nguyen, N.C.; Freund, R.M.; Parrilo, P.A.; Peraire, J.

2010-01-01

In this paper, we consider the optimal design of photonic crystal structures for two-dimensional square lattices. The mathematical formulation of the bandgap optimization problem leads to an infinite-dimensional Hermitian eigenvalue optimization problem parametrized by the dielectric material and the wave vector. To make the problem tractable, the original eigenvalue problem is discretized using the finite element method into a series of finite-dimensional eigenvalue problems for multiple values of the wave vector parameter. The resulting optimization problem is large-scale and non-convex, with low regularity and non-differentiable objective. By restricting to appropriate eigenspaces, we reduce the large-scale non-convex optimization problem via reparametrization to a sequence of small-scale convex semidefinite programs (SDPs) for which modern SDP solvers can be efficiently applied. Numerical results are presented for both transverse magnetic (TM) and transverse electric (TE) polarizations at several frequency bands. The optimized structures exhibit patterns which go far beyond typical physical intuition on periodic media design.

AMMOS: Automated Molecular Mechanics Optimization tool for in silico Screening

Directory of Open Access Journals (Sweden)

Pajeva Ilza

2008-10-01

Full Text Available Abstract Background Virtual or in silico ligand screening combined with other computational methods is one of the most promising methods to search for new lead compounds, thereby greatly assisting the drug discovery process. Despite considerable progresses made in virtual screening methodologies, available computer programs do not easily address problems such as: structural optimization of compounds in a screening library, receptor flexibility/induced-fit, and accurate prediction of protein-ligand interactions. It has been shown that structural optimization of chemical compounds and that post-docking optimization in multi-step structure-based virtual screening approaches help to further improve the overall efficiency of the methods. To address some of these points, we developed the program AMMOS for refining both, the 3D structures of the small molecules present in chemical libraries and the predicted receptor-ligand complexes through allowing partial to full atom flexibility through molecular mechanics optimization. Results The program AMMOS carries out an automatic procedure that allows for the structural refinement of compound collections and energy minimization of protein-ligand complexes using the open source program AMMP. The performance of our package was evaluated by comparing the structures of small chemical entities minimized by AMMOS with those minimized with the Tripos and MMFF94s force fields. Next, AMMOS was used for full flexible minimization of protein-ligands complexes obtained from a mutli-step virtual screening. Enrichment studies of the selected pre-docked complexes containing 60% of the initially added inhibitors were carried out with or without final AMMOS minimization on two protein targets having different binding pocket properties. AMMOS was able to improve the enrichment after the pre-docking stage with 40 to 60% of the initially added active compounds found in the top 3% to 5% of the entire compound collection

Biogeography-inspired multiobjective optimization for helping MEMS synthesis

Directory of Open Access Journals (Sweden)

Di Barba Paolo

2017-09-01

Full Text Available The aim of the paper is to assess the applicability of a multi-objective biogeography-based optimisation algorithm in MEMS synthesis. In order to test the performances of the proposed method in this research field, the optimal shape design of an electrostatic micromotor, and two different electro-thermo-elastic microactuators are considered as the case studies.

Policy Iteration for $H_\\infty $ Optimal Control of Polynomial Nonlinear Systems via Sum of Squares Programming.

Science.gov (United States)

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.

Optimization of radioactive waste management system by application of multiobjective linear programming

International Nuclear Information System (INIS)

Shimizu, Yoshiaki

1981-01-01

A mathematical procedure is proposed to make a radioactive waste management plan comprehensively. Since such planning is relevant to some different goals in management, decision making has to be formulated as a multiobjective optimization problem. A mathematical programming method was introduced to make a decision through an interactive manner which enables us to assess the preference of decision maker step by step among the conflicting objectives. The reference system taken as an example is the radioactive waste management system at the Research Reactor Institute of Kyoto University (KUR). Its linear model was built based on the experience in the actual management at KUR. The best-compromise model was then formulated as a multiobjective linear programming by the aid of the computational analysis through a conventional optimization. It was shown from the numerical results that the proposed approach could provide some useful informations to make an actual management plan. (author)

Hooked on Helping

Science.gov (United States)

Longhurst, James; McCord, Joan

2014-01-01

In this article, teens presenting at a symposium on peer-helping programs describe how caring for others fosters personal growth and builds positive group cultures. Their individual thoughts and opinions are expressed.

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)

Statistical mechanical analysis of linear programming relaxation for combinatorial optimization problems

Science.gov (United States)

Takabe, Satoshi; Hukushima, Koji

2016-05-01

Typical behavior of the linear programming (LP) problem is studied as a relaxation of the minimum vertex cover (min-VC), a type of integer programming (IP) problem. A lattice-gas model on the Erdös-Rényi random graphs of α -uniform hyperedges is proposed to express both the LP and IP problems of the min-VC in the common statistical mechanical model with a one-parameter family. Statistical mechanical analyses reveal for α =2 that the LP optimal solution is typically equal to that given by the IP below the critical average degree c =e in the thermodynamic limit. The critical threshold for good accuracy of the relaxation extends the mathematical result c =1 and coincides with the replica symmetry-breaking threshold of the IP. The LP relaxation for the minimum hitting sets with α ≥3 , minimum vertex covers on α -uniform random graphs, is also studied. Analytic and numerical results strongly suggest that the LP relaxation fails to estimate optimal values above the critical average degree c =e /(α -1 ) where the replica symmetry is broken.

Statistical mechanical analysis of linear programming relaxation for combinatorial optimization problems.

Science.gov (United States)

Takabe, Satoshi; Hukushima, Koji

2016-05-01

Typical behavior of the linear programming (LP) problem is studied as a relaxation of the minimum vertex cover (min-VC), a type of integer programming (IP) problem. A lattice-gas model on the Erdös-Rényi random graphs of α-uniform hyperedges is proposed to express both the LP and IP problems of the min-VC in the common statistical mechanical model with a one-parameter family. Statistical mechanical analyses reveal for α=2 that the LP optimal solution is typically equal to that given by the IP below the critical average degree c=e in the thermodynamic limit. The critical threshold for good accuracy of the relaxation extends the mathematical result c=1 and coincides with the replica symmetry-breaking threshold of the IP. The LP relaxation for the minimum hitting sets with α≥3, minimum vertex covers on α-uniform random graphs, is also studied. Analytic and numerical results strongly suggest that the LP relaxation fails to estimate optimal values above the critical average degree c=e/(α-1) where the replica symmetry is broken.

A Simulation Modeling Framework to Optimize Programs Using Financial Incentives to Motivate Health Behavior Change.

Science.gov (United States)

Basu, Sanjay; Kiernan, Michaela

2016-01-01

While increasingly popular among mid- to large-size employers, using financial incentives to induce health behavior change among employees has been controversial, in part due to poor quality and generalizability of studies to date. Thus, fundamental questions have been left unanswered: To generate positive economic returns on investment, what level of incentive should be offered for any given type of incentive program and among which employees? We constructed a novel modeling framework that systematically identifies how to optimize marginal return on investment from programs incentivizing behavior change by integrating commonly collected data on health behaviors and associated costs. We integrated "demand curves" capturing individual differences in response to any given incentive with employee demographic and risk factor data. We also estimated the degree of self-selection that could be tolerated: that is, the maximum percentage of already-healthy employees who could enroll in a wellness program while still maintaining positive absolute return on investment. In a demonstration analysis, the modeling framework was applied to data from 3000 worksite physical activity programs across the nation. For physical activity programs, the incentive levels that would optimize marginal return on investment ($367/employee/year) were higher than average incentive levels currently offered ($143/employee/year). Yet a high degree of self-selection could undermine the economic benefits of the program; if more than 17% of participants came from the top 10% of the physical activity distribution, the cost of the program would be expected to always be greater than its benefits. Our generalizable framework integrates individual differences in behavior and risk to systematically estimate the incentive level that optimizes marginal return on investment. © The Author(s) 2015.

Optimal design of distributed energy resource systems based on two-stage stochastic programming

International Nuclear Information System (INIS)

Yang, Yun; Zhang, Shijie; Xiao, Yunhan

2017-01-01

Highlights: • A two-stage stochastic programming model is built to design DER systems under uncertainties. • Uncertain energy demands have a significant effect on the optimal design. • Uncertain energy prices and renewable energy intensity have little effect on the optimal design. • The economy is overestimated if the system is designed without considering the uncertainties. • The uncertainty in energy prices has the significant and greatest effect on the economy. - Abstract: Multiple uncertainties exist in the optimal design of distributed energy resource (DER) systems. The expected energy, economic, and environmental benefits may not be achieved and a deficit in energy supply may occur if the uncertainties are not handled properly. This study focuses on the optimal design of DER systems with consideration of the uncertainties. A two-stage stochastic programming model is built in consideration of the discreteness of equipment capacities, equipment partial load operation and output bounds as well as of the influence of ambient temperature on gas turbine performance. The stochastic model is then transformed into its deterministic equivalent and solved. For an illustrative example, the model is applied to a hospital in Lianyungang, China. Comparative studies are performed to evaluate the effect of the uncertainties in load demands, energy prices, and renewable energy intensity separately and simultaneously on the system’s economy and optimal design. Results show that the uncertainties in load demands have a significant effect on the optimal system design, whereas the uncertainties in energy prices and renewable energy intensity have almost no effect. Results regarding economy show that it is obviously overestimated if the system is designed without considering the uncertainties.

Technology for Building Systems Integration and Optimization – Landscape Report

Energy Technology Data Exchange (ETDEWEB)

William Goetzler, Matt Guernsey, Youssef Bargach

2018-01-31

BTO's Commercial Building Integration (CBI) program helps advance a range of innovative building integration and optimization technologies and solutions, paving the way for high-performing buildings that could use 50-70% less energy than typical buildings. CBI’s work focuses on early stage technology innovation, with an emphasis on how components and systems work together and how whole buildings are integrated and optimized. This landscape study outlines the current body of knowledge, capabilities, and the broader array of solutions supporting integration and optimization in commercial buildings. CBI seeks to support solutions for both existing buildings and new construction, which often present very different challenges.

Optimal Land Use Management for Soil Erosion Control by Using an Interval-Parameter Fuzzy Two-Stage Stochastic Programming Approach

Science.gov (United States)

Han, Jing-Cheng; Huang, Guo-He; Zhang, Hua; Li, Zhong

2013-09-01

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 109 was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.

Optimal land use management for soil erosion control by using an interval-parameter fuzzy two-stage stochastic programming approach.

Science.gov (United States)

Han, Jing-Cheng; Huang, Guo-He; Zhang, Hua; Li, Zhong

2013-09-01

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 10(9) $ was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.

Optimization strategies based on sequential quadratic programming applied for a fermentation process for butanol production.

Science.gov (United States)

Pinto Mariano, Adriano; Bastos Borba Costa, Caliane; de Franceschi de Angelis, Dejanira; Maugeri Filho, Francisco; Pires Atala, Daniel Ibraim; Wolf Maciel, Maria Regina; Maciel Filho, Rubens

2009-11-01

In this work, the mathematical optimization of a continuous flash fermentation process for the production of biobutanol was studied. The process consists of three interconnected units, as follows: fermentor, cell-retention system (tangential microfiltration), and vacuum flash vessel (responsible for the continuous recovery of butanol from the broth). The objective of the optimization was to maximize butanol productivity for a desired substrate conversion. Two strategies were compared for the optimization of the process. In one of them, the process was represented by a deterministic model with kinetic parameters determined experimentally and, in the other, by a statistical model obtained using the factorial design technique combined with simulation. For both strategies, the problem was written as a nonlinear programming problem and was solved with the sequential quadratic programming technique. The results showed that despite the very similar solutions obtained with both strategies, the problems found with the strategy using the deterministic model, such as lack of convergence and high computational time, make the use of the optimization strategy with the statistical model, which showed to be robust and fast, more suitable for the flash fermentation process, being recommended for real-time applications coupling optimization and control.

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.

CiOpt: a program for optimization of the frequency response of linear circuits

OpenAIRE

Miró Sans, Joan Maria; Palà Schönwälder, Pere

1991-01-01

An interactive personal-computer program for optimizing the frequency response of linear lumped circuits (CiOpt) is presented. CiOpt has proved to be an efficient tool in improving designs where the inclusion of more accurate device models distorts the desired frequency response, as well as in device modeling. The outputs of CiOpt are the element values which best match the obtained and the desired frequency response. The optimization algorithms used (the Fletcher-Powell and Newton's methods,...

Optimization Models for Reaction Networks: Information Divergence, Quadratic Programming and Kirchhoff’s Laws

Directory of Open Access Journals (Sweden)

Julio Michael Stern

2014-03-01

Full Text Available This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.

Moving empirically supported practices to addiction treatment programs: recruiting supervisors to help in technology transfer.

Science.gov (United States)

Amodeo, Maryann; Storti, Susan A; Larson, Mary Jo

2010-05-01

Federal and state funding agencies are encouraging or mandating the use of empirically supported treatments in addiction programs, yet many programs have not moved in this direction (Forman, Bovasso, and Woody, 2001 ; Roman and Johnson, 2002 ; Willenbring et al., 2004 ). To improve the skills of counselors in community addiction programs, the authors developed an innovative Web-based course on Cognitive Behavioral Therapy (CBT), a widely accepted empirically-supported practice (ESP) for addiction. Federal funding supports this Web course and a randomized controlled trial to evaluate its effectiveness. Since supervisors often play a pivotal role in helping clinicians transfer learned skills from training courses to the workplace, the authors recruited supervisor-counselor teams, engaging 54 supervisors and 120 counselors. Lessons learned focus on supervisor recruitment and involvement, supervisors' perceptions of CBT, their own CBT skills and their roles in the study, and implications for technology transfer for the addiction field as a whole. Recruiting supervisors proved difficult because programs lacked clinical supervisors. Recruiting counselors was also difficult because programs were concerned about loss of third-party reimbursement. Across the addiction field, technology transfer will be severely hampered unless such infrastructure problems can be solved. Areas for further investigation are identified.

A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy

Science.gov (United States)

Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen

2018-01-01

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP

A singular value decomposition linear programming (SVDLP) optimization technique for circular cone based robotic radiotherapy.

Science.gov (United States)

Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen

2018-01-05

With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP

A "feasible direction" search for Lineal Programming problem solving

Directory of Open Access Journals (Sweden)

Jaime U Malpica Angarita

2003-07-01

Full Text Available The study presents an approach to solve linear programming problems with no artificial variables. A primal linear minimization problem is standard form and its associated dual linear maximization problem are used. Initially, the dual (or a partial dual program is solved by a "feasible direction" search, where the Karush-Kuhn-Tucker conditions help to verify its optimality and then its feasibility. The "feasible direction" search exploits the characteristics of the convex polyhedron (or prototype formed by the dual program constraints to find a starting point and then follows line segments, whose directions are found in afine subspaces defined by boundary hyperplanes of polyhedral faces, to find next points up to the (an optimal one. Them, the remaining dual constraints not satisfaced at that optimal dual point, if there are any, are handled as nonbasic variables of the primal program, which is to be solved by such "feasible direction" search.

Dynamic programming approach to optimization of approximate decision rules

KAUST Repository

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.

An efficient second-order SQP method for structural topology optimization

DEFF Research Database (Denmark)

Rojas Labanda, Susana; Stolpe, Mathias

2016-01-01

This article presents a Sequential Quadratic Programming (SQP) solver for structural topology optimization problems named TopSQP. The implementation is based on the general SQP method proposed in Morales et al. J Numer Anal 32(2):553–579 (2010) called SQP+. The topology optimization problem...... nonlinear solvers IPOPT and SNOPT. Numerical experiments on a large set of benchmark problems show good performance of TopSQP in terms of number of function evaluations. In addition, the use of second-order information helps to decrease the objective function value....

Mastering of musical rhythm by pre-school age children with speech disorders with the help of dance-correction program trainings

Directory of Open Access Journals (Sweden)

N.B. Petrenko

2016-08-01

Full Text Available Introduction: It is known that regular listening to specially selected music develops children’s cognitive abilities. Musical influence optimizes many important functions of brain: increases mental workability; accelerates processing of information; improves short term memory. Besides, sensitivity of visual and hearing analyzers strengthens, as well as regulation of arbitrary movements; indicators of verbal and non verbal intellect improve. Purpose: to determine peculiarities of musical rhythm’s mastering by pre-school age children with speech disorders with the help of dance-correction program trainings. Material: the categories of the tested children: children of age - 4-5 and 5-6 years with speech disorders and healthy pre-school age children. Children of 4-5 years’ age composed: main group (n=12, control group (n=16; group of healthy children (n=24. For assessment of verbal thinking and rhythm-motor (or dance abilities we used complex of tests of constantly increasing difficulty. Results: we found that under influence of dance-correcting exercises activation of rhythm-motor abilities and development of cognitive functions happened in children. We also found main functional peculiarities of musical rhythm’s mastering by pre-school age children. It was determined that by the end of pedagogic experiment, main groups of children approached to groups of healthy peers by all tested characteristics. Conclusions: it is recommended to include correcting components (fit ball - dance gymnastic, tales-therapy, logo-rhythm trainings, and game fitness in trainings by choreographic program.

Direct and Mediated Relationships Between Participation in a Telephonic Health Coaching Program and Health Behavior, Life Satisfaction, and Optimism.

Science.gov (United States)

Sears, Lindsay E; Coberley, Carter R; Pope, James E

2016-07-01

The aim of this study was to examine the direct and mediated effects of a telephonic health coaching program on changes to healthy behaviors, life satisfaction, and optimism. This longitudinal correlational study of 4881 individuals investigated simple and mediated relationships between participation in a telephonic health risk coaching program and outcomes from three annual Well-being Assessments. Program participation was directly related to improvements in healthy behaviors, life satisfaction and optimism, and indirect effects of coaching on these variables concurrently and over a one-year time lag were also supported. Given previous research that improvements to life satisfaction, optimism, and health behaviors are valuable for individuals, employers, and communities, a clearer understanding of intervention approaches that may impact these outcomes simultaneously can drive greater program effectiveness and value on investment.

Airline Maintenance Manpower Optimization from the De Novo Perspective

Science.gov (United States)

Liou, James J. H.; Tzeng, Gwo-Hshiung

Human resource management (HRM) is an important issue for today’s competitive airline marketing. In this paper, we discuss a multi-objective model designed from the De Novo perspective to help airlines optimize their maintenance manpower portfolio. The effectiveness of the model and solution algorithm is demonstrated in an empirical study of the optimization of the human resources needed for airline line maintenance. Both De Novo and traditional multiple objective programming (MOP) methods are analyzed. A comparison of the results with those of traditional MOP indicates that the proposed model and solution algorithm does provide better performance and an improved human resource portfolio.

Stochastic optimal control in infinite dimension dynamic programming and HJB equations

CERN Document Server

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 ...

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)

A generalized fuzzy credibility-constrained linear fractional programming approach for optimal irrigation water allocation under uncertainty

Science.gov (United States)

Zhang, Chenglong; Guo, Ping

2017-10-01

The vague and fuzzy parametric information is a challenging issue in irrigation water management problems. In response to this problem, a generalized fuzzy credibility-constrained linear fractional programming (GFCCFP) model is developed for optimal irrigation water allocation under uncertainty. The model can be derived from integrating generalized fuzzy credibility-constrained programming (GFCCP) into a linear fractional programming (LFP) optimization framework. Therefore, it can solve ratio optimization problems associated with fuzzy parameters, and examine the variation of results under different credibility levels and weight coefficients of possibility and necessary. It has advantages in: (1) balancing the economic and resources objectives directly; (2) analyzing system efficiency; (3) generating more flexible decision solutions by giving different credibility levels and weight coefficients of possibility and (4) supporting in-depth analysis of the interrelationships among system efficiency, credibility level and weight coefficient. The model is applied to a case study of irrigation water allocation in the middle reaches of Heihe River Basin, northwest China. Therefore, optimal irrigation water allocation solutions from the GFCCFP model can be obtained. Moreover, factorial analysis on the two parameters (i.e. λ and γ) indicates that the weight coefficient is a main factor compared with credibility level for system efficiency. These results can be effective for support reasonable irrigation water resources management and agricultural production.

Mental health first aid for the elderly: A pilot study of a training program adapted for helping elderly people.

Science.gov (United States)

Svensson, Bengt; Hansson, Lars

2017-06-01

Epidemiological studies have shown a high prevalence of mental illness among the elderly. Clinical data however indicate both insufficient detection and treatment of illnesses. Suggested barriers to treatment include conceptions that mental health symptoms belong to normal aging and lack of competence among staff in elderly care in detecting mental illness. A Mental Health First Aid (MHFA) training program for the elderly was developed and provided to staff in elderly care. The aim of this study was to investigate changes in knowledge in mental illness, confidence in helping a person, readiness to give help and attitudes towards persons with mental illness. Single group pre-test-post-test design. The study group included staff in elderly care from different places in Sweden (n = 139). Significant improvements in knowledge, confidence in helping an elderly person with mental illness and attitudes towards persons with mental illness are shown. Skills acquired during the course have been practiced during the follow-up. The adaption of MHFA training for staff working in elderly care gives promising results. Improvements in self-reported confidence in giving help, attitudes towards persons with mental illness and actual help given to persons with mental illness are shown. However, the study design allows no firm conclusions and a randomized controlled trail is needed to investigate the effectiveness of the program. Outcomes should include if the detection and treatment of mental illness among the elderly actually improved.

EABOT - Energetic analysis as a basis for robust optimization of trigeneration systems by linear programming

International Nuclear Information System (INIS)

Piacentino, A.; Cardona, F.

2008-01-01

The optimization of synthesis, design and operation in trigeneration systems for building applications is a quite complex task, due to the high number of decision variables, the presence of irregular heat, cooling and electric load profiles and the variable electricity price. Consequently, computer-aided techniques are usually adopted to achieve the optimal solution, based either on iterative techniques, linear or non-linear programming or evolutionary search. Large efforts have been made in improving algorithm efficiency, which have resulted in an increasingly rapid convergence to the optimal solution and in reduced calculation time; robust algorithm have also been formulated, assuming stochastic behaviour for energy loads and prices. This paper is based on the assumption that margins for improvements in the optimization of trigeneration systems still exist, which require an in-depth understanding of plant's energetic behaviour. Robustness in the optimization of trigeneration systems has more to do with a 'correct and comprehensive' than with an 'efficient' modelling, being larger efforts required to energy specialists rather than to experts in efficient algorithms. With reference to a mixed integer linear programming model implemented in MatLab for a trigeneration system including a pressurized (medium temperature) heat storage, the relevant contribute of thermoeconomics and energo-environmental analysis in the phase of mathematical modelling and code testing are shown

Optimal GENCO bidding strategy

Science.gov (United States)

Gao, Feng

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed

Optimal Allocation of Static Var Compensator via Mixed Integer Conic Programming

Energy Technology Data Exchange (ETDEWEB)

Zhang, Xiaohu [ORNL; Shi, Di [Global Energy Interconnection Research Institute North America (GEIRI North America), California; Wang, Zhiwei [Global Energy Interconnection Research Institute North America (GEIRI North America), California; Huang, Junhui [Global Energy Interconnection Research Institute North America (GEIRI North America), California; Wang, Xu [Global Energy Interconnection Research Institute North America (GEIRI North America), California; Liu, Guodong [ORNL; Tomsovic, Kevin [University of Tennessee, Knoxville (UTK)

2017-01-01

Shunt FACTS devices, such as, a Static Var Compensator (SVC), are capable of providing local reactive power compensation. They are widely used in the network to reduce the real power loss and improve the voltage profile. This paper proposes a planning model based on mixed integer conic programming (MICP) to optimally allocate SVCs in the transmission network considering load uncertainty. The load uncertainties are represented by a number of scenarios. Reformulation and linearization techniques are utilized to transform the original non-convex model into a convex second order cone programming (SOCP) model. Numerical case studies based on the IEEE 30-bus system demonstrate the effectiveness of the proposed planning model.

A Goal Programming Optimization Model for The Allocation of Liquid Steel Production

Science.gov (United States)

Hapsari, S. N.; Rosyidi, C. N.

2018-03-01

This research was conducted in one of the largest steel companies in Indonesia which has several production units and produces a wide range of steel products. One of the important products in the company is billet steel. The company has four Electric Arc Furnace (EAF) which produces liquid steel which must be procesed further to be billet steel. The billet steel plant needs to make their production process more efficient to increase the productvity. The management has four goals to be achieved and hence the optimal allocation of the liquid steel production is needed to achieve those goals. In this paper, a goal programming optimization model is developed to determine optimal allocation of liquid steel production in each EAF, to satisfy demand in 3 periods and the company goals, namely maximizing the volume of production, minimizing the cost of raw materials, minimizing maintenance costs, maximizing sales revenues, and maximizing production capacity. From the results of optimization, only maximizing production capacity goal can not achieve the target. However, the model developed in this papare can optimally allocate liquid steel so the allocation of production does not exceed the maximum capacity of the machine work hours and maximum production capacity.

Physical Activity Helps Seniors Stay Mobile

Science.gov (United States)

... Subscribe July 2014 Print this issue Health Capsule Physical Activity Helps Seniors Stay Mobile En español Send us your comments A carefully structured, moderate physical activity program helped vulnerable older people maintain their mobility. ...

Differential Spatio-temporal Multiband Satellite Image Clustering using K-means Optimization With Reinforcement Programming

Directory of Open Access Journals (Sweden)

Irene Erlyn Wina Rachmawan

2015-06-01

Full Text Available Deforestration is one of the crucial issues in Indonesia because now Indonesia has world's highest deforestation rate. In other hand, multispectral image delivers a great source of data for studying spatial and temporal changeability of the environmental such as deforestration area. This research present differential image processing methods for detecting nature change of deforestration. Our differential image processing algorithms extract and indicating area automatically. The feature of our proposed idea produce extracted information from multiband satellite image and calculate the area of deforestration by years with calculating data using temporal dataset. Yet, multiband satellite image consists of big data size that were difficult to be handled for segmentation. Commonly, K- Means clustering is considered to be a powerfull clustering algorithm because of its ability to clustering big data. However K-Means has sensitivity of its first generated centroids, which could lead into a bad performance. In this paper we propose a new approach to optimize K-Means clustering using Reinforcement Programming in order to clustering multispectral image. We build a new mechanism for generating initial centroids by implementing exploration and exploitation knowledge from Reinforcement Programming. This optimization will lead a better result for K-means data cluster. We select multispectral image from Landsat 7 in past ten years in Medawai, Borneo, Indonesia, and apply two segmentation areas consist of deforestration land and forest field. We made series of experiments and compared the experimental results of K-means using Reinforcement Programming as optimizing initiate centroid and normal K-means without optimization process. Keywords: Deforestration, Multispectral images, landsat, automatic clustering, K-means.

Optimizing Crawler4j using MapReduce Programming Model

Science.gov (United States)

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.

DETERMINATION OF OPTIMAL CONTOURS OF OPEN PIT MINE DURING OIL SHALE EXPLOITATION, BY MINEX 5.2.3. PROGRAM

Directory of Open Access Journals (Sweden)

Miroslav Ignjatović

2013-04-01

Full Text Available By examination and determination of optimal solution of technological processes of exploitation and oil shale processing from Aleksinac site and with adopted technical solution and exploitation of oil shale, derived a technical solution that optimize contour of the newly defined open pit mine. In the world, this problem is solved by using a computer program that has become the established standard for quick and efficient solution for this problem. One of the computer’s program, which can be used for determination of the optimal contours of open pit mines is Minex 5.2.3. program, produced in Australia in the Surpac Minex Group Pty Ltd Company, which is applied at the Mining and Metallurgy Institute Bor (no. of licenses are SSI - 24765 and SSI - 24766. In this study, authors performed 11 optimization of deposit geo - models in Minex 5.2.3. based on the tests results, performed in a laboratory for soil mechanics of Mining and Metallurgy Institute, Bor, on samples from the site of Aleksinac deposits.

Helping While Learning: A Skilled Group Helper Training Program.

Science.gov (United States)

Smaby, Marlowe H.; Tamminen, Armas W.

1983-01-01

Describes a developmental group training workshop for training experienced counselors to do group counseling. Discusses stages of training including exploration, understanding, and action, which can help counselors learn helping skills for counseling that can often transfer to their own interpersonal lives and interactions with others. (JAC)

Optimal placement of capacitors in a radial network using conic and mixed integer linear programming

Energy Technology Data Exchange (ETDEWEB)

Jabr, R.A. [Electrical, Computer and Communication Engineering Department, Notre Dame University, P.O. Box: 72, Zouk Mikhael, Zouk Mosbeh (Lebanon)

2008-06-15

This paper considers the problem of optimally placing fixed and switched type capacitors in a radial distribution network. The aim of this problem is to minimize the costs associated with capacitor banks, peak power, and energy losses whilst satisfying a pre-specified set of physical and technical constraints. The proposed solution is obtained using a two-phase approach. In phase-I, the problem is formulated as a conic program in which all nodes are candidates for placement of capacitor banks whose sizes are considered as continuous variables. A global solution of the phase-I problem is obtained using an interior-point based conic programming solver. Phase-II seeks a practical optimal solution by considering capacitor sizes as discrete variables. The problem in this phase is formulated as a mixed integer linear program based on minimizing the L1-norm of deviations from the phase-I state variable values. The solution to the phase-II problem is obtained using a mixed integer linear programming solver. The proposed method is validated via extensive comparisons with previously published results. (author)

Solving Bilevel Multiobjective Programming Problem by Elite Quantum Behaved Particle Swarm Optimization

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Tao Zhang

2012-01-01

Full Text Available An elite quantum behaved particle swarm optimization (EQPSO algorithm is proposed, in which an elite strategy is exerted for the global best particle to prevent premature convergence of the swarm. The EQPSO algorithm is employed for solving bilevel multiobjective programming problem (BLMPP in this study, which has never been reported in other literatures. Finally, we use eight different test problems to measure and evaluate the proposed algorithm, including low dimension and high dimension BLMPPs, as well as attempt to solve the BLMPPs whose theoretical Pareto optimal front is not known. The experimental results show that the proposed algorithm is a feasible and efficient method for solving BLMPPs.

An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications

International Nuclear Information System (INIS)

Santos Coelho, Leandro dos

2009-01-01

The reliability-redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability-redundancy optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solutions. One of these meta-heuristics is the particle swarm optimization (PSO). PSO is a population-based heuristic optimization technique inspired by social behavior of bird flocking and fish schooling. This paper presents an efficient PSO algorithm based on Gaussian distribution and chaotic sequence (PSO-GC) to solve the reliability-redundancy optimization problems. In this context, two examples in reliability-redundancy design problems are evaluated. Simulation results demonstrate that the proposed PSO-GC is a promising optimization technique. PSO-GC performs well for the two examples of mixed-integer programming in reliability-redundancy applications considered in this paper. The solutions obtained by the PSO-GC are better than the previously best-known solutions available in the recent literature

An efficient particle swarm approach for mixed-integer programming in reliability-redundancy optimization applications

Energy Technology Data Exchange (ETDEWEB)

Santos Coelho, Leandro dos [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR, Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: leandro.coelho@pucpr.br

2009-04-15

The reliability-redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability-redundancy optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solutions. One of these meta-heuristics is the particle swarm optimization (PSO). PSO is a population-based heuristic optimization technique inspired by social behavior of bird flocking and fish schooling. This paper presents an efficient PSO algorithm based on Gaussian distribution and chaotic sequence (PSO-GC) to solve the reliability-redundancy optimization problems. In this context, two examples in reliability-redundancy design problems are evaluated. Simulation results demonstrate that the proposed PSO-GC is a promising optimization technique. PSO-GC performs well for the two examples of mixed-integer programming in reliability-redundancy applications considered in this paper. The solutions obtained by the PSO-GC are better than the previously best-known solutions available in the recent literature.

Nutrient profiling can help identify foods of good nutritional quality for their price: a validation study with linear programming.

Science.gov (United States)

Maillot, Matthieu; Ferguson, Elaine L; Drewnowski, Adam; Darmon, Nicole

2008-06-01

Nutrient profiling ranks foods based on their nutrient content. They may help identify foods with a good nutritional quality for their price. This hypothesis was tested using diet modeling with linear programming. Analyses were undertaken using food intake data from the nationally representative French INCA (enquête Individuelle et Nationale sur les Consommations Alimentaires) survey and its associated food composition and price database. For each food, a nutrient profile score was defined as the ratio between the previously published nutrient density score (NDS) and the limited nutrient score (LIM); a nutritional quality for price indicator was developed and calculated from the relationship between its NDS:LIM and energy cost (in euro/100 kcal). We developed linear programming models to design diets that fulfilled increasing levels of nutritional constraints at a minimal cost. The median NDS:LIM values of foods selected in modeled diets increased as the levels of nutritional constraints increased (P = 0.005). In addition, the proportion of foods with a good nutritional quality for price indicator was higher (P linear programming and the nutrient profiling approaches indicates that nutrient profiling can help identify foods of good nutritional quality for their price. Linear programming is a useful tool for testing nutrient profiling systems and validating the concept of nutrient profiling.

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)

Dynamic Programming Optimization of Multi-rate Multicast Video-Streaming Services

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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.

Relationship between Maximum Principle and Dynamic Programming for Stochastic Recursive Optimal Control Problems and Applications

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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.

Contribution to the development of a food guide in Benin: linear programming for the optimization of local diets.

Science.gov (United States)

Levesque, Sarah; Delisle, Hélène; Agueh, Victoire

2015-03-01

Food guides are important tools for nutrition education. While developing a food guide in Benin, the objective was to determine the daily number of servings per food group and the portion sizes of common foods to be recommended. Linear programming (LP) was used to determine, for each predefined food group, the optimal number and size of servings of commonly consumed foods. Two types of constraints were introduced into the LP models: (i) WHO/FAO Recommended Nutrient Intakes and dietary guidelines for the prevention of chronic diseases; and (ii) dietary patterns based on local food consumption data recently collected in southern Benin in 541 adults. Dietary intakes of the upper tertile of participants for diet quality based on prevention and micronutrient adequacy scores were used in the LP algorithms. Southern area of the Republic of Benin. Local key-players in nutrition (n 30) from the government, academic institutions, international organizations and civil society were partners in the development of the food guide directed at the population. The number of servings per food group and the portion size for eight age-sex groups were determined. For four limiting micronutrients (Fe, Ca, folate and Zn), local diets could be optimized to meet only 70 % of the Recommended Nutrient Intakes, not 100 %. It was possible to determine the daily number of servings and the portion sizes of common foods that can be recommended in Benin with the help of LP to optimize local diets, although Recommended Nutrient Intakes were not fully met for a few critical micronutrients.

Pipe degradation investigations for optimization of flow-accelerated corrosion inspection location selection

International Nuclear Information System (INIS)

Chandra, S.; Habicht, P.; Chexal, B.; Mahini, R.; McBrine, W.; Esselman, T.; Horowitz, J.

1995-01-01

A large amount of piping in a typical nuclear power plant is susceptible to Flow-Accelerated Corrosion (FAC) wall thinning to varying degrees. A typical PAC monitoring program includes the wall thickness measurement of a select number of components in order to judge the structural integrity of entire systems. In order to appropriately allocate resources and maintain an adequate FAC program, it is necessary to optimize the selection of components for inspection by focusing on those components which provide the best indication of system susceptibility to FAC. A better understanding of system FAC predictability and the types of FAC damage encountered can provide some of the insight needed to better focus and optimize the inspection plan for an upcoming refueling outage. Laboratory examination of FAC damaged components removed from service at Northeast Utilities' (NU) nuclear power plants provides a better understanding of the damage mechanisms involved and contributing causes. Selected results of this ongoing study are presented with specific conclusions which will help NU to better focus inspections and thus optimize the ongoing FAC inspection program

Population-level effects of automated smoking cessation help programs: a randomized controlled trial.

Science.gov (United States)

Borland, Ron; Balmford, James; Benda, Peter

2013-03-01

To test the population impact of offering automated smoking cessation interventions via the internet and/or by mobile phone. Pragmatic randomized controlled trial with five conditions: offer of (i) minimal intervention control; (ii) QuitCoach personalized tailored internet-delivered advice program; (iii) onQ, an interactive automated text-messaging program; (iv) an integration of both QuitCoach and onQ; and (v) a choice of either alone or the combined program. Australia, via a mix of internet and telephone contacts. A total of 3530 smokers or recent quitters recruited from those interested in quitting, and seeking self-help resources (n = 1335) or cold-contacted from internet panels (n = 2195). The primary outcome was self-report of 6 months sustained abstinence at 7 months post-recruitment. Only 42.5% of those offered one of the interventions took it up to a minimal level. The intervention groups combined had a non-significantly higher 6-month sustained abstinence rate than the control [odds ratio (OR) = 1.48; 95% confidence interval (CI): 0.98-2.24] (missing cases treated as smokers), with no differences between the interventions. Among those who used an intervention, there was a significant overall increase in abstinence (OR = 1.95; CI: 1.04-3.67), but not clearly so when analysing only cases with reported outcomes. Success rates were greater among those recruited after seeking information compared to those cold-contacted. Smokers interested in quitting who were assigned randomly to an offer of either the QuitCoach internet-based support program and/or the interactive automated text-messaging program had non-significantly greater odds of quitting for at least 6 months than those randomized to an offer of a simple information website. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.

Fuzzy chance constrained linear programming model for scrap charge optimization in steel production

DEFF Research Database (Denmark)

Rong, Aiying; Lahdelma, Risto

2008-01-01

the uncertainty based on fuzzy set theory and constrain the failure risk based on a possibility measure. Consequently, the scrap charge optimization problem is modeled as a fuzzy chance constrained linear programming problem. Since the constraints of the model mainly address the specification of the product...

Optimal blood glucose control in diabetes mellitus treatment using dynamic programming based on Ackerman’s linear model

Science.gov (United States)

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.

Memory-Optimized Software Synthesis from Dataflow Program Graphs with Large Size Data Samples

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Hyunok Oh

2003-05-01

Full Text Available In multimedia and graphics applications, data samples of nonprimitive type require significant amount of buffer memory. This paper addresses the problem of minimizing the buffer memory requirement for such applications in embedded software synthesis from graphical dataflow programs based on the synchronous dataflow (SDF model with the given execution order of nodes. We propose a memory minimization technique that separates global memory buffers from local pointer buffers: the global buffers store live data samples and the local buffers store the pointers to the global buffer entries. The proposed algorithm reduces 67% memory for a JPEG encoder, 40% for an H.263 encoder compared with unshared versions, and 22% compared with the previous sharing algorithm for the H.263 encoder. Through extensive buffer sharing optimization, we believe that automatic software synthesis from dataflow program graphs achieves the comparable code quality with the manually optimized code in terms of memory requirement.

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

A case study in R and D productivity: Helping the program manager cope with job stress and improve communication effectiveness

Science.gov (United States)

Bodensteiner, W. D.; Gerloff, E. A.

1985-01-01

Certain structural changes in the Naval Material Command which resulted from a comparison of its operations to those of selected large-scale private sector companies are described. Central to the change was a reduction in the number of formal reports from systems commands to headquarters, and the provision of Program Management Assistance Teams (at the request of the program manager) to help resolve project problems. It is believed that these changes improved communication and information-processing, reduced program manager stress, and resulted in improved productivity.

Optimization of investments in gas networks

International Nuclear Information System (INIS)

Andre, J.

2010-09-01

The natural gas networks require very important investments to cope with a still growing demand and to satisfy the new regulatory constraints. The gas market deregulation imposed to the gas network operators, first, transparency rules of a natural monopoly to justify their costs and ultimately their tariffs, and, second, market fluidity objectives in order to facilitate access for competition to the end-users. These major investments are the main reasons for the use of optimization techniques aiming at reducing the costs. Due to the discrete choices (investment location, limited choice of additional capacities, timing) crossed with physical non linear constraints (flow/pressures relations in the pipe or operating ranges of compressors), the programs to solve are Large Mixed Non Linear Programs (MINLP). As these types of programs are known to be hard to solve exactly in polynomial times (NP-hard), advanced optimization methods have to be implemented to obtain realistic results. The objectives of this thesis are threefold. First, one states several investment problems modeling of natural gas networks from industrial world motivations. Second, one identifies the most suitable methods and algorithms to the formulated problems. Third, one exposes the main advantages and drawbacks of these methods with the help of numerical applications on real cases. (author)

Leakage characterization of top select transistor for program disturbance optimization in 3D NAND flash

Science.gov (United States)

Zhang, Yu; Jin, Lei; Jiang, Dandan; Zou, Xingqi; Zhao, Zhiguo; Gao, Jing; Zeng, Ming; Zhou, Wenbin; Tang, Zhaoyun; Huo, Zongliang

2018-03-01

In order to optimize program disturbance characteristics effectively, a characterization approach that measures top select transistor (TSG) leakage from bit-line is proposed to quantify TSG leakage under program inhibit condition in 3D NAND flash memory. Based on this approach, the effect of Vth modulation of two-cell TSG on leakage is evaluated. By checking the dependence of leakage and corresponding program disturbance on upper and lower TSG Vth, this approach is validated. The optimal Vth pattern with high upper TSG Vth and low lower TSG Vth has been suggested for low leakage current and high boosted channel potential. It is found that upper TSG plays dominant role in preventing drain induced barrier lowering (DIBL) leakage from boosted channel to bit-line, while lower TSG assists to further suppress TSG leakage by providing smooth potential drop from dummy WL to edge of TSG, consequently suppressing trap assisted band-to-band tunneling current (BTBT) between dummy WL and TSG.

Second Order Cone Programming (SOCP) Relaxation Based Optimal Power Flow with Hybrid VSC-HVDC Transmission and Active Distribution Networks

DEFF Research Database (Denmark)

Ding, Tao; Li, Cheng; Yang, Yongheng

2017-01-01

The detailed topology of renewable resource bases may have the impact on the optimal power flow of the VSC-HVDC transmission network. To address this issue, this paper develops an optimal power flow with the hybrid VSC-HVDC transmission and active distribution networks to optimally schedule...... the generation output and voltage regulation of both networks, which leads to a non-convex programming model. Furthermore, the non-convex power flow equations are based on the Second Order Cone Programming (SOCP) relaxation approach. Thus, the proposed model can be relaxed to a SOCP that can be tractably solved...

Environmental and Economic Optimization Model for Electric System Planning in Ningxia, China: Inexact Stochastic Risk-Aversion Programming Approach

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L. Ji

2015-01-01

Full Text Available The main goal of this paper is to provide a novel risk aversion model for long-term electric power system planning from the manager’s perspective with the consideration of various uncertainties. In the proposed method, interval parameter programming and two-stage stochastic programming are integrated to deal with the technical, economics, and policy uncertainties. Moreover, downside risk theory is introduced to balance the trade-off between the profit and risk according to the decision-maker’s risk aversion attitude. To verify the effectiveness and practical application of this approach, an inexact stochastic risk aversion model is developed for regional electric system planning and management in Ningxia Hui Autonomous Region, China. The series of solutions provide the decision-maker with the optimal investment strategy and operation management under different future emission reduction scenarios and risk-aversion levels. The results indicated that pollution control devices are still the main measures to achieve the current mitigation goal and the adjustment of generation structure would play an important role in the future cleaner electricity system with the stricter environmental policy. In addition, the model can be used for generating decision alternatives and helping decision-makers identify desired energy structure adjustment and pollutants/carbon mitigation abatement policies under various economic and system-reliability constraints.

A Novel Design and Optimization Software for Autonomous PV/Wind/Battery Hybrid Power Systems

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Ali M. Eltamaly

2014-01-01

Full Text Available This paper introduces a design and optimization computer simulation program for autonomous hybrid PV/wind/battery energy system. The main function of the new proposed computer program is to determine the optimum size of each component of the hybrid energy system for the lowest price of kWh generated and the best loss of load probability at highest reliability. This computer program uses the hourly wind speed, hourly radiation, and hourly load power with several numbers of wind turbine (WT and PV module types. The proposed computer program changes the penetration ratio of wind/PV with certain increments and calculates the required size of all components and the optimum battery size to get the predefined lowest acceptable probability. This computer program has been designed in flexible fashion that is not available in market available software like HOMER and RETScreen. Actual data for Saudi sites have been used with this computer program. The data obtained have been compared with these market available software. The comparison shows the superiority of this computer program in the optimal design of the autonomous PV/wind/battery hybrid system. The proposed computer program performed the optimal design steps in very short time and with accurate results. Many valuable results can be extracted from this computer program that can help researchers and decision makers.

Learning Based Approach for Optimal Clustering of Distributed Program's Call Flow Graph

Science.gov (United States)

Abofathi, Yousef; Zarei, Bager; Parsa, Saeed

Optimal clustering of call flow graph for reaching maximum concurrency in execution of distributable components is one of the NP-Complete problems. Learning automatas (LAs) are search tools which are used for solving many NP-Complete problems. In this paper a learning based algorithm is proposed to optimal clustering of call flow graph and appropriate distributing of programs in network level. The algorithm uses learning feature of LAs to search in state space. It has been shown that the speed of reaching to solution increases remarkably using LA in search process, and it also prevents algorithm from being trapped in local minimums. Experimental results show the superiority of proposed algorithm over others.

Semi-automatic tool to ease the creation and optimization of GPU programs

DEFF Research Database (Denmark)

Jepsen, Jacob

2014-01-01

We present a tool that reduces the development time of GPU-executable code. We implement a catalogue of common optimizations specific to the GPU architecture. Through the tool, the programmer can semi-automatically transform a computationally-intensive code section into GPU-executable form...... of the transformations can be performed automatically, which makes the tool usable for both novices and experts in GPU programming....

Using system dynamics for simulation and optimization of one coal industry system under fuzzy environment

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.

Robust Optimization Using Supremum of the Objective Function for Nonlinear Programming Problems

International Nuclear Information System (INIS)

Lee, Se Jung; Park, Gyung Jin

2014-01-01

In the robust optimization field, the robustness of the objective function emphasizes an insensitive design. In general, the robustness of the objective function can be achieved by reducing the change of the objective function with respect to the variation of the design variables and parameters. However, in conventional methods, when an insensitive design is emphasized, the performance of the objective function can be deteriorated. Besides, if the numbers of the design variables are increased, the numerical cost is quite high in robust optimization for nonlinear programming problems. In this research, the robustness index for the objective function and a process of robust optimization are proposed. Moreover, a method using the supremum of linearized functions is also proposed to reduce the computational cost. Mathematical examples are solved for the verification of the proposed method and the results are compared with those from the conventional methods. The proposed approach improves the performance of the objective function and its efficiency

Optimization of production planning in Czech agricultural co-operative via linear programming

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Jitka Janová

2009-01-01

Full Text Available The production planning is one of the key managerial decisions in agricultural business, which must be done periodically every year. Correct decision must cover the agriculture demands of planting the crops such as crop rotation restrictions or water resource scarcity, while the decision maker aims to plan the crop design in most profitable way in sense of maximizing the total profit from the crop yield. This decision problem represents the optimization of crop design and can be treated by the me­thods of linear programming which begun to be extensively used in agriculture production planning in USA during 50’s. There is ongoing research of mathematical programming applications in agriculture worldwide, but the results are not easily transferable to other localities due to the specific local restrictions in each country. In Czech Republic the farmers use for production planning mainly their expert knowledge and past experience. However, the mathematical programming approach enables find the true optimal solution of the problem, which especially in the problems with a great number of constraints is not easy to find intuitively. One of the possible barriers for using the general decision support systems (which are based on mathematical programming methods for agriculture production planning in Czech Republic is its expensiveness. The small farmer can not afford to buy the expensive software or to employ a mathematical programming specialist. The aim of this paper is to present a user friendly linear programming model of the typical agricultural production planning problem in Czech Republic which can be solved via software tools commonly available in any farm (e.g. EXCEL. The linear programming model covering the restrictions on total costs, crop rotation, thresholds for the total area sowed by particular crops, total amount of manure and the need of feed crops is developed. The model is applied in real-world problem of Czech agriculture

Does a self-referral counselling program reach doctors in need of help? A comparison with the general Norwegian doctor workforce

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Gude Tore

2007-03-01

Full Text Available Abstract Background Doctors have a relatively high degree of emotional distress, but seek help to a lesser degree and at a later stage than other academic groups. This can be deleterious for themselves and for their patients. Prevention programs have therefore been developed but it is unclear to what extent they reach doctors in need of help. This study describes doctors who participated in a self-referrral, easily accessible, stress relieving, counselling program in Norway, and compares them with a nationwide sample of Norwegian doctors. Methods Two hundred and twenty seven (94% of the doctors, 117 women and 110 men, who came to the resort centre Villa Sana, Modum, Norway, between August 2003 and July 2005, agreed to participate in the study. Socio-demographic data, reasons for and ways of help-seeking, sick-leave, symptoms of depression and anxiety, job stress and burnout were assessed by self-reporting questionnaires. Results Forty-nine percent of the Sana doctors were emotionally exhausted (Maslach compared with 25% of all Norwegian doctors. However, they did not differ on empathy and working capacity, the other two dimensions in Maslach's burnout inventory. Seventy-three percent of the Sana doctors could be in need of treatment for depression or anxiety based on their symptom distress scores, compared with 14% of men and 18% of women doctors in Norway. Twenty-one percent of the Sana doctors had a history of suicidal thoughts, including how to commit the act, as compared to 10% of Norwegian doctors in general. Conclusion Sana doctors displayed a higher degree of emotional exhaustion, symptoms of depression and anxiety as well as job related stress, compared with all Norwegian doctors. This may indicate that the program at Villa Sana to a large extent reaches doctors in need of help. The counselling intervention can help doctors to evaluate their professional and private situation, and, when necessary, enhance motivation for seeking adequate

A study on the optimization of radwaste treatment system: using goal programming

International Nuclear Information System (INIS)

Yang, Jin Yeong

1998-02-01

This study is concerned with the applications of linear goal programming techniques and artificial intelligence algorithm (fuzzy theory and genetic algorithm) to the analysis of management and operational problems in the radioactive processing system (RWPS). A typical RWPS is modeled as a linear functions to study and resolve the effects of conflicting objectives such as cost, limitation of released radioactivity to the environment, equipment utilization and total treatable radioactive waste volume before discharge and disposal. The developed model is validated and verified using actual data obtained from the RWPS at Kyoto University in Japan. The solution by goal programming would show the optimal operation point which is to maximize the total treatable radioactive waste volume and minimize the released radioactivity of liquid waste even under the restricted resources. But goal programming has a demerit that the target values are decided by decision maker arbitrarily. To complement the goal programming's demerit, the fuzzy set theory is introduced and the target values are analyzed by it. Genetic algorithm is combined with goal programming and the results by it is compared with that of goal programming only

Programs To Optimize Spacecraft And Aircraft Trajectories

Science.gov (United States)

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).

Generalized Second-Order Parametric Optimality Conditions in Semiinfinite Discrete Minmax Fractional Programming and Second-Order Univexity

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Ram Verma

2016-02-01

Full Text Available This paper deals with mainly establishing numerous sets of generalized second order paramertic sufficient optimality conditions for a semiinfinite discrete minmax fractional programming problem, while the results on semiinfinite discrete minmax fractional programming problem achieved based on some partitioning schemes under various types of generalized second order univexity assumptions. 

Developing optimal nurses work schedule using integer programming

Science.gov (United States)

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.

OPTIMIZATION OF MUD HAMMER DRILLING PERFORMANCE - A PROGRAM TO BENCHMARK THE VIABILITY OF ADVANCED MUD HAMMER DRILLING

Energy Technology Data Exchange (ETDEWEB)

Arnis Judzis

2003-01-01

This document details the progress to date on the ''OPTIMIZATION OF MUD HAMMER DRILLING PERFORMANCE -- A PROGRAM TO BENCHMARK THE VIABILITY OF ADVANCED MUD HAMMER DRILLING'' contract for the quarter starting October 2002 through December 2002. Even though we are awaiting the optimization portion of the testing program, accomplishments included the following: (1) Smith International participated in the DOE Mud Hammer program through full scale benchmarking testing during the week of 4 November 2003. (2) TerraTek acknowledges Smith International, BP America, PDVSA, and ConocoPhillips for cost-sharing the Smith benchmarking tests allowing extension of the contract to add to the benchmarking testing program. (3) Following the benchmark testing of the Smith International hammer, representatives from DOE/NETL, TerraTek, Smith International and PDVSA met at TerraTek in Salt Lake City to review observations, performance and views on the optimization step for 2003. (4) The December 2002 issue of Journal of Petroleum Technology (Society of Petroleum Engineers) highlighted the DOE fluid hammer testing program and reviewed last years paper on the benchmark performance of the SDS Digger and Novatek hammers. (5) TerraTek's Sid Green presented a technical review for DOE/NETL personnel in Morgantown on ''Impact Rock Breakage'' and its importance on improving fluid hammer performance. Much discussion has taken place on the issues surrounding mud hammer performance at depth conditions.

IT Workforce: Key Practices Help Ensure Strong Integrated Program Teams; Selected Departments Need to Assess Skill Gaps

Science.gov (United States)

2016-11-01

principles and steps associated with workforce planning that agencies can utilize in their efforts to assess and address IT skill gaps. See GAO-04-39...As another example, our prior review of the United States Department of Agriculture’s Farm Service Agency’s Modernize and Innovate the Delivery of...IT WORKFORCE Key Practices Help Ensure Strong Integrated Program Teams; Selected Departments Need to Assess Skill Gaps

A mixed-integer nonlinear programming approach to the optimal design of heat network in a polygeneration energy system

International Nuclear Information System (INIS)

Zhang, Jianyun; Liu, Pei; Zhou, Zhe; Ma, Linwei; Li, Zheng; Ni, Weidou

2014-01-01

Highlights: • Integration of heat streams with HRSG in a polygeneration system is studied. • A mixed-integer nonlinear programming model is proposed to optimize heat network. • Operating parameters and heat network configuration are optimized simultaneously. • The optimized heat network highly depends on the HRSG type and model specification. - Abstract: A large number of heat flows at various temperature and pressure levels exist in a polygeneration plant which co-produces electricity and chemical products. Integration of these external heat flows in a heat recovery steam generator (HRSG) has great potential to further enhance energy efficiency of such a plant; however, it is a challenging problem arising from the large design space of heat exchanger network. In this paper, a mixed-integer nonlinear programming model is developed for the design optimization of a HRSG with consideration of all alternative matches between the HRSG and external heat flows. This model is applied to four polygeneration cases with different HRSG types, and results indicate that the optimized heat network mainly depends on the HRSG type and the model specification

Optimal planning of gas turbine cogeneration system based on linear programming. Paper no. IGEC-1-ID09

International Nuclear Information System (INIS)

Oh, S.-D.; Kwak, H.-Y.

2005-01-01

An optimal planning for gas turbine cogeneration system has been studied. The planning problem considered in this study is to determine the optimal configuration of the system equipments and optimal operational policy of the system when the annual energy demands of electric power, heat and cooling are given a priori. The main benefit of the optimal planning is to minimize operational costs and to save energy by efficient energy utilization. A mixed-integer linear programming and the branch and bound algorithm have been adopted to obtain the optimal solution. Both the optimal configuration of the system equipments and the optimal operation policy has been obtained based on annual cost method. The planning method employed here may be applied to the planning problem of the cogeneration plant to any specific building or hotel. (author)

Alcohol e-Help: study protocol for a web-based self-help program to reduce alcohol use in adults with drinking patterns considered harmful, hazardous or suggestive of dependence in middle-income countries.

Science.gov (United States)

Schaub, Michael P; Tiburcio, Marcela; Martinez, Nora; Ambekar, Atul; Balhara, Yatan Pal Singh; Wenger, Andreas; Monezi Andrade, André Luiz; Padruchny, Dzianis; Osipchik, Sergey; Gehring, Elise; Poznyak, Vladimir; Rekve, Dag; Souza-Formigoni, Maria Lucia Oliveira

2018-02-01

Given the scarcity of alcohol prevention and alcohol use disorder treatments in many low and middle-income countries, the World Health Organization launched an e-health portal on alcohol and health that includes a Web-based self-help program. This paper presents the protocol for a multicentre randomized controlled trial (RCT) to test the efficacy of the internet-based self-help intervention to reduce alcohol use. Two-arm randomized controlled trial (RCT) with follow-up 6 months after randomization. Community samples in middle-income countries. People aged 18+, with Alcohol Use Disorders Identification Test (AUDIT) scores of 8+ indicating hazardous alcohol consumption. Offer of an internet-based self-help intervention, 'Alcohol e-Health', compared with a 'waiting list' control group. The intervention, adapted from a previous program with evidence of effectiveness in a high-income country, consists of modules to reduce or entirely stop drinking. The primary outcome measure is change in the Alcohol Use Disorders Identification Test (AUDIT) score assessed at 6-month follow-up. Secondary outcomes include self-reported the numbers of standard drinks and alcohol-free days in a typical week during the past 6 months, and cessation of harmful or hazardous drinking (AUDIT world-wide is considerable. © 2017 Society for the Study of Addiction.

An Optimal Turkish Private Pension Plan with a Guarantee Feature

Directory of Open Access Journals (Sweden)

Ayşegül İşcanog̃lu-Çekiç

2016-06-01

Full Text Available The Turkish Private Pension System is an investment system which aims to generate income for future consumption. This is a volunteer system, and the contributions are held in individual portfolios. Therefore, management of the funds is an important issue for both the participants and the insurance company. In this study, we propose an optimal private pension plan with a guarantee feature that is based on Constant Proportion Portfolio Insurance (CPPI. We derive a closed form formula for the optimal strategy with the help of dynamic programming. Moreover, our model is evaluated with numerical examples, and we compare its performance by implementing a sensitivity analysis.

Sequential Optimization of Global Sequence Alignments Relative to Different Cost Functions

KAUST Repository

Odat, Enas M.

2011-05-01

The purpose of this dissertation is to present a methodology to model global sequence alignment problem as directed acyclic graph which helps to extract all possible optimal alignments. Moreover, a mechanism to sequentially optimize sequence alignment problem relative to different cost functions is suggested. Sequence alignment is mostly important in computational biology. It is used to find evolutionary relationships between biological sequences. There are many algo- rithms that have been developed to solve this problem. The most famous algorithms are Needleman-Wunsch and Smith-Waterman that are based on dynamic program- ming. In dynamic programming, problem is divided into a set of overlapping sub- problems and then the solution of each subproblem is found. Finally, the solutions to these subproblems are combined into a final solution. In this thesis it has been proved that for two sequences of length m and n over a fixed alphabet, the suggested optimization procedure requires O(mn) arithmetic operations per cost function on a single processor machine. The algorithm has been simulated using C#.Net programming language and a number of experiments have been done to verify the proved statements. The results of these experiments show that the number of optimal alignments is reduced after each step of optimization. Furthermore, it has been verified that as the sequence length increased linearly then the number of optimal alignments increased exponentially which also depends on the cost function that is used. Finally, the number of executed operations increases polynomially as the sequence length increase linearly.

Networked Timetable Stability Improvement Based on a Bilevel Optimization Programming Model

Directory of Open Access Journals (Sweden)

Xuelei Meng

2014-01-01

Full Text Available Train timetable stability is the possibility to recover the status of the trains to serve as arranged according to the original timetable when the trains are disturbed. To improve the train timetable stability from the network perspective, the bilevel programming model is constructed, in which the upper level programming is to optimize the timetable stability on the network level and the lower is to improve the timetable stability on the dispatching railway segments. Timetable stability on the network level is defined with the variances of the utilization coefficients of the section capacity and station capacity. Weights of stations and sections are decided by the capacity index number and the degrees. The lower level programming focuses on the buffer time distribution plan of the trains operating on the sections and stations, taking the operating rules of the trains as constraints. A novel particle swarm algorithm is proposed and designed for the bilevel programming model. The computing case proves the feasibility of the model and the efficiency of the algorithm. The method outlined in this paper can be embedded in the networked train operation dispatching system.

DUKSUP: A Computer Program for High Thrust Launch Vehicle Trajectory Design and Optimization

Science.gov (United States)

Spurlock, O. Frank; Williams, Craig H.

2015-01-01

From the late 1960s through 1997, the leadership of NASAs Intermediate and Large class unmanned expendable launch vehicle projects resided at the NASA Lewis (now Glenn) Research Center (LeRC). One of LeRCs primary responsibilities --- trajectory design and performance analysis --- was accomplished by an internally-developed analytic three dimensional computer program called DUKSUP. Because of its Calculus of Variations-based optimization routine, this code was generally more capable of finding optimal solutions than its contemporaries. A derivation of optimal control using the Calculus of Variations is summarized including transversality, intermediate, and final conditions. The two point boundary value problem is explained. A brief summary of the codes operation is provided, including iteration via the Newton-Raphson scheme and integration of variational and motion equations via a 4th order Runge-Kutta scheme. Main subroutines are discussed. The history of the LeRC trajectory design efforts in the early 1960s is explained within the context of supporting the Centaur upper stage program. How the code was constructed based on the operation of the AtlasCentaur launch vehicle, the limits of the computers of that era, the limits of the computer programming languages, and the missions it supported are discussed. The vehicles DUKSUP supported (AtlasCentaur, TitanCentaur, and ShuttleCentaur) are briefly described. The types of missions, including Earth orbital and interplanetary, are described. The roles of flight constraints and their impact on launch operations are detailed (such as jettisoning hardware on heating, Range Safety, ground station tracking, and elliptical parking orbits). The computer main frames on which the code was hosted are described. The applications of the code are detailed, including independent check of contractor analysis, benchmarking, leading edge analysis, and vehicle performance improvement assessments. Several of DUKSUPs many major impacts on

Optimization of dairy cattle breeding programs for different environment with genotype by environment interaction

NARCIS (Netherlands)

Mulder, H.A.; Veerkamp, R.F.; Ducro, B.J.; Arendonk, van J.A.M.; Bijma, P.

2006-01-01

Dairy cattle breeding organizations tend to sell semen to breeders operating in different environments and genotype × environment interaction may play a role. The objective of this study was to investigate optimization of dairy cattle breeding programs for 2 environments with genotype × environment

Program Helps Simulate Neural Networks

Science.gov (United States)

Villarreal, James; Mcintire, Gary

1993-01-01

Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

Designing Abstractions for JavaScript Program Analysis

DEFF Research Database (Denmark)

Andreasen, Esben Sparre

JavaScript is a widely used dynamic programming language. What started out as a client-side scripting language for browsers, is now used for large applications in many different settings. As for other dynamic languages, JavaScript makes it easy to write programs quickly without being constrained...... by the language, and programmers exploit that power to write highly dynamic programs. Automated tools for helping programmers and optimizing programs are used successfully for many programming languages. Unfortunately, the automated tools for JavaScript are not as good as for other programming languages....... The program analyses, that the automated tools are built upon, are poorly suited to deal with the highly dynamic nature of JavaScript programs. The lack of language restrictions on the programmer are detrimental to the quality of program analyses for JavaScript. The aim of this dissertation is to address...

Spur gears: Optimal geometry, methods for generation and Tooth Contact Analysis (TCA) program

Science.gov (United States)

Litvin, Faydor L.; Zhang, Jiao

1988-01-01

The contents of this report include the following: (1) development of optimal geometry for crowned spur gears; (2) methods for their generation; and (3) tooth contact analysis (TCA) computer programs for the analysis of meshing and bearing contact on the crowned spur gears. The method developed for synthesis is used for the determination of the optimal geometry for crowned pinion surface and is directed to reduce the sensitivity of the gears to misalignment, localize the bearing contact, and guarantee the favorable shape and low level of the transmission errors. A new method for the generation of the crowned pinion surface has been proposed. This method is based on application of the tool with a surface of revolution that slightly deviates from a regular cone surface. The tool can be used as a grinding wheel or as a shaver. The crowned pinion surface can also be generated by a generating plane whose motion is provided by an automatic grinding machine controlled by a computer. The TCA program simulates the meshing and bearing contact of the misaligned gears. The transmission errors are also determined.

Automated design and optimization of flexible booster autopilots via linear programming, volume 1

Science.gov (United States)

Hauser, F. D.

1972-01-01

A nonlinear programming technique was developed for the automated design and optimization of autopilots for large flexible launch vehicles. This technique, which resulted in the COEBRA program, uses the iterative application of linear programming. The method deals directly with the three main requirements of booster autopilot design: to provide (1) good response to guidance commands; (2) response to external disturbances (e.g. wind) to minimize structural bending moment loads and trajectory dispersions; and (3) stability with specified tolerances on the vehicle and flight control system parameters. The method is applicable to very high order systems (30th and greater per flight condition). Examples are provided that demonstrate the successful application of the employed algorithm to the design of autopilots for both single and multiple flight conditions.

Interactive software tool to comprehend the calculation of optimal sequence alignments with dynamic programming.

Science.gov (United States)

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.

EPIC: Helping School Life and Family Support Each Other.

Science.gov (United States)

Montgomery, David

1992-01-01

Born out of a 1981 murder, Buffalo (New York) Public Schools' EPIC (Effective Parenting Information for Children) program successfully combines parenting, effective teaching, and community programs to help family and school life support each other. Under EPIC, teachers are advised to help students acquire 23 skills involving self-esteem, rules,…

Two-objective on-line optimization of supervisory control strategy

Energy Technology Data Exchange (ETDEWEB)

Nassif, N.; Kajl, S.; Sabourin, R. [Ecole de Technologie Superieure, Montreal (Canada)

2004-09-01

The set points of supervisory control strategy are optimized with respect to energy use and thermal comfort for existing HVAC systems. The set point values of zone temperatures, supply duct static pressure, and supply air temperature are the problem variables, while energy use and thermal comfort are the objective functions. The HVAC system model includes all the individual component models developed and validated against the monitored data of an existing VAV system. It serves to calculate energy use during the optimization process, whereas the actual energy use is determined by using monitoring data and the appropriate validated component models. A comparison, done for one summer week, of actual and optimal energy use shows that the on-line implementation of a genetic algorithm optimization program to determine the optimal set points of supervisory control strategy could save energy by 19.5%, while satisfying the minimum zone airflow rates and the thermal comfort. The results also indicate that the application of the two-objective optimization problem can help control daily energy use or daily building thermal comfort, thus saving more energy than the application of the one-objective optimization problem. (Author)

Element Partition Trees For H-Refined Meshes to Optimize Direct Solver Performance. Part I: Dynamic Programming

KAUST Repository

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

Element Partition Trees For H-Refined Meshes to Optimize Direct Solver Performance. Part I: Dynamic Programming

KAUST Repository

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

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.

Designing, programming, and optimizing a (small) quantum computer

Science.gov (United States)

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.

Optimal Strategy for Integrated Dynamic Inventory Control and Supplier Selection in Unknown Environment via Stochastic Dynamic Programming

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)

Decision-making methodology of optimal shielding materials by using fuzzy linear programming

International Nuclear Information System (INIS)

Kanai, Y.; Miura, T.; Hirao, Y.

2000-01-01

The main purpose of our studies are to select materials and determine the ratio of constituent materials as the first stage of optimum shielding design to suit the individual requirements of nuclear reactors, reprocessing facilities, casks for shipping spent fuel, etc. The parameters of the shield optimization are cost, space, weight and some shielding properties such as activation rates or individual irradiation and cooling time, and total dose rate for neutrons (including secondary gamma ray) and for primary gamma ray. Using conventional two-valued logic (i.e. crisp) approaches, huge combination calculations are needed to identify suitable materials for optimum shielding design. Also, re-computation is required for minor changes, as the approach does not react sensitively to the computation result. Present approach using a fuzzy linear programming method is much of the decision-making toward the satisfying solution might take place in fuzzy environment. And it can quickly and easily provide a guiding principle of optimal selection of shielding materials under the above-mentioned conditions. The possibility or reducing radiation effects by optimizing the ratio of constituent materials is investigated. (author)

Optimization of basic parameters in temperature-programmed gas chromatographic separations of multi-component samples within a given time

NARCIS (Netherlands)

Repka, D.; Krupcik, J.; Brunovska, A.; Leclercq, P.A.; Rijks, J.A.

1989-01-01

A new procedure is introduced for the optimization of column peak capacity in a given time. The opitmization focuses on temperature-programmed operating conditions, notably the initial temperature and hold time, and the programming rate. Based conceptually upon Lagrange functions, experiments were

Optimizing basin-scale coupled water quantity and water quality management with stochastic dynamic programming

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...

Constrained Quadratic Programming and Neurodynamics-Based Solver for Energy Optimization of Biped Walking Robots

Directory of Open Access Journals (Sweden)

Liyang Wang

2017-01-01

Full Text Available The application of biped robots is always trapped by their high energy consumption. This paper makes a contribution by optimizing the joint torques to decrease the energy consumption without changing the biped gaits. In this work, a constrained quadratic programming (QP problem for energy optimization is formulated. A neurodynamics-based solver is presented to solve the QP problem. Differing from the existing literatures, the proposed neurodynamics-based energy optimization (NEO strategy minimizes the energy consumption and guarantees the following three important constraints simultaneously: (i the force-moment equilibrium equation of biped robots, (ii frictions applied by each leg on the ground to hold the biped robot without slippage and tipping over, and (iii physical limits of the motors. Simulations demonstrate that the proposed strategy is effective for energy-efficient biped walking.

Optimization of land use of agricultural farms in Sumedang regency by using linear programming models

Science.gov (United States)

Zenis, F. M.; Supian, S.; Lesmana, E.

2018-03-01

Land is one of the most important assets for farmers in Sumedang Regency. Therefore, agricultural land should be used optimally. This study aims to obtain the optimal land use composition in order to obtain maximum income. The optimization method used in this research is Linear Programming Models. Based on the results of the analysis, the composition of land use for rice area of 135.314 hectares, corn area of 11.798 hectares, soy area of 2.290 hectares, and peanuts of 2.818 hectares with the value of farmers income of IDR 2.682.020.000.000,-/year. The results of this analysis can be used as a consideration in decisions making about cropping patterns by farmers.

Metrics Evolution in an Energy Research and Development Program

International Nuclear Information System (INIS)

Dixon, Brent

2011-01-01

All technology programs progress through three phases: Discovery, Definition, and Deployment. The form and application of program metrics needs to evolve with each phase. During the discovery phase, the program determines what is achievable. A set of tools is needed to define program goals, to analyze credible technical options, and to ensure that the options are compatible and meet the program objectives. A metrics system that scores the potential performance of technical options is part of this system of tools, supporting screening of concepts and aiding in the overall definition of objectives. During the definition phase, the program defines what specifically is wanted. What is achievable is translated into specific systems and specific technical options are selected and optimized. A metrics system can help with the identification of options for optimization and the selection of the option for deployment. During the deployment phase, the program shows that the selected system works. Demonstration projects are established and classical systems engineering is employed. During this phase, the metrics communicate system performance. This paper discusses an approach to metrics evolution within the Department of Energy's Nuclear Fuel Cycle R and D Program, which is working to improve the sustainability of nuclear energy.

Linear programming optimization of nuclear energy strategy with sodium-cooled fast reactors

International Nuclear Information System (INIS)

Lee, Je Whan; Jeong, Yong Hoon; Chang, Yoon Il; Chang, Soon Heung

2011-01-01

Nuclear power has become an essential part of electricity generation to meet the continuous growth of electricity demand. A Sodium-cooled Fast Reactor (SFR) was developed to extend uranium resource utilization under a growing nuclear energy scenario while concomitantly providing a nuclear waste management solution. Key questions in this scenario are when to introduce SFRs and how many reactors should be introduced. In this study, a methodology using Linear Programming is employed in order to quantify an optimized growth pattern of a nuclear energy system comprising light water reactors and SFRs. The optimization involves tradeoffs between SFR capital cost premiums and the total system U3O8 price premiums. Optimum nuclear growth patterns for several scenarios are presented, as well as sensitivity analyses of important input parameters

Program change management during nuclear power plant decommissioning

International Nuclear Information System (INIS)

Bushart, Sean; Kim, Karen; Naughton, Michael

2011-01-01

Decommissioning a nuclear power plant is a complex project. The project involves the coordination of several different departments and the management of changing plant conditions, programs, and regulations. As certain project Milestones are met, the evolution of such plant programs and regulations can help optimize project execution and cost. This paper will provide information about these Milestones and the plant departments and programs that change throughout a decommissioning project. The initial challenge in the decommissioning of a nuclear plant is the development of a definitive plan for such a complex project. EPRI has published several reports related to decommissioning planning. These earlier reports provided general guidance in formulating a Decommissioning Plan. This Change Management paper will draw from the experience gained in the last decade in decommissioning of nuclear plants. The paper discusses decommissioning in terms of a sequence of major Milestones. The plant programs, associated plans and actions, and staffing are discussed based upon experiences from the following power reactor facilities: Maine Yankee Atomic Power Plant, Yankee Nuclear Power Station, and the Haddam Neck Plant. Significant lessons learned from other sites are also discussed as appropriate. Planning is a crucial ingredient of successful decommissioning projects. The development of a definitive Decommissioning Plan can result in considerable project savings. The decommissioning plants in the U.S. have planned and executed their projects using different strategies based on their unique plant circumstances. However, experience has shown that similar project milestones and actions applied through all of these projects. This allows each plant to learn from the experiences of the preceding projects. As the plant transitions from an operating plant through decommissioning, the reduction and termination of defunct programs and regulations can help optimize all facets of

Multi-Objective Stochastic Optimization Programs for a Non-Life Insurance Company under Solvency Constraints

Directory of Open Access Journals (Sweden)

Massimiliano Kaucic

2015-09-01

Full Text Available In the paper, we introduce a multi-objective scenario-based optimization approach for chance-constrained portfolio selection problems. More specifically, a modified version of the normal constraint method is implemented with a global solver in order to generate a dotted approximation of the Pareto frontier for bi- and tri-objective programming problems. Numerical experiments are carried out on a set of portfolios to be optimized for an EU-based non-life insurance company. Both performance indicators and risk measures are managed as objectives. Results show that this procedure is effective and readily applicable to achieve suitable risk-reward tradeoff analysis.

Multiobjective Optimization of Aircraft Maintenance in Thailand Using Goal Programming: A Decision-Support Model

Directory of Open Access Journals (Sweden)

Yuttapong Pleumpirom

2012-01-01

Full Text Available The purpose of this paper is to develop the multiobjective optimization model in order to evaluate suppliers for aircraft maintenance tasks, using goal programming. The authors have developed a two-step process. The model will firstly be used as a decision-support tool for managing demand, by using aircraft and flight schedules to evaluate and generate aircraft-maintenance requirements, including spare-part lists. Secondly, they develop a multiobjective optimization model by minimizing cost, minimizing lead time, and maximizing the quality under various constraints in the model. Finally, the model is implemented in the actual airline's case.

Cultural Adaptation of a Cognitive Behavior Therapy Guided Self-Help Program for Mexican American Women with Binge Eating Disorders

Science.gov (United States)

Shea, Munyi; Cachelin, Fary; Uribe, Luz; Striegel, Ruth H.; Thompson, Douglas; Wilson, G. Terence

2012-01-01

Data on the compatibility of evidence-based treatment in ethnic minority groups are limited. This study utilized focus group interviews to elicit Mexican American women's (N = 12) feedback on a cognitive behavior therapy guided self-help program for binge eating disorders. Findings revealed 6 themes to be considered during the cultural adaptation…

Database Optimizing Services

Directory of Open Access Journals (Sweden)

Adrian GHENCEA

2010-12-01

Full Text Available Almost every organization has at its centre a database. The database provides support for conducting different activities, whether it is production, sales and marketing or internal operations. Every day, a database is accessed for help in strategic decisions. The satisfaction therefore of such needs is entailed with a high quality security and availability. Those needs can be realised using a DBMS (Database Management System which is, in fact, software for a database. Technically speaking, it is software which uses a standard method of cataloguing, recovery, and running different data queries. DBMS manages the input data, organizes it, and provides ways of modifying or extracting the data by its users or other programs. Managing the database is an operation that requires periodical updates, optimizing and monitoring.

Manipulation and handling processes off-line programming and optimization with use of K-Roset

Science.gov (United States)

Gołda, G.; Kampa, A.

2017-08-01

Contemporary trends in development of efficient, flexible manufacturing systems require practical implementation of modern “Lean production” concepts for maximizing customer value through minimizing all wastes in manufacturing and logistics processes. Every FMS is built on the basis of automated and robotized production cells. Except flexible CNC machine tools and other equipments, the industrial robots are primary elements of the system. In the studies, authors look for wastes of time and cost in real tasks of robots, during manipulation processes. According to aspiration for optimization of handling and manipulation processes with use of the robots, the application of modern off-line programming methods and computer simulation, is the best solution and it is only way to minimize unnecessary movements and other instructions. The modelling process of robotized production cell and offline programming of Kawasaki robots in AS-Language will be described. The simulation of robotized workstation will be realized with use of virtual reality software K-Roset. Authors show the process of industrial robot’s programs improvement and optimization in terms of minimizing the number of useless manipulator movements and unnecessary instructions. This is realized in order to shorten the time of production cycles. This will also reduce costs of handling, manipulations and technological process.

78 FR 57845 - Notice of Availability (NOA) for Strategic Network Optimization (SNO) Program Environmental...

Science.gov (United States)

2013-09-20

... Logistics Agency, DoD. ACTION: Notice of Availability (NOA) for Strategic Network Optimization (SNO) Program Environmental Assessment. SUMMARY: The Defense Logistics Agency (DLA) announces the availability of an...-0705 during normal business hours Monday through Friday, from 8:00 a.m. to 4:30 p.m. (EST) or by email...

A Bridge to Active Learning: A Summer Bridge Program Helps Students Maximize Their Active-Learning Experiences and the Active-Learning Experiences of Others

Science.gov (United States)

Cooper, Katelyn M.; Ashley, Michael; Brownell, Sara E.

2017-01-01

National calls to improve student academic success in college have sparked the development of bridge programs designed to help students transition from high school to college. We designed a 2-week Summer Bridge program that taught introductory biology content in an active-learning way. Through a set of exploratory interviews, we unexpectedly…

Application of goal programming to decision problem on optimal allocation of radiation workers

International Nuclear Information System (INIS)

Sa, Sangduk; Narita, Masakuni

1993-01-01

This paper is concerned with an optimal planning in a multiple objective decision-making problem of allocating radiation workers to workplaces associated with occupational exposure. The model problem is formulated with the application of goal programming which effectively followed up diverse and conflicting factors influencing the optimal decision. The formulation is based on the data simulating the typical situations encountered at the operating facilities such as nuclear power plants where exposure control is critical to the management. Multiple goals set by the decision-maker/manager who has the operational responsibilities for radiological protection are illustrated in terms of work requirements, exposure constraints of the places, desired allocation of specific personnel and so on. Test results of the model are considered to indicate that the model structure and its solution process can provide the manager with a good set of analysis of his problems in implementing the optimization review of radiation protection during normal operation. (author)

Using POGIL to Help Students Learn to Program

Science.gov (United States)

Hu, Helen H.; Shepherd, Tricia D.

2013-01-01

POGIL has been successfully implemented in a scientific computing course to teach science students how to program in Python. Following POGIL guidelines, the authors have developed guided inquiry activities that lead student teams to discover and understand programming concepts. With each iteration of the scientific computing course, the authors…

Improved decomposition–coordination and discrete differential dynamic programming for optimization of large-scale hydropower system

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

Cost-effectiveness of the "helping babies breathe" program in a missionary hospital in rural Tanzania.

Science.gov (United States)

Vossius, Corinna; Lotto, Editha; Lyanga, Sara; Mduma, Estomih; Msemo, Georgina; Perlman, Jeffrey; Ersdal, Hege L

2014-01-01

The Helping Babies Breathe" (HBB) program is an evidence-based curriculum in basic neonatal care and resuscitation, utilizing simulation-based training to educate large numbers of birth attendants in low-resource countries. We analyzed its cost-effectiveness at a faith-based Haydom Lutheran Hospital (HLH) in rural Tanzania. Data about early neonatal mortality and fresh stillbirth rates were drawn from a linked observational study during one year before and one year after full implementation of the HBB program. Cost data were provided by the Tanzanian Ministry of Health and Social Welfare (MOHSW), the research department at HLH, and the manufacturer of the training material Lærdal Global Health. Costs per life saved were USD 233, while they were USD 4.21 per life year gained. Costs for maintaining the program were USD 80 per life saved and USD 1.44 per life year gained. Costs per disease adjusted life year (DALY) averted ranged from International Dollars (ID; a virtual valuta corrected for purchasing power world-wide) 12 to 23, according to how DALYs were calculated. The HBB program is a low-cost intervention. Implementation in a very rural faith-based hospital like HLH has been highly cost-effective. To facilitate further global implementation of HBB a cost-effectiveness analysis including government owned institutions, urban hospitals and district facilities is desirable for a more diverse analysis to explore cost-driving factors and predictors of enhanced cost-effectiveness.

Cost-effectiveness of the "helping babies breathe" program in a missionary hospital in rural Tanzania.

Directory of Open Access Journals (Sweden)

Corinna Vossius

Full Text Available The Helping Babies Breathe" (HBB program is an evidence-based curriculum in basic neonatal care and resuscitation, utilizing simulation-based training to educate large numbers of birth attendants in low-resource countries. We analyzed its cost-effectiveness at a faith-based Haydom Lutheran Hospital (HLH in rural Tanzania.Data about early neonatal mortality and fresh stillbirth rates were drawn from a linked observational study during one year before and one year after full implementation of the HBB program. Cost data were provided by the Tanzanian Ministry of Health and Social Welfare (MOHSW, the research department at HLH, and the manufacturer of the training material Lærdal Global Health.Costs per life saved were USD 233, while they were USD 4.21 per life year gained. Costs for maintaining the program were USD 80 per life saved and USD 1.44 per life year gained. Costs per disease adjusted life year (DALY averted ranged from International Dollars (ID; a virtual valuta corrected for purchasing power world-wide 12 to 23, according to how DALYs were calculated.The HBB program is a low-cost intervention. Implementation in a very rural faith-based hospital like HLH has been highly cost-effective. To facilitate further global implementation of HBB a cost-effectiveness analysis including government owned institutions, urban hospitals and district facilities is desirable for a more diverse analysis to explore cost-driving factors and predictors of enhanced cost-effectiveness.

HIV Treatment and Prevention: A Simple Model to Determine Optimal Investment.

Science.gov (United States)

Juusola, Jessie L; Brandeau, Margaret L

2016-04-01

To create a simple model to help public health decision makers determine how to best invest limited resources in HIV treatment scale-up and prevention. A linear model was developed for determining the optimal mix of investment in HIV treatment and prevention, given a fixed budget. The model incorporates estimates of secondary health benefits accruing from HIV treatment and prevention and allows for diseconomies of scale in program costs and subadditive benefits from concurrent program implementation. Data sources were published literature. The target population was individuals infected with HIV or at risk of acquiring it. Illustrative examples of interventions include preexposure prophylaxis (PrEP), community-based education (CBE), and antiretroviral therapy (ART) for men who have sex with men (MSM) in the US. Outcome measures were incremental cost, quality-adjusted life-years gained, and HIV infections averted. Base case analysis indicated that it is optimal to invest in ART before PrEP and to invest in CBE before scaling up ART. Diseconomies of scale reduced the optimal investment level. Subadditivity of benefits did not affect the optimal allocation for relatively low implementation levels. The sensitivity analysis indicated that investment in ART before PrEP was optimal in all scenarios tested. Investment in ART before CBE became optimal when CBE reduced risky behavior by 4% or less. Limitations of the study are that dynamic effects are approximated with a static model. Our model provides a simple yet accurate means of determining optimal investment in HIV prevention and treatment. For MSM in the US, HIV control funds should be prioritized on inexpensive, effective programs like CBE, then on ART scale-up, with only minimal investment in PrEP. © The Author(s) 2015.

Analytical Model-Based Design Optimization of a Transverse Flux Machine

Energy Technology Data Exchange (ETDEWEB)

Hasan, Iftekhar; Husain, Tausif; Sozer, Yilmaz; Husain, Iqbal; Muljadi, Eduard

2017-02-16

This paper proposes an analytical machine design tool using magnetic equivalent circuit (MEC)-based particle swarm optimization (PSO) for a double-sided, flux-concentrating transverse flux machine (TFM). The magnetic equivalent circuit method is applied to analytically establish the relationship between the design objective and the input variables of prospective TFM designs. This is computationally less intensive and more time efficient than finite element solvers. A PSO algorithm is then used to design a machine with the highest torque density within the specified power range along with some geometric design constraints. The stator pole length, magnet length, and rotor thickness are the variables that define the optimization search space. Finite element analysis (FEA) was carried out to verify the performance of the MEC-PSO optimized machine. The proposed analytical design tool helps save computation time by at least 50% when compared to commercial FEA-based optimization programs, with results found to be in agreement with less than 5% error.

A Guided Online and Mobile Self-Help Program for Individuals With Eating Disorders: An Iterative Engagement and Usability Study.

Science.gov (United States)

Nitsch, Martina; Dimopoulos, Christina N; Flaschberger, Edith; Saffran, Kristina; Kruger, Jenna F; Garlock, Lindsay; Wilfley, Denise E; Taylor, Craig B; Jones, Megan

2016-01-11

engagement. This study identified salient usability and engagement features associated with participant motivation to use the Healthy Body Image Program and ultimately helped improve the program prior to its implementation. This research demonstrates that improvements in usability and engagement can be achieved by testing and adjusting intervention design and content prior to program launch. The results are consistent with related research and reinforce the need for further research to identify usage patterns and effective means for reducing dropout. Digital health research should include usability studies prior to efficacy trials to help create more user-friendly programs that have a higher likelihood of "real-world" adoption.

Helping Nevada School Children Become Sun Smart

Centers for Disease Control (CDC) Podcasts

This podcast features Christine Thompson, Community Programs Manager at the Nevada Cancer Coalition, and author of a recent study detailing a school-based program to help Nevada school children establish healthy sun safety habits and decrease UV exposure. Christine answers questions about her research and what impact her what impact the program had on children's skin health.

76 FR 48204 - Fund Availability Under VA's Homeless Providers Grant and Per Diem Program

Science.gov (United States)

2011-08-08

... programs addressing emotional, social, spiritual, and generative needs. Terminally Ill (1) Help... optimize reintegration such as life-skills education, recreational activities, and follow up case..., and medication education. Through this NOFA, VA seeks to renew the FY 2009 previous grant and per diem...

Optimal Control of Scalar Conservation Laws Using Linear/Quadratic Programming: Application to Transportation Networks

KAUST Repository

Li, Yanning

2014-03-01

This article presents a new optimal control framework for transportation networks in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi (H-J) equation and the commonly used triangular fundamental diagram, we pose the problem of controlling the state of the system on a network link, in a finite horizon, as a Linear Program (LP). We then show that this framework can be extended to an arbitrary transportation network, resulting in an LP or a Quadratic Program. Unlike many previously investigated transportation network control schemes, this method yields a globally optimal solution and is capable of handling shocks (i.e., discontinuities in the state of the system). As it leverages the intrinsic properties of the H-J equation used to model the state of the system, it does not require any approximation, unlike classical methods that are based on discretizations of the model. The computational efficiency of the method is illustrated on a transportation network. © 2014 IEEE.

Optimal Control of Scalar Conservation Laws Using Linear/Quadratic Programming: Application to Transportation Networks

KAUST Repository

Li, Yanning; Canepa, Edward S.; Claudel, Christian

2014-01-01

This article presents a new optimal control framework for transportation networks in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi (H-J) equation and the commonly used triangular fundamental diagram, we pose the problem of controlling the state of the system on a network link, in a finite horizon, as a Linear Program (LP). We then show that this framework can be extended to an arbitrary transportation network, resulting in an LP or a Quadratic Program. Unlike many previously investigated transportation network control schemes, this method yields a globally optimal solution and is capable of handling shocks (i.e., discontinuities in the state of the system). As it leverages the intrinsic properties of the H-J equation used to model the state of the system, it does not require any approximation, unlike classical methods that are based on discretizations of the model. The computational efficiency of the method is illustrated on a transportation network. © 2014 IEEE.

Two example applications of optimization techniques to US Department of Energy contractor radiation protection programs

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

REopt: A Platform for Energy System Integration and Optimization: Preprint

Energy Technology Data Exchange (ETDEWEB)

Simpkins, T.; Cutler, D.; Anderson, K.; Olis, D.; Elgqvist, E.; Callahan, M.; Walker, A.

2014-08-01

REopt is NREL's energy planning platform offering concurrent, multi-technology integration and optimization capabilities to help clients meet their cost savings and energy performance goals. The REopt platform provides techno-economic decision-support analysis throughout the energy planning process, from agency-level screening and macro planning to project development to energy asset operation. REopt employs an integrated approach to optimizing a site?s energy costs by considering electricity and thermal consumption, resource availability, complex tariff structures including time-of-use, demand and sell-back rates, incentives, net-metering, and interconnection limits. Formulated as a mixed integer linear program, REopt recommends an optimally-sized mix of conventional and renewable energy, and energy storage technologies; estimates the net present value associated with implementing those technologies; and provides the cost-optimal dispatch strategy for operating them at maximum economic efficiency. The REopt platform can be customized to address a variety of energy optimization scenarios including policy, microgrid, and operational energy applications. This paper presents the REopt techno-economic model along with two examples of recently completed analysis projects.

Dose optimization based on linear programming implemented in a system for treatment planning in Monte Carlo

International Nuclear Information System (INIS)

Ureba, A.; Palma, B. A.; Leal, A.

2011-01-01

Develop a more efficient method of optimization in relation to time, based on linear programming designed to implement a multi objective penalty function which also permits a simultaneous solution integrated boost situations considering two white volumes simultaneously.

Optimization of healthcare supply chain in context of macro-ergonomics factors by a unique mathematical programming approach.

Science.gov (United States)

Azadeh, A; Motevali Haghighi, S; Gaeini, Z; Shabanpour, N

2016-07-01

This study presents an integrated approach for analyzing the impact of macro-ergonomics factors in healthcare supply chain (HCSC) by data envelopment analysis (DEA). The case of this study is the supply chain (SC) of a real hospital. Thus, healthcare standards and macro-ergonomics factors are considered to be modeled by the mathematical programming approach. Over 28 subsidiary SC divisions with parallel missions and objectives are evaluated by analyzing inputs and outputs through DEA. Each division in this HCSC is considered as decision making unit (DMU). This approach can analyze the impact of macro-ergonomics factors on supply chain management (SCM) in healthcare sector. Also, this method ranks the relevant performance efficiencies of each HCSC. In this study by using proposed method, the most effective macro-ergonomics factor on HCSC is identified as "teamwork" issue. Also, this study would help managers to identify the areas of weaknesses in their SCM system and set improvement target plan for the related SCM system in healthcare industry. To the best of our knowledge, this is the first study for macro-ergonomics optimization of HCSC. Copyright © 2016 Elsevier Ltd and The Ergonomics Society. All rights reserved.

CAN-DO, CFD-based Aerodynamic Nozzle Design and Optimization program for supersonic/hypersonic wind tunnels

Science.gov (United States)

Korte, John J.; Kumar, Ajay; Singh, D. J.; White, J. A.

1992-01-01

A design program is developed which incorporates a modern approach to the design of supersonic/hypersonic wind-tunnel nozzles. The approach is obtained by the coupling of computational fluid dynamics (CFD) with design optimization. The program can be used to design a 2D or axisymmetric, supersonic or hypersonic, wind-tunnel nozzles that can be modeled with a calorically perfect gas. The nozzle design is obtained by solving a nonlinear least-squares optimization problem (LSOP). The LSOP is solved using an iterative procedure which requires intermediate flowfield solutions. The nozzle flowfield is simulated by solving the Navier-Stokes equations for the subsonic and transonic flow regions and the parabolized Navier-Stokes equations for the supersonic flow regions. The advantages of this method are that the design is based on the solution of the viscous equations eliminating the need to make separate corrections to a design contour, and the flexibility of applying the procedure to different types of nozzle design problems.

The Programming Optimization of Capacitorless 1T DRAM Based on the Dual-Gate TFET.

Science.gov (United States)

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

Science.gov (United States)

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.

Equipment cost optimization

International Nuclear Information System (INIS)

Ribeiro, E.M.; Farias, M.A.; Dreyer, S.R.B.

1995-01-01

Considering the importance of the cost of material and equipment in the overall cost profile of an oil company, which in the case of Petrobras, represents approximately 23% of the total operational cost or 10% of the sales, an organization for the optimization of such costs has been established within Petrobras. Programs are developed aiming at: optimization of life-cycle cost of material and equipment; optimization of industrial processes costs through material development. This paper describes the methodology used in the management of the development programs and presents some examples of concluded and ongoing programs, which are conducted in permanent cooperation with suppliers, technical laboratories and research institutions and have been showing relevant results

A two-stage stochastic programming model for the optimal design of distributed energy systems

International Nuclear Information System (INIS)

Zhou, Zhe; Zhang, Jianyun; Liu, Pei; Li, Zheng; Georgiadis, Michael C.; Pistikopoulos, Efstratios N.

2013-01-01

Highlights: ► The optimal design of distributed energy systems under uncertainty is studied. ► A stochastic model is developed using genetic algorithm and Monte Carlo method. ► The proposed system possesses inherent robustness under uncertainty. ► The inherent robustness is due to energy storage facilities and grid connection. -- Abstract: A distributed energy system is a multi-input and multi-output energy system with substantial energy, economic and environmental benefits. The optimal design of such a complex system under energy demand and supply uncertainty poses significant challenges in terms of both modelling and corresponding solution strategies. This paper proposes a two-stage stochastic programming model for the optimal design of distributed energy systems. A two-stage decomposition based solution strategy is used to solve the optimization problem with genetic algorithm performing the search on the first stage variables and a Monte Carlo method dealing with uncertainty in the second stage. The model is applied to the planning of a distributed energy system in a hotel. Detailed computational results are presented and compared with those generated by a deterministic model. The impacts of demand and supply uncertainty on the optimal design of distributed energy systems are systematically investigated using proposed modelling framework and solution approach.

Stress-constrained truss topology optimization problems that can be solved by linear programming

DEFF Research Database (Denmark)

Stolpe, Mathias; Svanberg, Krister

2004-01-01

We consider the problem of simultaneously selecting the material and determining the area of each bar in a truss structure in such a way that the cost of the structure is minimized subject to stress constraints under a single load condition. We show that such problems can be solved by linear...... programming to give the global optimum, and that two different materials are always sufficient in an optimal structure....

Online Adaptive Optimal Control of Vehicle Active Suspension Systems Using Single-Network Approximate Dynamic Programming

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.

A Hybrid Method for Modeling and Solving Supply Chain Optimization Problems with Soft and Logical Constraints

Directory of Open Access Journals (Sweden)

Paweł Sitek

2016-01-01

Full Text Available This paper presents a hybrid method for modeling and solving supply chain optimization problems with soft, hard, and logical constraints. Ability to implement soft and logical constraints is a very important functionality for supply chain optimization models. Such constraints are particularly useful for modeling problems resulting from commercial agreements, contracts, competition, technology, safety, and environmental conditions. Two programming and solving environments, mathematical programming (MP and constraint logic programming (CLP, were combined in the hybrid method. This integration, hybridization, and the adequate multidimensional transformation of the problem (as a presolving method helped to substantially reduce the search space of combinatorial models for supply chain optimization problems. The operation research MP and declarative CLP, where constraints are modeled in different ways and different solving procedures are implemented, were linked together to use the strengths of both. This approach is particularly important for the decision and combinatorial optimization models with the objective function and constraints, there are many decision variables, and these are summed (common in manufacturing, supply chain management, project management, and logistic problems. The ECLiPSe system with Eplex library was proposed to implement a hybrid method. Additionally, the proposed hybrid transformed model is compared with the MILP-Mixed Integer Linear Programming model on the same data instances. For illustrative models, its use allowed finding optimal solutions eight to one hundred times faster and reducing the size of the combinatorial problem to a significant extent.

Optimization of programming parameters in children with the advanced bionics cochlear implant.

Science.gov (United States)

Baudhuin, Jacquelyn; Cadieux, Jamie; Firszt, Jill B; Reeder, Ruth M; Maxson, Jerrica L

2012-05-01

Cochlear implants provide access to soft intensity sounds and therefore improved audibility for children with severe-to-profound hearing loss. Speech processor programming parameters, such as threshold (or T-level), input dynamic range (IDR), and microphone sensitivity, contribute to the recipient's program and influence audibility. When soundfield thresholds obtained through the speech processor are elevated, programming parameters can be modified to improve soft sound detection. Adult recipients show improved detection for low-level sounds when T-levels are set at raised levels and show better speech understanding in quiet when wider IDRs are used. Little is known about the effects of parameter settings on detection and speech recognition in children using today's cochlear implant technology. The overall study aim was to assess optimal T-level, IDR, and sensitivity settings in pediatric recipients of the Advanced Bionics cochlear implant. Two experiments were conducted. Experiment 1 examined the effects of two T-level settings on soundfield thresholds and detection of the Ling 6 sounds. One program set T-levels at 10% of most comfortable levels (M-levels) and another at 10 current units (CUs) below the level judged as "soft." Experiment 2 examined the effects of IDR and sensitivity settings on speech recognition in quiet and noise. Participants were 11 children 7-17 yr of age (mean 11.3) implanted with the Advanced Bionics High Resolution 90K or CII cochlear implant system who had speech recognition scores of 20% or greater on a monosyllabic word test. Two T-level programs were compared for detection of the Ling sounds and frequency modulated (FM) tones. Differing IDR/sensitivity programs (50/0, 50/10, 70/0, 70/10) were compared using Ling and FM tone detection thresholds, CNC (consonant-vowel nucleus-consonant) words at 50 dB SPL, and Hearing in Noise Test for Children (HINT-C) sentences at 65 dB SPL in the presence of four-talker babble (+8 signal

A Pareto-optimal moving average multigene genetic programming model for daily streamflow prediction

Science.gov (United States)

Danandeh Mehr, Ali; Kahya, Ercan

2017-06-01

Genetic programming (GP) is able to systematically explore alternative model structures of different accuracy and complexity from observed input and output data. The effectiveness of GP in hydrological system identification has been recognized in recent studies. However, selecting a parsimonious (accurate and simple) model from such alternatives still remains a question. This paper proposes a Pareto-optimal moving average multigene genetic programming (MA-MGGP) approach to develop a parsimonious model for single-station streamflow prediction. The three main components of the approach that take us from observed data to a validated model are: (1) data pre-processing, (2) system identification and (3) system simplification. The data pre-processing ingredient uses a simple moving average filter to diminish the lagged prediction effect of stand-alone data-driven models. The multigene ingredient of the model tends to identify the underlying nonlinear system with expressions simpler than classical monolithic GP and, eventually simplification component exploits Pareto front plot to select a parsimonious model through an interactive complexity-efficiency trade-off. The approach was tested using the daily streamflow records from a station on Senoz Stream, Turkey. Comparing to the efficiency results of stand-alone GP, MGGP, and conventional multi linear regression prediction models as benchmarks, the proposed Pareto-optimal MA-MGGP model put forward a parsimonious solution, which has a noteworthy importance of being applied in practice. In addition, the approach allows the user to enter human insight into the problem to examine evolved models and pick the best performing programs out for further analysis.

Optimizing preventive maintenance with maintenance templates

International Nuclear Information System (INIS)

Dozier, I.J.

1996-01-01

Rising operating costs has caused maintenance professionals to rethink their strategy for preventive maintenance (PM) programs. Maintenance Templates are pre-engineered PM task recommendations for a component type based on application of the component. Development of the maintenance template considers the dominant failure cause of the component and the type of preventive maintenance that can predict or prevent the failure from occurring. Maintenance template development also attempts to replace fixed frequency tasks with condition monitoring tasks such as vibration analysis or thermography. For those components that have fixed frequency PM intervals, consideration is given to the maintenance drivers such as criticality, environment and usage. This helps to maximize the PM frequency intervals and maximize the component availability. Maintenance Templates have been used at PECO Energy's Limerick Generating Station during the Reliability Centered Maintenance (RCM) Process to optimize their PM program. This paper describes the development and uses of the maintenance templates

Industrial waste recycling strategies optimization problem: mixed integer programming model and heuristics

Science.gov (United States)

Tang, Jiafu; Liu, Yang; Fung, Richard; Luo, Xinggang

2008-12-01

Manufacturers have a legal accountability to deal with industrial waste generated from their production processes in order to avoid pollution. Along with advances in waste recovery techniques, manufacturers may adopt various recycling strategies in dealing with industrial waste. With reuse strategies and technologies, byproducts or wastes will be returned to production processes in the iron and steel industry, and some waste can be recycled back to base material for reuse in other industries. This article focuses on a recovery strategies optimization problem for a typical class of industrial waste recycling process in order to maximize profit. There are multiple strategies for waste recycling available to generate multiple byproducts; these byproducts are then further transformed into several types of chemical products via different production patterns. A mixed integer programming model is developed to determine which recycling strategy and which production pattern should be selected with what quantity of chemical products corresponding to this strategy and pattern in order to yield maximum marginal profits. The sales profits of chemical products and the set-up costs of these strategies, patterns and operation costs of production are considered. A simulated annealing (SA) based heuristic algorithm is developed to solve the problem. Finally, an experiment is designed to verify the effectiveness and feasibility of the proposed method. By comparing a single strategy to multiple strategies in an example, it is shown that the total sales profit of chemical products can be increased by around 25% through the simultaneous use of multiple strategies. This illustrates the superiority of combinatorial multiple strategies. Furthermore, the effects of the model parameters on profit are discussed to help manufacturers organize their waste recycling network.

Helping Students Test Programs That Have Graphical User Interfaces

Directory of Open Access Journals (Sweden)

Matthew Thornton

2008-08-01

Full Text Available Within computer science education, many educators are incorporating software testing activities into regular programming assignments. Tools like JUnit and its relatives make software testing tasks much easier, bringing them into the realm of even introductory students. At the same time, many introductory programming courses are now including graphical interfaces as part of student assignments to improve student interest and engagement. Unfortunately, writing software tests for programs that have significant graphical user interfaces is beyond the skills of typical students (and many educators. This paper presents initial work at combining educationally oriented and open-source tools to create an infrastructure for writing tests for Java programs that have graphical user interfaces. Critically, these tools are intended to be appropriate for introductory (CS1/CS2 student use, and to dovetail with current teaching approaches that incorporate software testing in programming assignments. We also include in our findings our proposed approach to evaluating our techniques.

Flood Catastrophe Model for Designing Optimal Flood Insurance Program: Estimating Location-Specific Premiums in the Netherlands.

Science.gov (United States)

Ermolieva, T; Filatova, T; Ermoliev, Y; Obersteiner, M; de Bruijn, K M; Jeuken, A

2017-01-01

As flood risks grow worldwide, a well-designed insurance program engaging various stakeholders becomes a vital instrument in flood risk management. The main challenge concerns the applicability of standard approaches for calculating insurance premiums of rare catastrophic losses. This article focuses on the design of a flood-loss-sharing program involving private insurance based on location-specific exposures. The analysis is guided by a developed integrated catastrophe risk management (ICRM) model consisting of a GIS-based flood model and a stochastic optimization procedure with respect to location-specific risk exposures. To achieve the stability and robustness of the program towards floods with various recurrences, the ICRM uses stochastic optimization procedure, which relies on quantile-related risk functions of a systemic insolvency involving overpayments and underpayments of the stakeholders. Two alternative ways of calculating insurance premiums are compared: the robust derived with the ICRM and the traditional average annual loss approach. The applicability of the proposed model is illustrated in a case study of a Rotterdam area outside the main flood protection system in the Netherlands. Our numerical experiments demonstrate essential advantages of the robust premiums, namely, that they: (1) guarantee the program's solvency under all relevant flood scenarios rather than one average event; (2) establish a tradeoff between the security of the program and the welfare of locations; and (3) decrease the need for other risk transfer and risk reduction measures. © 2016 Society for Risk Analysis.

Help! A simple method for getting back-up help to the reference desk

Directory of Open Access Journals (Sweden)

Kenneth Furuta

2008-03-01

Full Text Available Using the "net send" command, native to Windows XP, librarians at the University of California, Riverside created a "help button" for the reference desk. The simple script file sends a message to librarians' workstations in their offices and logs the date and time of use. This paper describes that program.

Optimization Modeling with Spreadsheets

CERN Document Server

Baker, Kenneth R

2011-01-01

This introductory book on optimization (mathematical programming) includes coverage on linear programming, nonlinear programming, integer programming and heuristic programming; as well as an emphasis on model building using Excel and Solver.  The emphasis on model building (rather than algorithms) is one of the features that makes this book distinctive. Most books devote more space to algorithmic details than to formulation principles. These days, however, it is not necessary to know a great deal about algorithms in order to apply optimization tools, especially when relying on the sp

Tips for Helping a Person With Diabetes

Centers for Disease Control (CDC) Podcasts

2007-10-04

This podcast gives suggestions for helping a person with diabetes manage the disease.  Created: 10/4/2007 by National Diabetes Education Program (NDEP), a joint program of the Centers for Disease Control and Prevention and the National Institutes of Health.   Date Released: 11/6/2007.

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

Science.gov (United States)

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.

A novel linear programming approach to fluence map optimization for intensity modulated radiation therapy treatment planning

International Nuclear Information System (INIS)

Romeijn, H Edwin; Ahuja, Ravindra K; Dempsey, James F; Kumar, Arvind; Li, Jonathan G

2003-01-01

We present a novel linear programming (LP) based approach for efficiently solving the intensity modulated radiation therapy (IMRT) fluence-map optimization (FMO) problem to global optimality. Our model overcomes the apparent limitations of a linear-programming approach by approximating any convex objective function by a piecewise linear convex function. This approach allows us to retain the flexibility offered by general convex objective functions, while allowing us to formulate the FMO problem as a LP problem. In addition, a novel type of partial-volume constraint that bounds the tail averages of the differential dose-volume histograms of structures is imposed while retaining linearity as an alternative approach to improve dose homogeneity in the target volumes, and to attempt to spare as many critical structures as possible. The goal of this work is to develop a very rapid global optimization approach that finds high quality dose distributions. Implementation of this model has demonstrated excellent results. We found globally optimal solutions for eight 7-beam head-and-neck cases in less than 3 min of computational time on a single processor personal computer without the use of partial-volume constraints. Adding such constraints increased the running times by a factor of 2-3, but improved the sparing of critical structures. All cases demonstrated excellent target coverage (>95%), target homogeneity (<10% overdosing and <7% underdosing) and organ sparing using at least one of the two models

Search engine optimization

OpenAIRE

Marolt, Klemen

2013-01-01

Search engine optimization techniques, often shortened to “SEO,” should lead to first positions in organic search results. Some optimization techniques do not change over time, yet still form the basis for SEO. However, as the Internet and web design evolves dynamically, new optimization techniques flourish and flop. Thus, we looked at the most important factors that can help to improve positioning in search results. It is important to emphasize that none of the techniques can guarantee high ...

Optimization with PDE constraints ESF networking program 'OPTPDE'

CERN Document Server

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).

Structural optimization of static power control programs of nuclear power plants with WWER-1000

International Nuclear Information System (INIS)

Kokol, E.O.

2015-01-01

The question of possibility the power control programs switching for WWER-1000 is considered. The aim of this research is to determine the best program for the power control of nuclear reactor under cyclic diurnal behavior of electrical generation, as well as the switching implementation. The considered problem of finding the best control program refers to the multicriteria optimization class of problems. Operation of the nuclear power generation system simulated using the following power control programs: with constant average temperature of transfer fluid, with constant pressure in the reactor secondary circuit, with constant temperature in input of the nuclear reactor. The target function was proposed. It consists of three normalized criteria: the burn up fraction, the damage level of fuel rod array shells, as well as changes in the power values. When simulation of the nuclear power generation system operation within the life was done, the values of the selected criteria were obtained and inserted in the target function. The minimum of three values of the target function depending on the control program at current time defined the criterion of switching of considered static power control programs for nuclear power generation system

Integer programming and combinatorial optimization : 15th international conference, IPCO 2011, New York NY, USA, June 15-17, 2011 : proceedings

NARCIS (Netherlands)

Günlük, O.; Woeginger, G.J.

2011-01-01

This volume contains the 33 papers presented at IPCO 2011, the 15th Conference on Integer Programming and Combinatorial Optimization, held during June 15–17, 2011 at the IBM T.J. Watson Research Center in New York, USA. IPCO conferences are sponsored by the Mathematical Optimization Society. The

Energy Consumption Forecasting Using Semantic-Based Genetic Programming with Local Search Optimizer

Directory of Open Access Journals (Sweden)

Mauro Castelli

2015-01-01

Full Text Available Energy consumption forecasting (ECF is an important policy issue in today’s economies. An accurate ECF has great benefits for electric utilities and both negative and positive errors lead to increased operating costs. The paper proposes a semantic based genetic programming framework to address the ECF problem. In particular, we propose a system that finds (quasi-perfect solutions with high probability and that generates models able to produce near optimal predictions also on unseen data. The framework blends a recently developed version of genetic programming that integrates semantic genetic operators with a local search method. The main idea in combining semantic genetic programming and a local searcher is to couple the exploration ability of the former with the exploitation ability of the latter. Experimental results confirm the suitability of the proposed method in predicting the energy consumption. In particular, the system produces a lower error with respect to the existing state-of-the art techniques used on the same dataset. More importantly, this case study has shown that including a local searcher in the geometric semantic genetic programming system can speed up the search process and can result in fitter models that are able to produce an accurate forecasting also on unseen data.

Optimized programming algorithm for cylindrical and directional deep brain stimulation electrodes.

Science.gov (United States)

Anderson, Daria Nesterovich; Osting, Braxton; Vorwerk, Johannes; Dorval, Alan D; Butson, Christopher R

2018-04-01

Deep brain stimulation (DBS) is a growing treatment option for movement and psychiatric disorders. As DBS technology moves toward directional leads with increased numbers of smaller electrode contacts, trial-and-error methods of manual DBS programming are becoming too time-consuming for clinical feasibility. We propose an algorithm to automate DBS programming in near real-time for a wide range of DBS lead designs. Magnetic resonance imaging and diffusion tensor imaging are used to build finite element models that include anisotropic conductivity. The algorithm maximizes activation of target tissue and utilizes the Hessian matrix of the electric potential to approximate activation of neurons in all directions. We demonstrate our algorithm's ability in an example programming case that targets the subthalamic nucleus (STN) for the treatment of Parkinson's disease for three lead designs: the Medtronic 3389 (four cylindrical contacts), the direct STNAcute (two cylindrical contacts, six directional contacts), and the Medtronic-Sapiens lead (40 directional contacts). The optimization algorithm returns patient-specific contact configurations in near real-time-less than 10 s for even the most complex leads. When the lead was placed centrally in the target STN, the directional leads were able to activate over 50% of the region, whereas the Medtronic 3389 could activate only 40%. When the lead was placed 2 mm lateral to the target, the directional leads performed as well as they did in the central position, but the Medtronic 3389 activated only 2.9% of the STN. This DBS programming algorithm can be applied to cylindrical electrodes as well as novel directional leads that are too complex with modern technology to be manually programmed. This algorithm may reduce clinical programming time and encourage the use of directional leads, since they activate a larger volume of the target area than cylindrical electrodes in central and off-target lead placements.

Pressure cycling monitoring helps ensure the integrity of energy pipelines

Energy Technology Data Exchange (ETDEWEB)

Song, Peter; Lawrence, Doug; Keane, Sean; Ironside, Scott; Sutton, Aaron [Enbridge Pipelines Inc., Edmonton, AB (Canada)

2010-07-01

Enbridge Pipelines Inc. undertook a pressure cycling monitoring (PCM) program to see how pressure cycling severity (PCS) changes during line operations. The main purpose of this program is to make sure the integrity assessment interval is valid and to identify changes in operations that cause fatigue damage. The estimated fatigue life is obtained through fatigue analysis, which is based on Paris Law and uses certain data such as the operating pressure data from Enbridge's SCADA system. It serves as a measure of the PCS. When applied in an integrity management program, PCM helps maintain the integrity of pipelines by pinpointing segments whose operations have changed significantly. Among useful conclusions, it was found that a comparison between crack threat susceptibility indicators and PCS fluctuations help identify a change to crack threat susceptibility; also, the program helps identify notable changes to PCS that are caused by certain operational practices.

Risk averse optimal operation of a virtual power plant using two stage stochastic programming

International Nuclear Information System (INIS)

Tajeddini, Mohammad Amin; Rahimi-Kian, Ashkan; Soroudi, Alireza

2014-01-01

VPP (Virtual Power Plant) is defined as a cluster of energy conversion/storage units which are centrally operated in order to improve the technical and economic performance. This paper addresses the optimal operation of a VPP considering the risk factors affecting its daily operation profits. The optimal operation is modelled in both day ahead and balancing markets as a two-stage stochastic mixed integer linear programming in order to maximize a GenCo (generation companies) expected profit. Furthermore, the CVaR (Conditional Value at Risk) is used as a risk measure technique in order to control the risk of low profit scenarios. The uncertain parameters, including the PV power output, wind power output and day-ahead market prices are modelled through scenarios. The proposed model is successfully applied to a real case study to show its applicability and the results are presented and thoroughly discussed. - Highlights: • Virtual power plant modelling considering a set of energy generating and conversion units. • Uncertainty modelling using two stage stochastic programming technique. • Risk modelling using conditional value at risk. • Flexible operation of renewable energy resources. • Electricity price uncertainty in day ahead energy markets

A risk explicit interval linear programming model for uncertainty-based environmental economic optimization in the Lake Fuxian watershed, China.

Science.gov (United States)

Zhang, Xiaoling; Huang, Kai; Zou, Rui; Liu, Yong; Yu, Yajuan

2013-01-01

The conflict of water environment protection and economic development has brought severe water pollution and restricted the sustainable development in the watershed. A risk explicit interval linear programming (REILP) method was used to solve integrated watershed environmental-economic optimization problem. Interval linear programming (ILP) and REILP models for uncertainty-based environmental economic optimization at the watershed scale were developed for the management of Lake Fuxian watershed, China. Scenario analysis was introduced into model solution process to ensure the practicality and operability of optimization schemes. Decision makers' preferences for risk levels can be expressed through inputting different discrete aspiration level values into the REILP model in three periods under two scenarios. Through balancing the optimal system returns and corresponding system risks, decision makers can develop an efficient industrial restructuring scheme based directly on the window of "low risk and high return efficiency" in the trade-off curve. The representative schemes at the turning points of two scenarios were interpreted and compared to identify a preferable planning alternative, which has the relatively low risks and nearly maximum benefits. This study provides new insights and proposes a tool, which was REILP, for decision makers to develop an effectively environmental economic optimization scheme in integrated watershed management.

A Risk Explicit Interval Linear Programming Model for Uncertainty-Based Environmental Economic Optimization in the Lake Fuxian Watershed, China

Directory of Open Access Journals (Sweden)

Xiaoling Zhang

2013-01-01

Full Text Available The conflict of water environment protection and economic development has brought severe water pollution and restricted the sustainable development in the watershed. A risk explicit interval linear programming (REILP method was used to solve integrated watershed environmental-economic optimization problem. Interval linear programming (ILP and REILP models for uncertainty-based environmental economic optimization at the watershed scale were developed for the management of Lake Fuxian watershed, China. Scenario analysis was introduced into model solution process to ensure the practicality and operability of optimization schemes. Decision makers’ preferences for risk levels can be expressed through inputting different discrete aspiration level values into the REILP model in three periods under two scenarios. Through balancing the optimal system returns and corresponding system risks, decision makers can develop an efficient industrial restructuring scheme based directly on the window of “low risk and high return efficiency” in the trade-off curve. The representative schemes at the turning points of two scenarios were interpreted and compared to identify a preferable planning alternative, which has the relatively low risks and nearly maximum benefits. This study provides new insights and proposes a tool, which was REILP, for decision makers to develop an effectively environmental economic optimization scheme in integrated watershed management.

Workplace Financial Wellness Programs Help Employees Manage Health Care Changes.

Science.gov (United States)

Meyer, Cynthia; Smith, Michael C

Employers and employees are navigating major changes in health insurance benefits, including the move to high-deductible health plans in conjunction with health savings accounts (HSAs). The HSA offers unique benefits that could prove instrumental in helping workers both navigate current health care expenses and build a nest egg for much larger health care costs in retirement. Yet employees often don't understand the HSA and how to best use it. How can employers help employees make wise benefits choices that work for their personal financial circumstances?

Practical C++ programming

National Research Council Canada - National Science Library

Oualline, Steve

2003-01-01

... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 4 6 3 2. The Basics of Program Writing Programs from Conception to Execution Creating a Real Program Getting Help in Unix Getting Help in an IDE Programming...

Attribution Theory Applied to Helping Behaviour towards People with Intellectual Disabilities Who Challenge

Science.gov (United States)

Willner, Paul; Smith, Mark

2008-01-01

Introduction: Attribution theory posits that helping behaviour is determined in part by the potential helper's attributions and emotions regarding the behaviour that requires help. Specifically, helping is considered to be more likely if stability is perceived as low, generating optimism for change, and if controllability is perceived as low,…

Mathematical solution of multilevel fractional programming problem with fuzzy goal programming approach

Science.gov (United States)

Lachhwani, Kailash; Poonia, Mahaveer Prasad

2012-08-01

In this paper, we show a procedure for solving multilevel fractional programming problems in a large hierarchical decentralized organization using fuzzy goal programming approach. In the proposed method, the tolerance membership functions for the fuzzily described numerator and denominator part of the objective functions of all levels as well as the control vectors of the higher level decision makers are respectively defined by determining individual optimal solutions of each of the level decision makers. A possible relaxation of the higher level decision is considered for avoiding decision deadlock due to the conflicting nature of objective functions. Then, fuzzy goal programming approach is used for achieving the highest degree of each of the membership goal by minimizing negative deviational variables. We also provide sensitivity analysis with variation of tolerance values on decision vectors to show how the solution is sensitive to the change of tolerance values with the help of a numerical example.

Addressing holistic health and work empowerment through a body-mind-spirit intervention program among helping professionals in continuous education: A pilot study.

Science.gov (United States)

Ho, Rainbow T H; Sing, Cheuk Yan; Wong, Venus P Y

2016-01-01

To examine the effectiveness of a body-mind-spirit (BMS) intervention program in improving the holistic well-being and work empowerment among helping professionals in continuous education. Forty-four helping professionals, who were in their first-year part-time postgraduate study, participated in the present study. All participants attended a 3-day BMS intervention program which emphasized a holistic approach to health and well-being. Ratings on their levels of physical distress, daily functioning, affect, spirituality, and psychological empowerment at work were compared before and immediately after the intervention. Participants reported significantly lower levels of negative affect and physical distress, and were less spiritually disoriented after the intervention. Enhanced levels of daily functioning, positive affect, spiritual resilience, and tranquility were also reported. Results also suggested that participants were empowered at work, and specifically felt more able to make an impact on work outcomes. The 3-day BMS intervention program produced a positive and measurable effect on participants' holistic well-being and empowerment at work. Educators in related fields could incorporate holistic practices into the curriculum to better prepare the future practitioners, leading to better outcomes both to the professionals themselves and their clients or patients.

Help for the Entrepreneur. Unit 6. Level 3. Instructor Guide. PACE: Program for Acquiring Competence in Entrepreneurship. Third Edition. Research & Development Series No. 303-06.

Science.gov (United States)

Ohio State Univ., Columbus. Center on Education and Training for Employment.

This instructor guide for a unit on help for the entrepreneur in the PACE (Program for Acquiring Competence in Entrepreneurship) Program includes the full text of the student module and lesson plans, instructional suggestions, and other teacher resources. The competencies that are incorporated into this module are at Level 3 of learning--starting…

Optimization and analysis of decision trees and rules: Dynamic programming approach

KAUST Repository

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

KAUST Repository

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.

Topics in computational linear optimization

DEFF Research Database (Denmark)

Hultberg, Tim Helge

2000-01-01

Linear optimization has been an active area of research ever since the pioneering work of G. Dantzig more than 50 years ago. This research has produced a long sequence of practical as well as theoretical improvements of the solution techniques avilable for solving linear optimization problems...... of high quality solvers and the use of algebraic modelling systems to handle the communication between the modeller and the solver. This dissertation features four topics in computational linear optimization: A) automatic reformulation of mixed 0/1 linear programs, B) direct solution of sparse unsymmetric...... systems of linear equations, C) reduction of linear programs and D) integration of algebraic modelling of linear optimization problems in C++. Each of these topics is treated in a separate paper included in this dissertation. The efficiency of solving mixed 0-1 linear programs by linear programming based...

TEACHING OPTIMIZATION OF STUDENTS AT DESIGN OF BUILDINGS AND STRUCTURES FOUNDATIONS

Directory of Open Access Journals (Sweden)

MISURA Lid. V.

2016-04-01

Full Text Available Raising of problem. According to world statistics, more than 75 % of all violations of the normal operation of buildings and structures is due to deficiencies in the design, construction and operation of bases and foundations [1]. The costs to eliminate these negative effects can be compared only with the initial cost of construction, it speaks about the relevance of teaching subjects related to the design of foundations. On the other hand increased demands on the students' knowledge, raises the amount of information that needs to absorb at constant periods of instruction, which makes the current process optimization study of these disciplines. Purpose. The aim of the article is to present the software package that allows to facilitate and accelerate the calculation and check the parameters of foundations and bases for the design of buildings and structures. The software product is designed as an educational complex, which allows the student to help with the calculations in different levels of difficulty and test his knowledge. Conclusion. The program complex, which consists of a program for dimensioning the foundations, the program for calculating the parameters of the bases, of the database. It is confirmed stable operation of the school complex (the program, measures were taken to test the program, helped to make it stable. The training complex is designed only for shallow foundations, so the work will be continued.

Fifth French-German Conference on Optimization

CERN Document Server

1989-01-01

The 2-yearly French-German Conferences on Optimization review the state-of-the-art and the trends in the field. The proceedings of the Fifth Conference include papers on projective methods in linear programming (special session at the conference), nonsmooth optimization, two-level optimization, multiobjective optimization, partial inverse method, variational convergence, Newton type algorithms and flows and on practical applications of optimization. A. Ioffe and J.-Ph. Vial have contributed survey papers on, respectively second order optimality conditions and projective methods in linear programming.

How can we help students appreciate physics education?

Science.gov (United States)

Lin, Jia-Ling; Zaki, Eman; Schmidt, Jason; Woolston, Don

2004-03-01

Helping students appreciate physics education is a formidable task, considering that many students struggle to pass introductory physics courses. Numerous efforts have been made for this undertaking because it is an important step leading to successful learning. In an out-of-classroom academic program, the Supplemental Instruction (SI) Program, we have used the approach, INSPIRE (inquiry, network, skillfulness, perseverance, intuition, reasoning, and effort), to help more students value their experiences in these courses. The method basically includes key elements outlined by experts in physics education [1]. Student responses have been encouraging. Having undergraduates as facilitators in the program is advantageous in promoting principles of physics education. Their training emphasizes tenacity, resourcefulness, understanding, support, and teamwork, i.e. TRUST. We present the organization and focus of the SI Program, and discuss how these improve learning atmosphere and facilitate learning. [1] Edward F. Redish et al, Am J. Phys. 66(3), March 1998.

Development of a VVER-1000 core loading pattern optimization program based on perturbation theory

International Nuclear Information System (INIS)

Hosseini, Mohammad; Vosoughi, Naser

2012-01-01

Highlights: ► We use perturbation theory to find an optimum fuel loading pattern in a VVER-1000. ► We provide a software for in-core fuel management optimization. ► We consider two objectives for our method (perturbation theory). ► We show that perturbation theory method is very fast and accurate for optimization. - Abstract: In-core nuclear fuel management is one of the most important concerns in the design of nuclear reactors. Two main goals in core fuel loading pattern design optimization are maximizing the core effective multiplication factor in order to extract the maximum energy, and keeping the local power peaking factor lower than a predetermined value to maintain the fuel integrity. Because of the numerous possible patterns of fuel assemblies in the reactor core, finding the best configuration is so important and challenging. Different techniques for optimization of fuel loading pattern in the reactor core have been introduced by now. In this study, a software is programmed in C language to find an order of the fuel loading pattern of a VVER-1000 reactor core using the perturbation theory. Our optimization method is based on minimizing the radial power peaking factor. The optimization process launches by considering an initial loading pattern and the specifications of the fuel assemblies which are given as the input of the software. The results on a typical VVER-1000 reactor reveal that the method could reach to a pattern with an allowed radial power peaking factor and increases the cycle length 1.1 days, as well.

Optimal surveillance strategy for invasive species management when surveys stop after detection.

Science.gov (United States)

Guillera-Arroita, Gurutzeta; Hauser, Cindy E; McCarthy, Michael A

2014-05-01

Invasive species are a cause for concern in natural and economic systems and require both monitoring and management. There is a trade-off between the amount of resources spent on surveying for the species and conducting early management of occupied sites, and the resources that are ultimately spent in delayed management at sites where the species was present but undetected. Previous work addressed this optimal resource allocation problem assuming that surveys continue despite detection until the initially planned survey effort is consumed. However, a more realistic scenario is often that surveys stop after detection (i.e., follow a "removal" sampling design) and then management begins. Such an approach will indicate a different optimal survey design and can be expected to be more efficient. We analyze this case and compare the expected efficiency of invasive species management programs under both survey methods. We also evaluate the impact of mis-specifying the type of sampling approach during the program design phase. We derive analytical expressions that optimize resource allocation between monitoring and management in surveillance programs when surveys stop after detection. We do this under a scenario of unconstrained resources and scenarios where survey budget is constrained. The efficiency of surveillance programs is greater if a "removal survey" design is used, with larger gains obtained when savings from early detection are high, occupancy is high, and survey costs are not much lower than early management costs at a site. Designing a surveillance program disregarding that surveys stop after detection can result in an efficiency loss. Our results help guide the design of future surveillance programs for invasive species. Addressing program design within a decision-theoretic framework can lead to a better use of available resources. We show how species prevalence, its detectability, and the benefits derived from early detection can be considered.

Perceptions of Helpfulness of Teachers in Didactic Courses

Science.gov (United States)

Moate, Randall M.; Cox, Jane A.; Brown, Steven R.; West, Erin M.

2017-01-01

Thirty-five novice counselors completed a Q sort that assessed their perceptions of what was most helpful about teachers of didactic classes in their master's degree program. Participants perceived teachers who used a contextual teaching pedagogy and had an authentic, empathic, and compassionate way of being as helpful to their learning.

Structural optimization

CERN Document Server

MacBain, Keith M

2009-01-01

Intends to supplement the engineer's box of analysis and design tools making optimization as commonplace as the finite element method in the engineering workplace. This title introduces structural optimization and the methods of nonlinear programming such as Lagrange multipliers, Kuhn-Tucker conditions, and calculus of variations.

COST ANALYSIS AND OPTIMIZATION IN THE LOGISTIC SUPPLY CHAIN USING THE SIMPROLOGIC PROGRAM

OpenAIRE

Ilona MAŃKA; Adam MAŃKA

2016-01-01

This article aims to characterize the authorial SimProLOGIC program, version 2.1, which enables one to conduct a cost analysis of individual links, as well as the entire logistic supply chain (LSC). This article also presents an example of the analysis of the parameters, which characterize the supplier of subsystems in the examined logistic chain, and the results of the initial optimization, which makes it possible to improve the economic balance, as well as the level of customer servic...

Putting program evaluation into practice: enhancing the Girls Just Wanna Have Fun program.

Science.gov (United States)

Bean, Corliss N; Kendellen, Kelsey; Halsall, Tanya; Forneris, Tanya

2015-04-01

In recent years there has been a call for increased community physical activity and sport programs for female youth that are deliberately structured to foster positive developmental outcomes. In addition, researchers have recognized the need to empirically evaluate such programs to ensure that youth are provided with optimal opportunities to thrive. This study represents a utilization-focused evaluation of Girls Just Wanna Have Fun, a female-only physical activity-based life skills community program. A utilization-focused evaluation is particularly important when the evaluation is to help stakeholders utilize the findings in practice. The purpose of this study was twofold: (a) to gain an understanding of the ongoing successes and challenges after year two of program implementation and (b) to examine how the adaptations made based on feedback from the first year evaluation were perceived as impacting the program. From interviews with youth participants and program leaders, three main themes with eight sub-themes emerged. The main themes were: (a) applying lessons learned can make a significant difference, (b) continually implementing successful strategies, and (c) ongoing challenges. Overall, this evaluation represents an important step in understanding how to improve program delivery to better meet the needs of the participants in community-based programming. Copyright © 2014 Elsevier Ltd. All rights reserved.

Towards optimizing server performance in an educational MMORPG for teaching computer programming

Science.gov (United States)

Malliarakis, Christos; Satratzemi, Maya; Xinogalos, Stelios

2013-10-01

Web-based games have become significantly popular during the last few years. This is due to the gradual increase of internet speed, which has led to the ongoing multiplayer games development and more importantly the emergence of the Massive Multiplayer Online Role Playing Games (MMORPG) field. In parallel, similar technologies called educational games have started to be developed in order to be put into practice in various educational contexts, resulting in the field of Game Based Learning. However, these technologies require significant amounts of resources, such as bandwidth, RAM and CPU capacity etc. These amounts may be even larger in an educational MMORPG game that supports computer programming education, due to the usual inclusion of a compiler and the constant client/server data transmissions that occur during program coding, possibly leading to technical issues that could cause malfunctions during learning. Thus, the determination of the elements that affect the overall games resources' load is essential so that server administrators can configure them and ensure educational games' proper operation during computer programming education. In this paper, we propose a new methodology with which we can achieve monitoring and optimization of the load balancing, so that the essential resources for the creation and proper execution of an educational MMORPG for computer programming can be foreseen and bestowed without overloading the system.

A hybrid bird mating optimizer algorithm with teaching-learning-based optimization for global numerical optimization

Directory of Open Access Journals (Sweden)

Qingyang Zhang

2015-02-01

Full Text Available Bird Mating Optimizer (BMO is a novel meta-heuristic optimization algorithm inspired by intelligent mating behavior of birds. However, it is still insufficient in convergence of speed and quality of solution. To overcome these drawbacks, this paper proposes a hybrid algorithm (TLBMO, which is established by combining the advantages of Teaching-learning-based optimization (TLBO and Bird Mating Optimizer (BMO. The performance of TLBMO is evaluated on 23 benchmark functions, and compared with seven state-of-the-art approaches, namely BMO, TLBO, Artificial Bee Bolony (ABC, Particle Swarm Optimization (PSO, Fast Evolution Programming (FEP, Differential Evolution (DE, Group Search Optimization (GSO. Experimental results indicate that the proposed method performs better than other existing algorithms for global numerical optimization.

Increasing help-seeking and referrals for individuals at risk for suicide by decreasing stigma: the role of mass media.

Science.gov (United States)

Niederkrotenthaler, Thomas; Reidenberg, Daniel J; Till, Benedikt; Gould, Madelyn S

2014-09-01

Increasing help-seeking and referrals for at-risk individuals by decreasing stigma has been defined as Aspirational Goal 10 in the National Action Alliance for Suicide Prevention's Research Prioritization Task Force's 2014 prioritized research agenda. This article reviews the research evidence on the impact of mass media awareness campaigns on reducing stigma and increasing help-seeking. The review will focus on both beneficial and iatrogenic effects of suicide preventive interventions using media campaigns to target the broad public. A further focus is on collaboration between public health professionals and news media in order to reduce the risk of copycat behavior and enhance help-seeking behavior. Examples of multilevel approaches that include both mass media interventions and individual-level approaches to reduce stigma and increase referrals are provided as well. Multilevel suicide prevention programs that combine various approaches seem to provide the most promising results, but much more needs to be learned about the best possible composition of these programs. Major research and practice challenges include the identification of optimal ways to reach vulnerable populations who likely do not benefit from current awareness strategies. Caution is needed in all efforts that aim to reduce the stigma of suicidal ideation, mental illness, and mental health treatment in order to avoid iatrogenic effects. The article concludes with specific suggestions for research questions to help move this line of suicide research and practice forward. Copyright © 2014 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

A mixed integer linear programming approach for optimal DER portfolio, sizing, and placement in multi-energy microgrids

International Nuclear Information System (INIS)

Mashayekh, Salman; Stadler, Michael; Cardoso, Gonçalo; Heleno, Miguel

2017-01-01

Highlights: • This paper presents a MILP model for optimal design of multi-energy microgrids. • Our microgrid design includes optimal technology portfolio, placement, and operation. • Our model includes microgrid electrical power flow and heat transfer equations. • The case study shows advantages of our model over aggregate single-node approaches. • The case study shows the accuracy of the integrated linearized power flow model. - Abstract: Optimal microgrid design is a challenging problem, especially for multi-energy microgrids with electricity, heating, and cooling loads as well as sources, and multiple energy carriers. To address this problem, this paper presents an optimization model formulated as a mixed-integer linear program, which determines the optimal technology portfolio, the optimal technology placement, and the associated optimal dispatch, in a microgrid with multiple energy types. The developed model uses a multi-node modeling approach (as opposed to an aggregate single-node approach) that includes electrical power flow and heat flow equations, and hence, offers the ability to perform optimal siting considering physical and operational constraints of electrical and heating/cooling networks. The new model is founded on the existing optimization model DER-CAM, a state-of-the-art decision support tool for microgrid planning and design. The results of a case study that compares single-node vs. multi-node optimal design for an example microgrid show the importance of multi-node modeling. It has been shown that single-node approaches are not only incapable of optimal DER placement, but may also result in sub-optimal DER portfolio, as well as underestimation of investment costs.

Evaluation of mathematical methods and linear programming for optimization of the planning in radiotherapy

International Nuclear Information System (INIS)

Fernandes, Marco A.R.; Fernandes, David M.; Florentino, Helenice O.

2010-01-01

The work detaches the importance of the use of mathematical tools and computer systems for optimization of the planning in radiotherapy, seeking to the distribution of dose of appropriate radiation in the white volume that provides an ideal therapeutic rate between the tumor cells and the adjacent healthy tissues, extolled in the radiotherapy protocols. Examples of target volumes mathematically modeled are analyzed with the technique of linear programming, comparing the obtained results using the Simplex algorithm with those using the algorithm of Interior Points. The System Genesis II was used for obtaining of the isodose curves for the outline and geometry of fields idealized in the computer simulations, considering the parameters of a 10 MV photons beams. Both programming methods (Simplex and Interior Points) they resulted in a distribution of integral dose in the tumor volume and allow the adaptation of the dose in the critical organs inside of the restriction limits extolled. The choice of an or other method should take into account the facility and the need of limiting the programming time. The isodose curves, obtained with the Genesis II System, illustrate that the adjacent healthy tissues to the tumor receives larger doses than those reached in the computer simulations. More coincident values can be obtained altering the weights and some factors of minimization of the objective function. The prohibitive costs of the computer planning systems, at present available for radiotherapy, it motivates the researches to look for the implementation of simpler and so effective methods for optimization of the treatment plan. (author)

AUTOMATA PROGRAMS CONSTRUCTION FROM SPECIFICATION WITH AN ANT COLONY OPTIMIZATION ALGORITHM BASED ON MUTATION GRAPH

Directory of Open Access Journals (Sweden)

Daniil S. Chivilikhin

2014-11-01

Full Text Available The procedure of testing traditionally used in software engineering cannot guarantee program correctness; therefore verification is used at the excess requirements to programs reliability. Verification makes it possible to check certain properties of programs in all possible computational states; however, this process is very complex. In the model checking method a model of the program is built (often, manually and requirements in terms of temporal logic are formulated. Such temporal properties of the model can be checked automatically. The main issue in this framework is the gap between the program and its model. Automata-based programming paradigm gives the possibility to overcome this limitation. In this paradigm, program logic is represented using finite-state machines. The advantage of finite-state machines is that their models can be constructed automatically. The paper deals with the application of mutation-based ant colony optimization algorithm to the problem of finite-state machine construction from their specification, defined by test scenarios and temporal properties. The presented approach has been tested on the elevator doors control problem as well as on randomly generated data. Obtained results show the ant colony algorithm is two-three times faster than the previously used genetic algorithm. The proposed approach can be recommended for inferring control programs for critical systems.

Aether: leveraging linear programming for optimal cloud computing in genomics.

Science.gov (United States)

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.

A linear bi-level multi-objective program for optimal allocation of water resources.

Directory of Open Access Journals (Sweden)

Ijaz Ahmad

Full Text Available This paper presents a simple bi-level multi-objective linear program (BLMOLP with a hierarchical structure consisting of reservoir managers and several water use sectors under a multi-objective framework for the optimal allocation of limited water resources. Being the upper level decision makers (i.e., leader in the hierarchy, the reservoir managers control the water allocation system and tend to create a balance among the competing water users thereby maximizing the total benefits to the society. On the other hand, the competing water use sectors, being the lower level decision makers (i.e., followers in the hierarchy, aim only to maximize individual sectoral benefits. This multi-objective bi-level optimization problem can be solved using the simultaneous compromise constraint (SICCON technique which creates a compromise between upper and lower level decision makers (DMs, and transforms the multi-objective function into a single decision-making problem. The bi-level model developed in this study has been applied to the Swat River basin in Pakistan for the optimal allocation of water resources among competing water demand sectors and different scenarios have been developed. The application of the model in this study shows that the SICCON is a simple, applicable and feasible approach to solve the BLMOLP problem. Finally, the comparisons of the model results show that the optimization model is practical and efficient when it is applied to different conditions with priorities assigned to various water users.

Learning basic programming using CLIS through gamification

Science.gov (United States)

Prabawa, H. W.; Sutarno, H.; Kusnendar, J.; Rahmah, F.

2018-05-01

The difficulty of understanding programming concept is a major problem in basic programming lessons. Based on the results of preliminary studies, 60% of students reveal the monotonous of learning process caused by the limited number of media. Children Learning in Science (CLIS) method was chosen as solution because CLIS has facilitated students’ initial knowledge to be optimized into conceptual knowledge. Technological involvement in CLIS (gamification) helped students to understand basic programming concept. This research developed a media using CLIS method with gamification elements to increase the excitement of learning process. This research declared that multimedia is considered good by students, especially regarding the mechanical aspects of multimedia, multimedia elements and aspects of multimedia information structure. Multimedia gamification learning with the CLIS model showed increased number of students’ concept understanding.

Bi-Level Optimization for Available Transfer Capability Evaluation in Deregulated Electricity Market

Directory of Open Access Journals (Sweden)

Beibei Wang

2015-11-01

Full Text Available Available transfer capability (ATC is the transfer capability remaining in the physical transmission network for further commercial activity over and above already committed uses which needs to be posted in the electricity market to facilitate competition. ATC evaluation is a complicated task including the determination of total transfer capability (TTC and existing transfer capability (ETC. In the deregulated electricity market, ETC is decided by the independent system operator’s (ISO’s economic dispatch (ED. TTC can then be obtained by a continuation power flow (CPF method or by an optimal power flow (OPF method, based on the given ED solutions as well as the ETC. In this paper, a bi-level optimization framework for the ATC evaluation is proposed in which ATC results can be obtained simultaneously with the ED and ETC results in the deregulated electricity market. In this bi-level optimization model, ATC evaluation is formulated as the upper level problem and the ISO’s ED is the lower level problem. The bi-level model is first converted to a mathematic program with equilibrium constraints (MPEC by recasting the lower level problem as its Karush-Kuhn-Tucher (KKT optimality condition. Then, the MPEC is transformed into a mixed-integer linear programming (MILP problem, which can be solved with the help of available optimization software. In addition, case studies on PJM 5-bus, IEEE 30-bus, and IEEE 118-bus systems are presented to demonstrate the proposed methodology.

Up from Dependency: A New National Public Assistance Strategy. Supplement 3: A Self-Help Catalog.

Science.gov (United States)

Kotler, Martin; And Others

Self-help among low-income people is vitally important. In no area is self-help more important than in overcoming poverty's burdens and energizing the escape from poverty. This document comprises an inventory of self-help and mutual-help programs that feature active involvement of members of the low-income population. The programs in this…

Racing Sampling Based Microimmune Optimization Approach Solving Constrained Expected Value Programming

Directory of Open Access Journals (Sweden)

Kai Yang

2016-01-01

Full Text Available This work investigates a bioinspired microimmune optimization algorithm to solve a general kind of single-objective nonlinear constrained expected value programming without any prior distribution. In the study of algorithm, two lower bound sample estimates of random variables are theoretically developed to estimate the empirical values of individuals. Two adaptive racing sampling schemes are designed to identify those competitive individuals in a given population, by which high-quality individuals can obtain large sampling size. An immune evolutionary mechanism, along with a local search approach, is constructed to evolve the current population. The comparative experiments have showed that the proposed algorithm can effectively solve higher-dimensional benchmark problems and is of potential for further applications.

An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation

International Nuclear Information System (INIS)

Niknam, Taher; Azizipanah-Abarghooee, Rasoul; Narimani, Mohammad Rasoul

2012-01-01

Highlights: ► Proposes a stochastic model for optimal energy management. ► Consider uncertainties related to the forecasted values for load demand. ► Consider uncertainties of forecasted values of output power of wind and photovoltaic units. ► Consider uncertainties of forecasted values of market price. ► Present an improved multi-objective teaching–learning-based optimization. -- Abstract: This paper proposes a stochastic model for optimal energy management with the goal of cost and emission minimization. In this model, the uncertainties related to the forecasted values for load demand, available output power of wind and photovoltaic units and market price are modeled by a scenario-based stochastic programming. In the presented method, scenarios are generated by a roulette wheel mechanism based on probability distribution functions of the input random variables. Through this method, the inherent stochastic nature of the proposed problem is released and the problem is decomposed into a deterministic problem. An improved multi-objective teaching–learning-based optimization is implemented to yield the best expected Pareto optimal front. In the proposed stochastic optimization method, a novel self adaptive probabilistic modification strategy is offered to improve the performance of the presented algorithm. Also, a set of non-dominated solutions are stored in a repository during the simulation process. Meanwhile, the size of the repository is controlled by usage of a fuzzy-based clustering technique. The best expected compromise solution stored in the repository is selected via the niching mechanism in a way that solutions are encouraged to seek the lesser explored regions. The proposed framework is applied in a typical grid-connected micro grid in order to verify its efficiency and feasibility.

UMTS network planning, optimization, and inter-operation with GSM

CERN Document Server

Rahnema, Moe

2008-01-01

UMTS Network Planning, Optimization, and Inter-Operation with GSM is an accessible, one-stop reference to help engineers effectively reduce the time and costs involved in UMTS deployment and optimization. Rahnema includes detailed coverage from both a theoretical and practical perspective on the planning and optimization aspects of UMTS, and a number of other new techniques to help operators get the most out of their networks. Provides an end-to-end perspective, from network design to optimizationIncorporates the hands-on experiences of numerous researchersSingle

A model based on stochastic dynamic programming for determining China's optimal strategic petroleum reserve policy

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.

Dynamic Programming Algorithm for Generation of Optimal Elimination Trees for Multi-frontal Direct Solver Over H-refined Grids

KAUST Repository

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

Implementing a Coach-Delivered Dating Violence Prevention Program with High School Athletes.

Science.gov (United States)

Jaime, Maria Catrina D; McCauley, Heather L; Tancredi, Daniel J; Decker, Michele R; Silverman, Jay G; O'Connor, Brian; Miller, Elizabeth

2018-05-10

Teen dating violence and sexual violence are severe public health problems. Abusive behaviors within the context of dating or romantic relationships are associated with adverse health outcomes. Promoting positive bystander intervention and increasing knowledge of abusive behaviors are promising strategies for preventing dating and sexual violence. Coaching Boys Into Men (CBIM) is an evidence-based, athletic coach-delivered dating violence prevention program that has been shown to increase positive bystander behaviors and reduce abuse perpetration among high school male athletes. Identifying specific barriers and facilitators based on the coaches' experiences with program delivery combined with the coaches' and athletes' program perceptions may help optimize future CBIM implementation and sustainability. Semi-structured interviews with coaches (n = 36) explored the implementers' perspectives on strategies that worked well and potential barriers to program implementation. Ten focus groups with male athletes (n = 39) assessed their experiences with CBIM and the suitability of having their coaches deliver this program. Coaches described using the CBIM training cards and integrating program delivery during practice. Athletes reported coaches routinely delivering the CBIM program and adding their own personal stories or examples to the discussions. Key facilitators to program implementation include support from the violence prevention advocate, the ease of integrating CBIM into the sports season, and using the program materials. Barriers to implementation included finding sufficient time for the program, dynamics of delivering sensitive program content, and participant constraints. Coaches and athletes alike found the program feasible and acceptable to implement within the sports setting. Both coaches and athletes offered insights on the implementation and the feasibility and acceptability of CBIM within school-based athletic programs. These experiences by

Optimization of decision rules based on dynamic programming approach

KAUST Repository

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.

Tagging, Encoding, and Jones Optimality

DEFF Research Database (Denmark)

Danvy, Olivier; Lopez, Pablo E. Martinez

2003-01-01

A partial evaluator is said to be Jones-optimal if the result of specializing a self-interpreter with respect to a source program is textually identical to the source program, modulo renaming. Jones optimality has already been obtained if the self-interpreter is untyped. If the selfinterpreter...... is typed, however, residual programs are cluttered with type tags. To obtain the original source program, these tags must be removed. A number of sophisticated solutions have already been proposed. We observe, however, that with a simple representation shift, ordinary partial evaluation is already Jones......-optimal, modulo an encoding. The representation shift amounts to reading the type tags as constructors for higherorder abstract syntax. We substantiate our observation by considering a typed self-interpreter whose input syntax is higher-order. Specializing this interpreter with respect to a source program yields...

Genetic Algorithm (GA Method for Optimization of Multi-Reservoir Systems Operation

Directory of Open Access Journals (Sweden)

Shervin Momtahen

2006-01-01

Full Text Available A Genetic Algorithm (GA method for optimization of multi-reservoir systems operation is proposed in this paper. In this method, the parameters of operating policies are optimized using system simulation results. Hence, any operating problem with any sort of objective function, constraints and structure of operating policy can be optimized by GA. The method is applied to a 3-reservoir system and is compared with two traditional methods of Stochastic Dynamic Programming and Dynamic Programming and Regression. The results show that GA is superior both in objective function value and in computational speed. The proposed method is further improved using a mutation power updating rule and a varying period simulation method. The later is a novel procedure proposed in this paper that is believed to help in solving computational time problem in large systems. These revisions are evaluated and proved to be very useful in converging to better solutions in much less time. The final GA method is eventually evaluated as a very efficient procedure that is able to solve problems of large multi-reservoir system which is usually impossible by traditional methods. In fact, the real performance of the GA method starts where others fail to function.

Optimization of mechanical structures using particle swarm optimization

International Nuclear Information System (INIS)

Leite, Victor C.; Schirru, Roberto

2015-01-01

Several optimization problems are dealed with the particle swarm optimization (PSO) algorithm, there is a wide kind of optimization problems, it may be applications related to logistics or the reload of nuclear reactors. This paper discusses the use of the PSO in the treatment of problems related to mechanical structure optimization. The geometry and material characteristics of mechanical components are important for the proper functioning and performance of the systems were they are applied, particularly to the nuclear field. Calculations related to mechanical aspects are all made using ANSYS, while the PSO is programed in MATLAB. (author)

Optimization of mechanical structures using particle swarm optimization

Energy Technology Data Exchange (ETDEWEB)

Leite, Victor C.; Schirru, Roberto, E-mail: victor.coppo.leite@lmp.ufrj.br [Coordenacao dos Programas de Pos-Graduacao em Engenharia (LMP/PEN/COPPE/UFRJ), Rio de Janeiro, RJ (Brazil). Lab. de Monitoracao de Processos

2015-07-01

Several optimization problems are dealed with the particle swarm optimization (PSO) algorithm, there is a wide kind of optimization problems, it may be applications related to logistics or the reload of nuclear reactors. This paper discusses the use of the PSO in the treatment of problems related to mechanical structure optimization. The geometry and material characteristics of mechanical components are important for the proper functioning and performance of the systems were they are applied, particularly to the nuclear field. Calculations related to mechanical aspects are all made using ANSYS, while the PSO is programed in MATLAB. (author)

Hybrid Approximate Dynamic Programming Approach for Dynamic Optimal Energy Flow in the Integrated Gas and Power Systems

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...

Reducing refinery CO2 emissions through amine solvent upgrade and optimization

Energy Technology Data Exchange (ETDEWEB)

Alonso, Thiago V.; Valenzuela, Michelle [The Dow Chemical Company, Midland, MI (United States)

2012-07-01

Regional initiatives are underway to reduce and limit the emissions of greenhouse gases. With CO2 emissions making up over 80% of the greenhouse gases, cap-and-trade programs will focus on those industries that consume the most energy. Refineries are among the top energy consumers and are seeking opportunities to reduce usage. With tightening margins, energy management programs will not only help refineries meet CO{sub 2} emission regulations, but can also provide a competitive advantage. With the trend towards heavier and higher sulfur containing crudes, refineries are increasing processing capabilities, which can include capital-intensive projects and additional energy consumption. Energy conservation plans should include optimization of these processes. One area to consider includes the acid gas removal systems in refineries. Through the selection and use of optimal solvents and implementation of energy efficiency techniques, which require minimal capital investment and expenditures, refineries can reduce energy usage, overall CO{sub 2} emissions, and total cost in acid gas systems. This paper will discuss these approaches and share case studies detailing the implementation and results. (author)

Equipment reliability and life cycle optimization of a nuclear plant feedwater heater

International Nuclear Information System (INIS)

Thomas, Daniel; Coakley, Michael; Catapano, Michael; Svensson, Eric

2006-01-01

Many papers published over the last 25 years have strongly emphasized the need for an ongoing program of inspection and testing with subsequent failure cause analysis of feedwater heaters. Plants must be run more competitively; therefore, Utilities must lower operation and maintenance costs, while optimizing overall plant efficiency and capacity factor. One recognized area that needs to be addressed in accomplishing this goal is the heat cycle. This paper specifically deals with the feedwater heating system. Utility engineers must monitor feedwater heater performance in order to recognize degradation, identify and mitigate failure mechanisms, and prevent in-service failures thereby optimizing availability. Periodic tube plugging without complete analysis of the degraded/failed areas resolves the immediate need for return to service; however, heater life will not be optimized. This paper illustrates a complete life cycle management inspection, testing, and maintenance program implemented at Peach Bottom Atomic Power Station (PBAPS). Concerns that tubes may have been too conservatively plugged due to insufficient data and lack of root cause analysis, justified a program that included: - Removal of previously installed plugs; - Video-probe inspection of failed areas; - Extraction of tube samples for further analysis; - Eddy current testing of selected tubes; - Evaluation of the condition of 'insurance' plugged tubes for return to service; - Hydrostatic testing of selected individual tubes; - Final repair plan based on the results of the above program. This paper concludes that no single method of inspection or testing should solely be relied upon in establishing: - The extent of actual degraded conditions; - The mechanism(s) of failure; - The details of repair to be implemented. Evaluating all data affords the best chance in arresting problems and optimizing feedwater heater life. Problem heaters should be continuously monitored and inspected over time until the facts

Review: Optimization methods for groundwater modeling and management

Science.gov (United States)

Yeh, William W.-G.

2015-09-01

Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.

Constrained Optimization and Optimal Control for Partial Differential Equations

CERN Document Server

Leugering, Günter; Griewank, Andreas

2012-01-01

This special volume focuses on optimization and control of processes governed by partial differential equations. The contributors are mostly participants of the DFG-priority program 1253: Optimization with PDE-constraints which is active since 2006. The book is organized in sections which cover almost the entire spectrum of modern research in this emerging field. Indeed, even though the field of optimal control and optimization for PDE-constrained problems has undergone a dramatic increase of interest during the last four decades, a full theory for nonlinear problems is still lacking. The cont

College Students Rarely Seek Help Despite Serious Substance Use Problems

Science.gov (United States)

Caldeira, Kimberly M.; Kasperski, Sarah J.; Sharma, Eva; Vincent, Kathryn B.; O’Grady, Kevin E.; Wish, Eric D.; Arria, Amelia M.

2009-01-01

The prevalence of substance use disorders (SUD) and aspects of the help-seeking process among a high-risk sample of 946 students at one large public university were assessed in personal interviews during the first three years of college. After statistically adjusting for purposive sampling, an estimated 46.8%wt of all third-year students met DSM-IV criteria for SUD involving alcohol and/or marijuana at least once. Of 548 SUD cases, 3.6% perceived a need for help with substance use problems; 16.4% were encouraged by someone else to seek help. Help-seeking was rare among SUD cases (8.8%), but significantly elevated among individuals who perceived a need (90.0%) or experienced social pressures from parents (32.5%), friends (34.2%), or another person (58.3%). Resources accessed for help included educational programs (38%), health professionals (27%), and twelve-step programs (19%). College students have high rates of substance use problems but rarely recognize a need for treatment or seek help. Results highlight the opportunity for early intervention with college students with SUD. PMID:19553064

Genetic algorithms and fuzzy multiobjective optimization

CERN Document Server

Sakawa, Masatoshi

2002-01-01

Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation, and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty. There appears to be no book that is designed to present genetic algorithms for solving not only single-objective but also fuzzy and multiobjective optimization problems in a unified way. Genetic Algorithms And Fuzzy Multiobjective Optimization introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a w...

Robust and Reliable Portfolio Optimization Formulation of a Chance Constrained Problem

Directory of Open Access Journals (Sweden)

Sengupta Raghu Nandan

2017-02-01

Full Text Available We solve a linear chance constrained portfolio optimization problem using Robust Optimization (RO method wherein financial script/asset loss return distributions are considered as extreme valued. The objective function is a convex combination of portfolio’s CVaR and expected value of loss return, subject to a set of randomly perturbed chance constraints with specified probability values. The robust deterministic counterpart of the model takes the form of Second Order Cone Programming (SOCP problem. Results from extensive simulation runs show the efficacy of our proposed models, as it helps the investor to (i utilize extensive simulation studies to draw insights into the effect of randomness in portfolio decision making process, (ii incorporate different risk appetite scenarios to find the optimal solutions for the financial portfolio allocation problem and (iii compare the risk and return profiles of the investments made in both deterministic as well as in uncertain and highly volatile financial markets.

OpenMDAO: Framework for Flexible Multidisciplinary Design, Analysis and Optimization Methods

Science.gov (United States)

Heath, Christopher M.; Gray, Justin S.

2012-01-01

The OpenMDAO project is underway at NASA to develop a framework which simplifies the implementation of state-of-the-art tools and methods for multidisciplinary design, analysis and optimization. Foremost, OpenMDAO has been designed to handle variable problem formulations, encourage reconfigurability, and promote model reuse. This work demonstrates the concept of iteration hierarchies in OpenMDAO to achieve a flexible environment for supporting advanced optimization methods which include adaptive sampling and surrogate modeling techniques. In this effort, two efficient global optimization methods were applied to solve a constrained, single-objective and constrained, multiobjective version of a joint aircraft/engine sizing problem. The aircraft model, NASA's nextgeneration advanced single-aisle civil transport, is being studied as part of the Subsonic Fixed Wing project to help meet simultaneous program goals for reduced fuel burn, emissions, and noise. This analysis serves as a realistic test problem to demonstrate the flexibility and reconfigurability offered by OpenMDAO.

Risk-Based Two-Stage Stochastic Optimization Problem of Micro-Grid Operation with Renewables and Incentive-Based Demand Response Programs

Directory of Open Access Journals (Sweden)

Pouria Sheikhahmadi

2018-03-01

Full Text Available The operation problem of a micro-grid (MG in grid-connected mode is an optimization one in which the main objective of the MG operator (MGO is to minimize the operation cost with optimal scheduling of resources and optimal trading energy with the main grid. The MGO can use incentive-based demand response programs (DRPs to pay an incentive to the consumers to change their demands in the peak hours. Moreover, the MGO forecasts the output power of renewable energy resources (RERs and models their uncertainties in its problem. In this paper, the operation problem of an MGO is modeled as a risk-based two-stage stochastic optimization problem. To model the uncertainties of RERs, two-stage stochastic programming is considered and conditional value at risk (CVaR index is used to manage the MGO’s risk-level. Moreover, the non-linear economic models of incentive-based DRPs are used by the MGO to change the peak load. The numerical studies are done to investigate the effect of incentive-based DRPs on the operation problem of the MGO. Moreover, to show the effect of the risk-averse parameter on MGO decisions, a sensitivity analysis is carried out.

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.

Advanced Process Control Application and Optimization in Industrial Facilities

Directory of Open Access Journals (Sweden)

Howes S.

2015-01-01

Full Text Available This paper describes application of the new method and tool for system identification and PID tuning/advanced process control (APC optimization using the new 3G (geometric, gradient, gravity optimization method. It helps to design and implement control schemes directly inside the distributed control system (DCS or programmable logic controller (PLC. Also, the algorithm helps to identify process dynamics in closed-loop mode, optimizes controller parameters, and helps to develop adaptive control and model-based control (MBC. Application of the new 3G algorithm for designing and implementing APC schemes is presented. Optimization of primary and advanced control schemes stabilizes the process and allows the plant to run closer to process, equipment and economic constraints. This increases production rates, minimizes operating costs and improves product quality.

Dynamic Programming Algorithm for Generation of Optimal Elimination Trees for Multi-frontal Direct Solver Over H-refined Grids

KAUST Repository

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.

Optimization of planting pattern plan in Logung irrigation area using linear program

Science.gov (United States)

Wardoyo, Wasis; Setyono

2018-03-01

Logung irrigation area is located in Kudus Regency, Central Java Province, Indonesia. Irrigation area with 2810 Ha of extent is getting water supply from Logung dam. Yet, the utilization of water at Logung dam is not optimal and the distribution of water is still not evenly distributed. Therefore, this study will discuss about the optimization of irrigation water utilization based on the beginning of plant season. This optimization begins with the analysis of hydrology, climatology and river discharge in order to determine the irrigation water needs. After determining irrigation water needs, six alternatives of planting patterns with the different early planting periods, i.e. 1st November, 2nd November, 3rd November, 1st December, 2nd December, and 3rd December with the planting pattern of rice-secondary crop-sugarcane is introduced. It is continued by the analysis of water distribution conducted using linear program assisted by POM-Quantity method for Windows 3 with the reliable discharge limit and the available land area. Output of this calculation are to determine the land area that can be planted based on the type of plant and growing season, and to obtaine the profits of harvest yields. Based on the optimum area of each plant species with 6 alternatives, the most optimum area was obtained at the early planting periods on 3rd December with the production profit of Rp 113.397.338.854,- with the planting pattern of rice / beans / sugarcane-rice / beans / sugarcane-beans / sugarcane.

Optimal Energy Management for Microgrid with Stationary and Mobile Storage

Energy Technology Data Exchange (ETDEWEB)

Wang, Yubo; Wang, Bin; Zhang, Tianyang; Nazaripouya, Hamidreza; Chu, Chi-Cheng; Gadh, Rajit

2016-05-02

This paper studies energy management in a Microgrid (MG) with solar generation, Battery Energy Management System (BESS) and gridable (V2G) EVs. A two-stage stochastic optimization method is proposed to capture the intermittent solar generation and random EV user behaviors. It is subsequently formulated as a Mixed Integer Linear Programming (MILP) problem. To evalutate the proposed method, real solar generation, loads, BESS and EV data is used in Sample Average Approximation (SAA). Computational results show the correctness of the proposed method as well as steady and tightly bounded optimality gap. Comparisons demonstrate that the proposed stochastic method outperforms its deterministic counterpart at the expense of higher computational cost. It is also observed that moderate number of EVs helps to reduce the overall operational cost of the MG, which sheds light on future EV integration to the smart grid.

Tagging, Encoding, and Jones Optimality

DEFF Research Database (Denmark)

Danvy, Olivier; López, Pablo Ernesto Martínes

2003-01-01

A partial evaluator is said to be Jones-optimal if the result of specializing a self-interpreter with respect to a source program is textually identical to the source program, modulo renaming. Jones optimality has already been obtained if the self-interpreter is untyped. If the selfinterpreter...... is typed, however, residual programs are cluttered with type tags. To obtain the original source program, these tags must be removed. A number of sophisticated solutions have already been proposed. We observe, however, that with a simple representation shift, ordinary partial evaluation is already Jones...

Optimal energy management of the smart parking lot under demand response program in the presence of the electrolyser and fuel cell as hydrogen storage system

International Nuclear Information System (INIS)

Jannati, Jamil; Nazarpour, Daryoosh

2017-01-01

Highlights: • Energy management of IPL is considered in the presence of wind turbine and PV system. • The optimal charge and discharge powers of EVs, dispatch power of LDG are determined. • Charging/discharging decisions of electrolyser and fuel cell are determined. • Demand response program is used to manage the peak load to reduce the operation cost. • Global optimal is guaranteed in proposed model by mixed-integer linear programming. - Abstract: Nowadays, utilization of distributed generation sources and electric vehicles (EVs) are increased to reduce air pollution and greenhouse gas emissions. Also, intelligent parking lots (IPL) are increased in response to the increase in the number of EVs. Therefore, optimal operation of distributed generation sources and IPL in the power market without technical scheduling will follow some economic problems for the owner of IPL and some technical problems for the operator of distribution network. Therefore, in this paper, an optimal energy management has been proposed for an IPL which includes renewable energy sources (wind turbine and photovoltaic system) and local dispatchable generators (micro-turbines). Also, determination of optimal charge and discharge powers of hydrogen storage system (HSS) containing electrolyser, hydrogen storage tanks and fuel cell has been considered in the proposed model. Furthermore, the time-of-use rates of demand response program are proposed to flatten the load curve to reduce the operation cost of IPL. The objective function includes minimizing the operation costs of upstream grid and local dispatchable generators as well as charging and discharging cost of IPL subject to the technical and physical constraints under demand response program in the presence of HSS. The proposed model is formulated as a mixed-integer linear programming and solved using GAMS optimization software under CPLEX solver. Four case studies are investigated to validate the proposed model to show the positive

Developing a Model for Optimizing Inventory of Repairable Items at Single Operating Base

OpenAIRE

Le, Tin

2016-01-01

The use of EOQ model in inventory management is popular. However, EOQ models has many disadvantages, especially, when the model is applied to manage repairable items. In order to deal with high-cost and repairable items, Craig C. Sherbrooke introduced a model in his book “Optimal Inventory Modeling of Systems: Multi-Echelon Techniques”. The research focus is to implement and develop a program to execute the single-site in-ventory model for repairable items. The model helps to significantl...

Load flow optimization and optimal power flow

CERN Document Server

Das, J C

2017-01-01

This book discusses the major aspects of load flow, optimization, optimal load flow, and culminates in modern heuristic optimization techniques and evolutionary programming. In the deregulated environment, the economic provision of electrical power to consumers requires knowledge of maintaining a certain power quality and load flow. Many case studies and practical examples are included to emphasize real-world applications. The problems at the end of each chapter can be solved by hand calculations without having to use computer software. The appendices are devoted to calculations of line and cable constants, and solutions to the problems are included throughout the book.

Optimization of joint recycling process of drilling sludge and phosphogypsum

Directory of Open Access Journals (Sweden)

I. Yu. Ablieieva

2016-06-01

Full Text Available Joint recycling of drilling sludge and phosphogypsum with obtaining a building material is environmentally appropriate and cost-effective, as it helps not only to prevent environmental pollution, but also to solve the problem of rational nature management. Drilling sludge is a waste formed during drilling oil wells, and phosphogypsum is a waste of the chemical industry, formed as a result of the production of concentrational phosphoric acid. However, technogenic raw materials contain heavy metals that can be transformed into a finished product and leached out of it. The problem of minimizing the negative impact of pollutants is very important to reduce the risk to human health. The author's idea is to optimize ecological characteristics of drilling waste and phosphogypsum recycling process. The concentration of heavy metals in the extract of gypsum concrete was determined as the function of the target which depends primarily on structural and technological parameters. The purpose of the article is solution to mathematical programming task, i.e., finding optimal solutions for the factors values of drilling sludge and phosphogypsum recycling process. Mathematical programming solution to optimization problem of the gypsum concrete environmental characteristics (to minimize concentration of heavy metals in the extract was performed by the method of simple random search in the Borland C ++ programming environment using C programming language. It is necessary to observe the values of such factors to minimize concentration of heavy metals in the extract of gypsum concrete. The mass ratio of gypsum binder and drilling sludge is 2.93 units, the mass ratio of quick lime and gypsum binder is 0.09 units, the age of gypsum concrete is above 19 days, exposure time is 28 days.

A study of the use of linear programming techniques to improve the performance in design optimization problems

Science.gov (United States)

Young, Katherine C.; Sobieszczanski-Sobieski, Jaroslaw

1988-01-01

This project has two objectives. The first is to determine whether linear programming techniques can improve performance when handling design optimization problems with a large number of design variables and constraints relative to the feasible directions algorithm. The second purpose is to determine whether using the Kreisselmeier-Steinhauser (KS) function to replace the constraints with one constraint will reduce the cost of total optimization. Comparisons are made using solutions obtained with linear and non-linear methods. The results indicate that there is no cost saving using the linear method or in using the KS function to replace constraints.

Resource Allocation Optimization Model of Collaborative Logistics Network Based on Bilevel Programming

Directory of Open Access Journals (Sweden)

Xiao-feng Xu

2017-01-01

Full Text Available Collaborative logistics network resource allocation can effectively meet the needs of customers. It can realize the overall benefit maximization of the logistics network and ensure that collaborative logistics network runs orderly at the time of creating value. Therefore, this article is based on the relationship of collaborative logistics network supplier, the transit warehouse, and sellers, and we consider the uncertainty of time to establish a bilevel programming model with random constraints and propose a genetic simulated annealing hybrid intelligent algorithm to solve it. Numerical example shows that the method has stronger robustness and convergence; it can achieve collaborative logistics network resource allocation rationalization and optimization.

Helping Nevada School Children Become Sun Smart

Centers for Disease Control (CDC) Podcasts

2017-11-28

This podcast features Christine Thompson, Community Programs Manager at the Nevada Cancer Coalition, and author of a recent study detailing a school-based program to help Nevada school children establish healthy sun safety habits and decrease UV exposure. Christine answers questions about her research and what impact her what impact the program had on children’s skin health.  Created: 11/28/2017 by Preventing Chronic Disease (PCD), National Center for Chronic Disease Prevention and Health Promotion (NCCDPHP).   Date Released: 11/28/2017.

Conversion Rate Optimization : Visual Neuro Programming Principles

OpenAIRE

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...

Life cycle cost optimization of biofuel supply chains under uncertainties based on interval linear programming.

Science.gov (United States)

Ren, Jingzheng; Dong, Liang; Sun, Lu; Goodsite, Michael Evan; Tan, Shiyu; Dong, Lichun

2015-01-01

The aim of this work was to develop a model for optimizing the life cycle cost of biofuel supply chain under uncertainties. Multiple agriculture zones, multiple transportation modes for the transport of grain and biofuel, multiple biofuel plants, and multiple market centers were considered in this model, and the price of the resources, the yield of grain and the market demands were regarded as interval numbers instead of constants. An interval linear programming was developed, and a method for solving interval linear programming was presented. An illustrative case was studied by the proposed model, and the results showed that the proposed model is feasible for designing biofuel supply chain under uncertainties. Copyright © 2015 Elsevier Ltd. All rights reserved.