Learning oncogenetic networks by reducing to mixed integer linear programming.
Shahrabi Farahani, Hossein; Lagergren, Jens
2013-01-01
Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.
Optimized Waterspace Management and Scheduling Using Mixed-Integer Linear Programming
2016-01-01
TECHNICAL REPORT NSWC PCD TR 2015-003 OPTIMIZED WATERSPACE MANAGEMENT AND SCHEDULING USING MIXED-INTEGER LINEAR PROGRAMMING...constraints required for the mathematical formulation of the MCM scheduling problem pertaining to the survey constraints and logistics management . The...Floudas, Nonlinear and Mixed-Integer Optimization: Fundamentals and Applications, Oxford University Press, 1995. [10] M. J. Bays, A. Shende, D. J
DEFF Research Database (Denmark)
Ommen, Torben Schmidt; Markussen, Wiebke Brix; Elmegaard, Brian
2014-01-01
differences and differences between the solution found by each optimisation method. One of the investigated approaches utilises LP (linear programming) for optimisation, one uses LP with binary operation constraints, while the third approach uses NLP (non-linear programming). The LP model is used...... of selected units by 23%, while for a non-linear approach the increase can be higher than 39%. The results indicate a higher coherence between the two latter approaches, and that the MLP (mixed integer programming) optimisation is most appropriate from a viewpoint of accuracy and runtime. © 2014 Elsevier Ltd...
DEFF Research Database (Denmark)
Hernández, Adriana Carolina Luna; Aldana, Nelson Leonardo Diaz; Graells, Moises
2017-01-01
-side strategy, defined as a general mixed-integer linear programming by taking into account two stages for proper charging of the storage units. This model is considered as a deterministic problem that aims to minimize operating costs and promote self-consumption based on 24-hour ahead forecast data...
Mixed integer linear programming for maximum-parsimony phylogeny inference.
Sridhar, Srinath; Lam, Fumei; Blelloch, Guy E; Ravi, R; Schwartz, Russell
2008-01-01
Reconstruction of phylogenetic trees is a fundamental problem in computational biology. While excellent heuristic methods are available for many variants of this problem, new advances in phylogeny inference will be required if we are to be able to continue to make effective use of the rapidly growing stores of variation data now being gathered. In this paper, we present two integer linear programming (ILP) formulations to find the most parsimonious phylogenetic tree from a set of binary variation data. One method uses a flow-based formulation that can produce exponential numbers of variables and constraints in the worst case. The method has, however, proven extremely efficient in practice on datasets that are well beyond the reach of the available provably efficient methods, solving several large mtDNA and Y-chromosome instances within a few seconds and giving provably optimal results in times competitive with fast heuristics than cannot guarantee optimality. An alternative formulation establishes that the problem can be solved with a polynomial-sized ILP. We further present a web server developed based on the exponential-sized ILP that performs fast maximum parsimony inferences and serves as a front end to a database of precomputed phylogenies spanning the human genome.
An overview of solution methods for multi-objective mixed integer linear programming programs
DEFF Research Database (Denmark)
Andersen, Kim Allan; Stidsen, Thomas Riis
Multiple objective mixed integer linear programming (MOMIP) problems are notoriously hard to solve to optimality, i.e. finding the complete set of non-dominated solutions. We will give an overview of existing methods. Among those are interactive methods, the two phases method and enumeration...... methods. In particular we will discuss the existing branch and bound approaches for solving multiple objective integer programming problems. Despite the fact that branch and bound methods has been applied successfully to integer programming problems with one criterion only a few attempts has been made...
An overview of solution methods for multi-objective mixed integer linear programming programs
DEFF Research Database (Denmark)
Andersen, Kim Allan; Stidsen, Thomas Riis
Multiple objective mixed integer linear programming (MOMIP) problems are notoriously hard to solve to optimality, i.e. finding the complete set of non-dominated solutions. We will give an overview of existing methods. Among those are interactive methods, the two phases method and enumeration...... methods. In particular we will discuss the existing branch and bound approaches for solving multiple objective integer programming problems. Despite the fact that branch and bound methods has been applied successfully to integer programming problems with one criterion only a few attempts has been made...
Mixed-Integer Linear Programming Models for Teaching Assistant Assignment and Extensions
Directory of Open Access Journals (Sweden)
Xiaobo Qu
2017-01-01
Full Text Available In this paper, we develop mixed-integer linear programming models for assigning the most appropriate teaching assistants to the tutorials in a department. The objective is to maximize the number of tutorials that are taught by the most suitable teaching assistants, accounting for the fact that different teaching assistants have different capabilities and each teaching assistant’s teaching load cannot exceed a maximum value. Moreover, with optimization models, the teaching load allocation, a time-consuming process, does not need to be carried out in a manual manner. We have further presented a number of extensions that capture more practical considerations. Extensive numerical experiments show that the optimization models can be solved by an off-the-shelf solver and used by departments in universities.
A Mixed-Integer Linear Programming approach to wind farm layout and inter-array cable routing
DEFF Research Database (Denmark)
Fischetti, Martina; Leth, John-Josef; Borchersen, Anders Bech
2015-01-01
A Mixed-Integer Linear Programming (MILP) approach is proposed to optimize the turbine allocation and inter-array offshore cable routing. The two problems are considered with a two steps strategy, solving the layout problem first and then the cable problem. We give an introduction to both problem...
Baran, Richard; Northen, Trent R
2013-10-15
Untargeted metabolite profiling using liquid chromatography and mass spectrometry coupled via electrospray ionization is a powerful tool for the discovery of novel natural products, metabolic capabilities, and biomarkers. However, the elucidation of the identities of uncharacterized metabolites from spectral features remains challenging. A critical step in the metabolite identification workflow is the assignment of redundant spectral features (adducts, fragments, multimers) and calculation of the underlying chemical formula. Inspection of the data by experts using computational tools solving partial problems (e.g., chemical formula calculation for individual ions) can be performed to disambiguate alternative solutions and provide reliable results. However, manual curation is tedious and not readily scalable or standardized. Here we describe an automated procedure for the robust automated mass spectra interpretation and chemical formula calculation using mixed integer linear programming optimization (RAMSI). Chemical rules among related ions are expressed as linear constraints and both the spectra interpretation and chemical formula calculation are performed in a single optimization step. This approach is unbiased in that it does not require predefined sets of neutral losses and positive and negative polarity spectra can be combined in a single optimization. The procedure was evaluated with 30 experimental mass spectra and was found to effectively identify the protonated or deprotonated molecule ([M + H](+) or [M - H](-)) while being robust to the presence of background ions. RAMSI provides a much-needed standardized tool for interpreting ions for subsequent identification in untargeted metabolomics workflows.
Application of mixed-integer linear programming in a car seats assembling process
Directory of Open Access Journals (Sweden)
Jorge Iván Perez Rave
2011-12-01
Full Text Available In this paper, a decision problem involving a car parts manufacturing company is modeled in order to prepare the company for an increase in demand. Mixed-integer linear programming was used with the following decision variables: creating a second shift, purchasing additional equipment, determining the required work force, and other alternatives involving new manners of work distribution that make it possible to separate certain operations from some workplaces and integrate them into others to minimize production costs. The model was solved using GAMS. The solution consisted of programming 19 workers under a configuration that merges two workplaces and separates some operations from some workplaces. The solution did not involve purchasing additional machinery or creating a second shift. As a result, the manufacturing paradigms that had been valid in the company for over 14 years were broken. This study allowed the company to increase its productivity and obtain significant savings. It also shows the benefits of joint work between academia and companies, and provides useful information for professors, students and engineers regarding production and continuous improvement.
Mixed Integer Linear Programming For Exact Finite-Horizon Planning In Decentralized Pomdps
Aras, Raghav; Charpillet, Fran\\ccois
2007-01-01
We consider the problem of finding an n-agent joint-policy for the optimal finite-horizon control of a decentralized Pomdp (Dec-Pomdp). This is a problem of very high complexity (NEXP-hard in n >= 2). In this paper, we propose a new mathematical programming approach for the problem. Our approach is based on two ideas: First, we represent each agent's policy in the sequence-form and not in the tree-form, thereby obtaining a very compact representation of the set of joint-policies. Second, using this compact representation, we solve this problem as an instance of combinatorial optimization for which we formulate a mixed integer linear program (MILP). The optimal solution of the MILP directly yields an optimal joint-policy for the Dec-Pomdp. Computational experience shows that formulating and solving the MILP requires significantly less time to solve benchmark Dec-Pomdp problems than existing algorithms. For example, the multi-agent tiger problem for horizon 4 is solved in 72 secs with the MILP whereas existing ...
A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem
Montoya, Justin Vincent; Wood, Zachary Paul; Rathinam, Sivakumar; Malik, Waqar Ahmad
2010-01-01
Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.
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.
Poos, Alexandra M; Maicher, André; Dieckmann, Anna K; Oswald, Marcus; Eils, Roland; Kupiec, Martin; Luke, Brian; König, Rainer
2016-06-02
Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments.
Poos, Alexandra M.; Maicher, André; Dieckmann, Anna K.; Oswald, Marcus; Eils, Roland; Kupiec, Martin; Luke, Brian; König, Rainer
2016-01-01
Understanding telomere length maintenance mechanisms is central in cancer biology as their dysregulation is one of the hallmarks for immortalization of cancer cells. Important for this well-balanced control is the transcriptional regulation of the telomerase genes. We integrated Mixed Integer Linear Programming models into a comparative machine learning based approach to identify regulatory interactions that best explain the discrepancy of telomerase transcript levels in yeast mutants with deleted regulators showing aberrant telomere length, when compared to mutants with normal telomere length. We uncover novel regulators of telomerase expression, several of which affect histone levels or modifications. In particular, our results point to the transcription factors Sum1, Hst1 and Srb2 as being important for the regulation of EST1 transcription, and we validated the effect of Sum1 experimentally. We compiled our machine learning method leading to a user friendly package for R which can straightforwardly be applied to similar problems integrating gene regulator binding information and expression profiles of samples of e.g. different phenotypes, diseases or treatments. PMID:26908654
A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.
Röhl, Annika; Bockmayr, Alexander
2017-01-03
Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.
Automatic design of synthetic gene circuits through mixed integer non-linear programming.
Huynh, Linh; Kececioglu, John; Köppe, Matthias; Tagkopoulos, Ilias
2012-01-01
Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits.
Canepa, Edward S.
2013-09-01
Traffic sensing systems rely more and more on user generated (insecure) data, which can pose a security risk whenever the data is used for traffic flow control. In this article, we propose a new formulation for detecting malicious data injection in traffic flow monitoring systems by using the underlying traffic flow model. The state of traffic is modeled by the Lighthill- Whitham-Richards traffic flow model, which is a first order scalar conservation law with concave flux function. Given a set of traffic flow data generated by multiple sensors of different types, we show that the constraints resulting from this partial differential equation are mixed integer linear inequalities for a specific decision variable. We use this fact to pose the problem of detecting spoofing cyber attacks in probe-based traffic flow information systems as mixed integer linear feasibility problem. The resulting framework can be used to detect spoofing attacks in real time, or to evaluate the worst-case effects of an attack offliine. A numerical implementation is performed on a cyber attack scenario involving experimental data from the Mobile Century experiment and the Mobile Millennium system currently operational in Northern California. © American Institute of Mathematical Sciences.
Canepa, Edward S.
2013-01-01
Traffic sensing systems rely more and more on user generated (insecure) data, which can pose a security risk whenever the data is used for traffic flow control. In this article, we propose a new formulation for detecting malicious data injection in traffic flow monitoring systems by using the underlying traffic flow model. The state of traffic is modeled by the Lighthill-Whitham- Richards traffic flow model, which is a first order scalar conservation law with concave flux function. Given a set of traffic flow data, we show that the constraints resulting from this partial differential equation are mixed integer linear inequalities for some decision variable. We use this fact to pose the problem of detecting spoofing cyber-attacks in probe-based traffic flow information systems as mixed integer linear feasibility problem. The resulting framework can be used to detect spoofing attacks in real time, or to evaluate the worst-case effects of an attack offline. A numerical implementation is performed on a cyber-attack scenario involving experimental data from the Mobile Century experiment and the Mobile Millennium system currently operational in Northern California. © 2013 IEEE.
A Mixed-Integer Linear Programming approach to wind farm layout and inter-array cable routing
DEFF Research Database (Denmark)
Fischetti, Martina; Leth, John-Josef; Borchersen, Anders Bech
2015-01-01
A Mixed-Integer Linear Programming (MILP) approach is proposed to optimize the turbine allocation and inter-array offshore cable routing. The two problems are considered with a two steps strategy, solving the layout problem first and then the cable problem. We give an introduction to both problems...... and present the MILP models we developed to solve them. To deal with interference in the onshore cases, we propose an adaptation of the standard Jensen’s model, suitable for 3D cases. A simple Stochastic Programming variant of our model allows us to consider different wind scenarios in the optimization...
A mixed integer linear programming model applied in barge planning for Omya
Directory of Open Access Journals (Sweden)
David Bredström
2015-12-01
Full Text Available This article presents a mathematical model for barge transport planning on the river Rhine, which is part of a decision support system (DSS recently taken into use by the Swiss company Omya. The system is operated by Omya’s regional office in Cologne, Germany, responsible for distribution planning at the regional distribution center (RDC in Moerdijk, the Netherlands. The distribution planning is a vital part of supply chain management of Omya’s production of Norwegian high quality calcium carbonate slurry, supplied to European paper manufacturers. The DSS operates within a vendor managed inventory (VMI setting, where the customer inventories are monitored by Omya, who decides upon the refilling days and quantities delivered by barges. The barge planning problem falls into the category of inventory routing problems (IRP and is further characterized with multiple products, heterogeneous fleet with availability restrictions (the fleet is owned by third party, vehicle compartments, dependency of barge capacity on water-level, multiple customer visits, bounded customer inventories and rolling planning horizon. There are additional modelling details which had to be considered to make it possible to employ the model in practice at a sufficient level of detail. To the best of our knowledge, we have not been able to find similar models covering all these aspects in barge planning. This article presents the developed mixed-integer programming model and discusses practical experience with its solution. Briefly, it also puts the model into the context of the entire business case of value chain optimization in Omya.
Directory of Open Access Journals (Sweden)
Mbarek Elbounjimi
2015-11-01
Full Text Available Closed-loop supply chain network design is a critical issue due to its impact on both economic and environmental performances of the supply chain. In this paper, we address the problem of designing a multi-echelon, multi-product and capacitated closed-loop supply chain network. First, a mixed-integer linear programming formulation is developed to maximize the total profit. The main contribution of the proposed model is addressing two economic viability issues of closed-loop supply chain. The first issue is the collection of sufficient quantity of end-of-life products are assured by retailers against an acquisition price. The second issue is exploiting the benefits of colocation of forward facilities and reverse facilities. The presented model is solved by LINGO for some test problems. Computational results and sensitivity analysis are conducted to show the performance of the proposed model.
Directory of Open Access Journals (Sweden)
H Kazemipoor
2012-04-01
Full Text Available A multi-skilled project scheduling problem (MSPSP has been generally presented to schedule a project with staff members as resources. Each activity in project network requires different skills and also staff members have different skills, too. This causes the MSPSP becomes a special type of a multi-mode resource-constrained project scheduling problem (MM-RCPSP with a huge number of modes. Given the importance of this issue, in this paper, a mixed integer linear programming for the MSPSP is presented. Due to the complexity of the problem, a meta-heuristic algorithm is proposed in order to find near optimal solutions. To validate performance of the algorithm, results are compared against exact solutions solved by the LINGO solver. The results are promising and show that optimal or near-optimal solutions are derived for small instances and good solutions for larger instances in reasonable time.
Institute of Scientific and Technical Information of China (English)
Hai-ning KONG
2015-01-01
Byproduct gas is an important secondary energy in iron and steel industry, and its optimization is vital to cost reduction. With the development of iron and steel industry to be more eco-friendly, it is necessary to construct an integrated optimized system, taking economics, energy consumption and environment into consideration. Therefore, the environmental cost caused by pollutants discharge should be factored in total cost when optimizing byproduct gas distribution. A green mixed integer linear programming (MILP) model for the optimization of byproduct gases was established to reduce total cost, including both operation cost and environmental cost. The operation cost included penalty for gas deviation, costs of fuel and water consumption, holder booster trip penalty, and so forth; while the environmental cost consisted of penalties for both direct and indirect pollutants discharge. Case study showed that the proposed model brought an optimum solution and 2.2% of the total cost could be reduced compared with previous one.
Energy Technology Data Exchange (ETDEWEB)
DRIESSEN,BRIAN; SADEGH,NADER
2000-04-25
This work presents a method of finding near global optima to minimum-time trajectory generation problem for systems that would be linear if it were not for the presence of Coloumb friction. The required final state of the system is assumed to be maintainable by the system, and the input bounds are assumed to be large enough so that they can overcome the maximum static Coloumb friction force. Other than the previous work for generating minimum-time trajectories for non redundant robotic manipulators for which the path in joint space is already specified, this work represents, to the best of the authors' knowledge, the first approach for generating near global optima for minimum-time problems involving a nonlinear class of dynamic systems. The reason the optima generated are near global optima instead of exactly global optima is due to a discrete-time approximation of the system (which is usually used anyway to simulate such a system numerically). The method closely resembles previous methods for generating minimum-time trajectories for linear systems, where the core operation is the solution of a Phase I linear programming problem. For the nonlinear systems considered herein, the core operation is instead the solution of a mixed integer linear programming problem.
Li, Yanning
2013-10-01
This article presents a new robust control framework for transportation problems in which the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a Hamilton-Jacobi equation, we pose the problem of controlling the state of the system on a network link, using boundary flow control, as a Linear Program. Unlike many previously investigated transportation control schemes, this method yields a globally optimal solution and is capable of handling shocks (i.e. discontinuities in the state of the system). We also demonstrate that the same framework can handle robust control problems, in which the uncontrollable components of the initial and boundary conditions are encoded in intervals on the right hand side of inequalities in the linear program. The lower bound of the interval which defines the smallest feasible solution set is used to solve the robust LP (or MILP if the objective function depends on boolean variables). Since this framework leverages the intrinsic properties of the Hamilton-Jacobi equation used to model the state of the system, it is extremely fast. Several examples are given to demonstrate the performance of the robust control solution and the trade-off between the robustness and the optimality. © 2013 IEEE.
Institute of Scientific and Technical Information of China (English)
Meysam Kamalinejad; Majid Amidpour; S.M. Mousavi Naeynian
2015-01-01
Liquefied natural gas (LNG) is the most economical way of transporting natural gas (NG) over long distances. Liq-uefaction of NG using vapor compression refrigeration system requires high operating and capital cost. Due to lack of systematic design methods for multistage refrigeration cycles, conventional approaches to determine op-timal cycle are largely trial-and-error. In this paper a novel mixed integer non-linear programming (MINLP) model is introduced to select optimal synthesis of refrigeration systems to reduce both operating and capital costs of an LNG plant. Better conceptual understanding of design improvement is illustrated on composite curve (CC) and exergetic grand composite curve (EGCC) of pinch analysis diagrams. In this method a superstruc-ture representation of complex refrigeration system is developed to select and optimize key decision variables in refrigeration cycles (i.e. partition temperature, compression configuration, refrigeration features, refrigerant flow rate and economic trade-off). Based on this method a program (LNG-Pro) is developed which integrates VBA, Refprop and Excel MINLP Solver to automate the methodology. Design procedure is applied on a sample LNG plant to illustrate advantages of using this method which shows a 3.3% reduction in total shaft work consumption.
Directory of Open Access Journals (Sweden)
Esten I. Grøtli
2016-04-01
Full Text Available Large amounts of data are typically generated in applications such as surveillance of power lines and railways, inspection of gas pipes, and security surveillance. In the latter application it is a necessity that the data is transmitted to the control centre ``on-the-fly'' for analysis. Also missions related to other applications would greatly benefit from near real-time analysis and operator interaction based on captured data. This is the motivation behind this paper on coarse offline motion- and communication-planning for cooperating Unmanned Aerial Vehicles (UAVs. A Mixed-Integer Linear Programming (MILP problem is defined in order to solve the surveillance mission. To efficiently transmit the data back to the base station the vehicles are allowed to store data for later transmission and transmit via other vehicles, in addition to direct transmission. The paths obtained by solving the optimization problem are analyzed using a realistic radio propagation path loss simulator. If the radio propagation path loss exceeds the maximum design criterion the optimization problem is solved again with a stricter communication constraint, and the procedure is continued in an iterative manner until the criterion is met. The proposed algorithm is supported by simulations showing the resulting paths and communication topologies for different choices of delay tolerance.
Zhang, Huiling; Huang, Qingsheng; Bei, Zhendong; Wei, Yanjie; Floudas, Christodoulos A
2016-03-01
In this article, we present COMSAT, a hybrid framework for residue contact prediction of transmembrane (TM) proteins, integrating a support vector machine (SVM) method and a mixed integer linear programming (MILP) method. COMSAT consists of two modules: COMSAT_SVM which is trained mainly on position-specific scoring matrix features, and COMSAT_MILP which is an ab initio method based on optimization models. Contacts predicted by the SVM model are ranked by SVM confidence scores, and a threshold is trained to improve the reliability of the predicted contacts. For TM proteins with no contacts above the threshold, COMSAT_MILP is used. The proposed hybrid contact prediction scheme was tested on two independent TM protein sets based on the contact definition of 14 Å between Cα-Cα atoms. First, using a rigorous leave-one-protein-out cross validation on the training set of 90 TM proteins, an accuracy of 66.8%, a coverage of 12.3%, a specificity of 99.3% and a Matthews' correlation coefficient (MCC) of 0.184 were obtained for residue pairs that are at least six amino acids apart. Second, when tested on a test set of 87 TM proteins, the proposed method showed a prediction accuracy of 64.5%, a coverage of 5.3%, a specificity of 99.4% and a MCC of 0.106. COMSAT shows satisfactory results when compared with 12 other state-of-the-art predictors, and is more robust in terms of prediction accuracy as the length and complexity of TM protein increase. COMSAT is freely accessible at http://hpcc.siat.ac.cn/COMSAT/.
Bivium as a Mixed Integer Programming Problem
DEFF Research Database (Denmark)
Borghoff, Julia; Knudsen, Lars Ramkilde; Stolpe, Mathias
2009-01-01
Trivium is a stream cipher proposed for the eSTREAM project. Raddum introduced some reduced versions of Trivium, named Bivium A and Bivium B. In this article we present a numerical attack on the Biviums. The main idea is to transform the problem of solving a sparse system of quadratic equations...... over $GF(2)$ into a combinatorial optimization problem. We convert the Boolean equation system into an equation system over $\\mathbb{R}$ and formulate the problem of finding a $0$-$1$-valued solution for the system as a mixed-integer programming problem. This enables us to make use of several...... algorithms in the field of combinatorial optimization in order to find a solution for the problem and recover the initial state of Bivium. In particular this gives us an attack on Bivium B in estimated time complexity of $2^{63.7}$ seconds. But this kind of attack is also applicable to other cryptographic...
Directory of Open Access Journals (Sweden)
Samira Salahi
2016-08-01
Full Text Available Reduction of fossil resources, increasing the production of greenhouse gas emissions and demand growth lead to greater use of distributed energy resources in power system especially in distribution networks. Integrating these resources in order to supply local loads creates a new concept called micro-grid. Optimal operation of micro-grid in the specific time period is one of the most important problems of them. In this paper, the operation problem of micro-grids is modeled considering the economical, technical and environmental issues, as well as uncertainties related to loads, wind speed and solar radiation. The resulting model is a Mixed-Integer Non-Linear Programming (MINLP. To demonstrate the effectiveness of the proposed model, Bisheh village in Iran is considered as a case study. The results showed that considering load curtailment costs, the power losses of the main grid, the penalties of pollutant gasses emissions and the elimination of energy subsides will tremendous impacts on the operation of microgrids. Article History: Received March 12, 2016; Received in revised form June 20, 2016; Accepted July 2nd 2016; Available online How to Cite This Article: Salahi, S., and Bahramara, S. (2016 Modeling Operation Problem of Micro-grids Considering Economical, Technical and Environmental issues as Mixed-Integer Non-Linear Programming. Int. Journal of Renewable Energy Development, 5(2, 139-149. http://dx.doi.org/10.14710/ijred.5.2.139-149
Energy Technology Data Exchange (ETDEWEB)
Waddell, Lucas; Muldoon, Frank; Henry, Stephen Michael; Hoffman, Matthew John; Zwerneman, April Marie; Backlund, Peter; Melander, Darryl J.; Lawton, Craig R.; Rice, Roy Eugene
2017-09-01
In order to effectively plan the management and modernization of their large and diverse fleets of vehicles, Program Executive Office Ground Combat Systems (PEO GCS) and Program Executive Office Combat Support and Combat Service Support (PEO CS&CSS) commis- sioned the development of a large-scale portfolio planning optimization tool. This software, the Capability Portfolio Analysis Tool (CPAT), creates a detailed schedule that optimally prioritizes the modernization or replacement of vehicles within the fleet - respecting numerous business rules associated with fleet structure, budgets, industrial base, research and testing, etc., while maximizing overall fleet performance through time. This paper contains a thor- ough documentation of the terminology, parameters, variables, and constraints that comprise the fleet management mixed integer linear programming (MILP) mathematical formulation. This paper, which is an update to the original CPAT formulation document published in 2015 (SAND2015-3487), covers the formulation of important new CPAT features.
Designing Networks: A Mixed-Integer Linear Optimization Approach
Gounaris, Chrysanthos E; Kevrekidis, Ioannis G; Floudas, Christodoulos A
2015-01-01
Designing networks with specified collective properties is useful in a variety of application areas, enabling the study of how given properties affect the behavior of network models, the downscaling of empirical networks to workable sizes, and the analysis of network evolution. Despite the importance of the task, there currently exists a gap in our ability to systematically generate networks that adhere to theoretical guarantees for the given property specifications. In this paper, we propose the use of Mixed-Integer Linear Optimization modeling and solution methodologies to address this Network Generation Problem. We present a number of useful modeling techniques and apply them to mathematically express and constrain network properties in the context of an optimization formulation. We then develop complete formulations for the generation of networks that attain specified levels of connectivity, spread, assortativity and robustness, and we illustrate these via a number of computational case studies.
A fuzzy mixed integer programming for marketing planning
Directory of Open Access Journals (Sweden)
Abolfazl Danaei
2014-03-01
Full Text Available One of the primary concerns to market a product is to find appropriate channel to target customers. The recent advances on information technology have created new products with tremendous opportunities. This paper presents a mixed integer programming technique based on McCarthy's 4PS to locate suitable billboards for marketing newly introduced IPHONE product. The paper considers two types of information including age and income and tries to find the best places such that potential consumers aged 25-35 with high income visit the billboards and the cost of advertisement is minimized. The model is formulated in terms of mixed integer programming and it has been applied for potential customers who live in city of Tabriz, Iran. Using a typical software package, the model detects appropriate places in various parts of the city.
Relaxation and decomposition methods for mixed integer nonlinear programming
Nowak, Ivo; Bank, RE
2005-01-01
This book presents a comprehensive description of efficient methods for solving nonconvex mixed integer nonlinear programs, including several numerical and theoretical results, which are presented here for the first time. It contains many illustrations and an up-to-date bibliography. Because on the emphasis on practical methods, as well as the introduction into the basic theory, the book is accessible to a wide audience. It can be used both as a research and as a graduate text.
Domínguez, Luis F.
2010-12-01
This work introduces two algorithms for the solution of pure integer and mixed-integer bilevel programming problems by multiparametric programming techniques. The first algorithm addresses the integer case of the bilevel programming problem where integer variables of the outer optimization problem appear in linear or polynomial form in the inner problem. The algorithm employs global optimization techniques to convexify nonlinear terms generated by a reformulation linearization technique (RLT). A continuous multiparametric programming algorithm is then used to solve the reformulated convex inner problem. The second algorithm addresses the mixed-integer case of the bilevel programming problem where integer and continuous variables of the outer problem appear in linear or polynomial forms in the inner problem. The algorithm relies on the use of global multiparametric mixed-integer programming techniques at the inner optimization level. In both algorithms, the multiparametric solutions obtained are embedded in the outer problem to form a set of single-level (M)(I)(N)LP problems - which are then solved to global optimality using standard fixed-point (global) optimization methods. Numerical examples drawn from the open literature are presented to illustrate the proposed algorithms. © 2010 Elsevier Ltd.
Optimal power system management via mixed integer dynamic programming
Energy Technology Data Exchange (ETDEWEB)
Kwatny, H.G.; Mensah, E. [Drexel Univ., Philadelphia, PA (United States). Dept. of Mechanical Engineering and Mechanics; Niebur, D. [Drexel Univ., Philadelphia, PA (United States). Dept. of Electrical and Computer Engineering; Teolis, C. [Techno-Sciences Inc., Lanham, MD (United States)
2006-07-01
Power systems are comprised of continuous and discrete acting components and subsystems. This paper discussed a logical specification that was used to define the transition dynamics of the discrete subsystem. It also presented a computational tool that reduced the logical specification to a set of inequalities as well as the use of the transformed model in a dynamic programming approach to the design of the optimal feedback controls. An example of optimal load shedding within a power system with an aggregate induction motor and constant admittance loads was presented. Specifically, the paper outlined the problem and discussed the modeling of hybrid systems and the control problem. A solution to the optimal control problem was presented. The essential feature of the model was the characterization of the discrete subsystem in terms of a set of mixed-integer formulas. The case example showed how logical constraints involving system real variables, such as case excitation voltage, could be incorporated in the problem via transformation to mixed-integer formulas. 10 refs., 4 figs.
Mixed Integer Programming and Heuristic Scheduling for Space Communication
Lee, Charles H.; Cheung, Kar-Ming
2013-01-01
Optimal planning and scheduling for a communication network was created where the nodes within the network are communicating at the highest possible rates while meeting the mission requirements and operational constraints. The planning and scheduling problem was formulated in the framework of Mixed Integer Programming (MIP) to introduce a special penalty function to convert the MIP problem into a continuous optimization problem, and to solve the constrained optimization problem using heuristic optimization. The communication network consists of space and ground assets with the link dynamics between any two assets varying with respect to time, distance, and telecom configurations. One asset could be communicating with another at very high data rates at one time, and at other times, communication is impossible, as the asset could be inaccessible from the network due to planetary occultation. Based on the network's geometric dynamics and link capabilities, the start time, end time, and link configuration of each view period are selected to maximize the communication efficiency within the network. Mathematical formulations for the constrained mixed integer optimization problem were derived, and efficient analytical and numerical techniques were developed to find the optimal solution. By setting up the problem using MIP, the search space for the optimization problem is reduced significantly, thereby speeding up the solution process. The ratio of the dimension of the traditional method over the proposed formulation is approximately an order N (single) to 2*N (arraying), where N is the number of receiving antennas of a node. By introducing a special penalty function, the MIP problem with non-differentiable cost function and nonlinear constraints can be converted into a continuous variable problem, whose solution is possible.
Han, Kyung T.; Rudner, Lawrence M.
2014-01-01
This study uses mixed integer quadratic programming (MIQP) to construct multiple highly equivalent item pools simultaneously, and compares the results from mixed integer programming (MIP). Three different MIP/MIQP models were implemented and evaluated using real CAT item pool data with 23 different content areas and a goal of equal information…
Approximating electrical distribution networks via mixed-integer nonlinear programming
Energy Technology Data Exchange (ETDEWEB)
Lakhera, Sanyogita [Citibank, New York City, NY (United States); Shanbhag, Uday V. [Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign, 117 Transportation Building, 104 S. Mathews Ave., Urbana, IL 61801 (United States); McInerney, Michael K. [Construction Engineering Research Laboratory (CERL) (United States)
2011-02-15
Given urban data derived from a geographical information system (GIS), we consider the problem of constructing an estimate of the electrical distribution system of an urban area. We employ the image data to obtain an approximate electrical load distribution over a network of a prespecificed discretization. Together with partial information about existing substations, we determine the optimal placement of electrical substations to sustain such a load that minimizes the cost of capital and losses. This requires solving large-scale quadratic programs with discrete variables for which we present a novel penalization-smoothing scheme. The choice of locations allows one to determine the optimal flows in this network, as required by physical requirements which provide us with an approximation of the distribution network. Furthermore, the scheme allows for approximating systems in the presence of no-go areas, such as lakes and fields. We examine the performance of our algorithm on the solution of a set of location problems and observe that the scheme is capable of solving large-scale instances, well beyond the realm of existing mixed-integer nonlinear programming solvers. We conclude with a case study in which a stage-wise extension of this scheme is developed to reflect the temporal evolution of load. (author)
Deleting Outliers in Robust Regression with Mixed Integer Programming
Institute of Scientific and Technical Information of China (English)
Georgios Zioutas; Antonios Avramidis
2005-01-01
In robust regression we often have to decide how many are the unusual observations, which should be removed from the sample in order to obtain better fitting for the rest of the observations. Generally, we use the basic principle of LTS, which is to fit the majority of the data, identifying as outliers those points that cause the biggest damage to the robust fit. However, in the LTS regression method the choice of default values for high break down-point affects seriously the efficiency of the estimator. In the proposed approach we introduce penalty cost for discarding an outlier, consequently, the best fit for the majority of the data is obtained by discarding only catastrophic observations. This penalty cost is based on robust design weights and high break down-point residual scale taken from the LTS estimator. The robust estimation is obtained by solving a convex quadratic mixed integer programming problem, where in the objective function the sum of the squared residuals and penalties for discarding observations is minimized. The proposed mathematical programming formula is suitable for small-sample data. Moreover, we conduct a simulation study to compare other robust estimators with our approach in terms of their efficiency and robustness.
Mixed integer (0-1) fractional programming for decision support in paper production industry
Claassen, G.D.H.
2014-01-01
This paper presents an effective and efficient method for solving a special class of mixed integer fractional programming (FP) problems. We take a classical reformulation approach for continuous FP as a starting point and extend it for solving a more general class of mixed integer (0–1) fractional p
Constrained spacecraft reorientation using mixed integer convex programming
Tam, Margaret; Glenn Lightsey, E.
2016-10-01
A constrained attitude guidance (CAG) system is developed using convex optimization to autonomously achieve spacecraft pointing objectives while meeting the constraints imposed by on-board hardware. These constraints include bounds on the control input and slew rate, as well as pointing constraints imposed by the sensors. The pointing constraints consist of inclusion and exclusion cones that dictate permissible orientations of the spacecraft in order to keep objects in or out of the field of view of the sensors. The optimization scheme drives a body vector towards a target inertial vector along a trajectory that consists solely of permissible orientations in order to achieve the desired attitude for a given mission mode. The non-convex rotational kinematics are handled by discretization, which also ensures that the quaternion stays unity norm. In order to guarantee an admissible path, the pointing constraints are relaxed. Depending on how strict the pointing constraints are, the degree of relaxation is tuneable. The use of binary variables permits the inclusion of logical expressions in the pointing constraints in the case that a set of sensors has redundancies. The resulting mixed integer convex programming (MICP) formulation generates a steering law that can be easily integrated into an attitude determination and control (ADC) system. A sample simulation of the system is performed for the Bevo-2 satellite, including disturbance torques and actuator dynamics which are not modeled by the controller. Simulation results demonstrate the robustness of the system to disturbances while meeting the mission requirements with desirable performance characteristics.
A generalization of the MDS method by mixed integer linear and nonlinear mathematical models
Directory of Open Access Journals (Sweden)
Sadegh Niroomand
2014-09-01
Full Text Available The Multi-Dimensional Scaling (MDS method is used in statistics to detect hidden interrelations among multi-dimensional data and it has a wide range of applications. The method’s input is a matrix that describes the similarity/dissimilarity among objects of unknown dimension. The objects are generally reconstructed as points of a lower dimensional space to reveal the geometric configuration of the objects. The original MDS method uses Euclidean distance, for measuring both the distance of the reconstructed points and the bias of the reconstructed distances from the original similarity values. In this paper, these distances are distinguished, and distances other than Euclidean are also used, generalizing the MDS method. Two different distances may be used for the two different purposes. Therefore the instances of the generalized MDS model are denoted as model, where the first distance is the type of distance of the reconstructed points and the second one measures the bias of the reconstructed distances and the similarity values. In the case of and distances mixed-integer programming models are provided. The computational experiences show that the generalized model can catch the key properties of the original configuration, if any exist. Keywords: Multivariate Analysis; Multi-Dimensional Scaling; Optimization; Mixed Integer Linear Programming; Statistics.
Directory of Open Access Journals (Sweden)
Tessa Vanina Soetanto
2004-01-01
Full Text Available This paper presents a study about new heuristic algorithm performance compared to Mixed Integer Programming (MIP method in solving flowshop scheduling problem to reach minimum makespan. Performance appraisal is based on Efficiency Index (EI, Relative Error (RE and Elapsed Runtime. Abstract in Bahasa Indonesia : Makalah ini menyajikan penelitian tentang performance algoritma heuristik Pour terhadap metode Mixed Integer Programming (MIP dalam menyelesaikan masalah penjadwalan flowshop dengan tujuan meminimalkan makespan. Penilaian performance dilakukan berdasarkan nilai Efficiency Index (EI, Relative Error (RE dan Elapsed Runtime. Kata kunci: flowshop, makespan, algoritma heuristik Pour, Mixed Integer Programming.
Gorissen, Bram L; Hoffmann, Aswin L
2014-01-01
Current inverse treatment planning methods that optimize both catheter positions and dwell times in prostate HDR brachytherapy use surrogate linear or quadratic objective functions that have no direct interpretation in terms of dose-volume histogram (DVH) criteria, do not result in an optimum or have long solution times. We decrease the solution time of existing linear and quadratic dose-based programming models (LP and QP, respectively) to allow optimizing over potential catheter positions using mixed integer programming. An additional average speed-up of 75% can be obtained by stopping the solver at an early stage, without deterioration of the plan quality. For a fixed catheter configuration, the dwell time optimization model LP solves to optimality in less than 15 seconds, which confirms earlier results. We propose an iterative procedure for QP that allows to prescribe the target dose as an interval, while retaining independence between the solution time and the number of dose calculation points. This iter...
A mixed integer program to model spatial wildfire behavior and suppression placement decisions
Erin J. Belval; Yu Wei; Michael. Bevers
2015-01-01
Wildfire suppression combines multiple objectives and dynamic fire behavior to form a complex problem for decision makers. This paper presents a mixed integer program designed to explore integrating spatial fire behavior and suppression placement decisions into a mathematical programming framework. Fire behavior and suppression placement decisions are modeled using...
Canepa, Edward S.
2012-09-01
This article presents a new mixed integer programming formulation of the traffic density estimation problem in highways modeled by the Lighthill Whitham Richards equation. We first present an equivalent formulation of the problem using an Hamilton-Jacobi equation. Then, using a semi-analytic formula, we show that the model constraints resulting from the Hamilton-Jacobi equation result in linear constraints, albeit with unknown integers. We then pose the problem of estimating the density at the initial time given incomplete and inaccurate traffic data as a Mixed Integer Program. We then present a numerical implementation of the method using experimental flow and probe data obtained during Mobile Century experiment. © 2012 IEEE.
On the solution of mixed-integer nonlinear programming models for computer aided molecular design.
Ostrovsky, Guennadi M; Achenie, Luke E K; Sinha, Manish
2002-11-01
This paper addresses the efficient solution of computer aided molecular design (CAMD) problems, which have been posed as mixed-integer nonlinear programming models. The models of interest are those in which the number of linear constraints far exceeds the number of nonlinear constraints, and with most variables participating in the nonconvex terms. As a result global optimization methods are needed. A branch-and-bound algorithm (BB) is proposed that is specifically tailored to solving such problems. In a conventional BB algorithm, branching is performed on all the search variables that appear in the nonlinear terms. This translates to a large number of node traversals. To overcome this problem, we have proposed a new strategy for branching on a set of linear branchingfunctions, which depend linearly on the search variables. This leads to a significant reduction in the dimensionality of the search space. The construction of linear underestimators for a class of functions is also presented. The CAMD problem that is considered is the design of optimal solvents to be used as cleaning agents in lithographic printing.
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
Two classes of mixed-integer nonlinear bilevel programming problems are discussed. One is that the follower's functions are separable with respect to the follower's variables, and the other is that the follower's functions are convex if the follower's variables are not restricted to integers. A genetic algorithm based on an exponential distribution is proposed for the aforementioned problems. First, for each fixed leader's variable x, it is proved that the optimal solution y of the follower's mixed-integer programming can be obtained by solving associated relaxed problems, and according to the convexity of the functions involved, a simplified branch and bound approach is given to solve the follower's programming for the second class of problems. Furthermore, based on an exponential distribution with a parameter A, a new crossover operator is designed in which the best individuals are used to generate better offspring of crossover. The simulation results illustrate that the proposed algorithm is efficient and robust.
PON and WiMAX Convergence Network Planning Based on Mixed Integer Programming Model
Institute of Scientific and Technical Information of China (English)
Lv Miao; Chen Xue
2011-01-01
This article analyzes the characteristics of PON and WiMAX convergence network planning.Based on user coverage ratio,WiMAX channel allocation,cell radius,carrier-to-noise ratio threshold,and bandwidth constraint,we propose a mixed integer programming model solved by a Branch-Band and Heuristic Search method.Finally,the simulation result is given and analyzed.The planning method based on a mixed integer programming model can save 20 percentage of the overall planning cost,compared with the greedy algorithm.The relationship between the convergence network planning cost and frequency usage is also analyzed.The optimized planning result with the lowest cost can be acquired through the best frequency usage.
Enhanced index tracking modeling in portfolio optimization with mixed-integer programming z approach
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.
A Mixed Integer Programming Model Formulation for Solving the Lot-Sizing Problem
Mohammadi, Maryam
2012-01-01
This paper addresses a mixed integer programming (MIP) formulation for the multi-item uncapacitated lot-sizing problem that is inspired from the trailer manufacturer. The proposed MIP model has been utilized to find out the optimum order quantity, optimum order time, and the minimum total cost of purchasing, ordering, and holding over the predefined planning horizon. This problem is known as NP-hard problem. The model was presented in an optimal software form using LINGO 13.0.
DEFF Research Database (Denmark)
Jensen, Toke Koldborg; Kjærsgaard, Niels Christian
Slaughterhouses are major players in the pork supply chain, and supply and demand must be matched in order to generate the highest proﬁt. In particular, carcasses must be sorted in order to produce the “right” ﬁnal products from the “right” carcasses. We develop a mixed-integer programming (MIP) ...... at slaughterhouses. Finally, we comment on the expected effect of variations in the raw material supply and the demand as well as future research concerning joint modelling of supply chain aspects....
Cheng, Guanhui; Huang, Guohe; Dong, Cong; Xu, Ye; Chen, Xiujuan; Chen, Jiapei
2017-03-01
Due to the existence of complexities of heterogeneities, hierarchy, discreteness, and interactions in municipal solid waste management (MSWM) systems such as Beijing, China, a series of socio-economic and eco-environmental problems may emerge or worsen and result in irredeemable damages in the following decades. Meanwhile, existing studies, especially ones focusing on MSWM in Beijing, could hardly reflect these complexities in system simulations and provide reliable decision support for management practices. Thus, a framework of distributed mixed-integer fuzzy hierarchical programming (DMIFHP) is developed in this study for MSWM under these complexities. Beijing is selected as a representative case. The Beijing MSWM system is comprehensively analyzed in many aspects such as socio-economic conditions, natural conditions, spatial heterogeneities, treatment facilities, and system complexities, building a solid foundation for system simulation and optimization. Correspondingly, the MSWM system in Beijing is discretized as 235 grids to reflect spatial heterogeneity. A DMIFHP model which is a nonlinear programming problem is constructed to parameterize the Beijing MSWM system. To enable scientific solving of it, a solution algorithm is proposed based on coupling of fuzzy programming and mixed-integer linear programming. Innovations and advantages of the DMIFHP framework are discussed. The optimal MSWM schemes and mechanism revelations will be discussed in another companion paper due to length limitation.
Sakakibara, Kazutoshi; Tian, Yajie; Nishikawa, Ikuko
We discuss the planning of transportation by trucks over a multi-day period. Each truck collects loads from suppliers and delivers them to assembly plants or a truck terminal. By exploiting the truck terminal as a temporal storage, we aim to increase the load ratio of each truck and to minimize the lead time for transportation. In this paper, we show a mixed integer programming model which represents each product explicitly, and discuss the decomposition of the problem into a problem of delivery and storage, and a problem of vehicle routing. Based on this model, we propose a relax-and-fix type heuristic in which decision variables are fixed one by one by mathematical programming techniques such as branch-and-bound methods.
A Mixed Integer Programming Poultry Feed Ration Optimisation Problem Using the Bat Algorithm
Directory of Open Access Journals (Sweden)
Godfrey Chagwiza
2016-01-01
Full Text Available In this paper, a feed ration problem is presented as a mixed integer programming problem. An attempt to find the optimal quantities of Moringa oleifera inclusion into the poultry feed ration was done and the problem was solved using the Bat algorithm and the Cplex solver. The study used findings of previous research to investigate the effects of Moringa oleifera inclusion in poultry feed ration. The results show that the farmer is likely to gain US$0.89 more if Moringa oleifera is included in the feed ration. Results also show superiority of the Bat algorithm in terms of execution time and number of iterations required to find the optimum solution as compared with the results obtained by the Cplex solver. Results revealed that there is a significant economic benefit of Moringa oleifera inclusion into the poultry feed ration.
Energy Technology Data Exchange (ETDEWEB)
Linderoth, Jeff T. [University of Wisconsin-Madison; Luedtke, James R. [University of Wisconsin-Madison
2013-05-30
The mathematical modeling of systems often requires the use of both nonlinear and discrete components. Problems involving both discrete and nonlinear components are known as mixed-integer nonlinear programs (MINLPs) and are among the most challenging computational optimization problems. This research project added to the understanding of this area by making a number of fundamental advances. First, the work demonstrated many novel, strong, tractable relaxations designed to deal with non-convexities arising in mathematical formulation. Second, the research implemented the ideas in software that is available to the public. Finally, the work demonstrated the importance of these ideas on practical applications and disseminated the work through scholarly journals, survey publications, and conference presentations.
DEFF Research Database (Denmark)
Liu, Zhaoxi; Wu, Qiuwei; Oren, Shmuel S.
2016-01-01
This paper presents a distribution locational marginal pricing (DLMP) method through chance constrained mixed-integer programming designed to alleviate the possible congestion in the future distribution network with high penetration of electric vehicles (EVs). In order to represent the stochastic...... the driving requirement is below the predetermined confidence parameter. The efficacy of the proposed approach was demonstrated by case studies using a 33-bus distribution system of the Bornholm power system and the Danish driving data. The case study results show that the DLMP method through chance...... constrained MIP can successfully alleviate the congestion in the distribution network due to the EV charging while keeping the failure probability of EV charging not meeting driving needs below the predefined confidence....
Real-Time Hybrid In-Station Bus Dispatching Strategy Based on Mixed Integer Programming
Directory of Open Access Journals (Sweden)
Shi An
2016-07-01
Full Text Available The actual bus headway often deviates from the planned departure frequency because of external factors, such as traffic conditions and public transport demand, leading to transit resource waste and reducing the quality of service. In view of the existing shortcomings of the current dispatching strategy, a mixed integer programming model, integrating a bus-holding and stop-skipping strategy, is constructed to improve transit service with a minimum cost. The real-time optimal holding and stop-skipping strategies can be obtained by solving the proposed model using the Lagrangian relaxation algorithm. A numerical example is conducted using real transit GPS (Global Position System and IC (Intelligent Card data in Harbin. The results show that compared to a single control strategy, the proposed hybrid model is a better trade-off between the quality of the transit service and the operation cost. Notably, such a strategy would produce a minimal passengers’ average travel time coefficient. It is a great help for promoting the transit service level and increasing competitiveness.
Directory of Open Access Journals (Sweden)
Vahid Reza Ghezavati
2011-01-01
Full Text Available This research defines a new application of mathematical modeling to design a cellular manufacturing system integrated with group scheduling and layout aspects in an uncertain decision space under a supply chain characteristics. The aim is to present a mixed integer programming (MIP which optimizes cell formation, scheduling and layout decisions, concurrently where the suppliers are required to operate exceptional products. For this purpose, the time in which parts need to be operated on machines and also products' demand are uncertain and explained by set of scenarios. This model tries to optimize expected holding cost and the costs regarded to the suppliers network in a supply chain in order to outsource exceptional operations. Scheduling decisions in a cellular manufacturing framework is treated as group scheduling problem, which assumes that all parts in a part group are operated in the same cell and no inter-cellular transfer is required. An efficient hybrid method made of genetic algorithm (GA and simulated annealing (SA will be proposed to solve such a complex problem under an optimization rule as a sub-ordinate section. This integrative combination algorithm is compared with global solutions and also, a benchmark heuristic algorithm introduced in the literature. Finally, performance of the algorithm will be verified through some test problems.
Mixed integer programming for the resolution of GPS carrier phase ambiguities
Xu, Peiliang; Lachapelle, Gerard
2010-01-01
This arXiv upload is to clarify that the now well-known sorted QR MIMO decoder was first presented in the 1995 IUGG General Assembly. We clearly go much further in the sense that we directly incorporated reduction into this one step, non-exact suboptimal integer solution. Except for these first few lines up to this point, this paper is an unaltered version of the paper presented at the IUGG1995 Assembly in Boulder. Ambiguity resolution of GPS carrier phase observables is crucial in high precision geodetic positioning and navigation applications. It consists of two aspects: estimating the integer ambiguities in the mixed integer observation model and examining whether they are sufficiently accurate to be fixed as known nonrandom integers. We shall discuss the first point in this paper from the point of view of integer programming. A one-step nonexact approach is proposed by employing minimum diagonal pivoting Gaussian decompositions, which may be thought of as an improvement of the simple rounding-off method, ...
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.
Domínguez, Luis F.
2012-06-25
An algorithm for the solution of convex multiparametric mixed-integer nonlinear programming problems arising in process engineering problems under uncertainty is introduced. The proposed algorithm iterates between a multiparametric nonlinear programming subproblem and a mixed-integer nonlinear programming subproblem to provide a series of parametric upper and lower bounds. The primal subproblem is formulated by fixing the integer variables and solved through a series of multiparametric quadratic programming (mp-QP) problems based on quadratic approximations of the objective function, while the deterministic master subproblem is formulated so as to provide feasible integer solutions for the next primal subproblem. To reduce the computational effort when infeasibilities are encountered at the vertices of the critical regions (CRs) generated by the primal subproblem, a simplicial approximation approach is used to obtain CRs that are feasible at each of their vertices. The algorithm terminates when there does not exist an integer solution that is better than the one previously used by the primal problem. Through a series of examples, the proposed algorithm is compared with a multiparametric mixed-integer outer approximation (mp-MIOA) algorithm to demonstrate its computational advantages. © 2012 American Institute of Chemical Engineers (AIChE).
Mixed-integer programming methods for transportation and power generation problems
Damci Kurt, Pelin
This dissertation conducts theoretical and computational research to solve challenging problems in application areas such as supply chain and power systems. The first part of the dissertation studies a transportation problem with market choice (TPMC) which is a variant of the classical transportation problem in which suppliers with limited capacities have a choice of which demands (markets) to satisfy. We show that TPMC is strongly NP-complete. We consider a version of the problem with a service level constraint on the maximum number of markets that can be rejected and show that if the original problem is polynomial, its cardinality-constrained version is also polynomial. We propose valid inequalities for mixed-integer cover and knapsack sets with variable upper bound constraints, which appear as substructures of TPMC and use them in a branch-and-cut algorithm to solve this problem. The second part of this dissertation studies a unit commitment (UC) problem in which the goal is to minimize the operational cost of power generators over a time period subject to physical constraints while satisfying demand. We provide several exponential classes of multi-period ramping and multi-period variable upper bound inequalities. We prove the strength of these inequalities and describe polynomial-time separation algorithms. Computational results show the effectiveness of the proposed inequalities when used as cuts in a branch-and-cut algorithm to solve the UC problem. The last part of this dissertation investigates the effects of uncertain wind power on the UC problem. A two-stage robust model and a three-stage stochastic program are compared.
Directory of Open Access Journals (Sweden)
Veli ULUÇAM
2010-07-01
Full Text Available Abstract:This work presents a mixed integer linearprogramming method developed by using 0-1 variables forsolving aggregate production planning problem with thefollowing performance criteria: (1 maximize profit, (2minimize costs. The production planning activities are calledas aggregate production planning when both it is producedmore than one goods and the demand changes period byperiod. The purpose of aggregate production planning is not toprepare detailed plans for each goods, it is to do plans forwhole goods produced in the firm together to take in hand.Aggregate production planning is probably one of the mostimportant, yet least understood, jobs that a manager performs.However, all parts of the organization, operations, marketing,finance, and so on, must work together in the planning processto ensure that they are moving in harmony with one another.Aggregate production planning is such a method that canmove all parts of the organizations in same harmony.Özet:Bu çalışmada; kazançların maksimize edilirkenmaliyetlerin minimize edilebileceği bir bütünleşik üretimplanlama probleminin, 0–1 değişkenleri kullanılarakgeliştirilmiş karma tamsayılı doğrusal programlama tekniği ileçözümü anlatılmaktadır. Birden fazla çeşitte ürünün bir aradaüretildiği ve talebin dönemlere göre değişiklik gösterdiğidurumlarda üretim planlama faaliyetleri bütünleşik üretimplanlama olarak tanımlanır. Planlama çalışmalarınınbütünleşik olma niteliği, bu yöntemin tek tek ürünlerin detaylıplanlarının hazırlanması amacıyla değil, işletme tarafındanüretilen tüm ürünlerin bir arada ele alınarak planlamaçalışmalarının yapılmasıdır. Son yıllarda daha iyi anlaşıldığıüzere, bütünleşik üretim planlaması bir yöneticininperformansını etkileyen en önemli kriterdir. Bununla beraberbir işletmenin tüm bölümleri, üretim, pazarlama, finans vediğerleri aynı ahenk içersinde ve uyumlu olarak
Directory of Open Access Journals (Sweden)
Sílvia Maria Santana Mapa
2012-01-01
Full Text Available O objetivo do trabalho é avaliar a qualidade das soluções para o problema de localização-alocação de instalações geradas por um SIG-T (Sistema de Informação Geográfica para Transportes, obtidas após a utilização combinada das rotinas Localização de Facilidades e Problema do Transporte, quando comparadas com as soluções ótimas obtidas a partir de modelo matemático exato baseado em Programação Linear Inteira Mista (PLIM, desenvolvido externamente ao SIG. Os modelos foram aplicados a três simulações: a primeira propõe a abertura de fábricas e alocação de clientes no Estado de São Paulo; a segunda envolve um atacadista e um estudo de localização de centros de distribuição e alocação dos clientes varejistas; a terceira localiza creches em um contexto urbano, alocando a demanda. Os resultados mostraram que, quando se considera a capacidade das instalações, o modelo otimizante PLIM chegou a apresentar, em um dos cenários simulados, resultados até 37% melhores do que o SIG, além de propor locais diferentes para abertura de novas instalações. Quando não se considera a capacidade, o modelo SIG se mostrou tão eficiente quanto o modelo exato PLIM, chegando exatamente às mesmas soluções.This study aims to evaluate the quality of the solutions for facility location-allocation problems generated by a GIS-T (Geographic Information System for Transportation software. These solutions were obtained from combining the Facility Location and Transportation Problem routines, when compared with the optimal solutions, which were obtained using the exact mathematical model based on the Mixed Integer Linear Programming (MILP developed externally to the GIS. The models were applied to three simulations: the first one proposes set up businesses and customers' allocation in the state of São Paulo; the second involves a wholesaler and an investigation of distribution center location and retailers' allocation; and the third one
Winebrake, James J; Corbett, James J; Wang, Chengfeng; Farrell, Alexander E; Woods, Pippa
2005-04-01
Emissions from passenger ferries operating in urban harbors may contribute significantly to emissions inventories and commuter exposure to air pollution. In particular, ferries are problematic because of high emissions of oxides of nitrogen (NOx) and particulate matter (PM) from primarily unregulated diesel engines. This paper explores technical solutions to reduce pollution from passenger ferries operating in the New York-New Jersey Harbor. The paper discusses and demonstrates a mixed-integer, non-linear programming model used to identify optimal control strategies for meeting NOx and PM reduction targets for 45 privately owned commuter ferries in the harbor. Results from the model can be used by policy-makers to craft programs aimed at achieving least-cost reduction targets.
Yin, Sisi; Nishi, Tatsushi
2014-11-01
Quantity discount policy is decision-making for trade-off prices between suppliers and manufacturers while production is changeable due to demand fluctuations in a real market. In this paper, quantity discount models which consider selection of contract suppliers, production quantity and inventory simultaneously are addressed. The supply chain planning problem with quantity discounts under demand uncertainty is formulated as a mixed-integer nonlinear programming problem (MINLP) with integral terms. We apply an outer-approximation method to solve MINLP problems. In order to improve the efficiency of the proposed method, the problem is reformulated as a stochastic model replacing the integral terms by using a normalisation technique. We present numerical examples to demonstrate the efficiency of the proposed method.
Directory of Open Access Journals (Sweden)
Renata Melo e Silva de Oliveira
2015-03-01
Full Text Available Scheduling is a key factor for operations management as well as for business success. From industrial Job-shop Scheduling problems (JSSP, many optimization challenges have emerged since de 1960s when improvements have been continuously required such as bottlenecks allocation, lead-time reductions and reducing response time to requests. With this in perspective, this work aims to discuss 3 different optimization models for minimizing Makespan. Those 3 models were applied on 17 classical problems of examples JSSP and produced different outputs. The first model resorts on Mixed and Integer Programming (MIP and it resulted on optimizing 60% of the studied problems. The other models were based on Constraint Programming (CP and approached the problem in two different ways: a model CP1 is a standard IBM algorithm whereof restrictions have an interval structure that fail to solve 53% of the proposed instances, b Model CP-2 approaches the problem with disjunctive constraints and optimized 88% of the instances. In this work, each model is individually analyzed and then compared considering: i Optimization success performance, ii Computational processing time, iii Greatest Resource Utilization and, iv Minimum Work-in-process Inventory. Results demonstrated that CP-2 presented best results on criteria i and ii, but MIP was superior on criteria iii and iv and those findings are discussed at the final section of this work.
Meyer, Richard; DeCarlo, Raymond A
2012-01-01
This paper compares the embedding approach for solving hybrid optimal control problems to multi-parameter programming, mixed-integer programming, and gradient-descent based methods in the context of four published examples. The four examples include a spring-mass system, moving-target tracking for a mobile robot, two-tank filling, and a DC-DC boost converter. Numerical advantages of the embedding approach are set forth and validated for each example: significantly faster solution time, no ad hoc assumptions (such as predetermined mode sequences) or control models, lower performance index costs, and algorithm convergence when other methods fail. Specific (theoretical) advantages of the embedding approach over the other methods are also described: guaranteed existence of a solution under mild conditions, convexity of the embedded optimization problem solvable with traditional techniques such as sequential quadratic programming with no need for any mixed-integer programming, applicability to nonlinear systems, e...
The "Best" Algorithm for Solving Stochastic Mixed Integer Programs
2006-01-01
focuses on solving problems for which all second-stage scenarios can be enu- merated (Klein Haneveld and van der Vlerk 1998, Ahmed 2004). Two exceptions... Haneveld , W. K. and M. H. van der Vlerk. 1998. Stochastic integer programming: state of the art. Available via <http://citeseer.ist.psu. edu
Solving a Class of Stochastic Mixed-Integer Programs With Branch and Price
2006-01-01
with time windows (Desrosiers et al. [26], Ribeiro and Soumis [46]) The vehicle routing problem with time windows (VRPTW) is one important exemplar from...procedures for solving mixed-variables programming problems. Numerische Mathematik 4, 238–252 (1962) 10. Bertsimas, D.J.: A vehicle routing problem with...Oper. Res. 45, 649–661 (1997) 24. Desrochers, M., Desrosiers, J., Solomon, M.: 1992. A new optimization algorithm for the vehicle routing problem with
Cheng, Guanhui; Huang, Guohe; Dong, Cong; Xu, Ye; Chen, Jiapei; Chen, Xiujuan; Li, Kailong
2017-02-16
As presented in the first companion paper, distributed mixed-integer fuzzy hierarchical programming (DMIFHP) was developed for municipal solid waste management (MSWM) under complexities of heterogeneities, hierarchy, discreteness, and interactions. Beijing was selected as a representative case. This paper focuses on presenting the obtained schemes and the revealed mechanisms of the Beijing MSWM system. The optimal MSWM schemes for Beijing under various solid waste treatment policies and their differences are deliberated. The impacts of facility expansion, hierarchy, and spatial heterogeneities and potential extensions of DMIFHP are also discussed. A few of findings are revealed from the results and a series of comparisons and analyses. For instance, DMIFHP is capable of robustly reflecting these complexities in MSWM systems, especially for Beijing. The optimal MSWM schemes are of fragmented patterns due to the dominant role of the proximity principle in allocating solid waste treatment resources, and they are closely related to regulated ratios of landfilling, incineration, and composting. Communities without significant differences among distances to different types of treatment facilities are more sensitive to these ratios than others. The complexities of hierarchy and heterogeneities pose significant impacts on MSWM practices. Spatial dislocation of MSW generation rates and facility capacities caused by unreasonable planning in the past may result in insufficient utilization of treatment capacities under substantial influences of transportation costs. The problems of unreasonable MSWM planning, e.g., severe imbalance among different technologies and complete vacancy of ten facilities, should be gained deliberation of the public and the municipal or local governments in Beijing. These findings are helpful for gaining insights into MSWM systems under these complexities, mitigating key challenges in the planning of these systems, improving the related management
Uilhoorn, F. E.
2016-10-01
In this article, the stochastic modelling approach proposed by Box and Jenkins is treated as a mixed-integer nonlinear programming (MINLP) problem solved with a mesh adaptive direct search and a real-coded genetic class of algorithms. The aim is to estimate the real-valued parameters and non-negative integer, correlated structure of stationary autoregressive moving average (ARMA) processes. The maximum likelihood function of the stationary ARMA process is embedded in Akaike's information criterion and the Bayesian information criterion, whereas the estimation procedure is based on Kalman filter recursions. The constraints imposed on the objective function enforce stability and invertibility. The best ARMA model is regarded as the global minimum of the non-convex MINLP problem. The robustness and computational performance of the MINLP solvers are compared with brute-force enumeration. Numerical experiments are done for existing time series and one new data set.
Mixed integer evolution strategies for parameter optimization.
Li, Rui; Emmerich, Michael T M; Eggermont, Jeroen; Bäck, Thomas; Schütz, M; Dijkstra, J; Reiber, J H C
2013-01-01
Evolution strategies (ESs) are powerful probabilistic search and optimization algorithms gleaned from biological evolution theory. They have been successfully applied to a wide range of real world applications. The modern ESs are mainly designed for solving continuous parameter optimization problems. Their ability to adapt the parameters of the multivariate normal distribution used for mutation during the optimization run makes them well suited for this domain. In this article we describe and study mixed integer evolution strategies (MIES), which are natural extensions of ES for mixed integer optimization problems. MIES can deal with parameter vectors consisting not only of continuous variables but also with nominal discrete and integer variables. Following the design principles of the canonical evolution strategies, they use specialized mutation operators tailored for the aforementioned mixed parameter classes. For each type of variable, the choice of mutation operators is governed by a natural metric for this variable type, maximal entropy, and symmetry considerations. All distributions used for mutation can be controlled in their shape by means of scaling parameters, allowing self-adaptation to be implemented. After introducing and motivating the conceptual design of the MIES, we study the optimality of the self-adaptation of step sizes and mutation rates on a generalized (weighted) sphere model. Moreover, we prove global convergence of the MIES on a very general class of problems. The remainder of the article is devoted to performance studies on artificial landscapes (barrier functions and mixed integer NK landscapes), and a case study in the optimization of medical image analysis systems. In addition, we show that with proper constraint handling techniques, MIES can also be applied to classical mixed integer nonlinear programming problems.
Directory of Open Access Journals (Sweden)
Guillermo Cabrera G.
2012-01-01
Full Text Available We present a hybridization of two different approaches applied to the well-known Capacitated Facility Location Problem (CFLP. The Artificial Bee algorithm (BA is used to select a promising subset of locations (warehouses which are solely included in the Mixed Integer Programming (MIP model. Next, the algorithm solves the subproblem by considering the entire set of customers. The hybrid implementation allows us to bypass certain inherited weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. In this paper we demonstrate that BA can be significantly improved by use of the MIP algorithm. At the same time, our hybrid implementation allows the MIP algorithm to reach the optimal solution in a considerably shorter time than is needed to solve the model using the entire dataset directly within the model. Our hybrid approach outperforms the results obtained by each technique separately. It is able to find the optimal solution in a shorter time than each technique on its own, and the results are highly competitive with the state-of-the-art in large-scale optimization. Furthermore, according to our results, combining the BA with a mathematical programming approach appears to be an interesting research area in combinatorial optimization.
Purnomo, Muhammad Ridwan Andi; Satrio Wiwoho, Yoga
2016-01-01
Facility layout becomes one of production system factor that should be managed well, as it is designated for the location of production. In managing the layout, designing the layout by considering the optimal layout condition that supports the work condition is essential. One of the method for facility layout optimization is Mixed Integer Programming (MIP). In this study, the MIP is solved using Lingo 9.0 software and considering quantitative and qualitative objectives to be achieved simultaneously: minimizing material handling cost, maximizing closeness rating, and minimizing re-layout cost. The research took place in Rekayasa Wangdi as a make to order company, focusing on the making of concrete brick dough stirring machine with 10 departments involved. The result shows an improvement in the new layout for 333,72 points of objective value compared with the initial layout. As the conclusion, the proposed MIP is proven to be used to model facility layout problem under multi objective consideration for a more realistic look.
Detection of gross errors using mixed integer optimization approach in process industry
Institute of Scientific and Technical Information of China (English)
MEI Cong-li; SU Hong-ye; CHU Jian
2007-01-01
A novel mixed integer linear programming (NMILP) model for detection of gross errors is presented in this paper.Yamamura et al.(1988) designed a model for detection of gross errors and data reconciliation based on Akaike information criterion (AIC). But much computational cost is needed due to its combinational nature. A mixed integer linear programming (MILP)approach was performed to reduce the computational cost and enhance the robustness. But it loses the super performance of maximum likelihood estimation. To reduce the computational cost and have the merit of maximum likelihood estimation, the simultaneous data reconciliation method in an MILP framework is decomposed and replaced by an NMILP subproblem and a quadratic programming (QP) or a least squares estimation (LSE) subproblem. Simulation result of an industrial case shows the high efficiency of the method.
Ko, Andi Setiady; Chang, Ni-Bin
2008-07-01
Energy supply and use is of fundamental importance to society. Although the interactions between energy and environment were originally local in character, they have now widened to cover regional and global issues, such as acid rain and the greenhouse effect. It is for this reason that there is a need for covering the direct and indirect economic and environmental impacts of energy acquisition, transport, production and use. In this paper, particular attention is directed to ways of resolving conflict between economic and environmental goals by encouraging a power plant to consider co-firing biomass and refuse-derived fuel (RDF) with coal simultaneously. It aims at reducing the emission level of sulfur dioxide (SO(2)) in an uncertain environment, using the power plant in Michigan City, Indiana as an example. To assess the uncertainty by a comparative way both deterministic and grey nonlinear mixed integer programming (MIP) models were developed to minimize the net operating cost with respect to possible fuel combinations. It aims at generating the optimal portfolio of alternative fuels while maintaining the same electricity generation simultaneously. To ease the solution procedure stepwise relaxation algorithm was developed for solving the grey nonlinear MIP model. Breakeven alternative fuel value can be identified in the post-optimization stage for decision-making. Research findings show that the inclusion of RDF does not exhibit comparative advantage in terms of the net cost, albeit relatively lower air pollution impact. Yet it can be sustained by a charge system, subsidy program, or emission credit as the price of coal increases over time.
Optimal Airport Surface Traffic Planning Using Mixed-Integer Linear Programming
Roling, P.C.; Visser, H.G.
2008-01-01
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
Optimal Airport Surface Traffic Planning Using Mixed-Integer Linear Programming
Roling, P.C.; Visser, H.G.
2008-01-01
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
Optimization of Air Vehicle Operations Using Mixed-Integer Linear Programming
2006-01-01
heuristic and suboptimal. Tabu search can be used to solve difficult combinatorial optimization problems, e.g., the vehicle routing problem with...the mission. The method presented here can also accommodate fixed time windows, as in the Vehicle Routing Problem (VRP) (Toth et al, 2002...critical timing constraints, which set this problem apart from the classical Vehicle Routing Problem (VRP) (Toth et al, 2002). In the latter, rigid
微电网日内调度计划的混合整数规划模型%Mixed Integer Programming Model for Microgrid Intra-day Scheduling
Institute of Scientific and Technical Information of China (English)
孙浩; 张磊; 许海林; 汪隆君
2015-01-01
为进一步调和微电网日内调度计划模型求解速度与精度的矛盾，提出了一种适用于微电网日内调度计划的混合整数规划模型。构建了蓄电池充放电过程模型，能有效计及充放电循环次数和老化成本；在现有微电网经济调度模型的基础上，建立了考虑电压幅值的线性化潮流方程、基于解析几何的线性化支路容量、公共连接点功率交换和功率因数限制等约束模型。通过算例验证所提模型，结果表明：①所提模型求解精度高，快速稳定收敛于全局最优解，且不依赖于初值；②蓄电池充放电过程模型符合工程实际，克服了现有模型中蓄电池频繁充放电加剧老化的局限性。%A mixed integer programming model,which can be used in microgrid intra-day scheduling,is put forward in order to conciliate further the desired attributes of accuracy and computational performance in existing intra-day scheduling model.The model of storage battery charge/discharge process,which considers charge/discharge cycles and aging cost,is proposed. Compared with the conventional model for microgrid economical dispatching,the linearized constraint model,such as power flow with voltage amplitude limitation,branch capacity based on Cartesian geometry,transferring power and power factor of point of common couple et al,is established.The numerical results of case studies demonstrate that the proposed model and its solution are of the fast convergence and independent of initial points,and the storage battery model is in line with the practical engineering,overcoming the limitations of the existing model,which accelerates deterioration due to switching frequently between charge and discharge of storage battery.
Lütke Entrup, M.; Günther, H.O.; Beek, van P.; Grunow, M.; Seiler, T.
2005-01-01
In the production of perishable products such as dairy, meat or bakery goods, the consideration of shelf life in production planning is of particular importance. Retail customers with relatively low inventory turns can benefit significantly from longer product shelf life as wastage and out-of-stock
Makowski, D.; Hendrix, E.M.T.; Ittersum, van M.K.; Rossing, W.A.H.
2001-01-01
In complex decision problems, some objectives are not well quantified or are not introduced explicitly in optimization models. In view of this inherent limitation of models, solutions that are nearly optimal, i.e. deviating less than a predefined percentage from the optimal value of the quantified o
Directory of Open Access Journals (Sweden)
Mahdi Naderi-Beni
2012-04-01
Full Text Available In this paper, we study a supply chain problem where a whole seller/producer distributes goods among different retailers. Such problems are always faces with uncertainty with input data and we have to use various techniques to handle the uncertainty. The proposed model of this paper considers different input parameters such as demand, capacity and cost in trapezoid fuzzy forms and using two ranking methods, we handle the uncertainty. The results of the proposed model of this paper have been compared with the crisp and other existing fuzzy techniques using some randomly generated data. The preliminary results indicate that the proposed models of this paper provides better values for the objective function and do not increase the complexity of the resulted problem.
Energy Technology Data Exchange (ETDEWEB)
Shamloo, H.; Haghighi, A. [K.N. Toosi Univ. of Technology, Tehran (Iran, Islamic Republic of). Dept. of Civil Engineering
2009-07-01
The flow properties of pipes are affected by leaks. Leak detection methods based on hydraulic modelling and real data records aim to find a pipe's leak parameters including their number, location and size. Inverse Transient Analysis (ITA), generally in time domain, is a powerful approach to develop leak detection methods with considerable benefits. This paper introduced an ITA based leak detection method along with a numerical model developed for direct transient analysis of leaks in pipes using method of characteristics (MOC). Transient state flow was generated in pipe and the pressure fluctuations were sampled only at the end valve location. To minimize the effects of unsteadiness and uncertainties due to the numerical modeling and also practical problems caused by water hammer, the downstream end valve was considered to be closed gradually within a long enough time. Then, using the sampled data and a direct transient analysis model, a mixed integer nonlinear program was developed. A mixed genetic algorithm was used in which the binary chromosomes were decoded as mixed integer leak locations and real leak areas. In order to find unknown leak parameters in a pipe, an objective function was defined using the least squares criterion of differences between observed and calculated pressure heads at the valve location. The genetic algorithm was found to be a powerful and easy to use optimization tool to solve complicated mixed integer nonlinear program (MINLP) problems in leak detection. 24 refs., 1 tab., 7 figs.
Bilevel programming problems theory, algorithms and applications to energy networks
Dempe, Stephan; Pérez-Valdés, Gerardo A; Kalashnykova, Nataliya; Kalashnikova, Nataliya
2015-01-01
This book describes recent theoretical findings relevant to bilevel programming in general, and in mixed-integer bilevel programming in particular. It describes recent applications in energy problems, such as the stochastic bilevel optimization approaches used in the natural gas industry. New algorithms for solving linear and mixed-integer bilevel programming problems are presented and explained.
A mixed integer bi-level DEA model for bank branch performance evaluation by Stackelberg approach
Shafiee, Morteza; Lotfi, Farhad Hosseinzadeh; Saleh, Hilda; Ghaderi, Mehdi
2016-11-01
One of the most complicated decision making problems for managers is the evaluation of bank performance, which involves various criteria. There are many studies about bank efficiency evaluation by network DEA in the literature review. These studies do not focus on multi-level network. Wu (Eur J Oper Res 207:856-864, 2010) proposed a bi-level structure for cost efficiency at the first time. In this model, multi-level programming and cost efficiency were used. He used a nonlinear programming to solve the model. In this paper, we have focused on multi-level structure and proposed a bi-level DEA model. We then used a liner programming to solve our model. In other hand, we significantly improved the way to achieve the optimum solution in comparison with the work by Wu (2010) by converting the NP-hard nonlinear programing into a mixed integer linear programming. This study uses a bi-level programming data envelopment analysis model that embodies internal structure with Stackelberg-game relationships to evaluate the performance of banking chain. The perspective of decentralized decisions is taken in this paper to cope with complex interactions in banking chain. The results derived from bi-level programming DEA can provide valuable insights and detailed information for managers to help them evaluate the performance of the banking chain as a whole using Stackelberg-game relationships. Finally, this model was applied in the Iranian bank to evaluate cost efficiency.
DEFF Research Database (Denmark)
Escudero, Laureano F.; Monge, Juan Francisco; Morales, Dolores Romero
2015-01-01
In this paper we consider multiperiod mixed 0–1 linear programming models under uncertainty. We propose a risk averse strategy using stochastic dominance constraints (SDC) induced by mixed-integer linear recourse as the risk measure. The SDC strategy extends the existing literature to the multist...
Directory of Open Access Journals (Sweden)
Alexander Abuabara
2008-12-01
Full Text Available Este trabalho busca otimizar o planejamento do processo de corte unidimensional de tubos estruturais metálicos utilizados na fabricação de aeronaves leves agrícolas. Dois modelos de programação linear inteira mista são apresentados com o objetivo de minimizar as perdas do material cortado e considerando a possibilidade de gerar sobras com tamanhos suficientes para reaproveitamento (retalhos. Os modelos são resolvidos por meio de uma linguagem de modelagem usando um software de otimização. Para a validação dos modelos, dois experimentos computacionais foram realizados com dados reais de uma carteira de pedidos de uma aeronave leve voltada para o segmento do mercado agrícola, o Ipanema, produzido pela empresa brasileira Neiva/Embraer. As soluções dos modelos são comparadas com as soluções de uma heurística residual de arredondamento guloso da literatura e também com as soluções utilizadas pela empresa. Os resultados mostram que os modelos são úteis para apoiar as decisões envolvidas no planejamento deste processo de corte.This study aims to optimize the one-dimensional cutting process planning of structural metallic tubes used to build agricultural light aircrafts. Two mixed integer linear programming models are presented to minimize the waste of material cut and considering the possibility of generating surpluses with sizes sufficiently large for reuse (leftovers. The models are solved using a commercial modeling language and an optimization solver. For the validation of the models, two computational experiments were performed with actual data from the portfolio of a light aircraft designed for agricultural purposes, the Ipanema, produced by the Brazilian company Neiva/Embraer. The solutions of the models are compared with the solutions of a constructive heuristic of the literature and the solutions used by the company. The results show that the models are useful for being used in the planning of this cutting process.
Solow, Daniel
2014-01-01
This text covers the basic theory and computation for a first course in linear programming, including substantial material on mathematical proof techniques and sophisticated computation methods. Includes Appendix on using Excel. 1984 edition.
Mixed-integer vertex covers on bipartite graphs
Gerards, A.M.H.; Conforti, M.; Zambelli, G.; Fischetti, M.; Williamson, D.P.
2007-01-01
Let $A$ be the edge-node incidence matrix of a bipartite graph $G = (U, V ; E)$, $I$ be a subset of the nodes of $G$, and $b$ be a vector such that $2b$ is integral. We consider the following mixed-integer set: $X(G, b, I) = {x : Ax ≥ b, x ≥ 0, x_i$ integer for all $i ∈ I}$
Karloff, Howard
1991-01-01
To this reviewer’s knowledge, this is the first book accessible to the upper division undergraduate or beginning graduate student that surveys linear programming from the Simplex Method…via the Ellipsoid algorithm to Karmarkar’s algorithm. Moreover, its point of view is algorithmic and thus it provides both a history and a case history of work in complexity theory. The presentation is admirable; Karloff's style is informal (even humorous at times) without sacrificing anything necessary for understanding. Diagrams (including horizontal brackets that group terms) aid in providing clarity. The end-of-chapter notes are helpful...Recommended highly for acquisition, since it is not only a textbook, but can also be used for independent reading and study. —Choice Reviews The reader will be well served by reading the monograph from cover to cover. The author succeeds in providing a concise, readable, understandable introduction to modern linear programming. —Mathematics of Computing This is a textbook intend...
Wouda, H.E.; Beek, van P.; Vorst, van der J.G.A.J.; Tacke, H.
2002-01-01
Between 1995 and 1998 Nutricia acquired a number of dairy companies in Hungary. Each of these companies produced a wide variety of products for its regional market. Although alterations had been made to the production system in the last few years, production and transportation costs were still subst
Institute of Scientific and Technical Information of China (English)
李笕列; 荣冈
2009-01-01
Mixed integer linear programming(MILP)approach for simultaneous gross error detection and data reconciliation has been proved as an efficient Way to adjust process data with material,energy,and other balance constrains.BUt the efficiency will decrease significantly when this method IS applied in a large-scale problem because there are too many binary variables involved.In this article, an improved method IS proposed in order to generate gross error candidates with reliability factors before data rectification.Candidates are used in the MILP objectivc function to improve the emciency and accuracy by reducing the number of binary variables and gwmg accurate weights for suspected gross errors candidates.Performance of this improved method IS compared and discussed by applying the algorithm in a widely used industrial example.
Shoemaker, Christine; Wan, Ying
2016-04-01
Optimization of nonlinear water resources management issues which have a mixture of fixed (e.g. construction cost for a well) and variable (e.g. cost per gallon of water pumped) costs has been not well addressed because prior algorithms for the resulting nonlinear mixed integer problems have required many groundwater simulations (with different configurations of decision variable), especially when the solution space is multimodal. In particular heuristic methods like genetic algorithms have often been used in the water resources area, but they require so many groundwater simulations that only small systems have been solved. Hence there is a need to have a method that reduces the number of expensive groundwater simulations. A recently published algorithm for nonlinear mixed integer programming using surrogates was shown in this study to greatly reduce the computational effort for obtaining accurate answers to problems involving fixed costs for well construction as well as variable costs for pumping because of a substantial reduction in the number of groundwater simulations required to obtain an accurate answer. Results are presented for a US EPA hazardous waste site. The nonlinear mixed integer surrogate algorithm is general and can be used on other problems arising in hydrology with open source codes in Matlab and python ("pySOT" in Bitbucket).
Mixed-integer representations in control design mathematical foundations and applications
Prodan, Ionela; Olaru, Sorin; Niculescu, Silviu-Iulian
2016-01-01
In this book, the authors propose efficient characterizations of the non-convex regions that appear in many control problems, such as those involving collision/obstacle avoidance and, in a broader sense, in the description of feasible sets for optimization-based control design involving contradictory objectives. The text deals with a large class of systems that require the solution of appropriate optimization problems over a feasible region, which is neither convex nor compact. The proposed approach uses the combinatorial notion of hyperplane arrangement, partitioning the space by a finite collection of hyperplanes, to describe non-convex regions efficiently. Mixed-integer programming techniques are then applied to propose acceptable formulations of the overall problem. Multiple constructions may arise from the same initial problem, and their complexity under various parameters - space dimension, number of binary variables, etc. - is also discussed. This book is a useful tool for academic researchers and grad...
Institute of Scientific and Technical Information of China (English)
罗宗杰
2012-01-01
When the power grid blackout, making the power system restore normal operation quickly and efficiently can not only reduce the equipment damage of the power generation side, but also improve the load restoration speed of the power side. This paper focuses on the ability of the units which have not self-starting time limit, total system capacity constraints, start power limit, and the constraints of unit restoration priority, and gets the unit start sequence strategy for system restoration process through the mixed binary integer programming algorithm. It is concluded that: in a given recovery time, this strategy can get the optimal order of the system unit startup under the premise of ensuring the safe operation of the unit and the maximum generation capacity of system, and can reduce the post-reconstruction and load restoration time directly.%当电网大停电之后,运行人员快速高效地把电力系统恢复到正常运行状态,不仅可以降低发电侧的设备损害程度,还可提高用电侧负荷的恢复速度.重点分析了没有自启动能力的机组时间限制、系统总容量限制、启动功率限制、机组恢复优先权限制等条件,通过混合二元整数规划算法得到系统恢复过程中机组启动次序的策略.结论表明:在给定恢复时间内,采用这种策略能在保证机组的安全运行和系统的最大发电容量的前提下,得到系统机组启动的最优次序,可直接减少后期系统重构和负荷恢复的时间.
Directory of Open Access Journals (Sweden)
Deisemara Ferreira
2008-01-01
Full Text Available Neste artigo propomos um modelo de otimização inteira mista para o problema de dimensionamento e seqüenciamento dos lotes de produção em fábricas de refrigerantes de pequeno porte, com tempos e custos de set up de produção dependentes do seqüenciamento dos lotes. O modelo considera o estágio de envase como sendo o gargalo da produção da planta, o que é comum em fábricas de pequeno porte com uma única linha de envase, e restrições de lote mínimo do estágio de xaroparia. Variações da heurística relax and fix são propostas e comparadas na solução de exemplares do modelo, gerados com dados reais de uma fábrica localizada no interior do Estado de São Paulo. Os resultados mostram que as abordagens são capazes de gerar soluções melhores do que as utilizadas pela empresa.In this paper we propose a mixed integer programming model to the lot sizing and sequencing problem of a soft drink plant with sequence-dependent set up costs and times. The model considers that the bottling stage is the production bottleneck, which is common in small plants with only one production line, and minimum lot size constrains of the syrup stage. Variations of the relax and fix heuristic are proposed and compared. A computational study with instances generated based on real data from a plant situated in the State of São Paulo-Brazil is also presented. The results show that the approaches are capable to produce better solutions than the ones from the company.
Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks
Lee, Charles H.; Cheung, Kar-Ming
2012-01-01
In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.
Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks
Lee, Charles H.; Cheung, Kar-Ming
2012-01-01
In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.
Stochastic Dynamic Mixed-Integer Programming (SD-MIP)
2015-05-05
et al (2009), there is continuing interest in using SA methods for SP problems. This genre of methods creates a sequence of sampled subgradients...Phone Number Contact phone number if there is a problem with the report 213-740-6810 Organization / Institution name University of Southern California
Mixed-Integer Nonconvex Quadratic Optimization Relaxations and Performance Analysis
2016-10-11
formulates many online resource allocation and revenue management problems is the online linear program (0-1-ILP) in which the constraint matrix is...column is revealed without observing the future inputs and the goal is to maximize the overall objective function. In this paper, we provide a near
Presolving and regularization in mixed-integer second-order cone optimization
DEFF Research Database (Denmark)
Friberg, Henrik Alsing
Mixed-integer second-order cone optimization is a powerful mathematical framework capable of representing both logical conditions and nonlinear relationships in mathematical models of industrial optimization problems. What is more, solution methods are already part of many major commercial solvers...... both continuous and mixed-integer conic optimization in general, is discovered and treated. This part of the thesis continues the studies of facial reduction preceding the work of Borwein and Wolkowicz [17] in 1981, when the first algorithmic cure for these kinds of reliability issues were formulated....... An important distinction to make between continuous and mixed-integer optimization, however, is that the reliability issues occurring in mixed-integer optimization cannot be blamed on the practitioner’s formulation of the problem. Specifically, as shown, the causes for these issues may well lie within...
On the Branch and Cut Method for Multidimentional Mixed Integer Knapsack Problem
Mostafa Khorramizadeh; Zahra Rakhshandehroo
2014-01-01
In this paper, we examine the effect of the feasibility pump (FP) method on the branch and cut method for solving the multidimentional mixed integer knapsack problem. The feasibility pump is a heuristic method, trying to compute a feasible solution for mixed integer pro- gramming problems. Moreover, we consider two efficient strategies for using the feasibility pump in a branch and cut method and present some tables of numerical results, concerning the application and comparison of using thes...
Fast numerical methods for mixed-integer nonlinear model-predictive control
Kirches, Christian
2011-01-01
Christian Kirches develops a fast numerical algorithm of wide applicability that efficiently solves mixed-integer nonlinear optimal control problems. He uses convexification and relaxation techniques to obtain computationally tractable reformulations for which feasibility and optimality certificates can be given even after discretization and rounding.
CBLIB 2014: a benchmark library for conic mixed-integer and continuous optimization
DEFF Research Database (Denmark)
Friberg, Henrik Alsing
2016-01-01
The Conic Benchmark Library is an ongoing community-driven project aiming to challenge commercial and open source solvers on mainstream cone support. In this paper, 121 mixed-integer and continuous second-order cone problem instances have been selected from 11 categories as representative...
Wu, Z; Zhang, Y
2008-01-01
The double digestion problem for DNA restriction mapping has been proved to be NP-complete and intractable if the numbers of the DNA fragments become large. Several approaches to the problem have been tested and proved to be effective only for small problems. In this paper, we formulate the problem as a mixed-integer linear program (MIP) by following (Waterman, 1995) in a slightly different form. With this formulation and using state-of-the-art integer programming techniques, we can solve randomly generated problems whose search space sizes are many-magnitude larger than previously reported testing sizes.
Directory of Open Access Journals (Sweden)
Liliana Delgado Hidalgo
2010-07-01
Full Text Available A mixed integer linear programming model representing a two-echelon manufacturing system was implemented. Optimal deci- sions could be made about raw material/component provisioning by using the model. The model was programmed by using an algebraic modeller which was then integrated into a computational tool from which defining parameters could be managed as well as consulting the results once the model had been executed. The model was validated on a real manufacturing system; be- sides providing good representation of the system, optimal provisioning decisions were also reached. The article emphasises that such decisions cannot be made by using usual MRP reasoning.
A Composite Algorithm for Mixed Integer Constrained Nonlinear Optimization.
1980-01-01
algorithm (FLEX) developed by Paviani and Himmelblau [53] is a direct search algorithm for constrained, nonlinear problems. It uses a variation on the...given in an appendix to Himmelblau [32]. Two changes were made to the program as listed in the rcference. Between card number 1340 and 1350 the...1972, pp. 293-308 (32] Himmelblau , D. M., Applied Nonlinear Programming, McGraw-Hill, 1972 (33] Himmelblau , D. M., "A Uniform Evaluation of Unconstrained
Tuey, R. C.
1972-01-01
Computer solutions of linear programming problems are outlined. Information covers vector spaces, convex sets, and matrix algebra elements for solving simultaneous linear equations. Dual problems, reduced cost analysis, ranges, and error analysis are illustrated.
Lawson, C. L.; Krogh, F. T.; Gold, S. S.; Kincaid, D. R.; Sullivan, J.; Williams, E.; Hanson, R. J.; Haskell, K.; Dongarra, J.; Moler, C. B.
1982-01-01
The Basic Linear Algebra Subprograms (BLAS) library is a collection of 38 FORTRAN-callable routines for performing basic operations of numerical linear algebra. BLAS library is portable and efficient source of basic operations for designers of programs involving linear algebriac computations. BLAS library is supplied in portable FORTRAN and Assembler code versions for IBM 370, UNIVAC 1100 and CDC 6000 series computers.
Lawson, C. L.; Krogh, F. T.; Gold, S. S.; Kincaid, D. R.; Sullivan, J.; Williams, E.; Hanson, R. J.; Haskell, K.; Dongarra, J.; Moler, C. B.
1982-01-01
The Basic Linear Algebra Subprograms (BLAS) library is a collection of 38 FORTRAN-callable routines for performing basic operations of numerical linear algebra. BLAS library is portable and efficient source of basic operations for designers of programs involving linear algebriac computations. BLAS library is supplied in portable FORTRAN and Assembler code versions for IBM 370, UNIVAC 1100 and CDC 6000 series computers.
Relaxation Methods for Hyperbolic PDE Mixed-Integer Optimal Control Problems
Hante, Falk M.
2015-01-01
We extend the convergence analysis for methods solving PDE-constrained optimal control problems containing both discrete and continuous control decisions based on relaxation and rounding strategies to the class of first order semilinear hyperbolic systems in one space dimension. The results are obtained by novel a-priori estimates for the size of the relaxation gap based on the characteristic flow, fixed-point arguments and particular regularity theory for such mixed-integer control problems....
Li, J. C.; Gong, B.; Wang, H. G.
2016-08-01
Optimal development of shale gas fields involves designing a most productive fracturing network for hydraulic stimulation processes and operating wells appropriately throughout the production time. A hydraulic fracturing network design-determining well placement, number of fracturing stages, and fracture lengths-is defined by specifying a set of integer ordered blocks to drill wells and create fractures in a discrete shale gas reservoir model. The well control variables such as bottom hole pressures or production rates for well operations are real valued. Shale gas development problems, therefore, can be mathematically formulated with mixed-integer optimization models. A shale gas reservoir simulator is used to evaluate the production performance for a hydraulic fracturing and well control plan. To find the optimal fracturing design and well operation is challenging because the problem is a mixed integer optimization problem and entails computationally expensive reservoir simulation. A dynamic simplex interpolation-based alternate subspace (DSIAS) search method is applied for mixed integer optimization problems associated with shale gas development projects. The optimization performance is demonstrated with the example case of the development of the Barnett Shale field. The optimization results of DSIAS are compared with those of a pattern search algorithm.
Linear Projective Program Syntax
Bergstra, J.A.; Bethke, I.
2004-01-01
Based on an extremely simple program notation more advanced program features can be developed in linear projective program syntax such as conditional statements, while loops, recursion, use of an evaluation stack, object classes, method calls etc. Taking care of a cumulative and bottom up
Institute of Scientific and Technical Information of China (English)
陈理; 王克峰; 徐霄羽; 姚平经
2004-01-01
In this contribution we present an online scheduling algorithm for a real world multiproduct batch plant. The overall mixed integer nonlinear programming (MINLP) problem is hierarchically structured into a mixed integer linear programming (MILP) problem first and then a reduced dimensional MINLP problem, which are optimized by mathematical programming (MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA to exploit their complementary capacity. The key features of the hierarchical model are explained and illustrated with some real world cases from the multiproduct batch plants.
de Klerk, E.; Sotirov, R.; Truetsch, U.
2015-01-01
Recent progress in solving quadratic assignment problems (QAPs) from the QAPLIB (Quadratic Assignment Problem Library) test set has come from mixed-integer linear or quadratic programming models that are solved in a branch-and-bound framework. Semidefinite programming (SDP) bounds for QAPs have also
Topics in computational linear optimization
DEFF Research Database (Denmark)
Hultberg, Tim Helge
2000-01-01
. Linear optimization problems covers both linear programming problems, which are polynomially solvable, and mixed integer linear programming problems, which belong to the class of NP-hard problems. The three main reasons for the practical succes of linear optimization are: wide applicability, availabilty...... 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...
Introduction to stochastic programming and its applications to finance
Şimşek, Koray Deniz; Simsek, Koray Deniz
2008-01-01
Mathematical programming is one of a number of operations research techniques that employs mathematical optimization models to assist in decision making. Mathematical programming includes linear programming, integer programming, mixed-integer programming, nonlinear programming, stochastic programming, and goal programming. Mathematical programming models allow the decision maker to identify the “best” solution. This is in contrast to other mathematical tools that are in the arsenal of decisio...
A Mixed-Integer Optimization Framework for De Novo Peptide Identification.
Dimaggio, Peter A; Floudas, Christodoulos A
2007-01-01
A novel methodology for the de novo identification of peptides by mixed-integer optimization and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. Several problems are examined to illustrate the proposed method's ability to address (1) residue-dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum.
Klumpp, A. R.; Lawson, C. L.
1988-01-01
Routines provided for common scalar, vector, matrix, and quaternion operations. Computer program extends Ada programming language to include linear-algebra capabilities similar to HAS/S programming language. Designed for such avionics applications as software for Space Station.
Klumpp, A. R.; Lawson, C. L.
1988-01-01
Routines provided for common scalar, vector, matrix, and quaternion operations. Computer program extends Ada programming language to include linear-algebra capabilities similar to HAS/S programming language. Designed for such avionics applications as software for Space Station.
Institute of Scientific and Technical Information of China (English)
陈理; 王克峰; 徐霄羽; 姚平经
2004-01-01
In this contribution we present an online scheduling algorithm for a real world multiproduct batch plant.The overall mixed integer nonlinear programming (MINLP) problem is hierarchically structured into a mixed integer linear programming (MILP) problem first and then a reduced dimensional MINLP problem, which are optimized by mathematical programming (MP) and genetic algorithm (GA) respectively. The basis idea relies on combining MP with GA to exploit their complementary capacity. The key features of the hierarchical model are explained and illustrated with some real world cases from the multiproduct batch plants.
Linear Programming and Network Flows
Bazaraa, Mokhtar S; Sherali, Hanif D
2011-01-01
The authoritative guide to modeling and solving complex problems with linear programming-extensively revised, expanded, and updated The only book to treat both linear programming techniques and network flows under one cover, Linear Programming and Network Flows, Fourth Edition has been completely updated with the latest developments on the topic. This new edition continues to successfully emphasize modeling concepts, the design and analysis of algorithms, and implementation strategies for problems in a variety of fields, including industrial engineering, management science, operations research
Elementary linear programming with applications
Kolman, Bernard
1995-01-01
Linear programming finds the least expensive way to meet given needs with available resources. Its results are used in every area of engineering and commerce: agriculture, oil refining, banking, and air transport. Authors Kolman and Beck present the basic notions of linear programming and illustrate how they are used to solve important common problems. The software on the included disk leads students step-by-step through the calculations. The Second Edition is completely revised and provides additional review material on linear algebra as well as complete coverage of elementary linear program
Linear Programming across the Curriculum
Yoder, S. Elizabeth; Kurz, M. Elizabeth
2015-01-01
Linear programming (LP) is taught in different departments across college campuses with engineering and management curricula. Modeling an LP problem is taught in every linear programming class. As faculty teaching in Engineering and Management departments, the depth to which teachers should expect students to master this particular type of…
Generic Mathematical Programming Formulation and Solution for Computer-Aided Molecular Design
2015-01-01
This short communication presents a generic mathematical programming formulation for Computer-Aided Molecular Design (CAMD). A given CAMD problem, based on target properties, is formulated as a Mixed Integer Linear/Non-Linear Program (MILP/MINLP). The mathematical programming model presented here, which is formulated as an MILP/MINLP problem, considers first-order and second-order molecular groups for molecular structure representation and property estimation. It is shown that various CAMD pr...
Linear programming foundations and extensions
Vanderbei, Robert J
2001-01-01
Linear Programming: Foundations and Extensions is an introduction to the field of optimization. The book emphasizes constrained optimization, beginning with a substantial treatment of linear programming, and proceeding to convex analysis, network flows, integer programming, quadratic programming, and convex optimization. The book is carefully written. Specific examples and concrete algorithms precede more abstract topics. Topics are clearly developed with a large number of numerical examples worked out in detail. Moreover, Linear Programming: Foundations and Extensions underscores the purpose of optimization: to solve practical problems on a computer. Accordingly, the book is coordinated with free efficient C programs that implement the major algorithms studied: -The two-phase simplex method; -The primal-dual simplex method; -The path-following interior-point method; -The homogeneous self-dual methods. In addition, there are online JAVA applets that illustrate various pivot rules and variants of the simplex m...
LINEAR AND NONLINEAR SEMIDEFINITE PROGRAMMING
Directory of Open Access Journals (Sweden)
Walter Gómez Bofill
2014-12-01
Full Text Available This paper provides a short introduction to optimization problems with semidefinite constraints. Basic duality and optimality conditions are presented. For linear semidefinite programming some advances by dealing with degeneracy and the semidefinite facial reduction are discussed. Two relatively recent areas of application are presented. Finally a short overview of relevant literature on algorithmic approaches for efficiently solving linear and nonlinear semidefinite programming is provided.
A Mixed Integer Programming Model for Improving Theater Distribution Force Flow Analysis
2013-03-01
the introduction to LINGO in OPER 510. Next, I wish to thank LINDO Systems, particularly Kevin Cunningham, for software assistance with LINGO . I...viii Appendix A. LINGO 13 Settings File Contents .............................................................. 79 Appendix B. Additional Model...optimization software LINGO 13 (Lindo Systems Inc, 2012). A Decision Support System was built in the Excel environment where the user uploads a
Reliability in the Power System Modeled in a Multi- Stage Stochastic Mixed Integer Programming Model
DEFF Research Database (Denmark)
Simonsen Nielsen, Michael Pascal
Contributions from this article are that it takes the characteristics of the power system into account at different stages, which gives a more realistic presentation of the welfare aspects to be gained by an optimal operation/ dispatch of the power system. This article is utilizing a Multi...
Advertisement Scheduling in Television Channels: An Application of Mixed Integer Programming
ULUCAN, Aydın; Ülker, Onur
2005-01-01
In recent years, the increment in the number of TV channels, increased the digital broadcasting and wired homes. Developing of convalescence, television advertising has become much more important, commercial companies allocate their advertisement budgets on TV ads. TV channels need professional advertisement scheduling techniques in order to increase spot sales profitability and productivities. As per date, many methods have been evaluated in spot scheduling. The most important ones are, ‘int...
A Branch and Bound Algorithm for a Class of Biobjective Mixed Integer Programs
DEFF Research Database (Denmark)
Stidsen, Thomas Riis; Andersen, Kim Allan; Dammann, Bernd
2014-01-01
Pareto-optimal front). In this paper, we first give a survey of the newly developed branch and bound methods for solving MOMIP problems. After that, we propose a new branch and bound method for solving a subclass of MOMIP problems, where only two objectives are allowed, the integer variables are binary...
The Neighborhood Covering Heuristic (NCH) Approach for the General Mixed Integer Programming Problem
2006-05-31
array of blade processors, specifically configured for parallel computation, possibly as a Beowulf cluster . Additional revenue, $65,000 per year, will...The AMIPS hardware, a multi- node clustered computer installation, will be networked with the COMPASS simulator hardware. Because of the unique
CSIR Research Space (South Africa)
Mahlathi, Christopher
2016-10-01
Full Text Available , wastewater treatment plays a crucial role. In this work, a Streeter-Phelps dissolved oxygen model (DO) is implemented in a semi-hypothetical Upper Olifants River system to forecast instream dissolved oxygen profiles in response to different wastewater...
Directory of Open Access Journals (Sweden)
J. Fabian Lopez
2010-01-01
Full Text Available We consider a Pickup and Delivery Vehicle Routing Problem (PDP commonly encountered in real-world logistics operations. The problem involves a set of practical complications that have received little attention in the vehicle routing literature. In this problem, there are multiple vehicle types available to cover a set of pickup and delivery requests, each of which has pickup time windows and delivery time windows. Transportation orders and vehicle types must satisfy a set of compatibility constraints that specify which orders cannot be covered by which vehicle types. In addition we include some dock service capacity constraints as is required on common real world operations. This problem requires to be attended on large scale instances (orders ≥ 500, (vehicles ≥ 150. As a generalization of the traveling salesman problem, clearly this problem is NP-hard. The exact algorithms are too slow for large scale instances. The PDP-TWDS is both a packing problem (assign order to vehicles, and a routing problem (find the best route for each vehicle. We propose to solve the problem in three stages. The first stage constructs initials solutions at aggregate level relaxing some constraints on the original problem. The other two stages imposes time windows and dock service constraints. Our results are favorable finding good quality solutions in relatively short computational times.
A Linear Algorithm for the CMS Muon Drift Tubes Local Pattern Recognition
De Min, Alberto
2007-01-01
In this note a new, linear reconstruction algorithm is suggested for the CMS barrel muon chambers that detects the presence of a muon track, performs optimal pattern recognition, solves left-right ambiguities and fits the muon track segment within each chamber with a simple single-step procedure. The algorithm uses mixed-integer programming techniques developed in operations research. Compared to the sequential reconstruction method presently used it is potentially unbiased, more reliable and possibly faster.
Energy Technology Data Exchange (ETDEWEB)
Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami
2017-03-27
Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Results: Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs.
Energy Technology Data Exchange (ETDEWEB)
Binh, Do Quang [University of Technical Education Ho Chi Minh City (Viet Nam); Huy, Ngo Quang [University of Industry Ho Chi Minh City (Viet Nam); Hai, Nguyen Hoang [Centre for Research and Development of Radiation Technology, Ho Chi Minh City (Viet Nam)
2014-12-15
This paper presents a new approach based on a binary mixed integer coded genetic algorithm in conjunction with the weighted sum method for multi-objective optimization of fuel loading patterns for nuclear research reactors. The proposed genetic algorithm works with two types of chromosomes: binary and integer chromosomes, and consists of two types of genetic operators: one working on binary chromosomes and the other working on integer chromosomes. The algorithm automatically searches for the most suitable weighting factors of the weighting function and the optimal fuel loading patterns in the search process. Illustrative calculations are implemented for a research reactor type TRIGA MARK II loaded with the Russian VVR-M2 fuels. Results show that the proposed genetic algorithm can successfully search for both the best weighting factors and a set of approximate optimal loading patterns that maximize the effective multiplication factor and minimize the power peaking factor while satisfying operational and safety constraints for the research reactor.
On the linear programming bound for linear Lee codes.
Astola, Helena; Tabus, Ioan
2016-01-01
Based on an invariance-type property of the Lee-compositions of a linear Lee code, additional equality constraints can be introduced to the linear programming problem of linear Lee codes. In this paper, we formulate this property in terms of an action of the multiplicative group of the field [Formula: see text] on the set of Lee-compositions. We show some useful properties of certain sums of Lee-numbers, which are the eigenvalues of the Lee association scheme, appearing in the linear programming problem of linear Lee codes. Using the additional equality constraints, we formulate the linear programming problem of linear Lee codes in a very compact form, leading to a fast execution, which allows to efficiently compute the bounds for large parameter values of the linear codes.
Index coding via linear programming
Blasiak, Anna; Lubetzky, Eyal
2010-01-01
Index Coding has received considerable attention recently motivated in part by applications such as fast video-on-demand and efficient communication in wireless networks and in part by its connection to Network Coding. The basic setting of Index Coding encodes the side-information relation, the problem input, as an undirected graph and the fundamental parameter is the broadcast rate $\\beta$, the average communication cost per bit for sufficiently long messages (i.e. the non-linear vector capacity). Recent nontrivial bounds on $\\beta$ were derived from the study of other Index Coding capacities (e.g. the scalar capacity $\\beta_1$) by Bar-Yossef et al (FOCS'06), Lubetzky and Stav (FOCS'07) and Alon et al (FOCS'08). However, these indirect bounds shed little light on the behavior of $\\beta$ and its exact value remained unknown for \\emph{any graph} where Index Coding is nontrivial. Our main contribution is a hierarchy of linear programs whose solutions trap $\\beta$ between them. This enables a direct information-...
Institute of Scientific and Technical Information of China (English)
宗立军; 罗建军; 王明明; 袁建平
2016-01-01
For the obstacle avoidance problem of space robots,a mixed integer predictive controller is developed for the free-floating space robots.Firstly,in the frame of model predictive control method,the joint physical limits and obstacle avoidance requirement are described as inequality constraints of the optimal control problem,and the controller with linear quadratic programming form can be obtained.Secondly,priorities of the constraints in the controller are established based on the propositional logic theory to guarantee the optimal solution to be derived under the satisfaction of the maximum number of constraints,which effectively makes up for the deficiency issue that multiple constraints may lead to the optimal control problem infeasible when model predictive control method is used to control space robots.Finally,numerical simulations are conducted to prove the effectiveness of the proposed control strategy.%针对空间机器人完成任务时需要躲避障碍物的问题，提出一种自由漂浮空间机器人的混合整数预测控制方法。首先，在模型预测控制方法框架下，机械臂关节的物理限制，躲避障碍物的要求被统一描述为最优控制问题下的不等式约束，可以得到自由漂浮空间机器人具有线性二次规划形式的最优控制律。其次，基于命题逻辑建立控制问题中各约束的优先级，保证在最大程度地满足约束的情形下得到控制问题的解，有效弥补了模型预测控制方法用于空间机器人控制时，多约束可能导致最优控制问题不可行的不足。最后，仿真结果校验了所设计控制律的有效性。
Henriques, David; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R
2015-09-15
Systems biology models can be used to test new hypotheses formulated on the basis of previous knowledge or new experimental data, contradictory with a previously existing model. New hypotheses often come in the shape of a set of possible regulatory mechanisms. This search is usually not limited to finding a single regulation link, but rather a combination of links subject to great uncertainty or no information about the kinetic parameters. In this work, we combine a logic-based formalism, to describe all the possible regulatory structures for a given dynamic model of a pathway, with mixed-integer dynamic optimization (MIDO). This framework aims to simultaneously identify the regulatory structure (represented by binary parameters) and the real-valued parameters that are consistent with the available experimental data, resulting in a logic-based differential equation model. The alternative to this would be to perform real-valued parameter estimation for each possible model structure, which is not tractable for models of the size presented in this work. The performance of the method presented here is illustrated with several case studies: a synthetic pathway problem of signaling regulation, a two-component signal transduction pathway in bacterial homeostasis, and a signaling network in liver cancer cells. Supplementary data are available at Bioinformatics online. julio@iim.csic.es or saezrodriguez@ebi.ac.uk. © The Author 2015. Published by Oxford University Press.
Computer Program For Linear Algebra
Krogh, F. T.; Hanson, R. J.
1987-01-01
Collection of routines provided for basic vector operations. Basic Linear Algebra Subprogram (BLAS) library is collection from FORTRAN-callable routines for employing standard techniques to perform basic operations of numerical linear algebra.
Computer Program For Linear Algebra
Krogh, F. T.; Hanson, R. J.
1987-01-01
Collection of routines provided for basic vector operations. Basic Linear Algebra Subprogram (BLAS) library is collection from FORTRAN-callable routines for employing standard techniques to perform basic operations of numerical linear algebra.
Investigating Integer Restrictions in Linear Programming
Edwards, Thomas G.; Chelst, Kenneth R.; Principato, Angela M.; Wilhelm, Thad L.
2015-01-01
Linear programming (LP) is an application of graphing linear systems that appears in many Algebra 2 textbooks. Although not explicitly mentioned in the Common Core State Standards for Mathematics, linear programming blends seamlessly into modeling with mathematics, the fourth Standard for Mathematical Practice (CCSSI 2010, p. 7). In solving a…
Investigating Integer Restrictions in Linear Programming
Edwards, Thomas G.; Chelst, Kenneth R.; Principato, Angela M.; Wilhelm, Thad L.
2015-01-01
Linear programming (LP) is an application of graphing linear systems that appears in many Algebra 2 textbooks. Although not explicitly mentioned in the Common Core State Standards for Mathematics, linear programming blends seamlessly into modeling with mathematics, the fourth Standard for Mathematical Practice (CCSSI 2010, p. 7). In solving a…
Linear programming algorithms and applications
Vajda, S
1981-01-01
This text is based on a course of about 16 hours lectures to students of mathematics, statistics, and/or operational research. It is intended to introduce readers to the very wide range of applicability of linear programming, covering problems of manage ment, administration, transportation and a number of other uses which are mentioned in their context. The emphasis is on numerical algorithms, which are illustrated by examples of such modest size that the solutions can be obtained using pen and paper. It is clear that these methods, if applied to larger problems, can also be carried out on automatic (electronic) computers. Commercially available computer packages are, in fact, mainly based on algorithms explained in this book. The author is convinced that the user of these algorithms ought to be knowledgeable about the underlying theory. Therefore this volume is not merely addressed to the practitioner, but also to the mathematician who is interested in relatively new developments in algebraic theory and in...
Approximation Limits of Linear Programs (Beyond Hierarchies)
Braun, Gábor; Pokutta, Sebastian; Steurer, David
2012-01-01
We develop a framework for approximation limits of polynomial-size linear programs from lower bounds on the nonnegative ranks of suitably defined matrices. This framework yields unconditional impossibility results that are applicable to any linear program as opposed to only programs generated by hierarchies. Using our framework, we prove that O(n^{1/2-eps})-approximations for CLIQUE require linear programs of size 2^{n^\\Omega(eps)}. (This lower bound applies to linear programs using a certain encoding of CLIQUE as a linear optimization problem.) Moreover, we establish a similar result for approximations of semidefinite programs by linear programs. Our main ingredient is a quantitative improvement of Razborov's rectangle corruption lemma for the high error regime, which gives strong lower bounds on the nonnegative rank of certain perturbations of the unique disjointness matrix.
Linear-programming Decoding of Non-binary Linear Codes
Flanagan, Mark F; Byrne, Eimear; Greferath, Marcus
2007-01-01
We develop a framework for linear-programming (LP) decoding of non-binary linear codes over rings. We prove that the resulting LP decoder has the `maximum likelihood certificate' property, and we show that the decoder output is the lowest cost pseudocodeword. Equivalence between pseudocodewords of the linear program and pseudocodewords of graph covers is proved. LP decoding performance is illustrated for the (11,6,5) ternary Golay code with ternary PSK modulation over AWGN, and in this case it is shown that the LP decoder performance is comparable to codeword-error-rate-optimum hard-decision based decoding.
Song, Hyun-Seob; Goldberg, Noam; Mahajan, Ashutosh; Ramkrishna, Doraiswami
2017-08-01
Elementary (flux) modes (EMs) have served as a valuable tool for investigating structural and functional properties of metabolic networks. Identification of the full set of EMs in genome-scale networks remains challenging due to combinatorial explosion of EMs in complex networks. It is often, however, that only a small subset of relevant EMs needs to be known, for which optimization-based sequential computation is a useful alternative. Most of the currently available methods along this line are based on the iterative use of mixed integer linear programming (MILP), the effectiveness of which significantly deteriorates as the number of iterations builds up. To alleviate the computational burden associated with the MILP implementation, we here present a novel optimization algorithm termed alternate integer linear programming (AILP). Our algorithm was designed to iteratively solve a pair of integer programming (IP) and linear programming (LP) to compute EMs in a sequential manner. In each step, the IP identifies a minimal subset of reactions, the deletion of which disables all previously identified EMs. Thus, a subsequent LP solution subject to this reaction deletion constraint becomes a distinct EM. In cases where no feasible LP solution is available, IP-derived reaction deletion sets represent minimal cut sets (MCSs). Despite the additional computation of MCSs, AILP achieved significant time reduction in computing EMs by orders of magnitude. The proposed AILP algorithm not only offers a computational advantage in the EM analysis of genome-scale networks, but also improves the understanding of the linkage between EMs and MCSs. The software is implemented in Matlab, and is provided as supplementary information . hyunseob.song@pnnl.gov. Supplementary data are available at Bioinformatics online.
LISA: a linear structured system analysis program
Martinez-Martinez, Sinuhé; Mader, Theodor; Boukhobza, Taha; Hamelin, Frédéric
2007-01-01
International audience; In this paper the program LISA is presented. LISA is a flexible and portable program which has been developed to analyse structural properties of large scale linear and bilinear structured systems. More precisely, the program LISA contains programmed algorithms which allow us to apply recent results in the analysis of structured systems to some particular cases.
Solution Methods for Stochastic Dynamic Linear Programs.
1980-12-01
Linear Programming, IIASA , Laxenburg, Austria, June 2-6, 1980. [2] Aghili, P., R.H., Cramer and H.W. Thompson, "On the applicability of two- stage...Laxenburg, Austria, May, 1978. [52] Propoi, A. and V. Krivonozhko, ’The simplex method for dynamic linear programs", RR-78-14, IIASA , Vienna, Austria
Timetabling an Academic Department with Linear Programming.
Bezeau, Lawrence M.
This paper describes an approach to faculty timetabling and course scheduling that uses computerized linear programming. After reviewing the literature on linear programming, the paper discusses the process whereby a timetable was created for a department at the University of New Brunswick. Faculty were surveyed with respect to course offerings…
He, Li; Huang, Guo-He; Zeng, Guang-Ming; Lu, Hong-Wei
2009-01-01
The previous inexact mixed-integer linear programming (IMILP) method can only tackle problems with coefficients of the objective function and constraints being crisp intervals, while the existing inexact mixed-integer semi-infinite programming (IMISIP) method can only deal with single-objective programming problems as it merely allows the number of constraints to be infinite. This study proposes, an inexact mixed-integer bi-infinite programming (IMIBIP) method by incorporating the concept of functional intervals into the programming framework. Different from the existing methods, the IMIBIP can tackle the inexact programming problems that contain both infinite objectives and constraints. The developed method is applied to capacity planning of waste management systems under a variety of uncertainties. Four scenarios are considered for comparing the solutions of IMIBIP with those of IMILP. The results indicate that reasonable solutions can be generated by the IMIBIP method. Compared with IMILP, the system cost from IMIBIP would be relatively high since the fluctuating market factors are considered; however, the IMILP solutions are associated with a raised system reliability level and a reduced constraint violation risk level.
Zangiabadi, M.; H. R. MALEKI
2007-01-01
In the real-world optimization problems, coefficients of the objective function are not known precisely and can be interpreted as fuzzy numbers. In this paper we define the concepts of optimality for linear programming problems with fuzzy parameters based on those for multiobjective linear programming problems. Then by using the concept of comparison of fuzzy numbers, we transform a linear programming problem with fuzzy parameters to a multiobjective linear programming problem. To this end, w...
Neighborhood-following algorithms for linear programming
Institute of Scientific and Technical Information of China (English)
无
2004-01-01
［1］Kojima, M., Mizuno, S., Yoshise, A., A polynomial-time algorithm for a class of linear complementarity probems, Mathematical Programming, 1989, 44: 1-26.［2］Megiddo, N., Pathways to the optimal set in linear programming. in Progression Mathematical Programming:Interior Point and Related Methods, New York: Springer-Verlag, 1989, 131-158.［3］Monteiro, R. D. C., Adler, I., Interior path following primal-dual algorithms, Part Ⅰ: linear programming, Mathematical Programming, 1989, 44: 27-41.［4］Monteiro, R. D. C., Adler, I., Interior path following primal-dual algorithms, Part Ⅱ: convex quadratic programming, Mathematical Programming, 1989, 44: 43-46.［5］Wright, S. J., Primal-Dual Interior-Point Methods, Philadephia: SIAM Publications, 1997.［6］Mizuno, S., Todd, M. J., Ye, Y., On adaptive step primal-dual interior-point algorithms for linear programming,Mathematics of Operations Research, 1993, 18:964-981.［7］Gonzaga, C. C., The largest step path following algorithm for monotone linear complementarity problems, Mathematical Programming, 1997, 76: 309-332.［8］Sturm, J. F., Zhang, S., On a wide region of centers primal-dual interior point algorithms for linear programming,Tinbergen Institute Rotterdam, Erasmus University, Rotterdam, The Netherlands, 1995.［9］Hung, P., Ye, Y., An asymptotical O(√nL)-iteration path-following linear programming algorithm that uses wide neighborhoods, SIAM J.Optimization, 1996, 6: 570-586.［10］Ye, Y., Interior Point Algorithms: Theory and Analysis, New York: Wiley-Interscience Publication, 1997.［11］Güler, O., Ye, Y., Convergence behavior of interior-point algorithms. Mathematical Programming, 1993, 60:215-228.［12］Mehrotral, S., On the implementation of a primal-dual interior point mehtod, SIAM J. Optimization, 1992, 2(4):575-601.
Enhancement of Linear Circuit Program
DEFF Research Database (Denmark)
Gaunholt, Hans; Dabu, Mihaela; Beldiman, Octavian
1996-01-01
In this report a preliminary user friendly interface has been added to the LCP2 program making it possible to describe an electronic circuit by actually drawing the circuit on the screen. Component values and other options and parameters can easily be set by the aid of the interface. The interface...
Enhancement of Linear Circuit Program
DEFF Research Database (Denmark)
Gaunholt, Hans; Dabu, Mihaela; Beldiman, Octavian
1996-01-01
In this report a preliminary user friendly interface has been added to the LCP2 program making it possible to describe an electronic circuit by actually drawing the circuit on the screen. Component values and other options and parameters can easily be set by the aid of the interface. The interfac...
Linear Programming建模研讨%Modeling of Linear Programming
Institute of Scientific and Technical Information of China (English)
宋占奎; 於全收; 范光; 燕嬿; 胡杰军
2007-01-01
研究用图解法、simplexmethod和匈牙利法建立Linear Programming的数学模型并求得了最优解.结果表明:对仅有两个变量的Linear Programming,既可通过图解法求得最优解;也可用单纯形表简便地求得最优解;而对任务和人数不等的assignment problem,则用匈牙利法求最优解.
A Direct Heuristic Algorithm for Linear Programming
Indian Academy of Sciences (India)
S K Sen; A Ramful
2000-02-01
An (3) mathematically non-iterative heuristic procedure that needs no artificial variable is presented for solving linear programming problems. An optimality test is included. Numerical experiments depict the utility/scope of such a procedure.
Linear and integer programming made easy
Hu, T C
2016-01-01
Linear and integer programming are fundamental toolkits for data and information science and technology, particularly in the context of today’s megatrends toward statistical optimization, machine learning, and big data analytics. Drawn from over 30 years of classroom teaching and applied research experience, this textbook provides a crisp and practical introduction to the basics of linear and integer programming. The authors’ approach is accessible to students from all fields of engineering, including operations research, statistics, machine learning, control system design, scheduling, formal verification, and computer vision. Readers will learn to cast hard combinatorial problems as mathematical programming optimizations, understand how to achieve formulations where the objective and constraints are linear, choose appropriate solution methods, and interpret results appropriately. •Provides a concise introduction to linear and integer programming, appropriate for undergraduates, graduates, a short cours...
The Use of Linear Programming for Prediction.
Schnittjer, Carl J.
The purpose of the study was to develop a linear programming model to be used for prediction, test the accuracy of the predictions, and compare the accuracy with that produced by curvilinear multiple regression analysis. (Author)
Sausage Blending Using Multiple Objective Linear Programming
Steuer, Ralph E.
1984-01-01
Single objective cost minimization linear programming models are used as computerized decision-aids in sausage manufacturing (hot dogs, bologna, salami, etc.). However, sausage blending is clearly a problem with multiple conflicting criteria (cost, color, fat, protein, moisture, etc.) Presented in this paper is a vector-maximum/filtering MOLP (multiple objective linear programming) methodology for use as an improved decision-making approach with single formula sausage blending problems.
Generic Mathematical Programming Formulation and Solution for Computer-Aided Molecular Design
DEFF Research Database (Denmark)
Zhang, Lei; Cignitti, Stefano; Gani, Rafiqul
2015-01-01
This short communication presents a generic mathematical programming formulation for Computer-Aided Molecular Design (CAMD). A given CAMD problem, based on target properties, is formulated as a Mixed Integer Linear/Non-Linear Program (MILP/MINLP). The mathematical programming model presented here......, which is formulated as an MILP/MINLP problem, considers first-order and second-order molecular groups for molecular structure representation and property estimation. It is shown that various CAMD problems can be formulated and solved through this model....
Generic Mathematical Programming Formulation and Solution for Computer-Aided Molecular Design
DEFF Research Database (Denmark)
Zhang, Lei; Cignitti, Stefano; Gani, Rafiqul
2015-01-01
This short communication presents a generic mathematical programming formulation for Computer-Aided Molecular Design (CAMD). A given CAMD problem, based on target properties, is formulated as a Mixed Integer Linear/Non-Linear Program (MILP/MINLP). The mathematical programming model presented here......, which is formulated as an MILP/MINLP problem, considers first-order and second-order molecular groups for molecular structure representation and property estimation. It is shown that various CAMD problems can be formulated and solved through this model....
Maximum entropy signal restoration with linear programming
Energy Technology Data Exchange (ETDEWEB)
Mastin, G.A.; Hanson, R.J.
1988-05-01
Dantzig's bounded-variable method is used to express the maximum entropy restoration problem as a linear programming problem. This is done by approximating the nonlinear objective function with piecewise linear segments, then bounding the variables as a function of the number of segments used. The use of a linear programming approach allows equality constraints found in the traditional Lagrange multiplier method to be relaxed. A robust revised simplex algorithm is used to implement the restoration. Experimental results from 128- and 512-point signal restorations are presented.
Linear programming mathematics, theory and algorithms
1996-01-01
Linear Programming provides an in-depth look at simplex based as well as the more recent interior point techniques for solving linear programming problems. Starting with a review of the mathematical underpinnings of these approaches, the text provides details of the primal and dual simplex methods with the primal-dual, composite, and steepest edge simplex algorithms. This then is followed by a discussion of interior point techniques, including projective and affine potential reduction, primal and dual affine scaling, and path following algorithms. Also covered is the theory and solution of the linear complementarity problem using both the complementary pivot algorithm and interior point routines. A feature of the book is its early and extensive development and use of duality theory. Audience: The book is written for students in the areas of mathematics, economics, engineering and management science, and professionals who need a sound foundation in the important and dynamic discipline of linear programming.
Linear Programming With Applications to Educational Planning.
Carman, Robert A.
This document discusses the value of linear programing in finding minimum and maximum solutions to problems of resource allocation. Three models using this technique are given for the areas of educational finance, school district personnel compensation, and instructional program evaluation. (RA)
Fuzzy Multi-objective Linear Programming Approach
Directory of Open Access Journals (Sweden)
Amna Rehmat
2007-07-01
Full Text Available Traveling salesman problem (TSP is one of the challenging real-life problems, attracting researchers of many fields including Artificial Intelligence, Operations Research, and Algorithm Design and Analysis. The problem has been well studied till now under different headings and has been solved with different approaches including genetic algorithms and linear programming. Conventional linear programming is designed to deal with crisp parameters, but information about real life systems is often available in the form of vague descriptions. Fuzzy methods are designed to handle vague terms, and are most suited to finding optimal solutions to problems with vague parameters. Fuzzy multi-objective linear programming, an amalgamation of fuzzy logic and multi-objective linear programming, deals with flexible aspiration levels or goals and fuzzy constraints with acceptable deviations. In this paper, a methodology, for solving a TSP with imprecise parameters, is deployed using fuzzy multi-objective linear programming. An example of TSP with multiple objectives and vague parameters is discussed.
Fuzzy linear programming for bulb production
Siregar, I.; Suantio, H.; Hanifiah, Y.; Muchtar, M. A.; Nasution, T. H.
2017-01-01
The research was conducted at a bulb company. This company has a high market demand. The increasing of the market demand has caused the company’s production could not fulfill the demand due to production planning is not optimal. Bulb production planning is researched with the aim to enable the company to fulfill the market demand in accordance with the limited resources available. From the data, it is known that the company cannot reach the market demand in the production of the Type A and Type B bulb. In other hands, the Type C bulb is produced exceeds market demand. By using fuzzy linear programming, then obtained the optimal production plans and to reach market demand. Completion of the simple method is done by using software LINGO 13. Application of fuzzy linear programming is being able to increase profits amounted to 7.39% of the ordinary concept of linear programming.
Portfolio optimization using fuzzy linear programming
Pandit, Purnima K.
2013-09-01
Portfolio Optimization (PO) is a problem in Finance, in which investor tries to maximize return and minimize risk by carefully choosing different assets. Expected return and risk are the most important parameters with regard to optimal portfolios. In the simple form PO can be modeled as quadratic programming problem which can be put into equivalent linear form. PO problems with the fuzzy parameters can be solved as multi-objective fuzzy linear programming problem. In this paper we give the solution to such problems with an illustrative example.
Towards automatic synthesis of linear algebra programs
Energy Technology Data Exchange (ETDEWEB)
Boyle, J. M.
1979-01-01
Automating the writing of efficient computer programs from an abstract specification of the computation that they are to perform is discussed. Advantages offered by automatic synthesis of programs include economy, reliability, and improved service. The synthesis of simple linear algebra programs is considered in general and then illustrated for the usual matrix product, a column-oriented matrix product, a rank-one update matrix product, and a program to multiply three matrices. The accumulation of inner products and transformational implementation of program synthesis addressed. The discussion attempts to illustrate both the general strategy of the syntheses and how various tactics can be adapted to make the syntheses proceed deterministically to programs that are optimal with respect to certain criteria. (RWR)
A SCALED CENTRAL PATH FOR LINEAR PROGRAMMING
Institute of Scientific and Technical Information of China (English)
Ya-xiang Yuan
2001-01-01
Interior point methods are very efficient methods for solving large scale linear programming problems. The central path plays a very important role in interior point methods. In this paper we propose a new central path, which scales the variables. Thus it has the advantage of forcing the path to have roughly the same distance from each active constraint boundary near the solution.
Spline smoothing of histograms by linear programming
Bennett, J. O.
1972-01-01
An algorithm for an approximating function to the frequency distribution is obtained from a sample of size n. To obtain the approximating function a histogram is made from the data. Next, Euclidean space approximations to the graph of the histogram using central B-splines as basis elements are obtained by linear programming. The approximating function has area one and is nonnegative.
A HYBRID METHOD FOR LINEAR PROGRAMMING
Institute of Scientific and Technical Information of China (English)
XIU Naihua; WU Fang
1999-01-01
In this paper, a hybrid method for linear programming is established.Its search direction is defined as a combination of two directions insimplex method and affine-scaling interior point method.The method is proven to have some promising convergence properties.The relation among the new method, the simplex method and the affine-scalinginteriorpoint method is discussed.
Generalised Assignment Matrix Methodology in Linear Programming
Jerome, Lawrence
2012-01-01
Discrete Mathematics instructors and students have long been struggling with various labelling and scanning algorithms for solving many important problems. This paper shows how to solve a wide variety of Discrete Mathematics and OR problems using assignment matrices and linear programming, specifically using Excel Solvers although the same…
Controller design approach based on linear programming.
Tanaka, Ryo; Shibasaki, Hiroki; Ogawa, Hiromitsu; Murakami, Takahiro; Ishida, Yoshihisa
2013-11-01
This study explains and demonstrates the design method for a control system with a load disturbance observer. Observer gains are determined by linear programming (LP) in terms of the Routh-Hurwitz stability criterion and the final-value theorem. In addition, the control model has a feedback structure, and feedback gains are determined to be the linear quadratic regulator. The simulation results confirmed that compared with the conventional method, the output estimated by our proposed method converges to a reference input faster when a load disturbance is added to a control system. In addition, we also confirmed the effectiveness of the proposed method by performing an experiment with a DC motor.
The simplex method of linear programming
Ficken, FA
2015-01-01
This concise but detailed and thorough treatment discusses the rudiments of the well-known simplex method for solving optimization problems in linear programming. Geared toward undergraduate students, the approach offers sufficient material for readers without a strong background in linear algebra. Many different kinds of problems further enrich the presentation. The text begins with examinations of the allocation problem, matrix notation for dual problems, feasibility, and theorems on duality and existence. Subsequent chapters address convex sets and boundedness, the prepared problem and boun
Joint shape segmentation with linear programming
Huang, Qixing
2011-01-01
We present an approach to segmenting shapes in a heterogenous shape database. Our approach segments the shapes jointly, utilizing features from multiple shapes to improve the segmentation of each. The approach is entirely unsupervised and is based on an integer quadratic programming formulation of the joint segmentation problem. The program optimizes over possible segmentations of individual shapes as well as over possible correspondences between segments from multiple shapes. The integer quadratic program is solved via a linear programming relaxation, using a block coordinate descent procedure that makes the optimization feasible for large databases. We evaluate the presented approach on the Princeton segmentation benchmark and show that joint shape segmentation significantly outperforms single-shape segmentation techniques. © 2011 ACM.
Evolving evolutionary algorithms using linear genetic programming.
Oltean, Mihai
2005-01-01
A new model for evolving Evolutionary Algorithms is proposed in this paper. The model is based on the Linear Genetic Programming (LGP) technique. Every LGP chromosome encodes an EA which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization, the Traveling Salesman Problem and the Quadratic Assignment Problem are evolved by using the considered model. Numerical experiments show that the evolved Evolutionary Algorithms perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.
Dynamic Pricing Criteria in Linear Programming
1988-07-01
Dantzig, M.A.H. Dempster and M. Kallio, eds.), pp. 631- 662, IIASA , Laxenburg, Austria. [23] Karmarkar, N. (1984). A new polynomial-time algorithm for...simplex method, in Large Scale Linear Programming (G.B. Dantzig, M.A.H. Dempster and M. Kallio, eds.), pp. 55-66, IIASA , Laxenburg, Austria. [39] Perold...M.J. Kallio, eds.), pp. 67-96, IIASA , Laxenburg, Austria. [40] Pyle, L.D. (1987). Generalizations of the simplex algorithm, Department of Compvter
PRIMAL PERTURBATION SIMPLEX ALGORITHMS FOR LINEAR PROGRAMMING
Institute of Scientific and Technical Information of China (English)
Ping-qi Pan
2000-01-01
In this paper, we propose two new perturbation simplex variants. Solving linear programming problems without introducing artificial variables, each of the two uses the dual pivot rule to achieve primal feasibility, and then the primal pivot rule to achieve optimality. The second algorithm, a modification of the first, is designed to handle highly degenerate problems more etficiently. Some interesting results concerning merit of the perturbation are established. Numerical results from preliminary tests are also reported.
A LINEAR PROGRAMMING MODEL OF THE GASEOUSDIFFUSION ISOTOPE-SEPARATION PROCESS,
ISOTOPE SEPARATION, LINEAR PROGRAMMING ), (*GASEOUS DIFFUSION SEPARATION, LINEAR PROGRAMMING ), (* LINEAR PROGRAMMING , GASEOUS DIFFUSION SEPARATION), NUCLEAR REACTORS, REACTOR FUELS, URANIUM, PURIFICATION
Modeling of non-linear CHP efficiency curves in distributed energy systems
DEFF Research Database (Denmark)
Milan, Christian; Stadler, Michael; Cardoso, Gonçalo
2015-01-01
Distributed energy resources gain an increased importance in commercial and industrial building design. Combined heat and power (CHP) units are considered as one of the key technologies for cost and emission reduction in buildings. In order to make optimal decisions on investment and operation...... for these technologies, detailed system models are needed. These models are often formulated as linear programming problems to keep computational costs and complexity in a reasonable range. However, CHP systems involve variations of the efficiency for large nameplate capacity ranges and in case of part load operation......, which can be even of non-linear nature. Since considering these characteristics would turn the models into non-linear problems, in most cases only constant efficiencies are assumed. This paper proposes possible solutions to address this issue. For a mixed integer linear programming problem two...
Regularized multiple criteria linear programs for classification
Institute of Scientific and Technical Information of China (English)
SHI Yong; TIAN YingJie; CHEN XiaoJun; ZHANG Peng
2009-01-01
Although multiple criteria mathematical program (MCMP), as an alternative method of classification, has been used in various real-life data mining problems, its mathematical structure of solvability is still challengeable. This paper proposes a regularized multiple criteria linear program (RMCLP) for two classes of classification problems. It first adds some regularization terms in the objective function of the known multiple criteria linear program (MCLP) model for possible existence of solution. Then the paper describes the mathematical framework of the solvability. Finally, a series of experimental tests are conducted to illustrate the performance of the proposed RMCLP with the existing methods: MCLP, multiple criteria quadratic program (MCQP), and support vector machine (SVM). The results of four publicly available datasets and a real-life credit dataset all show that RMCLP is a competitive method in classification. Furthermore, this paper explores an ordinal RMCLP (ORMCLP) model for ordinal multi-group problems. Comparing ORMCLP with traditional methods such as One-Against-One, One-Against-The rest on large-scale credit card dataset, experimental results show that both ORMCLP and RMCLP perform well.
Integer programming for the generalized high school timetabling problem
DEFF Research Database (Denmark)
Kristiansen, Simon; Sørensen, Matias; Stidsen, Thomas Riis
2015-01-01
Recently, the XHSTT format for high school timetabling was introduced. It provides a uniform way of modeling problem instances and corresponding solutions. The format supports a wide variety of constraints, and currently 38 real-life instances from 11 different countries are available. Thereby......, the XHSTT format serves as a common ground for researchers within this area. This paper describes the first exact method capable of handling an arbitrary instance of the XHSTT format. The method is based on a mixed-integer linear programming (MIP) model, which is solved in two steps with a commercial...
DEFF Research Database (Denmark)
Zhan, Yiduo; Zheng, Qipeng; Wang, Jianhui
2016-01-01
, the probability distribution function is determined by not only input parameters but also decision variables. To deal with the nonlinear constraints in our model, a quasi-exact solution approach is then introduced to reformulate the multistage stochastic investment model to a mixed-integer linear programming......Power generation expansion planning needs to deal with future uncertainties carefully, given that the invested generation assets will be in operation for a long time. Many stochastic programming models have been proposed to tackle this challenge. However, most previous works assume predetermined...
Neighborhood-following algorithms for linear programming
Institute of Scientific and Technical Information of China (English)
AI Wenbao
2004-01-01
In this paper, we present neighborhood-following algorithms for linear programming. When the neighborhood is a wide neighborhood, our algorithms are wide neighborhood primal-dual interior point algorithms. If the neighborhood degenerates into the central path, our algorithms also degenerate into path-following algorithms. We prove that our algorithms maintain the O(√nL)-iteration complexity still, while the classical wide neighborhood primal-dual interior point algorithms have only the O(nL)-iteration complexity. We also proved that the algorithms are quadratic convergence if the optimal vertex is nondegenerate. Finally, we show some computational results of our algorithms.
Verifying Time Petri Nets by Linear Programming
Institute of Scientific and Technical Information of China (English)
李宣东
2001-01-01
The paper proposes an approach to solving some verification problems of time Petri nets using linear programming. The approach is based on the observation that for loop-closed time Petri nets, it is only necessary to investigate a finite prefix of an untimed run of the underlying Petri net. Using the technique the paper gives solutions to reachability and bounded delay timing analysis problems. For both problems algorithms are given, that are decision procedures for loop-closed time Petri nets, and semi-decision procedures for general time Petri nets.
Grammar Based Genetic Programming Using Linear Representations
Institute of Scientific and Technical Information of China (English)
ZHANGHong; LUYinan; WANGFei
2003-01-01
In recent years,there has been a great interest in genetic programming(GP),which is used to solve many applications such as data mining,electronic engineering and pattern recognition etc.. Genetic programming paradigm as a from of adaptive learning is a functional approach to many problems that require a nonfixed representation and GP typically operates on a population of parse which usually represent computer programs whose nodes have single data type.In this paper GP using context-free grammars(CFGs) is described.This technique separates search space from solution space through a genotype to phenotype mapping.The genotypes and phenotypes of the individuals both act on different linear representations.A phenotype is postfix expression,a new method of representing which is described by making use of the definition and related features of a context-free grammar,i.e.a genotype is a variable length,linear valid genome determined by a simplifled derivation tree(SDT) generated from a context-free grammar.A CFG is used to specify how the possible solutions are created according to experiential knowledge and to direct legal crossover(ormutation)operations without any explicit reference to the process of program generation and parsing,and automatically ensuring typing and syntax correctness.Some related definitions involving genetic operators are described.Fitness evaluation is given.This technique is applied to a symbol regression problem-the identification of nonlinear dynamic characteristics of cushioning packaging.Experimental results show this method can flnd good relations between variables and is better than basic GP without a grammar.Future research on it is outlined.
On a Natural Dynamics for Linear Programming
Straszak, Damian
2015-01-01
In this paper we study dynamics inspired by Physarum polycephalum (a slime mold) for solving linear programs [NTY00, IJNT11, JZ12]. These dynamics are arrived at by a local and mechanistic interpretation of the inner workings of the slime mold and a global optimization perspective has been lacking even in the simplest of instances. Our first result is an interpretation of the dynamics as an optimization process. We show that Physarum dynamics can be seen as a steepest-descent type algorithm on a certain Riemannian manifold. Moreover, we prove that the trajectories of Physarum are in fact paths of optimizers to a parametrized family of convex programs, in which the objective is a linear cost function regularized by an entropy barrier. Subsequently, we rigorously establish several important properties of solution curves of Physarum. We prove global existence of such solutions and show that they have limits, being optimal solutions of the underlying LP. Finally, we show that the discretization of the Physarum dy...
Consensus contact prediction by linear programming.
Gao, Xin; Bu, Dongbo; Li, Shuai Cheng; Li, Ming; Xu, Jinbo
2007-01-01
Protein inter-residue contacts are of great use for protein structure determination or prediction. Recent CASP events have shown that a few accurately predicted contacts can help improve both computational efficiency and prediction accuracy of the ab inito folding methods. This paper develops an integer linear programming (ILP) method for consensus-based contact prediction. In contrast to the simple "majority voting" method assuming that all the individual servers are equal and independent, our method evaluates their correlations using the maximum likelihood method and constructs some latent independent servers using the principal component analysis technique. Then, we use an integer linear programming model to assign weights to these latent servers in order to maximize the deviation between the correct contacts and incorrect ones; our consensus prediction server is the weighted combination of these latent servers. In addition to the consensus information, our method also uses server-independent correlated mutation (CM) as one of the prediction features. Experimental results demonstrate that our contact prediction server performs better than the "majority voting" method. The accuracy of our method for the top L/5 contacts on CASP7 targets is 73.41%, which is much higher than previously reported studies. On the 16 free modeling (FM) targets, our method achieves an accuracy of 37.21%.
An Algorithm for Linearly Constrained Nonlinear Programming Programming Problems.
1980-01-01
ALGORITHM FOR LINEARLY CONSTRAINED NONLINEAR PROGRAMMING PROBLEMS Mokhtar S. Bazaraa and Jamie J. Goode In this paper an algorithm for solving a linearly...distance pro- gramr.ing, as in the works of Bazaraa and Goode 12], and Wolfe [16 can be used for solving this problem. Special methods that take advantage of...34 Pacific Journal of Mathematics, Volume 16, pp. 1-3, 1966. 2. M. S. Bazaraa and J. j. Goode, "An Algorithm for Finding the Shortest Element of a
Ensemble segmentation using efficient integer linear programming.
Alush, Amir; Goldberger, Jacob
2012-10-01
We present a method for combining several segmentations of an image into a single one that in some sense is the average segmentation in order to achieve a more reliable and accurate segmentation result. The goal is to find a point in the "space of segmentations" which is close to all the individual segmentations. We present an algorithm for segmentation averaging. The image is first oversegmented into superpixels. Next, each segmentation is projected onto the superpixel map. An instance of the EM algorithm combined with integer linear programming is applied on the set of binary merging decisions of neighboring superpixels to obtain the average segmentation. Apart from segmentation averaging, the algorithm also reports the reliability of each segmentation. The performance of the proposed algorithm is demonstrated on manually annotated images from the Berkeley segmentation data set and on the results of automatic segmentation algorithms.
Robust Control Design via Linear Programming
Keel, L. H.; Bhattacharyya, S. P.
1998-01-01
This paper deals with the problem of synthesizing or designing a feedback controller of fixed dynamic order. The closed loop specifications considered here are given in terms of a target performance vector representing a desired set of closed loop transfer functions connecting various signals. In general these point targets are unattainable with a fixed order controller. By enlarging the target from a fixed point set to an interval set the solvability conditions with a fixed order controller are relaxed and a solution is more easily enabled. Results from the parametric robust control literature can be used to design the interval target family so that the performance deterioration is acceptable, even when plant uncertainty is present. It is shown that it is possible to devise a computationally simple linear programming approach that attempts to meet the desired closed loop specifications.
Solving a Fully Fuzzy Linear Programming Problem through Compromise Programming
Haifang Cheng; Weilai Huang; Jianhu Cai
2013-01-01
In the current literatures, there are several models of fully fuzzy linear programming (FFLP) problems where all the parameters and variables were fuzzy numbers but the constraints were crisp equality or inequality. In this paper, an FFLP problem with fuzzy equality constraints is discussed, and a method for solving this FFLP problem is also proposed. We first transform the fuzzy equality constraints into the crisp inequality ones using the measure of the similarity, which is interpreted as t...
Linear Index Coding via Semidefinite Programming
Chlamtac, Eden
2011-01-01
In the index coding problem, introduced by Birk and Kol (INFOCOM, 1998), the goal is to broadcast an n bit word to n receivers (one bit per receiver), where the receivers have side information represented by a graph G. The objective is to minimize the length of a codeword sent to all receivers which allows each receiver to learn its bit. For linear index coding, the minimum possible length is known to be equal to a graph parameter called minrank (Bar-Yossef et al., FOCS, 2006). We show a polynomial time algorithm that, given an n vertex graph G with minrank k, finds a linear index code for G of length $\\widetilde{O}(n^{f(k)})$, where f(k) depends only on k. For example, for k=3 we obtain f(3) ~ 0.2574. Our algorithm employs a semidefinite program (SDP) introduced by Karger, Motwani and Sudan (J. ACM, 1998) for graph coloring and its refined analysis due to Arora, Chlamtac and Charikar (STOC, 2006). Since the SDP we use is not a relaxation of the minimization problem we consider, a crucial component of our ana...
LPNORM: A linear programming normative analysis code
de Caritat, Patrice; Bloch, John; Hutcheon, Ian
1994-04-01
The computer code LPNORM implements the mathematical method of linear programming to calculate the mineralogical makeup of mineral mixtures, such as rock, sediment, or soil samples, from their bulk geochemical composition and from the mineralogical (or geochemical) composition of the contained minerals. This method simultaneously solves the set of linear equations governing the distribution of oxides into these minerals, subject to an objective function and a set of basic constraints. LPNORM allows the user to specify what minerals will be considered for normative analysis, what their composition is (in terms of mineral formula or geochemical composition), and whether to maximize mineral abundances, minimize slack variables (oxides that can not be accounted for), or do both at once in the objective function. Independent knowledge about the abundance of one or several of the minerals in the sample can be entered as additional equality or inequality constraints. Trial-and-error approach enables the user to "optimize" the composition of one or a few of the contained minerals. Results of comparative tests, highlighting the efficiency, as well as the shortcomings, of LPNORM are presented.
Linear programming for learning in neural networks
Raghavan, Raghu
1991-08-01
The authors have previously proposed a network of probabilistic cellular automata (PCAs) as part of an image recognition system designed to integrate model-based and data-driven approaches in a connectionist framework. The PCA arises from some natural requirements on the system which include incorporation of prior knowledge such as in inference rules, locality of inferences, and full parallelism. This network has been applied to recognize objects in both synthetic and in real data. This approach achieves recognition through the short-, rather than the long-time behavior of the dynamics of the PCA. In this paper, some methods are developed for learning the connection strengths by solving linear inequalities: the figures of merit are tendencies or directions of movement of the dynamical system. These 'dynamical' figures of merit result in inequality constraints on the connection strengths which are solved by linear (LP) or quadratic programs (QP). An algorithm is described for processing a large number of samples to determine weights for the PCA. The work may be regarded as either pointing out another application for constrained optimization, or as pointing out the need to extend the perceptron and similar methods for learning. The extension is needed because the neural network operates on a different principle from that for which the perceptron method was devised.
Polynomial Linear Programming with Gaussian Belief Propagation
Bickson, Danny; Shental, Ori; Dolev, Danny
2008-01-01
Interior-point methods are state-of-the-art algorithms for solving linear programming (LP) problems with polynomial complexity. Specifically, the Karmarkar algorithm typically solves LP problems in time O(n^{3.5}), where $n$ is the number of unknown variables. Karmarkar's celebrated algorithm is known to be an instance of the log-barrier method using the Newton iteration. The main computational overhead of this method is in inverting the Hessian matrix of the Newton iteration. In this contribution, we propose the application of the Gaussian belief propagation (GaBP) algorithm as part of an efficient and distributed LP solver that exploits the sparse and symmetric structure of the Hessian matrix and avoids the need for direct matrix inversion. This approach shifts the computation from realm of linear algebra to that of probabilistic inference on graphical models, thus applying GaBP as an efficient inference engine. Our construction is general and can be used for any interior-point algorithm which uses the Newt...
Genetic Programming Transforms in Linear Regression Situations
Castillo, Flor; Kordon, Arthur; Villa, Carlos
The chapter summarizes the use of Genetic Programming (GP) inMultiple Linear Regression (MLR) to address multicollinearity and Lack of Fit (LOF). The basis of the proposed method is applying appropriate input transforms (model respecification) that deal with these issues while preserving the information content of the original variables. The transforms are selected from symbolic regression models with optimal trade-off between accuracy of prediction and expressional complexity, generated by multiobjective Pareto-front GP. The chapter includes a comparative study of the GP-generated transforms with Ridge Regression, a variant of ordinary Multiple Linear Regression, which has been a useful and commonly employed approach for reducing multicollinearity. The advantages of GP-generated model respecification are clearly defined and demonstrated. Some recommendations for transforms selection are given as well. The application benefits of the proposed approach are illustrated with a real industrial application in one of the broadest empirical modeling areas in manufacturing - robust inferential sensors. The chapter contributes to increasing the awareness of the potential of GP in statistical model building by MLR.
Stochastic linear programming models, theory, and computation
Kall, Peter
2011-01-01
This new edition of Stochastic Linear Programming: Models, Theory and Computation has been brought completely up to date, either dealing with or at least referring to new material on models and methods, including DEA with stochastic outputs modeled via constraints on special risk functions (generalizing chance constraints, ICC’s and CVaR constraints), material on Sharpe-ratio, and Asset Liability Management models involving CVaR in a multi-stage setup. To facilitate use as a text, exercises are included throughout the book, and web access is provided to a student version of the authors’ SLP-IOR software. Additionally, the authors have updated the Guide to Available Software, and they have included newer algorithms and modeling systems for SLP. The book is thus suitable as a text for advanced courses in stochastic optimization, and as a reference to the field. From Reviews of the First Edition: "The book presents a comprehensive study of stochastic linear optimization problems and their applications. … T...
Offering model for a virtual power plant based on stochastic programming
DEFF Research Database (Denmark)
PandŽić, Hrvoje; Morales González, Juan Miguel; Conejo, Antonio J.
2013-01-01
electricity in both the day-ahead and the balancing markets seeking to maximize its expected profit. Such model is mathematically rigorous, yet computationally efficient.The offering problem is cast as a two-stage stochastic mixed-integer linear programming model which maximizes the virtual power plant......A virtual power plant aggregates various local production/consumption units that act in the market as a single entity. This paper considers a virtual power plant consisting of an intermittent source, a storage facility, and a dispatchable power plant. The virtual power plant sells and purchases...
Linear and integer programming theory and practice
Sierksma, Gerard
2001-01-01
Linear optimisation; basic concepts; Dantzig's simplex method; duality and optimality; sensitivity analysis; karmarkar's interior path method; integer linear optimisation; linear network models; computational complexity issues; model building, case studies, and advanced techniques; solutions to selected exercises. Appendices: linear algebra; convexity; graph theory; optimisation theory; computer package INTPM.
Polytope Representations for Linear-Programming Decoding of Non-Binary Linear Codes
Skachek, Vitaly; Byrne, Eimear; Greferath, Marcus
2007-01-01
In previous work, we demonstrated how decoding of a non-binary linear code could be formulated as a linear-programming problem. In this paper, we study different polytopes for use with linear-programming decoding, and show that for many classes of codes these polytopes yield a complexity advantage for decoding. These representations lead to polynomial-time decoders for a wide variety of classical non-binary linear codes.
A semidefinite programming approach for solving Multiobjective Linear Programming
Blanco, Víctor; Ben-Ali, Safae El-Haj
2011-01-01
Several algorithms are available in the literature for finding the entire set of Pareto-optimal solutions in MultiObjective Linear Programming (MOLP). However, it has not been proposed so far an interior point algorithm that finds all Pareto-optimal solutions of MOLP. We present an explicit construction, based on a transformation of any MOLP into a finite sequence of SemiDefinite Programs (SDP), the solutions of which give the entire set of Pareto-optimal extreme points solutions of MOLP. These SDP problems are solved by interior point methods; thus our approach provides a pseudo-polynomial interior point methodology to find the set of Pareto-optimal solutions of MOLP.
A Fuzzy Linear Programming Approach for Aggregate Production Planning
DEFF Research Database (Denmark)
Iris, Cagatay; Cevikcan, Emre
2014-01-01
a mathematical programming framework for aggregate production planning problem under imprecise data environment. After providing background information about APP problem, together with fuzzy linear programming, the fuzzy linear programming model of APP is solved on an illustrative example for different a...
Analytic central path, sensitivity analysis and parametric linear programming
A.G. Holder; J.F. Sturm; S. Zhang (Shuzhong)
1998-01-01
textabstractIn this paper we consider properties of the central path and the analytic center of the optimal face in the context of parametric linear programming. We first show that if the right-hand side vector of a standard linear program is perturbed, then the analytic center of the optimal face
Comparison of open-source linear programming solvers.
Energy Technology Data Exchange (ETDEWEB)
Gearhart, Jared Lee; Adair, Kristin Lynn; Durfee, Justin David.; Jones, Katherine A.; Martin, Nathaniel; Detry, Richard Joseph
2013-10-01
When developing linear programming models, issues such as budget limitations, customer requirements, or licensing may preclude the use of commercial linear programming solvers. In such cases, one option is to use an open-source linear programming solver. A survey of linear programming tools was conducted to identify potential open-source solvers. From this survey, four open-source solvers were tested using a collection of linear programming test problems and the results were compared to IBM ILOG CPLEX Optimizer (CPLEX) [1], an industry standard. The solvers considered were: COIN-OR Linear Programming (CLP) [2], [3], GNU Linear Programming Kit (GLPK) [4], lp_solve [5] and Modular In-core Nonlinear Optimization System (MINOS) [6]. As no open-source solver outperforms CPLEX, this study demonstrates the power of commercial linear programming software. CLP was found to be the top performing open-source solver considered in terms of capability and speed. GLPK also performed well but cannot match the speed of CLP or CPLEX. lp_solve and MINOS were considerably slower and encountered issues when solving several test problems.
The Uniqueness of Optimal Solution for Linear Programming Problem
Institute of Scientific and Technical Information of China (English)
QuanlingWei; HongYan; JunWang
2004-01-01
This paper investigates an old problem in operations research, the uniqueness of the optimal solution to a linear programming problem. We discuss the problem on a general polyhedron, give some equivalent conditions for uniqueness testing. In addition, we discuss the implementation issues for linear programming based decision making procedures,which motivated this research.
Fuzzy variable linear programming with fuzzy technical coefficients
Directory of Open Access Journals (Sweden)
Sanwar Uddin Ahmad
2012-11-01
Full Text Available Normal 0 false false false EN-US X-NONE X-NONE Fuzzy linear programming is an application of fuzzy set theory in linear decision making problems and most of these problems are related to linear programming with fuzzy variables. In this paper an approximate but convenient method for solving these problems with fuzzy non-negative technical coefficient and without using the ranking functions, is proposed. With the help of numerical examples, the method is illustrated.
A Generalised Concept of Dominance in Linear Programming Models
Drynan, Ross G.
1987-01-01
The notion of dominance most familiar to agricultural economists is perhaps the decision theoretic concept entailed in comparing one risky prospect to others. But dominance concepts are also relevant in the linear programming context, for example in identifying redundant constraints. In this note, the standard concept of dominance in linear programming is generalized by defining dominance with respect to differing levels of information about the programming problem.
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 tim...... 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.......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...
Pop, P.C.; Still, Georg J.
1999-01-01
In linear programming it is known that an appropriate non-homogeneous Farkas Lemma leads to a short proof of the strong duality results for a pair of primal and dual programs. By using a corresponding generalized Farkas lemma we give a similar proof of the strong duality results for semidefinite
Theoretical and algorithmic advances in multi-parametric programming and control
Pistikopoulos, Efstratios N.
2012-04-21
This paper presents an overview of recent theoretical and algorithmic advances, and applications in the areas of multi-parametric programming and explicit/multi-parametric model predictive control (mp-MPC). In multi-parametric programming, advances include areas such as nonlinear multi-parametric programming (mp-NLP), bi-level programming, dynamic programming and global optimization for multi-parametric mixed-integer linear programming problems (mp-MILPs). In multi-parametric/explicit MPC (mp-MPC), advances include areas such as robust multi-parametric control, multi-parametric nonlinear MPC (mp-NMPC) and model reduction in mp-MPC. A comprehensive framework for multi-parametric programming and control is also presented. Recent applications include a hydrogen storage device, a fuel cell power generation system, an unmanned autonomous vehicle (UAV) and a hybrid pressure swing adsorption (PSA) system. © 2012 Springer-Verlag.
Linear System of Equations, Matrix Inversion, and Linear Programming Using MS Excel
El-Gebeily, M.; Yushau, B.
2008-01-01
In this note, we demonstrate with illustrations two different ways that MS Excel can be used to solve Linear Systems of Equation, Linear Programming Problems, and Matrix Inversion Problems. The advantage of using MS Excel is its availability and transparency (the user is responsible for most of the details of how a problem is solved). Further, we…
Directory of Open Access Journals (Sweden)
Yi-hua Zhong
2013-01-01
Full Text Available Recently, various methods have been developed for solving linear programming problems with fuzzy number, such as simplex method and dual simplex method. But their computational complexities are exponential, which is not satisfactory for solving large-scale fuzzy linear programming problems, especially in the engineering field. A new method which can solve large-scale fuzzy number linear programming problems is presented in this paper, which is named a revised interior point method. Its idea is similar to that of interior point method used for solving linear programming problems in crisp environment before, but its feasible direction and step size are chosen by using trapezoidal fuzzy numbers, linear ranking function, fuzzy vector, and their operations, and its end condition is involved in linear ranking function. Their correctness and rationality are proved. Moreover, choice of the initial interior point and some factors influencing the results of this method are also discussed and analyzed. The result of algorithm analysis and example study that shows proper safety factor parameter, accuracy parameter, and initial interior point of this method may reduce iterations and they can be selected easily according to the actual needs. Finally, the method proposed in this paper is an alternative method for solving fuzzy number linear programming problems.
A New Finite Continuation Algorithm for Linear Programming
DEFF Research Database (Denmark)
Madsen, Kaj; Nielsen, Hans Bruun; Pinar, Mustafa
1996-01-01
We describe a new finite continuation algorithm for linear programming. The dual of the linear programming problem with unit lower and upper bounds is formulated as an $\\ell_1$ minimization problem augmented with the addition of a linear term. This nondifferentiable problem is approximated...... by a smooth problem. It is shown that the minimizers of the smooth problem define a family of piecewise-linear paths as a function of a smoothing parameter. Based on this property, a finite algorithm that traces these paths to arrive at an optimal solution of the linear program is developed. The smooth...... problems are solved by a Newton-type algorithm. Preliminary numerical results indicate that the new algorithm is promising....
Hybrid heuristic and mathematical programming in oil pipelines networks: Use of immigrants
Institute of Scientific and Technical Information of China (English)
DE LA CRUZ J.M.; HERR(A)N-GONZ(A)LEZ A.; RISCO-MART(I)N J.L.; ANDR(E)S-TORO B.
2005-01-01
We solve the problem of petroleum products distribution through oil pipelines networks. This problem is modelled and solved using two techniques: A heuristic method like a multiobjective evolutionary algorithm and Mathematical Programming. In the multiobjective evolutionary algorithm, several objective functions are defined to express the goals of the solutions as well as the preferences among them. Some constraints are included as hard objective functions and some are evaluated through a repairing function to avoid infeasible solutions. In the Mathematical Programming approach the multiobjective optimization is solved using the Constraint Method in Mixed Integer Linear Programming. Some constraints of the mathematical model are nonlinear, so they are linearized. The results obtained with both methods for one concrete network are presented. They are compared with a hybrid solution, where we use the results obtained by Mathematical Programming as the seed of the evolutionary algorithm.
An approach for solving linear fractional programming problems ...
African Journals Online (AJOL)
An approach for solving linear fractional programming problems. ... Journal of the Nigerian Association of Mathematical Physics. Journal Home · ABOUT · Advanced Search ... Open Access DOWNLOAD FULL TEXT Subscription or Fee Access ...
Evolutionary Programming for IP/MIP Problems with Linear Constraints
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
In this paper, we propose a modified evolutionary programming with dynamic domain for solving nonlinear IP/MIP problems with linear constraints, without involving penalty function or any transformation for the problem to a linear model or others. The numerical results show that the new algorithm gives a satisfactory performance in which it works of high speed and accuracy in IP/MIP problems.
EZLP: An Interactive Computer Program for Solving Linear Programming Problems. Final Report.
Jarvis, John J.; And Others
Designed for student use in solving linear programming problems, the interactive computer program described (EZLP) permits the student to input the linear programming model in exactly the same manner in which it would be written on paper. This report includes a brief review of the development of EZLP; narrative descriptions of program features,…
Institute of Scientific and Technical Information of China (English)
LiliHan; RongshengLi
2004-01-01
This paper proposes a sufficient condition, if the properties of the bilevel programming satisfied the condition, we can solve the bilevel programming by solving single level programming. It becomes easy and simple to solve the bilevel programming.
Linear programming with positive semi-definite matrices
Directory of Open Access Journals (Sweden)
Lasserre J. B.
1996-01-01
Full Text Available We consider the general linear programming problem over the cone of positive semi-definite matrices. We first provide a simple sufficient condition for existence of optimal solutions and absence of a duality gap without requiring existence of a strictly feasible solution. We then simply characterize the analogues of the standard concepts of linear programming, i.e., extreme points, basis, reduced cost, degeneracy, pivoting step as well as a Simplex-like algorithm.
Composition of portfolio by multiple criteria linear programming
2012-01-01
This work is dealing with portfolio optimization of corporate bonds available on Stuttgart stock exchange by methods of linear programming. Firstly there is a theoretical description of financial market, bonds and stock exchange. In next part there is a description of basics of linear programming and some details of multiple criteria decision. Big part of work is devote of practical problem of optimization of bond portfolio by methods which are described in theoretical part. Data of practical...
Linear Programming and Its Application to Pattern Recognition Problems
Omalley, M. J.
1973-01-01
Linear programming and linear programming like techniques as applied to pattern recognition problems are discussed. Three relatively recent research articles on such applications are summarized. The main results of each paper are described, indicating the theoretical tools needed to obtain them. A synopsis of the author's comments is presented with regard to the applicability or non-applicability of his methods to particular problems, including computational results wherever given.
Directory of Open Access Journals (Sweden)
M. Abbasi
2013-01-01
Full Text Available Today’s competitive world conditions and shortened product life cycles have led to the rise of attention towards new product development issue which can guarantee both growth and survival of organizations. The agility of new product development is directed by the efficiency and efficacy of knowledge management skills of an organization. A key issue in thorough success of such networks is the developed knowledge preservation amongst the members. Thus, it is important that reliable relations can be established between the members in order to promote further interactions. To do so, an integrated framework is developed in this paper to configure the new product development network so that sustainable collaborations can be maintained amongst the entities. The proposed framework consists of the network configuration in addition to the supplier selection phase. They are taken into consideration using a biobjective mathematical model in which incurred costs and suppliers' superiority determine the final configuration of the network. Finally, different numerical instances are solved to address the applicability of the proposed model.
Smalley, Hannah K; Keskinocak, Pinar; Swann, Julie; Hinman, Alan
2015-11-17
In addition to improved sanitation, hygiene, and better access to safe water, oral cholera vaccines can help to control the spread of cholera in the short term. However, there is currently no systematic method for determining the best allocation of oral cholera vaccines to minimize disease incidence in a population where the disease is endemic and resources are limited. We present a mathematical model for optimally allocating vaccines in a region under varying levels of demographic and incidence data availability. The model addresses the questions of where, when, and how many doses of vaccines to send. Considering vaccine efficacies (which may vary based on age and the number of years since vaccination), we analyze distribution strategies which allocate vaccines over multiple years. Results indicate that, given appropriate surveillance data, targeting age groups and regions with the highest disease incidence should be the first priority, followed by other groups primarily in order of disease incidence, as this approach is the most life-saving and cost-effective. A lack of detailed incidence data results in distribution strategies which are not cost-effective and can lead to thousands more deaths from the disease. The mathematical model allows for what-if analysis for various vaccine distribution strategies by providing the ability to easily vary parameters such as numbers and sizes of regions and age groups, risk levels, vaccine price, vaccine efficacy, production capacity and budget.
Linear Programming for Vocational Education Planning. Interim Report.
Young, Robert C.; And Others
The purpose of the paper is to define for potential users of vocational education management information systems a quantitative analysis technique and its utilization to facilitate more effective planning of vocational education programs. Defining linear programming (LP) as a management technique used to solve complex resource allocation problems…
Planning Student Flow with Linear Programming: A Tunisian Case Study.
Bezeau, Lawrence
A student flow model in linear programming format, designed to plan the movement of students into secondary and university programs in Tunisia, is described. The purpose of the plan is to determine a sufficient number of graduating students that would flow back into the system as teachers or move into the labor market to meet fixed manpower…
Linear combination of forecasts with numerical adjustment via MINIMAX non-linear programming
Directory of Open Access Journals (Sweden)
Jairo Marlon Corrêa
2016-03-01
Full Text Available This paper proposes a linear combination of forecasts obtained from three forecasting methods (namely, ARIMA, Exponential Smoothing and Artificial Neural Networks whose adaptive weights are determined via a multi-objective non-linear programming problem, which seeks to minimize, simultaneously, the statistics: MAE, MAPE and MSE. The results achieved by the proposed combination are compared with the traditional approach of linear combinations of forecasts, where the optimum adaptive weights are determined only by minimizing the MSE; with the combination method by arithmetic mean; and with individual methods
Resource-oriented Programming Based on Linear Logic
Directory of Open Access Journals (Sweden)
Valerie Novitzká
2007-08-01
Full Text Available In our research we consider programming as logical reasoning over types.Linear logic with its resource-oriented features yields a proper means for our approachbecause it enables to consider about resources as in real life: after their use they areexhausted. Computation then can be regarded as proof search. In our paper we presenthow space and time can be introduced into this logic and we discuss several programminglanguages based on linear logic.
An adaptive genetic algorithm for solving bilevel linear programming problem
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Bilevel linear programming, which consists of the objective functions of the upper level and lower level, is a useful tool for modeling decentralized decision problems.Various methods are proposed for solving this problem. Of all the algorithms, the genetic algorithm is an alternative to conventional approaches to find the solution of the bilevel linear programming. In this paper, we describe an adaptive genetic algorithm for solving the bilevel linear programming problem to overcome the difficulty of determining the probabilities of crossover and mutation. In addition, some techniques are adopted not only to deal with the difficulty that most of the chromosomes may be infeasible in solving constrained optimization problem with genetic algorithm but also to improve the efficiency of the algorithm. The performance of this proposed algorithm is illustrated by the examples from references.
Linear program differentiation for single-channel speech separation
DEFF Research Database (Denmark)
Pearlmutter, Barak A.; Olsson, Rasmus Kongsgaard
2006-01-01
Many apparently difficult problems can be solved by reduction to linear programming. Such problems are often subproblems within larger systems. When gradient optimisation of the entire larger system is desired, it is necessary to propagate gradients through the internally-invoked LP solver....... For instance, when an intermediate quantity z is the solution to a linear program involving constraint matrix A, a vector of sensitivities dE/dz will induce sensitivities dE/dA. Here we show how these can be efficiently calculated, when they exist. This allows algorithmic differentiation to be applied...... to algorithms that invoke linear programming solvers as subroutines, as is common when using sparse representations in signal processing. Here we apply it to gradient optimisation of over complete dictionaries for maximally sparse representations of a speech corpus. The dictionaries are employed in a single...
From Parity and Payoff Games to Linear Programming
Schewe, Sven
This paper establishes a surprising reduction from parity and mean payoff games to linear programming problems. While such a connection is trivial for solitary games, it is surprising for two player games, because the players have opposing objectives, whose natural translations into an optimisation problem are minimisation and maximisation, respectively. Our reduction to linear programming circumvents the need for concurrent minimisation and maximisation by replacing one of them, the maximisation, by approximation. The resulting optimisation problem can be translated to a linear programme by a simple space transformation, which is inexpensive in the unit cost model, but results in an exponential growth of the coefficients. The discovered connection opens up unexpected applications - like μ-calculus model checking - of linear programming in the unit cost model, and thus turns the intriguing academic problem of finding a polynomial time algorithm for linear programming in this model of computation (and subsequently a strongly polynomial algorithm) into a problem of paramount practical importance: All advancements in this area can immediately be applied to accelerate solving parity and payoff games, or to improve their complexity analysis.
Boolean approach to zero-one linear programming
Energy Technology Data Exchange (ETDEWEB)
Hulme, B.L.; Baca, L.S.
1984-07-01
Problems of minimizing or maximizing a linear objective function in zero-one variables subject to linear constraints can be solved by Boolean algebraic methods. This report gives both a general procedure for stating such problems in Boolean form and a solution procedure that has been implemented by a SETS user program. The program uses the Boolean algebraic formula manipulation techniques of the SETS language. Sample problems illustrate how to make optimum choices in the contexts of physical protection, packing knapsacks, designing manufacturing processes and making assignments.
Support vector classification algorithm based on variable parameter linear programming
Institute of Scientific and Technical Information of China (English)
Xiao Jianhua; Lin Jian
2007-01-01
To solve the problems of SVM in dealing with large sample size and asymmetric distributed samples, a support vector classification algorithm based on variable parameter linear programming is proposed.In the proposed algorithm, linear programming is employed to solve the optimization problem of classification to decrease the computation time and to reduce its complexity when compared with the original model.The adjusted punishment parameter greatly reduced the classification error resulting from asymmetric distributed samples and the detailed procedure of the proposed algorithm is given.An experiment is conducted to verify whether the proposed algorithm is suitable for asymmetric distributed samples.
Analytic central path, sensitivity analysis and parametric linear programming
A.G. Holder; Sturm, J.F.; Zhang, Shuzhong
1998-01-01
textabstractIn this paper we consider properties of the central path and the analytic center of the optimal face in the context of parametric linear programming. We first show that if the right-hand side vector of a standard linear program is perturbed, then the analytic center of the optimal face is one-side differentiable with respect to the perturbation parameter. In that case we also show that the whole analytic central path shifts in a uniform fashion. When the objective vector is pertur...
A new heuristic algorithm for general integer linear programming problems
Institute of Scientific and Technical Information of China (English)
GAO Pei-wang; CAI Ying
2006-01-01
A new heuristic algorithm is proposed for solving general integer linear programming problems.In the algorithm,the objective function hyperplane is used as a cutting plane,and then by introducing a special set of assistant sets,an efficient heuristic search for the solution to the integer linear program is carried out in the sets on the objective function hyperplane.A simple numerical example shows that the algorithm is efficient for some problems,and therefore,of practical interest.
Applied Research of Enterprise Cost Control Based on Linear Programming
Directory of Open Access Journals (Sweden)
Yu Shuo
2015-01-01
This paper researches the enterprise cost control through the linear programming model, and analyzes the restriction factors of the labor of enterprise production, raw materials, processing equipment, sales price, and other factors affecting the enterprise income, so as to obtain an enterprise cost control model based on the linear programming. This model can calculate rational production mode in the case of limited resources, and acquire optimal enterprise income. The production guiding program and scheduling arrangement of the enterprise can be obtained through calculation results, so as to provide scientific and effective guidance for the enterprise production. This paper adds the sensitivity analysis in the linear programming model, so as to learn about the stability of the enterprise cost control model based on linear programming through the sensitivity analysis, and verify the rationality of the model, and indicate the direction for the enterprise cost control. The calculation results of the model can provide a certain reference for the enterprise planning in the market economy environment, which have strong reference and practical significance in terms of the enterprise cost control.
THE SEPARATION OF URANIUM ISOTOPES BY GASEOUS DIFFUSION: A LINEAR PROGRAMMING MODEL,
URANIUM, ISOTOPE SEPARATION), (*GASEOUS DIFFUSION SEPARATION, LINEAR PROGRAMMING ), (* LINEAR PROGRAMMING , GASEOUS DIFFUSION SEPARATION), MATHEMATICAL MODELS, GAS FLOW, NUCLEAR REACTORS, OPERATIONS RESEARCH
Short Rational Generating Functions For Multiobjective Linear Integer Programming
Blanco, Victor
2007-01-01
This paper presents an algorithm for solving multiobjective integer programming problems. The algorithm uses Barvinok's rational functions of the polytope that defines the feasible region and provides as output the entire set of nondominated solutions for the problem. Theoretical complexity results on the algorithm are provided in the paper and an implementation of the algorithm shows that it is useful for solving multiobjective integer linear programs.
On filter-successive linearization methods for nonlinear semidefinite programming
Institute of Scientific and Technical Information of China (English)
无
2009-01-01
In this paper we present a filter-successive linearization method with trust region for solutions of nonlinear semidefinite programming. Such a method is based on the concept of filter for nonlinear programming introduced by Fletcher and Leyffer in 2002. We describe the new algorithm and prove its global convergence under weaker assumptions. Some numerical results are reported and show that the new method is potentially effcient.
On filter-successive linearization methods for nonlinear semidefinite programming
Institute of Scientific and Technical Information of China (English)
LI ChengJin; SUN WenYui
2009-01-01
In this paper we present a filter-successive linearization method with trust region for solutions of nonlinear semidefinite programming. Such a method is based on the concept of filter for nonlinear programming introduced by Fletcher and Leyffer in 2002. We describe the new algorithm and prove its global convergence under weaker assumptions. Some numerical results are reported and show that the new method is potentially efficient.
ON ALTERNATIVE OPTIMAL SOLUTIONS TO QUASIMONOTONIC PROGRAMMING WITH LINEAR CONSTRAINTS
Institute of Scientific and Technical Information of China (English)
Xue Shengjia
2007-01-01
In this paper, the nonlinear programming problem with quasimonotonic ( both quasiconvex and quasiconcave )objective function and linear constraints is considered. With the decomposition theorem of polyhedral sets, the structure of optimal solution set for the programming problem is depicted. Based on a simplified version of the convex simplex method,the uniqueness condition of optimal solution and the computational procedures to determine all optimal solutions are given, if the uniqueness condition is not satisfied. An illustrative example is also presented.
Linear programming bounds for unitary space time codes
Creignou, Jean
2008-01-01
The linear programming method is applied to the space $\\U_n(\\C)$ of unitary matrices in order to obtain bounds for codes relative to the diversity sum and the diversity product. Theoretical and numerical results improving previously known bounds are derived.
Smoothing Newton Algorithm for Linear Programming over Symmetric Cones
Institute of Scientific and Technical Information of China (English)
LIU Xiaohong; NI Tie
2009-01-01
By using the theory of Euclidean Jordan algebras,based on a new class of smoothing functions,the QiSun-Zhou's smoothing Newton algorithm is extended to solve linear programming over symmetric cones (SCLP).The algorithm is globally convergent under suitable assumptions.
Linear decomposition approach for a class of nonconvex programming problems.
Shen, Peiping; Wang, Chunfeng
2017-01-01
This paper presents a linear decomposition approach for a class of nonconvex programming problems by dividing the input space into polynomially many grids. It shows that under certain assumptions the original problem can be transformed and decomposed into a polynomial number of equivalent linear programming subproblems. Based on solving a series of liner programming subproblems corresponding to those grid points we can obtain the near-optimal solution of the original problem. Compared to existing results in the literature, the proposed algorithm does not require the assumptions of quasi-concavity and differentiability of the objective function, and it differs significantly giving an interesting approach to solving the problem with a reduced running time.
Train repathing in emergencies based on fuzzy linear programming.
Meng, Xuelei; Cui, Bingmou
2014-01-01
Train pathing is a typical problem which is to assign the train trips on the sets of rail segments, such as rail tracks and links. This paper focuses on the train pathing problem, determining the paths of the train trips in emergencies. We analyze the influencing factors of train pathing, such as transferring cost, running cost, and social adverse effect cost. With the overall consideration of the segment and station capability constraints, we build the fuzzy linear programming model to solve the train pathing problem. We design the fuzzy membership function to describe the fuzzy coefficients. Furthermore, the contraction-expansion factors are introduced to contract or expand the value ranges of the fuzzy coefficients, coping with the uncertainty of the value range of the fuzzy coefficients. We propose a method based on triangular fuzzy coefficient and transfer the train pathing (fuzzy linear programming model) to a determinate linear model to solve the fuzzy linear programming problem. An emergency is supposed based on the real data of the Beijing-Shanghai Railway. The model in this paper was solved and the computation results prove the availability of the model and efficiency of the algorithm.
A goal programming procedure for solving fuzzy multiobjective fractional linear programming problems
Tunjo Perić; Zoran Babić; Sead Rešić
2014-01-01
This paper presents a modification of Pal, Moitra and Maulik's goal programming procedure for fuzzy multiobjective linear fractional programming problem solving. The proposed modification of the method allows simpler solving of economic multiple objective fractional linear programming (MOFLP) problems, enabling the obtained solutions to express the preferences of the decision maker defined by the objective function weights. The proposed method is tested on the production planning example.
Boolean Variables in Economic Models Solved by Linear Programming
Directory of Open Access Journals (Sweden)
Lixandroiu D.
2014-12-01
Full Text Available The article analyses the use of logical variables in economic models solved by linear programming. Focus is given to the presentation of the way logical constraints are obtained and of the definition rules based on predicate logic. Emphasis is also put on the possibility to use logical variables in constructing a linear objective function on intervals. Such functions are encountered when costs or unitary receipts are different on disjunct intervals of production volumes achieved or sold. Other uses of Boolean variables are connected to constraint systems with conditions and the case of a variable which takes values from a finite set of integers.
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…
Secure Linear Programming Using Privacy-Preserving Simplex
Saxena, Amitabh; Hoogh, Sebastiaan J
2009-01-01
The SecureSCM project (www.securescm.org) aims to develop cryptographic solutions to the problem of data sharing in Supply Chain Optimization (SCO). The SCO problem has a precise mathematical structure. It is an instance of the general Linear Programming (LP) problem. However, standard techniques for LP problems are not suitable for this purpose because they require participants to reveal private data needed as input to the algorithm. The risk of revealing this information far exceeds the benefits gained. Therefore, the aim of the project is to develop efficient techniques for securely solving LP problems. In this paper we give a summary of work done in the cryptographic aspects of the project. We describe the state-of-the art building blocks for secure linear programming along with an analysis of their complexity.
Planning under uncertainty solving large-scale stochastic linear programs
Energy Technology Data Exchange (ETDEWEB)
Infanger, G. (Stanford Univ., CA (United States). Dept. of Operations Research Technische Univ., Vienna (Austria). Inst. fuer Energiewirtschaft)
1992-12-01
For many practical problems, solutions obtained from deterministic models are unsatisfactory because they fail to hedge against certain contingencies that may occur in the future. Stochastic models address this shortcoming, but up to recently seemed to be intractable due to their size. Recent advances both in solution algorithms and in computer technology now allow us to solve important and general classes of practical stochastic problems. We show how large-scale stochastic linear programs can be efficiently solved by combining classical decomposition and Monte Carlo (importance) sampling techniques. We discuss the methodology for solving two-stage stochastic linear programs with recourse, present numerical results of large problems with numerous stochastic parameters, show how to efficiently implement the methodology on a parallel multi-computer and derive the theory for solving a general class of multi-stage problems with dependency of the stochastic parameters within a stage and between different stages.
Multiobjective fuzzy stochastic linear programming problems with inexact probability distribution
Energy Technology Data Exchange (ETDEWEB)
Hamadameen, Abdulqader Othman [Optimization, Department of Mathematical Sciences, Faculty of Science, UTM (Malaysia); Zainuddin, Zaitul Marlizawati [Department of Mathematical Sciences, Faculty of Science, UTM (Malaysia)
2014-06-19
This study deals with multiobjective fuzzy stochastic linear programming problems with uncertainty probability distribution which are defined as fuzzy assertions by ambiguous experts. The problem formulation has been presented and the two solutions strategies are; the fuzzy transformation via ranking function and the stochastic transformation when α{sup –}. cut technique and linguistic hedges are used in the uncertainty probability distribution. The development of Sen’s method is employed to find a compromise solution, supported by illustrative numerical example.
A Spreadsheet-Based, Matrix Formulation Linear Programming Lesson
DEFF Research Database (Denmark)
Harrod, Steven
2009-01-01
The article focuses on the spreadsheet-based, matrix formulation linear programming lesson. According to the article, it makes a higher level of theoretical mathematics approachable by a wide spectrum of students wherein many may not be decision sciences or quantitative methods majors. Moreover......, it is consistent with the Arganbright Principles because the arrays and functions are clear in their operation and easily manipulated by the user....
An MC2 Linear Programming Approaches to Portfolios
Institute of Scientific and Technical Information of China (English)
ZHOU Zong-fang; TANG Xiao-wo; SHI Yong
2002-01-01
Portfolios is a well-known investment technique of handling multiple stocks, bonds and securities. However, the previous portfolios investment lack of incorporating the various possible opinions from several experts on an given portfolios investment problem. This paper proposes an MC2 linear programming approach to determining weighted coefficients of portfolios that involves multiple experts. The numerical example of the paper shows that the proposed approach likely outperforms the current techniques of portfolios in dealing with the case of multiple experts.
Efficient numerical methods for entropy-linear programming problems
Gasnikov, A. V.; Gasnikova, E. B.; Nesterov, Yu. E.; Chernov, A. V.
2016-04-01
Entropy-linear programming (ELP) problems arise in various applications. They are usually written as the maximization of entropy (minimization of minus entropy) under affine constraints. In this work, new numerical methods for solving ELP problems are proposed. Sharp estimates for the convergence rates of the proposed methods are established. The approach described applies to a broader class of minimization problems for strongly convex functionals with affine constraints.
Solving linear integer programming problems by a novel neural model.
Cavalieri, S
1999-02-01
The paper deals with integer linear programming problems. As is well known, these are extremely complex problems, even when the number of integer variables is quite low. Literature provides examples of various methods to solve such problems, some of which are of a heuristic nature. This paper proposes an alternative strategy based on the Hopfield neural network. The advantage of the strategy essentially lies in the fact that hardware implementation of the neural model allows for the time required to obtain a solution so as not depend on the size of the problem to be solved. The paper presents a particular class of integer linear programming problems, including well-known problems such as the Travelling Salesman Problem and the Set Covering Problem. After a brief description of this class of problems, it is demonstrated that the original Hopfield model is incapable of supplying valid solutions. This is attributed to the presence of constant bias currents in the dynamic of the neural model. A demonstration of this is given and then a novel neural model is presented which continues to be based on the same architecture as the Hopfield model, but introduces modifications thanks to which the integer linear programming problems presented can be solved. Some numerical examples and concluding remarks highlight the solving capacity of the novel neural model.
Linear-programming approach to electricity demand-curtailment planning
Energy Technology Data Exchange (ETDEWEB)
Allentuck, J; Carroll, O; Schnader, M
1980-05-01
Curtailment planning at a generally rudimentary level has been undertaken by the governments of some twenty states. Many utilities have demand-curtailment plans, however, these are often incorporated in plans for meeting capacity shortages. In at least five states, there are apparently no curtailment plans either at the state-government level or at the utility level. Moreover, none of the existing electricity demand curtailment plans examined included an explicit statement of the planners' objective in arriving at a specified sharing of the burdens of curtailment among consumer classes. Yet clearly the actual allocations of such burdens will affect the cost of the shortage. Since a study of state planning failed to yield a clear-cut indication of which of many possible curtailment allocation schemes would best serve as a point of departure for the design of an optimal curtailment strategy to deal with prolonged supply deficiencies, it was then decided to use a linear-programming approach. The advantages of such an approach are examined first, after which some important conceptual and practical problems in the design of a specific linear-programming model are addressed. A mathematical statement of the model is then followed by a brief review the principal methodological shortcomings of the linear-programming approach. Finally, the authors discuss how the analysis might be expanded to account for inter-regional and other secondary effects of curtailment.
The Hardness of Finding Linear Ranking Functions for Lasso Programs
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Amir M. Ben-Amram
2014-08-01
Full Text Available Finding whether a linear-constraint loop has a linear ranking function is an important key to understanding the loop behavior, proving its termination and establishing iteration bounds. If no preconditions are provided, the decision problem is known to be in coNP when variables range over the integers and in PTIME for the rational numbers, or real numbers. Here we show that deciding whether a linear-constraint loop with a precondition, specifically with partially-specified input, has a linear ranking function is EXPSPACE-hard over the integers, and PSPACE-hard over the rationals. The precise complexity of these decision problems is yet unknown. The EXPSPACE lower bound is derived from the reachability problem for Petri nets (equivalently, Vector Addition Systems, and possibly indicates an even stronger lower bound (subject to open problems in VAS theory. The lower bound for the rationals follows from a novel simulation of Boolean programs. Lower bounds are also given for the problem of deciding if a linear ranking-function supported by a particular form of inductive invariant exists. For loops over integers, the problem is PSPACE-hard for convex polyhedral invariants and EXPSPACE-hard for downward-closed sets of natural numbers as invariants.
Short-term hydropower production planning by stochastic programming
DEFF Research Database (Denmark)
Fleten, Stein-Erik; Kristoffersen, Trine
2008-01-01
Within the framework of multi-stage mixed-integer linear stochastic programming we develop a short-term production plan for a price-taking hydropower plant operating under uncertainty. Current production must comply with the day-ahead commitments of the previous day which makes short......-term production planning a matter of spatial distribution among the reservoirs of the plant. Day-ahead market prices and reservoir inflows are, however, uncertain beyond the current operation day and water must be allocated among the reservoirs in order to strike a balance between current profits and expected...... future profits. A demonstration is presented with data from a Norwegian hydropower producer and the Nordic power market at Nord Pool....
Linear Programming Graphic Method Modelling%Linear Programming图解法建模研讨
Institute of Scientific and Technical Information of China (English)
宋占奎
2010-01-01
对仅有两个变量的Linear Programming,通过图解法求最优解.建立了数学模型并求得了最优解.从图解法可以直观地看出,仅有两个变量的Linear Programming的解有唯一最优解、无穷多个最优解、无界解和无可行解四种情况.若其有最优解,则必定会在其顶点上得到;若在多个顶点上得到最优解,则其有无穷多个最优解.
Lima, Ricardo
2016-06-16
This paper addresses the solution of a cardinality Boolean quadratic programming problem using three different approaches. The first transforms the original problem into six mixed-integer linear programming (MILP) formulations. The second approach takes one of the MILP formulations and relies on the specific features of an MILP solver, namely using starting incumbents, polishing, and callbacks. The last involves the direct solution of the original problem by solvers that can accomodate the nonlinear combinatorial problem. Particular emphasis is placed on the definition of the MILP reformulations and their comparison with the other approaches. The results indicate that the data of the problem has a strong influence on the performance of the different approaches, and that there are clear-cut approaches that are better for some instances of the data. A detailed analysis of the results is made to identify the most effective approaches for specific instances of the data. © 2016 Springer Science+Business Media New York
A Primal-Dual Interior Point-Linear Programming Algorithm for MPC
DEFF Research Database (Denmark)
Edlund, Kristian; Sokoler, Leo Emil; Jørgensen, John Bagterp
2009-01-01
Constrained optimal control problems for linear systems with linear constraints and an objective function consisting of linear and l1-norm terms can be expressed as linear programs. We develop an efficient primal-dual interior point algorithm for solution of such linear programs. The algorithm...
A LINEAR-PROGRAMMING ALGORITHM FOR INVARIANT POLYHEDRAL-SETS OF DISCRETE-TIME LINEAR-SYSTEMS
TENDAM, AA; NIEUWENHUIS, JW
1995-01-01
In this paper we formulate necessary and sufficient conditions for an arbitrary polyhedral set to be a positively invariant set of a linear discrete-time system. Polyhedral cones and linear subspaces are included in the analysis. A linear programming algorithm is presented that enables practical
Optimal traffic control in highway transportation networks using linear programming
Li, Yanning
2014-06-01
This article presents a framework for the optimal control of boundary flows on transportation networks. The state of the system is modeled by a first order scalar conservation law (Lighthill-Whitham-Richards PDE). Based on an equivalent formulation of the Hamilton-Jacobi PDE, the problem of controlling the state of the system on a network link in a finite horizon can be posed as a Linear Program. Assuming all intersections in the network are controllable, we show that the optimization approach can be extended to an arbitrary transportation network, preserving linear constraints. Unlike previously investigated transportation network control schemes, this framework leverages the intrinsic properties of the Halmilton-Jacobi equation, and does not require any discretization or boolean variables on the link. Hence this framework is very computational efficient and provides the globally optimal solution. The feasibility of this framework is illustrated by an on-ramp metering control example.
M Sakawa; Kato, K.
2009-01-01
This paper considers stochastic two-level linear programming problems. Using the concept of chance constraints and probability maximization, original problems are transformed into deterministic ones. An interactive fuzzy programming method is presented for deriving a satisfactory solution efficiently with considerations of overall satisfactory balance.
On a Linear Program for Minimum-Weight Triangulation
Yousefi, Arman
2011-01-01
Minimum-weight triangulation (MWT) is NP-hard. It has a polynomial-time constant-factor approximation algorithm, and a variety of effective polynomial- time heuristics that, for many instances, can find the exact MWT. Linear programs (LPs) for MWT are well-studied, but previously no connection was known between any LP and any approximation algorithm or heuristic for MWT. Here we show the first such connections: for an LP formulation due to Dantzig et al. (1985): (i) the integrality gap is bounded by a constant; (ii) given any instance, if the aforementioned heuristics find the MWT, then so does the LP.
A recurrent neural network for solving bilevel linear programming problem.
He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie; Huang, Junjian
2014-04-01
In this brief, based on the method of penalty functions, a recurrent neural network (NN) modeled by means of a differential inclusion is proposed for solving the bilevel linear programming problem (BLPP). Compared with the existing NNs for BLPP, the model has the least number of state variables and simple structure. Using nonsmooth analysis, the theory of differential inclusions, and Lyapunov-like method, the equilibrium point sequence of the proposed NNs can approximately converge to an optimal solution of BLPP under certain conditions. Finally, the numerical simulations of a supply chain distribution model have shown excellent performance of the proposed recurrent NNs.
A new algorithm for solving linear programming problems
Directory of Open Access Journals (Sweden)
Andrés Leonardo Ramírez Leal
2012-08-01
Full Text Available Linear programming (LP is one of the most widely-applied techniques in operations research. Many methods have been developed and several others are being proposed for solving LP problems, including the famous simplex method and interior point algorithms. This study was aimed at introducing a new method for solving LP problems. The proposed algorithm starts from an interior point and then carries out orthogonal projections using parametric straight lines to move between the interior and polyhedron frontier defining the feasible region until reaching the extreme optimal point.
A linear programming approach for optimal contrast-tone mapping.
Wu, Xiaolin
2011-05-01
This paper proposes a novel algorithmic approach of image enhancement via optimal contrast-tone mapping. In a fundamental departure from the current practice of histogram equalization for contrast enhancement, the proposed approach maximizes expected contrast gain subject to an upper limit on tone distortion and optionally to other constraints that suppress artifacts. The underlying contrast-tone optimization problem can be solved efficiently by linear programming. This new constrained optimization approach for image enhancement is general, and the user can add and fine tune the constraints to achieve desired visual effects. Experimental results demonstrate clearly superior performance of the new approach over histogram equalization and its variants.
Polymorphic Uncertain Linear Programming for Generalized Production Planning Problems
Directory of Open Access Journals (Sweden)
Xinbo Zhang
2014-01-01
Full Text Available A polymorphic uncertain linear programming (PULP model is constructed to formulate a class of generalized production planning problems. In accordance with the practical environment, some factors such as the consumption of raw material, the limitation of resource and the demand of product are incorporated into the model as parameters of interval and fuzzy subsets, respectively. Based on the theory of fuzzy interval program and the modified possibility degree for the order of interval numbers, a deterministic equivalent formulation for this model is derived such that a robust solution for the uncertain optimization problem is obtained. Case study indicates that the constructed model and the proposed solution are useful to search for an optimal production plan for the polymorphic uncertain generalized production planning problems.
Brachytherapy seed localization using geometric and linear programming techniques.
Singh, Vikas; Mukherjee, Lopamudra; Xu, Jinhui; Hoffmann, Kenneth R; Dinu, Petru M; Podgorsak, Matthew
2007-09-01
We propose an optimization algorithm to solve the brachytherapy seed localization problem in prostate brachytherapy. Our algorithm is based on novel geometric approaches to exploit the special structure of the problem and relies on a number of key observations which help us formulate the optimization problem as a minimization integer program (IP). Our IP model precisely defines the feasibility polyhedron for this problem using a polynomial number of half-spaces; the solution to its corresponding linear program is rounded to yield an integral solution to our task of determining correspondences between seeds in multiple projection images. The algorithm is efficient in theory as well as in practice and performs well on simulation data (approximately 98% accuracy) and real X-ray images (approximately 95% accuracy). We present in detail the underlying ideas and an extensive set of performance evaluations based on our implementation.
OPTIMIZED AGRICULTURAL PLANNING OF SUGARCANE USING LINEAR PROGRAMMING
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Maximiliano Salles Scarpari* and Edgar Gomes Ferreira de Beauclair**
2010-03-01
Full Text Available Optimized agricultural planning is a fundamental activity in business profitability because it can increase the returns from an operation with low additional costs. Nonetheless, the use of operations research adapted to sugarcane plantation management is still limited, resulting in decision-making at management level being primarily empirical. The goal of this work was to develop an optimized planning model for sugarcane farming using a linear programming tool. The program language used was General Algebraic Modelling System (GAMS as this system was seen to be an excellent tool to allow profit maximization and harvesting time schedule optimization in the sugar mill studied. The results presented support this optimized planning model as being a very useful tool for sugarcane management.
Analisis Optimalisasi Produksi dengan Linear Programming Melalui Metode Simpleks
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Teguh Sriwidadi
2013-11-01
Full Text Available PD Utama Jaya Plasindo is a trading company which processes plastic resin. In the daily process, the company has many problems or constraints in production planning. Uncertainty demand of goods fluctuation has effect on shortage or surplus production. Other problems are raw materials, machine work hour, labour work hour, and the demand of the products. Simplex Method of Linear Programming purposes to maximize profit in linear function, Profit = 37 A + 46 B + 38 C + 46 D and linear functions of the seven constraints, Raw materials = 7 A + 5,8 B + 8,6 C + 7,6 D ≤ 180.000 grams, Machine Work Hour = 2,5 A + 3 B + 2 C + 2,5 D ≤86.400 seconds, Labor Work Hour = 5 A + 5 B + 5 C + 5 D ≤ 115.200 seconds, Demands of GRX 25 = A ≤2400 pcs, demand of GTW 25 = B ≤ 7200 pcs, demand of GTX 25 = C ≤ 3000 pcs, and demand of GTX 25 M = D ≤ 6600 pcs. The total profit earned by PD Utama Jaya Plasindo from plastic buckle products is Rp 837,000.00 per day and for 20 efective days is Rp 16,752,000.00 with the assumption of profit is in accordance with fixed objective and constraint functions.
How to Use Linear Programming for Information System Performances Optimization
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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.
AN APPLICATION FOR EFFICIENT TELECOMMUNICATION NETWORKS PROVISIONING USING LINEAR PROGRAMMING
Directory of Open Access Journals (Sweden)
Maria Augusta Soares Machado
2015-03-01
Full Text Available This paper presents a practical proposition for the application of the Linear Programming quantitative method in order to assist planning and control of customer circuit delivery activities in telecommunications companies working with the corporative market. Based upon data provided for by a telecom company operating in Brazil, the Linear Programming method was employed for one of the classical problems of determining the optimum mix of production quantities for a set of five products of that company: Private Telephone Network, Internet Network, Intranet Network, Low Speed Data Network, and High Speed Data Network, in face of several limitations of the productive resources, seeking to maximize the company’s monthly revenue. By fitting the production data available into a primary model, observation was made as to what number of monthly activations for each product would be mostly optimized in order to achieve maximum revenues in the company. The final delivery of a complete network was not observed but the delivery of the circuits that make it up, and this was a limiting factor for the study herein, which, however, brings an innovative proposition for the planning of private telecommunications network provisioning.
Assembling networks of microbial genomes using linear programming
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Holloway Catherine
2010-11-01
Full Text Available Abstract Background Microbial genomes exhibit complex sets of genetic affinities due to lateral genetic transfer. Assessing the relative contributions of parent-to-offspring inheritance and gene sharing is a vital step in understanding the evolutionary origins and modern-day function of an organism, but recovering and showing these relationships is a challenging problem. Results We have developed a new approach that uses linear programming to find between-genome relationships, by treating tables of genetic affinities (here, represented by transformed BLAST e-values as an optimization problem. Validation trials on simulated data demonstrate the effectiveness of the approach in recovering and representing vertical and lateral relationships among genomes. Application of the technique to a set comprising Aquifex aeolicus and 75 other thermophiles showed an important role for large genomes as 'hubs' in the gene sharing network, and suggested that genes are preferentially shared between organisms with similar optimal growth temperatures. We were also able to discover distinct and common genetic contributors to each sequenced representative of genus Pseudomonas. Conclusions The linear programming approach we have developed can serve as an effective inference tool in its own right, and can be an efficient first step in a more-intensive phylogenomic analysis.
Final Report---Optimization Under Nonconvexity and Uncertainty: Algorithms and Software
Energy Technology Data Exchange (ETDEWEB)
Jeff Linderoth
2011-11-06
the goal of this work was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems. The focus of the work done in the continuation was on Mixed Integer Nonlinear Programs (MINLP)s and Mixed Integer Linear Programs (MILP)s, especially those containing a great deal of symmetry.
Non-linear Total Energy Optimisation of a Fleet of Power Plants
Nolle, Lars; Biegler-König, Friedrich; Deeskow, Peter
In order to optimise the energy production in a fleet of power plants, it is necessary to solve a mixed integer optimisation problem. Traditionally, the continuous parts of the problem are linearized and a Simplex scheme is applied. Alternatively, heuristic "bionic" optimisation methods can be used without having to linearize the problem. Weare going to demonstrate this approach by modelling power plant blocks with fast Neural Networks and optimising the operation of multi-block power plants over one day with Simulated Annealing.
Direct linear programming solver in C for structural applications
Damkilde, L.; Hoyer, O.; Krenk, S.
1994-08-01
An optimization problem can be characterized by an object-function, which is maximized, and restrictions, which limit the variation of the variables. A subclass of optimization is Linear Programming (LP), where both the object-function and the restrictions are linear functions of the variables. The traditional solution methods for LP problems are based on the simplex method, and it is customary to allow only non-negative variables. Compared to other optimization routines the LP solvers are more robust and the optimum is reached in a finite number of steps and is not sensitive to the starting point. For structural applications many optimization problems can be linearized and solved by LP routines. However, the structural variables are not always non-negative, and this requires a reformation, where a variable x is substituted by the difference of two non-negative variables, x(sup + ) and x(sup - ). The transformation causes a doubling of the number of variables, and in a computer implementation the memory allocation doubles and for a typical problem the execution time at least doubles. This paper describes a LP solver written in C, which can handle a combination of non-negative variables and unlimited variables. The LP solver also allows restart, and this may reduce the computational costs if the solution to a similar LP problem is known a priori. The algorithm is based on the simplex method, and differs only in the logical choices. Application of the new LP solver will at the same time give both a more direct problem formulation and a more efficient program.
APPLICATION OF LINEAR PROGRAMMING TO FACILITY MAINTENANCE PROBLEMS IN THE NAVY SHORE ESTABLISHMENT.
LINEAR PROGRAMMING ), (*NAVAL SHORE FACILITIES, MAINTENANCE), (*MAINTENANCE, COSTS, MATHEMATICAL MODELS, MANAGEMENT PLANNING AND CONTROL, MANPOWER, FEASIBILITY STUDIES, OPTIMIZATION, MANAGEMENT ENGINEERING.
A Goal Programming Model for Selection and Scheduling of Advertisements on Online News Media
DEFF Research Database (Denmark)
Manik, Prerna; Gupta, Anshu; Jha, P. C.
2016-01-01
for several websites that provide free services to internet users. The website management team needs to do a lot of planning and optimally schedule various advertisements (ads) to maximize revenue, taking care of advertisers' needs under system constraints. In this paper, we have considered the case of news...... banners. Many ads compete with each other for their placement on a webpage on a specific banner, based on partition requirement, at specific time interval(s). Here, we have formulated a mixed integer 0-1 linear programming advertisement scheduling problem to maximize the revenue over planning horizon......Digital revolution has resulted in a paradigm shift in the field of marketing with online advertising becoming increasingly popular as it offers the reach, range, scale and interactivity to organizations to influence their target customers. Moreover, web advertisement is the primary revenue stream...
Compact Mathematical Programs For DEC-MDPs With Structured Agent Interactions
Mostafa, Hala
2012-01-01
To deal with the prohibitive complexity of calculating policies in Decentralized MDPs, researchers have proposed models that exploit structured agent interactions. Settings where most agent actions are independent except for few actions that affect the transitions and/or rewards of other agents can be modeled using Event-Driven Interactions with Complex Rewards (EDI-CR). Finding the optimal joint policy can be formulated as an optimization problem. However, existing formulations are too verbose and/or lack optimality guarantees. We propose a compact Mixed Integer Linear Program formulation of EDI-CR instances. The key insight is that most action sequences of a group of agents have the same effect on a given agent. This allows us to treat these sequences similarly and use fewer variables. Experiments show that our formulation is more compact and leads to faster solution times and better solutions than existing formulations.
Model of flow programming in combination pipeline; Modelo de programacao de fluxo em polidutos
Energy Technology Data Exchange (ETDEWEB)
Matagao, Leandro; Stebel, Sergio L. [Agencia Nacional de Petroleo, Rio de Janeiro, RJ (Brazil). Programa de Recursos Humanos; Arruda, L.V.R. de [Universite de Nice Sophia-Antipolis (France)]|[Centro Federal de Educacao Tecnologica do Parana, Curitiba, PR (Brazil); Neves Junior, Flavio [Universite Paul Sabatier, Toulouse Cedex (France)]|[Centro Federal de Educacao Tecnologica do Parana, Curitiba, PR (Brazil)
2000-07-01
This work proposes a pipeline multi-product scheduling methodology, developing an auxiliary quantitative model for the operational decision-make process. It will be developed a study upon a tank farm and a pipeline connecting a refinery to a harbor. The objective is the minimization of an economic cost function. This function includes storage tanks load and unload, operation costs and also the cost of plug products. Mixed integer linear programming techniques were used. It is taken into account the pipeline operational constraints, pumping sequence constraints and tankage constraints. The proposed model will allow to obtain a product pumping sequence that minimizes both storage tanks changeover and the number of pumping interface inclusions. (author)
Alternation-Trading Proofs, Linear Programming, and Lower Bounds
Williams, Ryan
2010-01-01
A fertile area of recent research has demonstrated concrete polynomial time lower bounds for solving natural hard problems on restricted computational models. Among these problems are Satisfiability, Vertex Cover, Hamilton Path, Mod6-SAT, Majority-of-Majority-SAT, and Tautologies, to name a few. The proofs of these lower bounds follow a certain proof-by-contradiction strategy that we call alternation-trading. An important open problem is to determine how powerful such proofs can possibly be. We propose a methodology for studying these proofs that makes them amenable to both formal analysis and automated theorem proving. We prove that the search for better lower bounds can often be turned into a problem of solving a large series of linear programming instances. Implementing a small-scale theorem prover based on this result, we extract new human-readable time lower bounds for several problems. This framework can also be used to prove concrete limitations on the current techniques.
Manipulating Multi-qudit Entanglement Witnesses by Using Linear Programming
Jafarizadeh, M A; Najarbashi, G
2006-01-01
A new class of entanglement witnesses (EWs) called reduction type entanglement witnesses is introduced, which can detect some multi-qudit entangeled states including PPT ones with Hilbert space of dimension $d_{_{1}}\\otimes d_{_{2}}\\otimes...\\otimes d_{_{n}}$. The novelty of this work comes from the fact that the feasible regions turn out to be convex polygons, hence the manipulation of these EWs reduces to linear programming which can be solved \\emph{exactly} by using simplex method. The decomposability and non-decomposability of these EWs are studied and it is shown that it has a close connection with eigenvalues and optimality of EWs. Also using the Jamio\\l kowski isomorphism, the corresponding possible positive maps, including the generalized reduction maps of Ref. \\cite{Hall1}, are obtained.
Microgrid Reliability Modeling and Battery Scheduling Using Stochastic Linear Programming
Energy Technology Data Exchange (ETDEWEB)
Cardoso, Goncalo; Stadler, Michael; Siddiqui, Afzal; Marnay, Chris; DeForest, Nicholas; Barbosa-Povoa, Ana; Ferrao, Paulo
2013-05-23
This paper describes the introduction of stochastic linear programming into Operations DER-CAM, a tool used to obtain optimal operating schedules for a given microgrid under local economic and environmental conditions. This application follows previous work on optimal scheduling of a lithium-iron-phosphate battery given the output uncertainty of a 1 MW molten carbonate fuel cell. Both are in the Santa Rita Jail microgrid, located in Dublin, California. This fuel cell has proven unreliable, partially justifying the consideration of storage options. Several stochastic DER-CAM runs are executed to compare different scenarios to values obtained by a deterministic approach. Results indicate that using a stochastic approach provides a conservative yet more lucrative battery schedule. Lower expected energy bills result, given fuel cell outages, in potential savings exceeding 6percent.
An integer linear programming for a comprehensive reverse supply chain
Directory of Open Access Journals (Sweden)
Hoda Mahmoudi
2014-12-01
Full Text Available Reverse supply chain is a cycle of recovery for the products and materials used by the customers but can be returned to the chain performing some operations. Due to significance of reverse supply chain in the content of environmental and economical aspects, we formulate a mathematical model of reverse multi-layer multi-product supply chain for minimizing the total costs including returning, disassembly, processing, recycling, remanufacturing, and distribution centers. The presented model is an integer linear programming model being solved using Lingo 9 software. Numerical experiments are conducted to gain insight into the proposed model. The solutions provide a decision aid stream strengthening the concept of reverse supply network design and analysis for profit-making organization.
Linear programming phase unwrapping for dual-wavelength digital holography.
Wang, Zhaomin; Jiao, Jiannan; Qu, Weijuan; Yang, Fang; Li, Hongru; Tian, Ailing; Asundi, Anand
2017-01-20
A linear programming phase unwrapping method in dual-wavelength digital holography is proposed and verified experimentally. The proposed method uses the square of height difference as a convergence standard and theoretically gives the boundary condition in a searching process. A simulation was performed by unwrapping step structures at different levels of Gaussian noise. As a result, our method is capable of recovering the discontinuities accurately. It is robust and straightforward. In the experiment, a microelectromechanical systems sample and a cylindrical lens were measured separately. The testing results were in good agreement with true values. Moreover, the proposed method is applicable not only in digital holography but also in other dual-wavelength interferometric techniques.
Towards lexicographic multi-objective linear programming using grossone methodology
Cococcioni, Marco; Pappalardo, Massimo; Sergeyev, Yaroslav D.
2016-10-01
Lexicographic Multi-Objective Linear Programming (LMOLP) problems can be solved in two ways: preemptive and nonpreemptive. The preemptive approach requires the solution of a series of LP problems, with changing constraints (each time the next objective is added, a new constraint appears). The nonpreemptive approach is based on a scalarization of the multiple objectives into a single-objective linear function by a weighted combination of the given objectives. It requires the specification of a set of weights, which is not straightforward and can be time consuming. In this work we present both mathematical and software ingredients necessary to solve LMOLP problems using a recently introduced computational methodology (allowing one to work numerically with infinities and infinitesimals) based on the concept of grossone. The ultimate goal of such an attempt is an implementation of a simplex-like algorithm, able to solve the original LMOLP problem by solving only one single-objective problem and without the need to specify finite weights. The expected advantages are therefore obvious.
Analyzing Nonblocking Switching Networks using Linear Programming (Duality)
Ngo, Hung Q; Le, Anh N; Nguyen, Thanh-Nhan
2012-01-01
The main task in analyzing a switching network design (including circuit-, multirate-, and photonic-switching) is to determine the minimum number of some switching components so that the design is non-blocking in some sense (e.g., strict- or wide-sense). We show that, in many cases, this task can be accomplished with a simple two-step strategy: (1) formulate a linear program whose optimum value is a bound for the minimum number we are seeking, and (2) specify a solution to the dual program, whose objective value by weak duality immediately yields a sufficient condition for the design to be non-blocking. We illustrate this technique through a variety of examples, ranging from circuit to multirate to photonic switching, from unicast to $f$-cast and multicast, and from strict- to wide-sense non-blocking. The switching architectures in the examples are of Clos-type and Banyan-type, which are the two most popular architectural choices for designing non-blocking switching networks. To prove the result in the multir...
Split diversity in constrained conservation prioritization using integer linear programming.
Chernomor, Olga; Minh, Bui Quang; Forest, Félix; Klaere, Steffen; Ingram, Travis; Henzinger, Monika; von Haeseler, Arndt
2015-01-01
Phylogenetic diversity (PD) is a measure of biodiversity based on the evolutionary history of species. Here, we discuss several optimization problems related to the use of PD, and the more general measure split diversity (SD), in conservation prioritization.Depending on the conservation goal and the information available about species, one can construct optimization routines that incorporate various conservation constraints. We demonstrate how this information can be used to select sets of species for conservation action. Specifically, we discuss the use of species' geographic distributions, the choice of candidates under economic pressure, and the use of predator-prey interactions between the species in a community to define viability constraints.Despite such optimization problems falling into the area of NP hard problems, it is possible to solve them in a reasonable amount of time using integer programming. We apply integer linear programming to a variety of models for conservation prioritization that incorporate the SD measure.We exemplarily show the results for two data sets: the Cape region of South Africa and a Caribbean coral reef community. Finally, we provide user-friendly software at http://www.cibiv.at/software/pda.
lireza Fallah-Tafti, A.; Sahraeian, Rashed; Tavakkoli-Moghaddam, Reza; Moeinipour, Masoud
2014-03-01
In this article, we first propose a closed-loop supply chain network design that integrates network design decisions in both forward and reverse supply chain networks into a unified structure as well as incorporates the tactical decisions with strategic ones (e.g., facility location and supplier selection) at each period. To do so, various conflicting objectives and constraints are simultaneously taken into account in the presence of some uncertain parameters, such as cost coefficients and customer demands. Then, we propose a novel interactive possibilistic approach based on the well-known STEP method to solve the multi-objective mixed-integer linear programming model. To validate the presented model and solution method, a numerical test is accomplished through the application of the proposed possibilistic-STEM algorithm. The computational results demonstrate suitability of the presented model and solution method.
Institute of Scientific and Technical Information of China (English)
Yuanyuan ZOU; Shaoyuan LI
2007-01-01
In this paper,a linear programming method is proposed to solve model predictive control for a class of hybrid systems.Firstly,using the(max,+)algebra,a typical subclass of hybrid systems called max-plus-linear(MPL)systems is obtained.And then,model predictive control(MPC)framework is extended to MPL systems.In general,the nonlinear optimization approach or extended linear complementarity problem(ELCP)were applied to solve the MPL-MPC optimization problem.A new optimization method based on canonical forms for max-min-plus-scaling(MMPS)functions (using the operations maximization,minimization,addition and scalar multiplication)with linear constraints on the inputs is presented.The proposed approach consists in solving several linear programming problems and is more efficient than nonlinear optimization.The validity of the algorithm is illustrated by an example.
Zhao, Yingfeng; Liu, Sanyang
2016-01-01
We present a practical branch and bound algorithm for globally solving generalized linear multiplicative programming problem with multiplicative constraints. To solve the problem, a relaxation programming problem which is equivalent to a linear programming is proposed by utilizing a new two-phase relaxation technique. In the algorithm, lower and upper bounds are simultaneously obtained by solving some linear relaxation programming problems. Global convergence has been proved and results of so...
Near-Regular Structure Discovery Using Linear Programming
Huang, Qixing
2014-06-02
Near-regular structures are common in manmade and natural objects. Algorithmic detection of such regularity greatly facilitates our understanding of shape structures, leads to compact encoding of input geometries, and enables efficient generation and manipulation of complex patterns on both acquired and synthesized objects. Such regularity manifests itself both in the repetition of certain geometric elements, as well as in the structured arrangement of the elements. We cast the regularity detection problem as an optimization and efficiently solve it using linear programming techniques. Our optimization has a discrete aspect, that is, the connectivity relationships among the elements, as well as a continuous aspect, namely the locations of the elements of interest. Both these aspects are captured by our near-regular structure extraction framework, which alternates between discrete and continuous optimizations. We demonstrate the effectiveness of our framework on a variety of problems including near-regular structure extraction, structure-preserving pattern manipulation, and markerless correspondence detection. Robustness results with respect to geometric and topological noise are presented on synthesized, real-world, and also benchmark datasets. © 2014 ACM.
Setting Inventory Levels of CONWIP Flow Lines via Linear Programming
Directory of Open Access Journals (Sweden)
Stefan Helber
2011-04-01
Full Text Available This paper treats the problem of setting the inventory level and optimizing the buffer allocation of closed-loop flow lines operating under the constant-work-in-process (CONWIP protocol. We solve a very large but simple linear program that models an entire simulation run of a closed-loop flow line in discrete time to determine a production rate estimate of the system. This approach introduced in Helber, Schimmelpfeng, Stolletz, and Lagershausen (2011 for open flow lines with limited buffer capacities is extended to closed-loop CONWIP flow lines. Via this method, both the CONWIP level and the buffer allocation can be optimized simultaneously. The first part of a numerical study deals with the accuracy of the method. In the second part, we focus on the relationship between the CONWIP inventory level and the short-term profit. The accuracy of the method turns out to be best for such configurations that maximize production rate and/or short-term profit.
Fitting boxes to Manhattan scenes using linear integer programming
Li, Minglei
2016-02-19
We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption. The method first aligns the point cloud with a per-building local coordinate system, and then fits axis-aligned planes to the point cloud through an iterative regularization process. The refined planes partition the space of the data into a series of compact cubic cells (candidate boxes) spanning the entire 3D space of the input data. We then choose to approximate the target building by the assembly of a subset of these candidate boxes using a binary linear programming formulation. The objective function is designed to maximize the point cloud coverage and the compactness of the final model. Finally, all selected boxes are merged into a lightweight polygonal mesh model, which is suitable for interactive visualization of large scale urban scenes. Experimental results and a comparison with state-of-the-art methods demonstrate the effectiveness of the proposed framework.
Dense image registration through MRFs and efficient linear programming.
Glocker, Ben; Komodakis, Nikos; Tziritas, Georgios; Navab, Nassir; Paragios, Nikos
2008-12-01
In this paper, we introduce a novel and efficient approach to dense image registration, which does not require a derivative of the employed cost function. In such a context, the registration problem is formulated using a discrete Markov random field objective function. First, towards dimensionality reduction on the variables we assume that the dense deformation field can be expressed using a small number of control points (registration grid) and an interpolation strategy. Then, the registration cost is expressed using a discrete sum over image costs (using an arbitrary similarity measure) projected on the control points, and a smoothness term that penalizes local deviations on the deformation field according to a neighborhood system on the grid. Towards a discrete approach, the search space is quantized resulting in a fully discrete model. In order to account for large deformations and produce results on a high resolution level, a multi-scale incremental approach is considered where the optimal solution is iteratively updated. This is done through successive morphings of the source towards the target image. Efficient linear programming using the primal dual principles is considered to recover the lowest potential of the cost function. Very promising results using synthetic data with known deformations and real data demonstrate the potentials of our approach.
Accurate construction of consensus genetic maps via integer linear programming.
Wu, Yonghui; Close, Timothy J; Lonardi, Stefano
2011-01-01
We study the problem of merging genetic maps, when the individual genetic maps are given as directed acyclic graphs. The computational problem is to build a consensus map, which is a directed graph that includes and is consistent with all (or, the vast majority of) the markers in the input maps. However, when markers in the individual maps have ordering conflicts, the resulting consensus map will contain cycles. Here, we formulate the problem of resolving cycles in the context of a parsimonious paradigm that takes into account two types of errors that may be present in the input maps, namely, local reshuffles and global displacements. The resulting combinatorial optimization problem is, in turn, expressed as an integer linear program. A fast approximation algorithm is proposed, and an additional speedup heuristic is developed. Our algorithms were implemented in a software tool named MERGEMAP which is freely available for academic use. An extensive set of experiments shows that MERGEMAP consistently outperforms JOINMAP, which is the most popular tool currently available for this task, both in terms of accuracy and running time. MERGEMAP is available for download at http://www.cs.ucr.edu/~yonghui/mgmap.html.
Maximum likelihood pedigree reconstruction using integer linear programming.
Cussens, James; Bartlett, Mark; Jones, Elinor M; Sheehan, Nuala A
2013-01-01
Large population biobanks of unrelated individuals have been highly successful in detecting common genetic variants affecting diseases of public health concern. However, they lack the statistical power to detect more modest gene-gene and gene-environment interaction effects or the effects of rare variants for which related individuals are ideally required. In reality, most large population studies will undoubtedly contain sets of undeclared relatives, or pedigrees. Although a crude measure of relatedness might sometimes suffice, having a good estimate of the true pedigree would be much more informative if this could be obtained efficiently. Relatives are more likely to share longer haplotypes around disease susceptibility loci and are hence biologically more informative for rare variants than unrelated cases and controls. Distant relatives are arguably more useful for detecting variants with small effects because they are less likely to share masking environmental effects. Moreover, the identification of relatives enables appropriate adjustments of statistical analyses that typically assume unrelatedness. We propose to exploit an integer linear programming optimisation approach to pedigree learning, which is adapted to find valid pedigrees by imposing appropriate constraints. Our method is not restricted to small pedigrees and is guaranteed to return a maximum likelihood pedigree. With additional constraints, we can also search for multiple high-probability pedigrees and thus account for the inherent uncertainty in any particular pedigree reconstruction. The true pedigree is found very quickly by comparison with other methods when all individuals are observed. Extensions to more complex problems seem feasible.
A Novel Tabular Form of the Simplex Method for Solving Linear Programming Problems
Directory of Open Access Journals (Sweden)
Akaninyene Obot
2016-01-01
Full Text Available A new tabular form of the simplex method for solving linear programming problems is presented in this paper. There are many variants of the simplex method. The existing different tabular forms of the simplex method are difficult to comprehend, boring, not straight forward, confusing and tedious. The results obtained based on the proposed method are simpler and computationally more efficient for calculations of linear programs, than other competing simplex methods by other writers. The proposed method could be applied to solve Operations Research based problems in fuzzy linear programming, goal programming, transportation and assignment problems, game problems, and for carrying out sensitivity analysis in linear programming.
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
A New Method for Solving Fuzzy Linear Programs with Trapezoidal Fuzzy Numbers
Directory of Open Access Journals (Sweden)
Jagdeep Kaur
2011-12-01
Full Text Available Ganesan and Veeramani [Fuzzy linear programs with trapezoidal fuzzy numbers, Annals of Operations Research 143 (2006 305-315.] proposed a new method for solving a special type of fuzzy linear programming problems. In this paper a new method, named as Mehar's method, is proposed for solving the same type of fuzzy linear programming problems and it is shown that it is easy to apply the Mehar's method as compared to the existing method for solving the same type of fuzzy linear programming problems.
Frost, Susan A.; Bodson, Marc; Acosta, Diana M.
2009-01-01
The Next Generation (NextGen) transport aircraft configurations being investigated as part of the NASA Aeronautics Subsonic Fixed Wing Project have more control surfaces, or control effectors, than existing transport aircraft configurations. Conventional flight control is achieved through two symmetric elevators, two antisymmetric ailerons, and a rudder. The five effectors, reduced to three command variables, produce moments along the three main axes of the aircraft and enable the pilot to control the attitude and flight path of the aircraft. The NextGen aircraft will have additional redundant control effectors to control the three moments, creating a situation where the aircraft is over-actuated and where a simple relationship does not exist anymore between the required effector deflections and the desired moments. NextGen flight controllers will incorporate control allocation algorithms to determine the optimal effector commands and attain the desired moments, taking into account the effector limits. Approaches to solving the problem using linear programming and quadratic programming algorithms have been proposed and tested. It is of great interest to understand their relative advantages and disadvantages and how design parameters may affect their properties. In this paper, we investigate the sensitivity of the effector commands with respect to the desired moments and show on some examples that the solutions provided using the l2 norm of quadratic programming are less sensitive than those using the l1 norm of linear programming.
Proximity search heuristics for wind farm optimal layout
DEFF Research Database (Denmark)
Fischetti, Martina; Monaci, Michele
2016-01-01
A heuristic framework for turbine layout optimization in a wind farm is proposed that combines ad-hoc heuristics and mixed-integer linear programming. In our framework, large-scale mixed-integer programming models are used to iteratively refine the current best solution according to the recently-...
A two-phase linear programming approach for redundancy allocation problems
Directory of Open Access Journals (Sweden)
Hsieh Yi-Chih
2002-01-01
Full Text Available Provision of redundant components in parallel is an efficient way to increase the system reliability, however, the weight, volume and cost of the system will increase simultaneously. This paper proposes a new two-phase linear programming approach for solving the nonlinear redundancy allocation problems subject to multiple linear constraints. The first phase is used to approximately allocate the resource by using a general linear programming, while the second phase is used to re-allocate the slacks of resource by using a 0-1 integer linear programming. Numerical results demonstrate the effectiveness and efficiency of the proposed approach.
Gorissen, B.L.; Blanc, J.P.C.; den Hertog, D.; Ben-Tal, A.
We propose a new way to derive tractable robust counterparts of a linear program based on the duality between the robust (“pessimistic”) primal problem and its “optimistic” dual. First we obtain a new convex reformulation of the dual problem of a robust linear program, and then show how to construct
DEFF Research Database (Denmark)
Parlesak, Alexandr; Tetens, Inge; Jensen, Jørgen Dejgård;
2016-01-01
using linear programming. The FBs were defined depending on the type of constraints applied: cultural acceptability (C), or dietary guidelines (D), or nutrient recommendations (N), or cultural acceptability and nutrient recommendations (CN), or dietary guidelines and nutrient recommendations (DN......: Use of linear programming facilitates the generation of low-cost food baskets that are nutritionally adequate, health promoting, and culturally acceptable....
A linear circuit analysis program with stiff systems capability
Cook, C. H.; Bavuso, S. J.
1973-01-01
Several existing network analysis programs have been modified and combined to employ a variable topological approach to circuit translation. Efficient numerical integration techniques are used for transient analysis.
An Adaptive Linear Approximation Algorithm for Copositive Programs
Bundfuss, Stefan; Dur, Mirjam
2009-01-01
We study linear optimization problems over the cone of copositive matrices. These problems appear in nonconvex quadratic and binary optimization; for instance, the maximum clique problem and other combinatorial problems can be reformulated as such problems. We present new polyhedral inner and outer
Application of linear programming techniques for controlling linear dynamic plants in real time
Gabasov, R.; Kirillova, F. M.; Ha, Vo Thi Thanh
2016-03-01
The problem of controlling a linear dynamic plant in real time given its nondeterministic model and imperfect measurements of the inputs and outputs is considered. The concepts of current distributions of the initial state and disturbance parameters are introduced. The method for the implementation of disclosable loop using the separation principle is described. The optimal control problem under uncertainty conditions is reduced to the problems of optimal observation, optimal identification, and optimal control of the deterministic system. To extend the domain where a solution to the optimal control problem under uncertainty exists, a two-stage optimal control method is proposed. Results are illustrated using a dynamic plant of the fourth order.
Global optimization over linear constraint non-convex programming problem
Institute of Scientific and Technical Information of China (English)
ZHANG Gui-Jun; WU Ti-Huan; YE Rong; YANG Hai-qing
2005-01-01
A improving Steady State Genetic Algorithm for global optimization over linear constraint non-convex programmin g problem is presented. By convex analyzing, the primal optimal problem can be converted to an equivalent problem, in which only the information of convex extremes of feasible space is included, and is more easy for GAs to solve. For avoiding invalid genetic operators, a redesigned convex crossover operator is also performed in evolving. As a integrality, the quality of two problem is proven, and a method is also given to get all extremes in linear constraint space. Simulation result show that new algorithm not only converges faster, but also can maintain an diversity population, and can get the global optimum of test problem.
A linear programming approach for placement of applicants to academic programs.
Kassa, Biniyam Asmare
2013-01-01
This paper reports a linear programming approach for placement of applicants to study programs developed and implemented at the college of Business & Economics, Bahir Dar University, Bahir Dar, Ethiopia. The approach is estimated to significantly streamline the placement decision process at the college by reducing required man hour as well as the time it takes to announce placement decisions. Compared to the previous manual system where only one or two placement criteria were considered, the new approach allows the college's management to easily incorporate additional placement criteria, if needed. Comparison of our approach against manually constructed placement decisions based on actual data for the 2012/13 academic year suggested that about 93 percent of the placements from our model concur with the actual placement decisions. For the remaining 7 percent of placements, however, the actual placements made by the manual system display inconsistencies of decisions judged against the very criteria intended to guide placement decisions by the college's program management office. Overall, the new approach proves to be a significant improvement over the manual system in terms of efficiency of the placement process and the quality of placement decisions.
Potential function methods for approximately solving linear programming problems theory and practice
Bienstock, Daniel
2002-01-01
Potential Function Methods For Approximately Solving Linear Programming Problems breaks new ground in linear programming theory. The book draws on the research developments in three broad areas: linear and integer programming, numerical analysis, and the computational architectures which enable speedy, high-level algorithm design. During the last ten years, a new body of research within the field of optimization research has emerged, which seeks to develop good approximation algorithms for classes of linear programming problems. This work both has roots in fundamental areas of mathematical programming and is also framed in the context of the modern theory of algorithms. The result of this work, in which Daniel Bienstock has been very much involved, has been a family of algorithms with solid theoretical foundations and with growing experimental success. This book will examine these algorithms, starting with some of the very earliest examples, and through the latest theoretical and computational developments.
Indarsih, Indrati, Ch. Rini
2016-02-01
In this paper, we define variance of the fuzzy random variables through alpha level. We have a theorem that can be used to know that the variance of fuzzy random variables is a fuzzy number. We have a multi-objective linear programming (MOLP) with fuzzy random of objective function coefficients. We will solve the problem by variance approach. The approach transform the MOLP with fuzzy random of objective function coefficients into MOLP with fuzzy of objective function coefficients. By weighted methods, we have linear programming with fuzzy coefficients and we solve by simplex method for fuzzy linear programming.
Image Enhancement Using Linear Programming Method for High Dynamic Range Image
Directory of Open Access Journals (Sweden)
S. Yamini
2014-01-01
Full Text Available Now a days in Telecommunication areas the contrast gain is considered as a major constraints. For the enhancement purpose, the technique called Histogram Equalization is involved, but due to over enhancement and not such gain is been obtained. So that for the OCTM is been proposed, where the constraints of HE is been rectified. OCTM gives better efficiency and it is been solved by Linear programming. In this paper the enhancement of HDR Image using linear Programming is done. According to it HDR Image is constructed using the multiple exposures and its contrast is enhanced using the OCTM method using Linear Programming.
Diet models with linear goal programming: impact of achievement functions
Gerdessen, J.C.; Vries, de J.H.M.
2015-01-01
Background/Objectives: Diet models based on goal programming (GP) are valuable tools in designing diets that comply with nutritional, palatability and cost constraints. Results derived from GP models are usually very sensitive to the type of achievement function that is chosen. This paper aims to pr
A Way to Find All the Optimal Solutions in Linear Programming
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
With the expression theorem of convex polyhedron, this paper gives the general expres sion for the solutions in standard linear programming problems. And the calculation procedures in determining the optimal solutions are also given.
The Expansion of Dynamic Solving Process About a Class of Non-linear Programming Problems
Institute of Scientific and Technical Information of China (English)
ZANG Zhen-chun
2001-01-01
In this paper, we research non-linear programming problems which have a given specialstructure, some simple forms of this kind structure have been solved in some papers, here we focus on othercomplex ones.
An introduction to fuzzy linear programming problems theory, methods and applications
Kaur, Jagdeep
2016-01-01
The book presents a snapshot of the state of the art in the field of fully fuzzy linear programming. The main focus is on showing current methods for finding the fuzzy optimal solution of fully fuzzy linear programming problems in which all the parameters and decision variables are represented by non-negative fuzzy numbers. It presents new methods developed by the authors, as well as existing methods developed by others, and their application to real-world problems, including fuzzy transportation problems. Moreover, it compares the outcomes of the different methods and discusses their advantages/disadvantages. As the first work to collect at one place the most important methods for solving fuzzy linear programming problems, the book represents a useful reference guide for students and researchers, providing them with the necessary theoretical and practical knowledge to deal with linear programming problems under uncertainty.
On-Off Minimum-Time Control With Limited Fuel Usage: Global Optima Via Linear Programming
Energy Technology Data Exchange (ETDEWEB)
DRIESSEN,BRIAN
1999-09-01
A method for finding a global optimum to the on-off minimum-time control problem with limited fuel usage is presented. Each control can take on only three possible values: maximum, zero, or minimum. The simplex method for linear systems naturally yields such a solution for the re-formulation presented herein because it always produces an extreme point solution to the linear program. Numerical examples for the benchmark linear flexible system are presented.
1979-01-01
The computer program Linear SCIDNT which evaluates rotorcraft stability and control coefficients from flight or wind tunnel test data is described. It implements the maximum likelihood method to maximize the likelihood function of the parameters based on measured input/output time histories. Linear SCIDNT may be applied to systems modeled by linear constant-coefficient differential equations. This restriction in scope allows the application of several analytical results which simplify the computation and improve its efficiency over the general nonlinear case.
Oki, Eiji
2012-01-01
Explaining how to apply to mathematical programming to network design and control, Linear Programming and Algorithms for Communication Networks: A Practical Guide to Network Design, Control, and Management fills the gap between mathematical programming theory and its implementation in communication networks. From the basics all the way through to more advanced concepts, its comprehensive coverage provides readers with a solid foundation in mathematical programming for communication networks. Addressing optimization problems for communication networks, including the shortest path problem, max f
Arc-Search Infeasible Interior-Point Algorithm for Linear Programming
Yang, Yaguang
2014-01-01
Mehrotra's algorithm has been the most successful infeasible interior-point algorithm for linear programming since 1990. Most popular interior-point software packages for linear programming are based on Mehrotra's algorithm. This paper proposes an alternative algorithm, arc-search infeasible interior-point algorithm. We will demonstrate, by testing Netlib problems and comparing the test results obtained by arc-search infeasible interior-point algorithm and Mehrotra's algorithm, that the propo...
A Comparative Study of Redundant Constraints Identification Methods in Linear Programming Problems
Directory of Open Access Journals (Sweden)
Paulraj S.
2010-01-01
Full Text Available The objective function and the constraints can be formulated as linear functions of independent variables in most of the real-world optimization problems. Linear Programming (LP is the process of optimizing a linear function subject to a finite number of linear equality and inequality constraints. Solving linear programming problems efficiently has always been a fascinating pursuit for computer scientists and mathematicians. The computational complexity of any linear programming problem depends on the number of constraints and variables of the LP problem. Quite often large-scale LP problems may contain many constraints which are redundant or cause infeasibility on account of inefficient formulation or some errors in data input. The presence of redundant constraints does not alter the optimal solutions(s. Nevertheless, they may consume extra computational effort. Many researchers have proposed different approaches for identifying the redundant constraints in linear programming problems. This paper compares five of such methods and discusses the efficiency of each method by solving various size LP problems and netlib problems. The algorithms of each method are coded by using a computer programming language C. The computational results are presented and analyzed in this paper.
Directory of Open Access Journals (Sweden)
Seyed Abolghasem Mortazavi
2014-03-01
Full Text Available Water resources sustainability is one of the major issues in the agricultural sustainability. In this study sustainability of water resources has been investigated by use of linear and non-linear models in six models based on optimal utilization of water resources in the north parts farms of Iran because of incorrect use of agricultural water resources, from 2011 to 2012. Also “gross margin per a unit of water consumption” and “employment per a unit of water consumption” are used as indicators for assessing the sustainability of cropping patterns. The results show that cropping pattern of fractional goal programming (FGP model has been near to current situation and has shown realistic conditions according to expertise and advantage of this area in cultivation of certain crops. So the FGP model has desirability in each of indicators than other five models.
Donkers, M C F; Heemels, W P M H
2011-01-01
In this paper, we present two control laws that are tailored for control applications in which computational and/or communication resources are scarce. Namely, we consider minimum attention control, where the `attention' that a control task requires is minimised given certain performance requirements, and anytime attention control, where the performance under the `attention' given by a scheduler is maximised. Here, we interpret `attention' as the inverse of the time elapsed between two consecutive executions of a control task. By focussing on linear plants, by allowing for only a finite number of possible intervals between two subsequent executions of the control task, by making a novel extension to the notion of control Lyapunov functions and taking these novel extended control Lyapunov function to be infinity-norm-based, we can formulate the aforementioned control problems as online linear programs, which can be solved efficiently. Furthermore, we provide techniques to construct suitable infinity-norm-based...
Solution for integer linear bilevel programming problems using orthogonal genetic algorithm
Institute of Scientific and Technical Information of China (English)
Hong Li; Li Zhang; Yongchang Jiao
2014-01-01
An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit program-ming. An orthogonal genetic algorithm is developed for solving the binary linear implicit programming problem based on the ortho-gonal design. The orthogonal design with the factor analysis, an experimental design method is applied to the genetic algorithm to make the algorithm more robust, statistical y sound and quickly convergent. A crossover operator formed by the orthogonal array and the factor analysis is presented. First, this crossover operator can generate a smal but representative sample of points as off-spring. After al of the better genes of these offspring are selected, a best combination among these offspring is then generated. The simulation results show the effectiveness of the proposed algo-rithm.
FSILP: fuzzy-stochastic-interval linear programming for supporting municipal solid waste management.
Li, Pu; Chen, Bing
2011-04-01
Although many studies on municipal solid waste management (MSW management) were conducted under uncertain conditions of fuzzy, stochastic, and interval coexistence, the solution to the conventional linear programming problems of integrating fuzzy method with the other two was inefficient. In this study, a fuzzy-stochastic-interval linear programming (FSILP) method is developed by integrating Nguyen's method with conventional linear programming for supporting municipal solid waste management. The Nguyen's method was used to convert the fuzzy and fuzzy-stochastic linear programming problems into the conventional linear programs, by measuring the attainment values of fuzzy numbers and/or fuzzy random variables, as well as superiority and inferiority between triangular fuzzy numbers/triangular fuzzy-stochastic variables. The developed method can effectively tackle uncertainties described in terms of probability density functions, fuzzy membership functions, and discrete intervals. Moreover, the method can also improve upon the conventional interval fuzzy programming and two-stage stochastic programming approaches, with advantageous capabilities that are easily achieved with fewer constraints and significantly reduces consumption time. The developed model was applied to a case study of municipal solid waste management system in a city. The results indicated that reasonable solutions had been generated. The solution can help quantify the relationship between the change of system cost and the uncertainties, which could support further analysis of tradeoffs between the waste management cost and the system failure risk.
Multi-Objective Fuzzy Linear Programming In Agricultural Production Planning
Directory of Open Access Journals (Sweden)
H.M.I.U. Herath
2015-08-01
Full Text Available Abstract Modern agriculture is characterized by a series of conflicting optimization criteria that obstruct the decision-making process in the planning of agricultural production. Such criteria are usually net profit total cost total production etc. At the same time the decision making process in the agricultural production planning is often conducted with data that accidentally occur in nature or that are fuzzy not deterministic. Such data are the yields of various crops the prices of products and raw materials demand for the product the available quantities of production factors such as water labor etc. In this paper a fuzzy multi-criteria mathematical programming model is presented. This model is applied in a region of 10 districts in Sri Lanka where paddy is cultivated under irrigated and rain fed water in the two main seasons called Yala and Maha and the optimal production plan is achieved. This study was undertaken to find out the optimal allocation of land for paddy to get a better yield while satisfying the two conflicting objectives profit maximizing and cost minimizing subjected to the utilizing of water constraint and the demand constraint. Only the availability of land constraint is considered as a crisp in nature while objectives and other constraints are treated as fuzzy. It is observed that the MOFLP is an effective method to handle more than a single objective occurs in an uncertain vague environment.
Linearized Programming of Memristors for Artificial Neuro-Sensor Signal Processing
Yang, Changju; Kim, Hyongsuk
2016-01-01
A linearized programming method of memristor-based neural weights is proposed. Memristor is known as an ideal element to implement a neural synapse due to its embedded functions of analog memory and analog multiplication. Its resistance variation with a voltage input is generally a nonlinear function of time. Linearization of memristance variation about time is very important for the easiness of memristor programming. In this paper, a method utilizing an anti-serial architecture for linear programming is proposed. The anti-serial architecture is composed of two memristors with opposite polarities. It linearizes the variation of memristance due to complimentary actions of two memristors. For programming a memristor, additional memristor with opposite polarity is employed. The linearization effect of weight programming of an anti-serial architecture is investigated and memristor bridge synapse which is built with two sets of anti-serial memristor architecture is taken as an application example of the proposed method. Simulations are performed with memristors of both linear drift model and nonlinear model. PMID:27548186
Linearized Programming of Memristors for Artificial Neuro-Sensor Signal Processing.
Yang, Changju; Kim, Hyongsuk
2016-08-19
A linearized programming method of memristor-based neural weights is proposed. Memristor is known as an ideal element to implement a neural synapse due to its embedded functions of analog memory and analog multiplication. Its resistance variation with a voltage input is generally a nonlinear function of time. Linearization of memristance variation about time is very important for the easiness of memristor programming. In this paper, a method utilizing an anti-serial architecture for linear programming is proposed. The anti-serial architecture is composed of two memristors with opposite polarities. It linearizes the variation of memristance due to complimentary actions of two memristors. For programming a memristor, additional memristor with opposite polarity is employed. The linearization effect of weight programming of an anti-serial architecture is investigated and memristor bridge synapse which is built with two sets of anti-serial memristor architecture is taken as an application example of the proposed method. Simulations are performed with memristors of both linear drift model and nonlinear model.
Solving fully fuzzy multiple objective linear programming problems: A new perspective
A. Hadi-Vencheh; Z Rezaei; S. Razipour
2014-01-01
In this paper a systematic process has been proposed to solve a fully fuzzy multi objective linear programming problem (FFMOLPP). Using the utility vector the MOLPP is transferred to a single objective programming and this single fuzzy object problem is simply solved by one of the fuzzy approaches.A numerical example is then given to show applicability of the proposed approach.
DEFF Research Database (Denmark)
Cetin, Bilge Kartal; Prasad, Neeli R.; Prasad, Ramjee
2011-01-01
of the maximum lifetime routing problem that considers the operation modes of the node. Solution of the linear programming gives the upper analytical bound for the network lifetime. In order to illustrate teh application of the optimization model, we solved teh problem for different parameter settings...... protocols, and the energy model for transmission. In this paper, we tackle the routing challenge for maximum lifetime of the sensor network. We introduce a novel linear programming approach to the maximum lifetime routing problem. To the best of our knowledge, this is the first mathematical programming...
Object matching using a locally affine invariant and linear programming techniques.
Li, Hongsheng; Huang, Xiaolei; He, Lei
2013-02-01
In this paper, we introduce a new matching method based on a novel locally affine-invariant geometric constraint and linear programming techniques. To model and solve the matching problem in a linear programming formulation, all geometric constraints should be able to be exactly or approximately reformulated into a linear form. This is a major difficulty for this kind of matching algorithm. We propose a novel locally affine-invariant constraint which can be exactly linearized and requires a lot fewer auxiliary variables than other linear programming-based methods do. The key idea behind it is that each point in the template point set can be exactly represented by an affine combination of its neighboring points, whose weights can be solved easily by least squares. Errors of reconstructing each matched point using such weights are used to penalize the disagreement of geometric relationships between the template points and the matched points. The resulting overall objective function can be solved efficiently by linear programming techniques. Our experimental results on both rigid and nonrigid object matching show the effectiveness of the proposed algorithm.
Extensions to the Multilevel Programming Problem
1988-05-01
READS IN A 0-1 BILEVEL PROGRAMMING C PROBLEM (BLPP) AND CREATES THE MATHEMATICAL C PROGRAMMING PROBLEM WHICH CAN BE SOLVED TO FIND C THE SOLUTION OF THE...CODES DEVELOPED BY DR. PAUL A. JENSEN. C IT IS USED TO SOLVE THE 0-1 INTEGER PROGRAMMING C PROBLEM DERIVED FROM THE 0-1 BLPP. C IMPLICIT INTEGER (A-Z...233 PROGRAM BLPPRD C C *****PURPOSE C THIS PROGRAM READS IN A BILEVEL PROGRAMMING C PROBLEM (BLPP) OR A MIXED INTEGER BLPP AND CREATES C THE
Optimal CH-47 AND C-130 Workload Balance
2011-03-01
42 LINGO -based Model Development .......................................................................42 Summary...58 Appendix A. LINGO -Based Model...scenario and network is the first step towards developing a mixed integer linear program in the LINGO ® software environment. The program development
Detecting Non-Dominated Extreme Points for Multiple Objective Linear Programming
Directory of Open Access Journals (Sweden)
S. F. Tantawy
2007-01-01
Full Text Available Efficient extreme points in decision space of multiple objective linear programming (MOLP may not map to non dominated extreme points in objective space under the linear mapping, condition that efficient extreme points have a non dominated extreme is given, the important of this study is that the decision-Maker may depends on extreme points of the set of the objective space than that of the decision space since they have fewer extreme points.
Directory of Open Access Journals (Sweden)
A.K. Bhunia
2013-04-01
Full Text Available This paper deals with a deterministic inventory model developed for deteriorating items having two separate storage facilities (owned and rented warehouses due to limited capacity of the existing storage (owned warehouse with linear time dependent demand (increasing over a fixed finite time horizon. The model is formulated with infinite replenishment and the successive replenishment cycle lengths are in arithmetic progression. Partially backlogged shortages are allowed. The stocks of rented warehouse (RW are transported to the owned warehouse (OW in continuous release pattern. For this purpose, the model is formulated as a constrained non-linear mixed integer programming problem. For solving the problem, an advanced genetic algorithm (GA has been developed. This advanced GA is based on ranking selection, elitism, whole arithmetic crossover and non-uniform mutation dependent on the age of the population. Our objective is to determine the optimal replenishment number, lot-size of two-warehouses (OW and RW by maximizing the profit function. The model is illustrated with four numerical examples and sensitivity analyses of the optimal solution are performed with respect to different parameters.
Method for solving fully fuzzy linear programming problems using deviation degree measure
Institute of Scientific and Technical Information of China (English)
Haifang Cheng; Weilai Huang; Jianhu Cai
2013-01-01
A new ful y fuzzy linear programming (FFLP) prob-lem with fuzzy equality constraints is discussed. Using deviation degree measures, the FFLP problem is transformed into a crispδ-parametric linear programming (LP) problem. Giving the value of deviation degree in each constraint, the δ-fuzzy optimal so-lution of the FFLP problem can be obtained by solving this LP problem. An algorithm is also proposed to find a balance-fuzzy optimal solution between two goals in conflict: to improve the va-lues of the objective function and to decrease the values of the deviation degrees. A numerical example is solved to il ustrate the proposed method.
DEFF Research Database (Denmark)
Ren, Jingzheng; Dong, Liang; Sun, Lu
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...
The Knowledge of Expert Opinion in Intuitionistic Fuzzy Linear Programming Problem
Directory of Open Access Journals (Sweden)
A. Nagoorgani
2015-01-01
Full Text Available In real life, information available for certain situations is vague and such uncertainty is unavoidable. One possible solution is to consider the knowledge of experts on the parameters involved as intuitionistic fuzzy data. We examine a linear programming problem in which all the coefficients are intuitionistic in nature. An approach is presented to solve an intuitionistic fuzzy linear programming problem. In this proposed approach, a procedure for allocating limited resources effectively among competing demands is developed. An example is given to highlight the illustrated study.
A novel recurrent neural network with finite-time convergence for linear programming.
Liu, Qingshan; Cao, Jinde; Chen, Guanrong
2010-11-01
In this letter, a novel recurrent neural network based on the gradient method is proposed for solving linear programming problems. Finite-time convergence of the proposed neural network is proved by using the Lyapunov method. Compared with the existing neural networks for linear programming, the proposed neural network is globally convergent to exact optimal solutions in finite time, which is remarkable and rare in the literature of neural networks for optimization. Some numerical examples are given to show the effectiveness and excellent performance of the new recurrent neural network.
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.
Solving fully fuzzy multiple objective linear programming problems: A new perspective
Directory of Open Access Journals (Sweden)
A. Hadi-Vencheh
2014-08-01
Full Text Available In this paper a systematic process has been proposed to solve a fully fuzzy multi objective linear programming problem (FFMOLPP. Using the utility vector the MOLPP is transferred to a single objective programming and this single fuzzy object problem is simply solved by one of the fuzzy approaches.A numerical example is then given to show applicability of the proposed approach.
A mathematical programming approach for sequential clustering of dynamic networks
Silva, Jonathan C.; Bennett, Laura; Papageorgiou, Lazaros G.; Tsoka, Sophia
2016-02-01
A common analysis performed on dynamic networks is community structure detection, a challenging problem that aims to track the temporal evolution of network modules. An emerging area in this field is evolutionary clustering, where the community structure of a network snapshot is identified by taking into account both its current state as well as previous time points. Based on this concept, we have developed a mixed integer non-linear programming (MINLP) model, SeqMod, that sequentially clusters each snapshot of a dynamic network. The modularity metric is used to determine the quality of community structure of the current snapshot and the historical cost is accounted for by optimising the number of node pairs co-clustered at the previous time point that remain so in the current snapshot partition. Our method is tested on social networks of interactions among high school students, college students and members of the Brazilian Congress. We show that, for an adequate parameter setting, our algorithm detects the classes that these students belong more accurately than partitioning each time step individually or by partitioning the aggregated snapshots. Our method also detects drastic discontinuities in interaction patterns across network snapshots. Finally, we present comparative results with similar community detection methods for time-dependent networks from the literature. Overall, we illustrate the applicability of mathematical programming as a flexible, adaptable and systematic approach for these community detection problems. Contribution to the Topical Issue "Temporal Network Theory and Applications", edited by Petter Holme.
Optimising an integrated crop-livestock farm using risk programming
Directory of Open Access Journals (Sweden)
SE Visagie
2004-06-01
Full Text Available Numerous studies have analysed farm planning decisions focusing on producer risk preferences. Few studies have focussed on the farm planning decisions in an integrated croplivestock farm context. Income variability and means of managing risk continues to receive much attention in farm planning research. Different risk programming models have attempted to focus on minimising the income variability of farm activities. This study attempts to identify the optimal mix of crops and the number of animals the farm needs to keep in the presence of crop production risk for a range of risk levels. A mixed integer linear programming model was developed to model the decision environment faced by an integrated crop-livestock farmer. The deviation of income from the expected value was used as a measure of risk. A case study is presented with representative data from a farm in the Swartland area. An investigation of the results of the model under different constraints shows that, in general, strategies that depend on crop rotation principles are preferred to strategies that follow mono-crop production practices.
Cooke, C. H.
1975-01-01
STICAP (Stiff Circuit Analysis Program) is a FORTRAN 4 computer program written for the CDC-6400-6600 computer series and SCOPE 3.0 operating system. It provides the circuit analyst a tool for automatically computing the transient responses and frequency responses of large linear time invariant networks, both stiff and nonstiff (algorithms and numerical integration techniques are described). The circuit description and user's program input language is engineer-oriented, making simple the task of using the program. Engineering theories underlying STICAP are examined. A user's manual is included which explains user interaction with the program and gives results of typical circuit design applications. Also, the program structure from a systems programmer's viewpoint is depicted and flow charts and other software documentation are given.
Dyehouse, Melissa; Bennett, Deborah; Harbor, Jon; Childress, Amy; Dark, Melissa
2009-01-01
Logic models are based on linear relationships between program resources, activities, and outcomes, and have been used widely to support both program development and evaluation. While useful in describing some programs, the linear nature of the logic model makes it difficult to capture the complex relationships within larger, multifaceted…
2007-11-02
scarce resources ( Bazaraa vii). The modeling capabilities linear programming provides has made it a success in many fields of study. Since the...Planning and Programming of Facility Construction Projects. 12 May 1994. Bazaraa , Mokhtar S., John J Jarvis and Hanif D. Sherali. Linear Programming
Linear circuit analysis program for IBM 1620 Monitor 2, 1311/1443 data processing system /CIRCS/
Hatfield, J.
1967-01-01
CIRCS is modification of IBSNAP Circuit Analysis Program, for use on smaller systems. This data processing system retains the basic dc, transient analysis, and FORTRAN 2 formats. It can be used on the IBM 1620/1311 Monitor I Mod 5 system, and solves a linear network containing 15 nodes and 45 branches.
DEFF Research Database (Denmark)
Cetin, Bilge Kartal; Prasad, Neeli R.; Prasad, Ramjee
2011-01-01
of the maximum lifetime routing problem that considers the operation modes of the node. Solution of the linear programming gives the upper analytical bound for the network lifetime. In order to illustrate teh application of the optimization model, we solved teh problem for different parameter settings...
EXPECTED NUMBER OF ITERATIONS OF INTERIOR-POINT ALGORITHMS FOR LINEAR PROGRAMMING
Institute of Scientific and Technical Information of China (English)
Si-ming Huang
2005-01-01
We study the behavior of some polynomial interior-point algorithms for solving random linear programming (LP) problems. We show that the expected and anticipated number of iterations of these algorithms is bounded above by O(n1,5). The random LP problem is Todd's probabilistic model with the Cauchy distribution.
Average number of iterations of some polynomial interior-point——Algorithms for linear programming
Institute of Scientific and Technical Information of China (English)
黄思明
2000-01-01
We study the behavior of some polynomial interior-point algorithms for solving random linear programming (LP) problems. We show that the average number of iterations of these algorithms, coupled with a finite termination technique, is bounded above by O( n1.5). The random LP problem is Todd’s probabilistic model with the standard Gauss distribution.
Research and evaluation of the effectiveness of e-learning in the case of linear programming
Directory of Open Access Journals (Sweden)
Ljiljana Miletić
2016-04-01
Full Text Available The paper evaluates the effectiveness of the e-learning approach to linear programming. The goal was to investigate how proper use of information and communication technologies (ICT and interactive learning helps to improve high school students’ understanding, learning and retention of advanced non-curriculum material. The hypothesis was that ICT and e-learning is helpful in teaching linear programming methods. In the first phase of the research, a module of lessons for linear programming (LP was created using the software package Loomen Moodle and other interactive software packages such as Geogebra. In the second phase, the LP module was taught as a short course to two groups of high school students. These two groups of students were second-grade students in a Croatian high school. In Class 1, the module was taught using ICT and e-learning, while the module was taught using classical methods in Class 2. The action research methodology was an integral part in delivering the course to both student groups. The sample student groups were carefully selected to ensure that differences in background knowledge and learning potential were statistically negligible. Relevant data was collected while delivering the course. Statistical analysis of the collected data showed that the student group using the e-learning method produced better results than the group using a classical learning method. These findings support previous results on the effectiveness of e-learning, and also establish a specific approach to e-learning in linear programming.
A linear programming model of diet choice of free-living beavers
Nolet, BA; VanderVeer, PJ; Evers, EGJ; Ottenheim, MM
1995-01-01
Linear programming has been remarkably successful in predicting the diet choice of generalist herbivores. We used this technique to test the diet choice of free-living beavers (Castor fiber) in the Biesbosch (The Netherlands) under different Foraging goals, i.e. maximization of intake of energy,
Huitzing, HA
2004-01-01
This article shows how set covering with item sampling (SCIS) methods can be used in the analysis and preanalysis of linear programming models for test assembly (LPTA). LPTA models can construct tests, fulfilling a set of constraints set by the test assembler. Sometimes, no solution to the LPTA mode
Velazquez-Marti, B.; Annevelink, E.
2009-01-01
Much bio-energy can be obtained from wood pruning operations in forests and fruit orchards. Several spatial studies have been carried out for biomass surveys, and many linear programming models have been developed to model the logistics of bio-energy chains. These models can assist in determining th
Time-Staged Methods in Linear Programming, Comments and Early History.
1980-06-01
ENG77-06761. This paper was presented at "Workshop on Large-Scale Linear Programming*, June 2-6, 1980, International Institute for Applied Systems Analysis , Austria...This paper is a more polished version of the talk which I delivered opening the International Institute for Applied Systems Analysis Workshop on Large
Directory of Open Access Journals (Sweden)
S. F. Tantawy
2007-01-01
Full Text Available We presented a feasible direction method to find all optimal extreme points for the linear programming problem. Our method depends on the conjugate gradient projection method starting with an initial point we generate a sequence of feasible directions towards all alternative extremes.
A Multi-Objective Fuzzy Linear Programming Model for Cash Flow Management
Directory of Open Access Journals (Sweden)
A. M. El-Kholy
2014-08-01
Full Text Available Although significant research work has been conducted on cash flow forecast, planning, and management, the objective is constantly the maximization of profit/final cash balance, or minimization of total project cost. This paper presents a multi-objective fuzzy linear programming model (FLP for resolving the optimization problem of three conflicting objectives: final cash balance, cost of money, and initial cash balance. The proposed model depends on Jiang et al. (2011 Model. In the new formulation, both the advanced payment and delay of owner's progress payment one period were considered. Literature concerned with cash flow studies and models for construction projects was reviewed. Fuzzy linear programming applications in literature was presented and it's concept was then described. Jiang et al. (2011 Model is presented. The proposed model development was then presented. The proposed model was validated using an example project. An optimization of each individual objective was performed with a linear programming (LP software (Lindo that gave the upper and lower bounds for the multi-objective analysis. Fuzzy linear programming was then applied to optimize the solution. Four cases are considered: considering advanced payment and delay of owner's progress payment one period simultaneously, then separately, and neglecting advanced payment and delay of owner's progress payment. Penalty of delayed payment have been also considered. Analysis of the results revealed that the model is an effective decision making tool to be used by industry practitioners with reasonable accuracy.
A linear programming model of diet choice of free-living beavers
Nolet, BA; VanderVeer, PJ; Evers, EGJ; Ottenheim, MM
1995-01-01
Linear programming has been remarkably successful in predicting the diet choice of generalist herbivores. We used this technique to test the diet choice of free-living beavers (Castor fiber) in the Biesbosch (The Netherlands) under different Foraging goals, i.e. maximization of intake of energy, nit
Secret Message Decryption: Group Consulting Projects Using Matrices and Linear Programming
Gurski, Katharine F.
2009-01-01
We describe two short group projects for finite mathematics students that incorporate matrices and linear programming into fictional consulting requests presented as a letter to the students. The students are required to use mathematics to decrypt secret messages in one project involving matrix multiplication and inversion. The second project…
Average number of iterations of some polynomial interior-point--Algorithms for linear programming
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
We study the behavior of some polynomial interior-point algorithms for solving random linear programming (LP) problems. We show that the average number of iterations of these algorithms, coupled with a finite termination technique, is bounded above by O(n1.5). The random LP problem is Todd's probabilistic model with the standard Gauss distribution.
The fastclime Package for Linear Programming and Large-Scale Precision Matrix Estimation in R.
Pang, Haotian; Liu, Han; Vanderbei, Robert
2014-02-01
We develop an R package fastclime for solving a family of regularized linear programming (LP) problems. Our package efficiently implements the parametric simplex algorithm, which provides a scalable and sophisticated tool for solving large-scale linear programs. As an illustrative example, one use of our LP solver is to implement an important sparse precision matrix estimation method called CLIME (Constrained L1 Minimization Estimator). Compared with existing packages for this problem such as clime and flare, our package has three advantages: (1) it efficiently calculates the full piecewise-linear regularization path; (2) it provides an accurate dual certificate as stopping criterion; (3) it is completely coded in C and is highly portable. This package is designed to be useful to statisticians and machine learning researchers for solving a wide range of problems.
A Generalized Fuzzy Integer Programming Approach for Environmental Management under Uncertainty
Directory of Open Access Journals (Sweden)
Y. R. Fan
2014-01-01
Full Text Available In this study, a generalized fuzzy integer programming (GFIP method is developed for planning waste allocation and facility expansion under uncertainty. The developed method can (i deal with uncertainties expressed as fuzzy sets with known membership functions regardless of the shapes (linear or nonlinear of these membership functions, (ii allow uncertainties to be directly communicated into the optimization process and the resulting solutions, and (iii reflect dynamics in terms of waste-flow allocation and facility-capacity expansion. A stepwise interactive algorithm (SIA is proposed to solve the GFIP problem and generate solutions expressed as fuzzy sets. The procedures of the SIA method include (i discretizing the membership function grade of fuzzy parameters into a set of α-cut levels; (ii converting the GFIP problem into an inexact mixed-integer linear programming (IMILP problem under each α-cut level; (iii solving the IMILP problem through an interactive algorithm; and (iv approximating the membership function for decision variables through statistical regression methods. The developed GFIP method is applied to a municipal solid waste (MSW management problem to facilitate decision making on waste flow allocation and waste-treatment facilities expansion. The results, which are expressed as discrete or continuous fuzzy sets, can help identify desired alternatives for managing MSW under uncertainty.
Directory of Open Access Journals (Sweden)
Chandra Nagasuma R
2009-02-01
Full Text Available Abstract Background A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN from transcript profiling data. Results The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting problem and solved finally by formulating a Linear Program (LP. A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known
Snow, L. S.; Kuhn, A. E.
1975-01-01
Previous error analyses conducted by the Guidance and Dynamics Branch of NASA have used the Guidance Analysis Program (GAP) as the trajectory simulation tool. Plans are made to conduct all future error analyses using the Space Vehicle Dynamics Simulation (SVDS) program. A study was conducted to compare the inertial measurement unit (IMU) error simulations of the two programs. Results of the GAP/SVDS comparison are presented and problem areas encountered while attempting to simulate IMU errors, vehicle performance uncertainties and environmental uncertainties using SVDS are defined. An evaluation of the SVDS linear error analysis capability is also included.
Zhang, Yunong; Wang, Jun
2002-06-01
A recurrent neural network called the dual neural network is proposed in this Letter for solving the strictly convex quadratic programming problems. Compared to other recurrent neural networks, the proposed dual network with fewer neurons can solve quadratic programming problems subject to equality, inequality, and bound constraints. The dual neural network is shown to be globally exponentially convergent to optimal solutions of quadratic programming problems. In addition, compared to neural networks containing high-order nonlinear terms, the dynamic equation of the proposed dual neural network is piecewise linear, and the network architecture is thus much simpler. The global convergence behavior of the dual neural network is demonstrated by an illustrative numerical example.
Directory of Open Access Journals (Sweden)
Roslee Rajikan
2017-02-01
Full Text Available Differences in socioeconomic profile may influences healthy food choices, particularly among individuals with low socioeconomic status. Thus, high-energy dense foods become the preferences compared to high nutritional content foods due to their cheaper price. The present study aims to develop healthy and palatable diet at the minimum cost based on Malaysian Dietary Guidelines 2010 and Recommended Nutrient Intake 2005 via linear programming. A total of 96 female adults from low socioeconomic families in Johor, South East Malaysia have been recruited for the present study. Anthropometric measurement; weight and height, socio-demographic information and 3-days food record have been collected from the subjects. In addition, data on food prices have also been collected. Then, a linear programming model has been developed to select the cheapest food combinations that could fulfil all the nutritional recommendations and palatable constraints in order to capture common dietary habit of the locals. Subsequently, healthy seven-days menus have been created using the optimal food servings estimated from the linear programming model. Dietary data have shown that the average energy intake among low-income adult women (1871 ± 317 kcal/day is less than the nutrient recommendation. Thus, from the linear programming analysis, the minimum food cost has been estimated at RM6.55 (2.15 USD for the total energy intake of 2000 kcal per day for a female adult which meets the recommendation of MDG 2010 and RNI 2005. In conclusion, linear programming may be a useful tool to develop healthy and palatable diets at a minimal cost in managing dietary problems among low socioeconomic groups where food expenditure becomes an important restraining factor. Eventually, low socioeconomics female adults would improve their nutritional intake by making wiser food choices to meet all the nutritional requirements, which lead to healthier life.
Linear programming models and methods of matrix games with payoffs of triangular fuzzy numbers
Li, Deng-Feng
2016-01-01
This book addresses two-person zero-sum finite games in which the payoffs in any situation are expressed with fuzzy numbers. The purpose of this book is to develop a suite of effective and efficient linear programming models and methods for solving matrix games with payoffs in fuzzy numbers. Divided into six chapters, it discusses the concepts of solutions of matrix games with payoffs of intervals, along with their linear programming models and methods. Furthermore, it is directly relevant to the research field of matrix games under uncertain economic management. The book offers a valuable resource for readers involved in theoretical research and practical applications from a range of different fields including game theory, operational research, management science, fuzzy mathematical programming, fuzzy mathematics, industrial engineering, business and social economics. .
A Two-Phase Support Method for Solving Linear Programs: Numerical Experiments
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Mohand Bentobache
2012-01-01
Full Text Available We develop a single artificial variable technique to initialize the primal support method for solving linear programs with bounded variables. We first recall the full artificial basis technique, then we will present the proposed algorithm. In order to study the performances of the suggested algorithm, an implementation under the MATLAB programming language has been developed. Finally, we carry out an experimental study about CPU time and iterations number on a large set of the NETLIB test problems. These test problems are practical linear programs modelling various real-life problems arising from several fields such as oil refinery, audit staff scheduling, airline scheduling, industrial production and allocation, image restoration, multisector economic planning, and data fitting. It has been shown that our approach is competitive with our implementation of the primal simplex method and the primal simplex algorithm implemented in the known open-source LP solver LP_SOLVE.
Katz, Josh M; Winter, Carl K; Buttrey, Samuel E; Fadel, James G
2012-03-01
Western and guideline based diets were compared to determine if dietary improvements resulting from following dietary guidelines reduce acrylamide intake. Acrylamide forms in heat treated foods and is a human neurotoxin and animal carcinogen. Acrylamide intake from the Western diet was estimated with probabilistic techniques using teenage (13-19 years) National Health and Nutrition Examination Survey (NHANES) food consumption estimates combined with FDA data on the levels of acrylamide in a large number of foods. Guideline based diets were derived from NHANES data using linear programming techniques to comport to recommendations from the Dietary Guidelines for Americans, 2005. Whereas the guideline based diets were more properly balanced and rich in consumption of fruits, vegetables, and other dietary components than the Western diets, acrylamide intake (mean±SE) was significantly greater (Plinear programming and results demonstrate that linear programming techniques can be used to model specific diets for the assessment of toxicological and nutritional dietary components.
Applied Integer Programming Modeling and Solution
Chen, Der-San; Dang, Yu
2011-01-01
An accessible treatment of the modeling and solution of integer programming problems, featuring modern applications and software In order to fully comprehend the algorithms associated with integer programming, it is important to understand not only how algorithms work, but also why they work. Applied Integer Programming features a unique emphasis on this point, focusing on problem modeling and solution using commercial software. Taking an application-oriented approach, this book addresses the art and science of mathematical modeling related to the mixed integer programming (MIP) framework and
A User’s Manual for Interactive Linear Control Programs on IBM/3033.
1982-12-01
assistance in solving aDplications of linear control theory. The Linear Control Program ( LINCON ) and its user’s guide satisfy this need. A series of...Do 1473 DITION OF I’OV Of I OISOLRTE SIN 0102-014-4401 SECURITY CLASIPICATION OF To PAGE (WnT =e0 #~ LINCON consists of twu groups: matrix manipulation... LINCON ) was f.irst developed by Melsa [ 1] and adapted for batch use a- NPS by Desjardins C 2]. Alth:’agh the original intent of this thesis was simply
National Research Council Canada - National Science Library
María del Rosario Pérez-Salazar; Nicolás Francisco Mateo-Díaz; Rogelio García-Rodríguez; Carlos Eusebio Mar-Orozco; Lidilia Cruz-Rivero
2015-01-01
.... The decision regarding facility location is the central issue in solid waste management. A mixed integer linear programming model of the capacitated facility location problem is proposed and then a genetic algorithm is designed to optimize the model...
Stability of multi-objective bi-level linear programming problems under fuzziness
Directory of Open Access Journals (Sweden)
Abo-Sinna Mahmoud A.
2013-01-01
Full Text Available This paper deals with multi-objective bi-level linear programming problems under fuzzy environment. In the proposed method, tentative solutions are obtained and evaluated by using the partial information on preference of the decision-makers at each level. The existing results concerning the qualitative analysis of some basic notions in parametric linear programming problems are reformulated to study the stability of multi-objective bi-level linear programming problems. An algorithm for obtaining any subset of the parametric space, which has the same corresponding Pareto optimal solution, is presented. Also, this paper established the model for the supply-demand interaction in the age of electronic commerce (EC. First of all, the study uses the individual objectives of both parties as the foundation of the supply-demand interaction. Subsequently, it divides the interaction, in the age of electronic commerce, into the following two classifications: (i Market transactions, with the primary focus on the supply demand relationship in the marketplace; and (ii Information service, with the primary focus on the provider and the user of information service. By applying the bi-level programming technique of interaction process, the study will develop an analytical process to explain how supply-demand interaction achieves a compromise or why the process fails. Finally, a numerical example of information service is provided for the sake of illustration.
Zimmermann, Karl-Heinz; Achtziger, Wolfgang
2001-09-01
The size of a systolic array synthesized from a uniform recurrence equation, whose computations are mapped by a linear function to the processors, matches the problem size. In practice, however, there exist several limiting factors on the array size. There are two dual schemes available to derive arrays of smaller size from large-size systolic arrays based on the partitioning of the large-size arrays into subarrays. In LSGP, the subarrays are clustered one-to-one into the processors of a small-size array, while in LPGS, the subarrays are serially assigned to a reduced-size array. In this paper, we propose a common methodology for both LSGP and LPGS based on polyhedral partitionings of large-size k-dimensional systolic arrays which are synthesized from n-dimensional uniform recurrences by linear mappings for allocation and timing. In particular, we address the optimization problem of finding optimal piecewise linear timing functions for small-size arrays. These are mappings composed of linear timing functions for the computations of the subarrays. We study a continuous approximation of this problem by passing from piecewise linear to piecewise quasi-linear timing functions. The resultant problem formulation is then a quadratic programming problem which can be solved by standard algorithms for nonlinear optimization problems.
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Frison, Gianluca; Skajaa, Anders
2015-01-01
We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear...... is significantly faster than several state-of-the-art IPMs based on sparse linear algebra, and 2) warm-start reduces the average number of iterations by 35-40%.......We develop an efficient homogeneous and self-dual interior-point method (IPM) for the linear programs arising in economic model predictive control of constrained linear systems with linear objective functions. The algorithm is based on a Riccati iteration procedure, which is adapted to the linear...
Fixed-dimensional parallel linear programming via relative {Epsilon}-approximations
Energy Technology Data Exchange (ETDEWEB)
Goodrich, M.T.
1996-12-31
We show that linear programming in IR{sup d} can be solved deterministically in O((log log n){sup d}) time using linear work in the PRAM model of computation, for any fixed constant d. Our method is developed for the CRCW variant of the PRAM parallel computation model, and can be easily implemented to run in O(log n(log log n){sup d-1}) time using linear work on an EREW PRAM. A key component in these algorithms is a new, efficient parallel method for constructing E-nets and E-approximations (which have wide applicability in computational geometry). In addition, we introduce a new deterministic set approximation for range spaces with finite VC-exponent, which we call the {delta}-relative {epsilon}-approximation, and we show how such approximations can be efficiently constructed in parallel.
LDRD final report on massively-parallel linear programming : the parPCx system.
Energy Technology Data Exchange (ETDEWEB)
Parekh, Ojas (Emory University, Atlanta, GA); Phillips, Cynthia Ann; Boman, Erik Gunnar
2005-02-01
This report summarizes the research and development performed from October 2002 to September 2004 at Sandia National Laboratories under the Laboratory-Directed Research and Development (LDRD) project ''Massively-Parallel Linear Programming''. We developed a linear programming (LP) solver designed to use a large number of processors. LP is the optimization of a linear objective function subject to linear constraints. Companies and universities have expended huge efforts over decades to produce fast, stable serial LP solvers. Previous parallel codes run on shared-memory systems and have little or no distribution of the constraint matrix. We have seen no reports of general LP solver runs on large numbers of processors. Our parallel LP code is based on an efficient serial implementation of Mehrotra's interior-point predictor-corrector algorithm (PCx). The computational core of this algorithm is the assembly and solution of a sparse linear system. We have substantially rewritten the PCx code and based it on Trilinos, the parallel linear algebra library developed at Sandia. Our interior-point method can use either direct or iterative solvers for the linear system. To achieve a good parallel data distribution of the constraint matrix, we use a (pre-release) version of a hypergraph partitioner from the Zoltan partitioning library. We describe the design and implementation of our new LP solver called parPCx and give preliminary computational results. We summarize a number of issues related to efficient parallel solution of LPs with interior-point methods including data distribution, numerical stability, and solving the core linear system using both direct and iterative methods. We describe a number of applications of LP specific to US Department of Energy mission areas and we summarize our efforts to integrate parPCx (and parallel LP solvers in general) into Sandia's massively-parallel integer programming solver PICO (Parallel Interger and
Directory of Open Access Journals (Sweden)
Weihua Jin
2013-01-01
Full Text Available This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal solid waste treatment system. Compared to the traditional interactive binary analysis, this approach has fewer limitations and is able to reduce the complexity in solving the inexact linear programming problems and inexact quadratic programming problems. The implementation of this approach was performed using the Genetic Algorithm Solver of MATLAB (trademark of MathWorks. The paper explains the genetic-algorithms-based method and presents details on the computation procedures for each type of inexact operation programming problems. A comparison of the results generated by the proposed method based on genetic algorithms with those produced by the traditional interactive binary analysis method is also presented.
Directory of Open Access Journals (Sweden)
Feng Liu
2013-06-01
Full Text Available In this study, we research the problem of network revenue management with customer choice based on the Origin-Destination (O-D demands. By dividing customers into different segments according to O-D pairs, we consider a network capacity control problem where each customer chooses the open product within the segment he belongs to. Starting with a Markov Decision Process (MDP formulation, we approximate the value function with an affine function of the state vector. The affine function approximation results in a new Linear Program (LP which yields tighter bounds than the Choice-based Deterministic Linear Program (CDLP. We give a column generation procedure for solving the LP within a desired optimality tolerance and present numerical results which show the policy perform from our solution approach can outperform that from the CDLP.
Linear programming to build food-based dietary guidelines: Romanian food baskets
DEFF Research Database (Denmark)
Parlesak, Alexandr; Robertson, Aileen; Hondru, Gabriela
basket that incorporates only WHO food-based dietary guidelines does not meet all the recommended nutrient intake values for, for example, vitamins A, D, K, iodine and calcium. • The version of a Romanian fully nutritious, health-promoting food basket for a family (two adults, two children) costs 19.......65 lei (~€ 4.46) for a day. • Key nutrients, primarily vitamin D, calcium, potassium and iron, were found to control the overall price. • The least expensive basket (one day’s rations) is monotonous and the linear programming approach is used to select a wide range of foods that can be recommended......, potatoes and fish and considerably less meat, fats, oils and sugar. In conclusion, the linear programming methodology can facilitate the development of national dietary recommendations that meet both recommended nutrient intake values and WHO food-based dietary guidelines in a cost-efficient manner. How...
Interior point algorithm for linear programming used in transmission network synthesis
Energy Technology Data Exchange (ETDEWEB)
Sanchez, I.G.; Romero, R.; Mantovani, J.R.S. [Electrical Engineering Department, UNESP, CEP 15385000, Ilha Solteira, SP (Brazil); Garcia, A. [Power Systems Department, University of Campinas, UNICAMP, CEP 13083970, Campinas, SP (Brazil)
2005-09-15
This article presents a well-known interior point method (IPM) used to solve problems of linear programming that appear as sub-problems in the solution of the long-term transmission network expansion planning problem. The linear programming problem appears when the transportation model is used, and when there is the intention to solve the planning problem using a constructive heuristic algorithm (CHA), or a branch-and-bound algorithm. This paper shows the application of the IPM in a CHA. A good performance of the IPM was obtained, and then it can be used as tool inside algorithms used to solve the planning problem. Illustrative tests are shown, using electrical systems known in the specialized literature. (author) [Transmission network synthesis; Interior point method; Relaxed optimization models; Network expansion planning; Transportation model; Constructive heuristic algorithms].
Directory of Open Access Journals (Sweden)
Bahram Izadi
2013-09-01
Full Text Available Among various statistical and data mining discriminant analysis proposed so far for group classification, linear programming discriminant analysis have recently attracted the researchers’ interest. This study evaluates multi-group discriminant linear programming (MDLP for classification problems against well-known methods such as neural networks, support vector machine, and so on. MDLP is less complex compared to other methods and does not suffer from local optima. However, sometimes classification becomes infeasible due to insufficient data in databases such as in the case of an Internet Service Provider (ISP small and medium-sized market considered in this research. This study proposes a fuzzy Delphi method to select and gather required data. The results show that the performance of MDLP is better than other methods with respect to correct classification, at least for small and medium-sized datasets.
Refining and end use study of coal liquids II - linear programming analysis
Energy Technology Data Exchange (ETDEWEB)
Lowe, C.; Tam, S.
1995-12-31
A DOE-funded study is underway to determine the optimum refinery processing schemes for producing transportation fuels that will meet CAAA regulations from direct and indirect coal liquids. The study consists of three major parts: pilot plant testing of critical upgrading processes, linear programming analysis of different processing schemes, and engine emission testing of final products. Currently, fractions of a direct coal liquid produced form bituminous coal are being tested in sequence of pilot plant upgrading processes. This work is discussed in a separate paper. The linear programming model, which is the subject of this paper, has been completed for the petroleum refinery and is being modified to handle coal liquids based on the pilot plant test results. Preliminary coal liquid evaluation studies indicate that, if a refinery expansion scenario is adopted, then the marginal value of the coal liquid (over the base petroleum crude) is $3-4/bbl.
Multistep Linear Programming Approaches for Decoding Low-Density Parity-Check Codes
Institute of Scientific and Technical Information of China (English)
LIU Haiyang; MA Lianrong; CHEN Jie
2009-01-01
The problem of improving the performance of linear programming (LP) decoding of low-density padty-check (LDPC) codes is considered in this paper. A multistep linear programming (MLP) algorithm was developed for decoding LDPC codes that includes a slight increase in computational complexity. The MLP decoder adaptively adds new constraints which are compatible with a selected check node to refine the re-sults when an error is reported by the odginal LP decoder. The MLP decoder result is shown to have the maximum-likelihood (ML) certificate property. Simulations with moderate block length LDPC codes suggest that the MLP decoder gives better performance than both the odginal LP decoder and the conventional sum-product (SP) decoder.
A new neural network model for solving random interval linear programming problems.
Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza
2017-05-01
This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique.
DEFF Research Database (Denmark)
Parlesak, A.; Tetens, Inge; Dejgård Jensen, Jørgen
2016-01-01
programming. The FBs were defined using five different constraints: cultural acceptability (CA), or dietary guidelines (DG), or nutrient recommendations (N), or cultural acceptability and nutrient recommendations (CAN), or dietary guidelines and nutrient recommendations (DGN). The variety and number of foods...... variety of foods contained from 70 (CAN) to 134 (DGN) foods and cost between DKK 60 (€ 8.1, N) and DKK 125 (€ 16.8, DGN). Ensuring that the food baskets cover both dietary guidelines and nutrient recommendations doubled the cost while cultural acceptability (CAN) tripled it. Use of linear programming...
Lehtinen, B.; Geyser, L. C.
1984-01-01
AESOP is a computer program for use in designing feedback controls and state estimators for linear multivariable systems. AESOP is meant to be used in an interactive manner. Each design task that the program performs is assigned a "function" number. The user accesses these functions either (1) by inputting a list of desired function numbers or (2) by inputting a single function number. In the latter case the choice of the function will in general depend on the results obtained by the previously executed function. The most important of the AESOP functions are those that design,linear quadratic regulators and Kalman filters. The user interacts with the program when using these design functions by inputting design weighting parameters and by viewing graphic displays of designed system responses. Supporting functions are provided that obtain system transient and frequency responses, transfer functions, and covariance matrices. The program can also compute open-loop system information such as stability (eigenvalues), eigenvectors, controllability, and observability. The program is written in ANSI-66 FORTRAN for use on an IBM 3033 using TSS 370. Descriptions of all subroutines and results of two test cases are included in the appendixes.
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.
The solution of the optimization problem of small energy complexes using linear programming methods
Ivanin, O. A.; Director, L. B.
2016-11-01
Linear programming methods were used for solving the optimization problem of schemes and operation modes of distributed generation energy complexes. Applicability conditions of simplex method, applied to energy complexes, including installations of renewable energy (solar, wind), diesel-generators and energy storage, considered. The analysis of decomposition algorithms for various schemes of energy complexes was made. The results of optimization calculations for energy complexes, operated autonomously and as a part of distribution grid, are presented.
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....
Institute of Scientific and Technical Information of China (English)
HUANG Hui; FEI Pu-sheng; YUAN Yuan
2005-01-01
A primal-dual infeasible-interior-point algorithm for multiple objective linear programming (MOLP) problems was presented. In contrast to the current MOLP algorithm,moving through the interior of polytope but not confining the iterates within the feasible region in our proposed algorithm result in a solution approach that is quite different and less sensitive to problem size, so providing the potential to dramatically improve the practical computation effectiveness.
Bin-objective shape optimization based on linear programming model of arch dam
Institute of Scientific and Technical Information of China (English)
JIN Hai; LIN Gao; YANG Ming-sheng
2007-01-01
Bin-objective shape optimization of arch dam based on linear programming model is discussed to minimize both dam volume and maximal tensile stress. The importance of weight coefficient of the above two objectives is chosen according to the value of importance ratio. The influence of weight coefficient to the optimization result is discussed in detail and the numerical example shows that both the model and method proposed is doable.
A linear programming formulation of Mader's edge-disjoint paths problem
Keijsper, J.C.M.; Pendavingh, R.A.; Stougie, L.
2006-01-01
We give a dual pair of linear programs for a minâ€“max result of Mader describing the maximum number of edge-disjoint T-paths in a graph G=(V,E) with TV. We conclude that there exists a polynomial-time algorithm (based on the ellipsoid method) for finding the maximum number of T-paths in a
A linear programming approach to characterizing norm bounded uncertainty from experimental data
Scheid, R. E.; Bayard, D. S.; Yam, Y.
1991-01-01
The linear programming spectral overbounding and factorization (LPSOF) algorithm, an algorithm for finding a minimum phase transfer function of specified order whose magnitude tightly overbounds a specified nonparametric function of frequency, is introduced. This method has direct application to transforming nonparametric uncertainty bounds (available from system identification experiments) into parametric representations required for modern robust control design software (i.e., a minimum-phase transfer function multiplied by a norm-bounded perturbation).
A O(n^8) X O(n^7) Linear Programming Model of the Traveling Salesman Problem
Diaby, Moustapha
2008-01-01
In this paper, we present a new linear programming (LP) formulation of the Traveling Salesman Problem (TSP). The proposed model has O(n^8) variables and O(n^7) constraints, where n is the number of cities. Our numerical experimentation shows that computational times for the proposed linear program are several orders of magnitude smaller than those for the existing model [3].
Institute of Scientific and Technical Information of China (English)
ZhuDetong
2004-01-01
This paper proposes a nonmonotonic backtracking trust region algorithm via bilevel linear programming for solving the general multicommodity minimal cost flow problems. Using the duality theory of the linear programming and convex theory, the generalized directional derivative of the general multicommodity minimal cost flow problems is derived. The global convergence and superlinear convergence rate of the proposed algorithm are established under some mild conditions.
Schipper, R.A.; Stoorvogel, J.J.; Jansen, D.M.
1995-01-01
The paper deals with linear programming as a tool for land use analysis at the sub-regional level. A linear programming model of a case study area, the Neguev settlement in the Atlantic zone of Costa Rica, is presented. The matrix of the model includes five submatrices each encompassing a different
IESIP - AN IMPROVED EXPLORATORY SEARCH TECHNIQUE FOR PURE INTEGER LINEAR PROGRAMMING PROBLEMS
Fogle, F. R.
1994-01-01
IESIP, an Improved Exploratory Search Technique for Pure Integer Linear Programming Problems, addresses the problem of optimizing an objective function of one or more variables subject to a set of confining functions or constraints by a method called discrete optimization or integer programming. Integer programming is based on a specific form of the general linear programming problem in which all variables in the objective function and all variables in the constraints are integers. While more difficult, integer programming is required for accuracy when modeling systems with small numbers of components such as the distribution of goods, machine scheduling, and production scheduling. IESIP establishes a new methodology for solving pure integer programming problems by utilizing a modified version of the univariate exploratory move developed by Robert Hooke and T.A. Jeeves. IESIP also takes some of its technique from the greedy procedure and the idea of unit neighborhoods. A rounding scheme uses the continuous solution found by traditional methods (simplex or other suitable technique) and creates a feasible integer starting point. The Hook and Jeeves exploratory search is modified to accommodate integers and constraints and is then employed to determine an optimal integer solution from the feasible starting solution. The user-friendly IESIP allows for rapid solution of problems up to 10 variables in size (limited by DOS allocation). Sample problems compare IESIP solutions with the traditional branch-and-bound approach. IESIP is written in Borland's TURBO Pascal for IBM PC series computers and compatibles running DOS. Source code and an executable are provided. The main memory requirement for execution is 25K. This program is available on a 5.25 inch 360K MS DOS format diskette. IESIP was developed in 1990. IBM is a trademark of International Business Machines. TURBO Pascal is registered by Borland International.
A new gradient-based neural network for solving linear and quadratic programming problems.
Leung, Y; Chen, K Z; Jiao, Y C; Gao, X B; Leung, K S
2001-01-01
A new gradient-based neural network is constructed on the basis of the duality theory, optimization theory, convex analysis theory, Lyapunov stability theory, and LaSalle invariance principle to solve linear and quadratic programming problems. In particular, a new function F(x, y) is introduced into the energy function E(x, y) such that the function E(x, y) is convex and differentiable, and the resulting network is more efficient. This network involves all the relevant necessary and sufficient optimality conditions for convex quadratic programming problems. For linear programming and quadratic programming (QP) problems with unique and infinite number of solutions, we have proven strictly that for any initial point, every trajectory of the neural network converges to an optimal solution of the QP and its dual problem. The proposed network is different from the existing networks which use the penalty method or Lagrange method, and the inequality constraints are properly handled. The simulation results show that the proposed neural network is feasible and efficient.
Zörnig, Peter
2015-08-01
We present integer programming models for some variants of the farthest string problem. The number of variables and constraints is substantially less than that of the integer linear programming models known in the literature. Moreover, the solution of the linear programming-relaxation contains only a small proportion of noninteger values, which considerably simplifies the rounding process. Numerical tests have shown excellent results, especially when a small set of long sequences is given.
Directory of Open Access Journals (Sweden)
Zhongwen Li
2016-06-01
Full Text Available Microgrids (MGs are presented as a cornerstone of smart grids. With the potential to integrate intermittent renewable energy sources (RES in a flexible and environmental way, the MG concept has gained even more attention. Due to the randomness of RES, load, and electricity price in MG, the forecast errors of MGs will affect the performance of the power scheduling and the operating cost of an MG. In this paper, a combined stochastic programming and receding horizon control (SPRHC strategy is proposed for microgrid energy management under uncertainty, which combines the advantages of two-stage stochastic programming (SP and receding horizon control (RHC strategy. With an SP strategy, a scheduling plan can be derived that minimizes the risk of uncertainty by involving the uncertainty of MG in the optimization model. With an RHC strategy, the uncertainty within the MG can be further compensated through a feedback mechanism with the lately updated forecast information. In our approach, a proper strategy is also proposed to maintain the SP model as a mixed integer linear constrained quadratic programming (MILCQP problem, which is solvable without resorting to any heuristics algorithms. The results of numerical experiments explicitly demonstrate the superiority of the proposed strategy for both island and grid-connected operating modes of an MG.
Wavelet-linear genetic programming: A new approach for modeling monthly streamflow
Ravansalar, Masoud; Rajaee, Taher; Kisi, Ozgur
2017-06-01
The streamflows are important and effective factors in stream ecosystems and its accurate prediction is an essential and important issue in water resources and environmental engineering systems. A hybrid wavelet-linear genetic programming (WLGP) model, which includes a discrete wavelet transform (DWT) and a linear genetic programming (LGP) to predict the monthly streamflow (Q) in two gauging stations, Pataveh and Shahmokhtar, on the Beshar River at the Yasuj, Iran were used in this study. In the proposed WLGP model, the wavelet analysis was linked to the LGP model where the original time series of streamflow were decomposed into the sub-time series comprising wavelet coefficients. The results were compared with the single LGP, artificial neural network (ANN), a hybrid wavelet-ANN (WANN) and Multi Linear Regression (MLR) models. The comparisons were done by some of the commonly utilized relevant physical statistics. The Nash coefficients (E) were found as 0.877 and 0.817 for the WLGP model, for the Pataveh and Shahmokhtar stations, respectively. The comparison of the results showed that the WLGP model could significantly increase the streamflow prediction accuracy in both stations. Since, the results demonstrate a closer approximation of the peak streamflow values by the WLGP model, this model could be utilized for the simulation of cumulative streamflow data prediction in one month ahead.
Automatic tracking of linear features on SPOT images using dynamic programming
Bonnefon, Regis; Dherete, Pierre; Desachy, Jacky
1999-12-01
Detection of geographic elements on images is important in the perspective of adding new elements in geographic databases which are sometimes old and so, some elements are not represented. Our goal is to look for linear features like roads, rivers or railways on SPOT images with a resolution of 10 meters. Several methods allow this detection to be realized and may be classified in three categories: (1) Detection operators: the best known is the DUDA Road Operator which determine the belonging degree of a pixel to a linear feature from several 5 X 5 filters. Results are often unsatisfactory. It exists too the Infinite Size Exponential Filter (ISEF), which is a derivative filter and allows edge, valley or roof profile to be found on the image. It can be utilized as an additional information for others methods. (2) Structural tracking: from a starting point, an analysis in several directions is performed to determine the best next point (features may be: homogeneity of radiometry, contrast with environment, ...). From this new point and with an updated direction, the process goes on. Difficulty of these methods is the consideration of occlusions (bridges, tunnels, dense vegetation, ...). (3) Dynamic programming: F* algorithm and snakes are the best known. They allow a path with a minimal cost to be found in a search window. Occlusions are not a problem but two points or more near the searched linear feature must be known to define the window. The method described below is a mixture of structural tracking and dynamic programming (F* algorithm).
Programming methodology and performance issues for advanced computer architectures. [Linear algebra
Energy Technology Data Exchange (ETDEWEB)
Dongarra, J.J.; Sorensen, D.C.; Connolly, K.; Patterson, J.
1987-01-01
This paper will describe some recent attempts to construct transportable numerical software for high performance computers. Restructuring algorithms in terms of simple linear algebra modules is reviewed. This technique has proved very successful in obtaining a high level of transportability without severe loss of performance on a wide variety of both vector and parallel computers. The use of modules to encapsulate parallelism and reduce the ratio of data movement to floating point operations has been demonstrably effective for regular problems such as those found in dense linear algebra. In other situations it may be necessary to express explicitly parallel algorithms. We also present a programming methodology that is useful for constructing new parallel algorithms which require sophisticated synchronization at a large grain level. We describe the SCHEDULE package which provides an environment for developing and analyzing explicitly parallel programs in Fortran which aare portable. This package now includes a preprocessor to achieve complete portability of user level code and also a graphics post processor for performance analysis and debugging. We discuss details of porting both the SCHEDULE package and user code. Examples from linear algebra, and partial differential equations are used to illustrate the utility of this approach.
On the graph traversal method for evaluating linear binary-chain programs
Institute of Scientific and Technical Information of China (English)
陈阳军
1999-01-01
Grahne et al. have presented a graph algorithm for evaluating a subset of recursive queries. This method consists of two phases. In the first phase, the method transforms a linear binary-chain program into a set of equations over expressions containing predicate symbols. In the second phase, a graph is constructed from the equations and the answers are produced by traversing the relevant paths. A new algorithm is described which requires less time than Grahne’ s. The key idea of the improvement is to reduce the search space that will be traversed when a query is invoked. Further, the evaluation of cyclic data is speeded up by generating most answers directly in terms of the answers already found and the associated "path information" instead of traversing the corresponding paths as usual. In this way, this algorithm achieves a linear time complexity for both acyclic and most of cyclic data.
On Pseudocodewords and Improved Union Bound of Linear Programming Decoding of HDPC Codes
Gidon, Ohad
2012-01-01
In this paper, we present an improved union bound on the Linear Programming (LP) decoding performance of the binary linear codes transmitted over an additive white Gaussian noise channels. The bounding technique is based on the second-order of Bonferroni-type inequality in probability theory, and it is minimized by Prim's minimum spanning tree algorithm. The bound calculation needs the fundamental cone generators of a given parity-check matrix rather than only their weight spectrum, but involves relatively low computational complexity. It is targeted to high-density parity-check codes, where the number of their generators is extremely large and these generators are spread densely in the Euclidean space. We explore the generator density and make a comparison between different parity-check matrix representations. That density effects on the improvement of the proposed bound over the conventional LP union bound. The paper also presents a complete pseudo-weight distribution of the fundamental cone generators for ...
Optimisation of substrate blends in anaerobic co-digestion using adaptive linear programming.
García-Gen, Santiago; Rodríguez, Jorge; Lema, Juan M
2014-12-01
Anaerobic co-digestion of multiple substrates has the potential to enhance biogas productivity by making use of the complementary characteristics of different substrates. A blending strategy based on a linear programming optimisation method is proposed aiming at maximising COD conversion into methane, but simultaneously maintaining a digestate and biogas quality. The method incorporates experimental and heuristic information to define the objective function and the linear restrictions. The active constraints are continuously adapted (by relaxing the restriction boundaries) such that further optimisations in terms of methane productivity can be achieved. The feasibility of the blends calculated with this methodology was previously tested and accurately predicted with an ADM1-based co-digestion model. This was validated in a continuously operated pilot plant, treating for several months different mixtures of glycerine, gelatine and pig manure at organic loading rates from 1.50 to 4.93 gCOD/Ld and hydraulic retention times between 32 and 40 days at mesophilic conditions.
Solutions to estimation problems for scalar hamilton-jacobi equations using linear programming
Claudel, Christian G.
2014-01-01
This brief presents new convex formulations for solving estimation problems in systems modeled by scalar Hamilton-Jacobi (HJ) equations. Using a semi-analytic formula, we show that the constraints resulting from a HJ equation are convex, and can be written as a set of linear inequalities. We use this fact to pose various (and seemingly unrelated) estimation problems related to traffic flow-engineering as a set of linear programs. In particular, we solve data assimilation and data reconciliation problems for estimating the state of a system when the model and measurement constraints are incompatible. We also solve traffic estimation problems, such as travel time estimation or density estimation. For all these problems, a numerical implementation is performed using experimental data from the Mobile Century experiment. In the context of reproducible research, the code and data used to compute the results presented in this brief have been posted online and are accessible to regenerate the results. © 2013 IEEE.
On Implicit Active Constraints in Linear Semi-Infinite Programs with Unbounded Coefficients
Energy Technology Data Exchange (ETDEWEB)
Goberna, M. A., E-mail: mgoberna@ua.es [Alicante University, Dep. of Statistics and Operations Research (Spain); Lancho, G. A., E-mail: lanchoga@mixteco.utm.mx [Universidad Tecnologica de Mixteca, Instituto de Fisica y Matematicas (Mexico); Todorov, M. I., E-mail: maxim.todorov@udlap.mx [UDLA, Dep. of Physics and Mathematics (Mexico); Vera de Serio, V. N., E-mail: vvera@uncu.edu.ar [Universidad Nacional de Cuyo, Facultad de Ciencias Economicas, Instituto de Ciencias Basicas (Argentina)
2011-04-15
The concept of implicit active constraints at a given point provides useful local information about the solution set of linear semi-infinite systems and about the optimal set in linear semi-infinite programming provided the set of gradient vectors of the constraints is bounded, commonly under the additional assumption that there exists some strong Slater point. This paper shows that the mentioned global boundedness condition can be replaced by a weaker local condition (LUB) based on locally active constraints (active in a ball of small radius whose center is some nominal point), providing geometric information about the solution set and Karush-Kuhn-Tucker type conditions for the optimal solution to be strongly unique. The maintaining of the latter property under sufficiently small perturbations of all the data is also analyzed, giving a characterization of its stability with respect to these perturbations in terms of the strong Slater condition, the so-called Extended-Nuernberger condition, and the LUB condition.
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.
Jung, Ho-Won; El Emam, Khaled
2014-01-01
Background A linear programming (LP) model was proposed to create de-identified data sets that maximally include spatial detail (e.g., geocodes such as ZIP or postal codes, census blocks, and locations on maps) while complying with the HIPAA Privacy Rule’s Expert Determination method, i.e., ensuring that the risk of re-identification is very small. The LP model determines the transition probability from an original location of a patient to a new randomized location. However, it has a limitati...
APPLICATION OF LINEAR PROGRAMMING FOR DORMITORY DEVELOPMENT PLAN AT PETRA CHRISTIAN UNIVERSITY
Directory of Open Access Journals (Sweden)
Connie Susilawati
2001-01-01
Full Text Available Dormitory is a very important facility which have to be provided by a university. A survey to Petra Christian University’s students has been conducted to understand the required facilities and their financial ability. Linear programming has been used to calculate number of rooms and area of each facility which could satisfy the constraints and to obtain optimum profit. Number of bedrooms, number of bathrooms, and area of each facility, such as: living room, dining room, common room, cafeteria, book shop, mini market, phone booths, sport facilities, and parking space are recommended. Since the investment is financially feasible, the dormitory could be built in the future.
An Integer Linear Programming Model for the Radiotherapy Treatment Scheduling Problem
Burke, Edmund K; Petrovic, Sanja
2011-01-01
Radiotherapy represents an important phase of treatment for a large number of cancer patients. It is essential that resources used to deliver this treatment are employed effectively. This paper presents a new integer linear programming model for real-world radiotherapy treatment scheduling and analyses the effectiveness of using this model on a daily basis in a hospital. Experiments are conducted varying the days on which schedules can be created. Results obtained using real-world data from the Nottingham University Hospitals NHS Trust, UK, are presented and show how the proposed model can be used with different policies in order to achieve good quality schedules.
INFORMATION SECURITY RISKS OPTIMIZATION IN CLOUDY SERVICES ON THE BASIS OF LINEAR PROGRAMMING
Directory of Open Access Journals (Sweden)
I. A. Zikratov
2013-01-01
Full Text Available The paper discusses theoretical aspects of secure cloud services creation for information processing of various confidentiality degrees. A new approach to the reasoning of information security composition in distributed computing structures is suggested, presenting the problem of risk assessment as an extreme problem of decisionmaking. Linear programming method application is proved to minimize the risk of information security for given performance security in compliance with the economic balance for the maintenance of security facilities and cost of services. An example is given to illustrate the obtained theoretical results.
Optimal local dimming for LED-backlit LCD displays via linear programming
DEFF Research Database (Denmark)
Shu, Xiao; Wu, Xiaolin; Forchhammer, Søren
2012-01-01
LED-backlit LCD displays hold the promise of improving the image quality while reducing the energy consumption with signal-dependent local dimming. To fully realize such potentials we propose a novel local dimming technique that jointly optimizes the intensities of LED backlights...... 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...
On Pseudocodewords and Decision Regions of Linear Programming Decoding of HDPC Codes
Lifshitz, Asi
2011-01-01
In this paper we explore the decision regions of Linear Programming (LP) decoding. We compare the decision regions of an LP decoder, a Belief Propagation (BP) decoder and the optimal Maximum Likelihood (ML) decoder. We study the effect of minimal-weight pseudocodewords on LP decoding. We present global optimization as a method for finding the minimal pseudoweight of a given code as well as the number of minimal-weight generators. We present a complete pseudoweight distribution for the [24; 12; 8] extended Golay code, and provide justifications of why the pseudoweight distribution alone cannot be used for obtaining a tight upper bound on the error probability.
Fleming, P.
1983-01-01
A design technique is proposed for linear regulators in which a feedback controller of fixed structure is chosen to minimize an integral quadratic objective function subject to the satisfaction of integral quadratic constraint functions. Application of a nonlinear programming algorithm to this mathematically tractable formulation results in an efficient and useful computer aided design tool. Particular attention is paid to computational efficiency and various recommendations are made. Two design examples illustrate the flexibility of the approach and highlight the special insight afforded to the designer. One concerns helicopter longitudinal dynamics and the other the flight dynamics of an aerodynamically unstable aircraft.
Adaptive broadband beamformer for nonuniform linear array based on second order cone programming
Institute of Scientific and Technical Information of China (English)
Chen Peng; Hou Chaohuan; Ma Xiaochuan; Cao Zhiqian; Liang Yicong; Yan Sheng
2009-01-01
Adaptive broadband beamforming is a key issue in array applications. The adaptive broadband beamformer with tapped delay line (TDL) structure for nonuniform linear array (NLA) is designed according to the rule of minimizing the beamformer's output power while keeping the distortionless response (DR) in the direction of desired signal and keeping the constant beamwidth (CB) with the prescribed sidelobe level over the whole operating band. This kind of beamforming problem can be solved with the interior-point method after being converted to the form of standard second order cone programming (SOCP). The computer simulations are presented which illustrate the effectiveness of our bearaformer.
A KIND OF FUZZY MULTI-OBJECTIVE LINEAR PROGRAMMING PROBLEMS BASED ON INTERVAL VALUED FUZZY SETS
Institute of Scientific and Technical Information of China (English)
XU Jiuping
2001-01-01
This paper presents a general solution procedure and an interactive fuzzy satisfying method for a kind of fuzzy multi-objective linear programming problems based on interval valued fuzzy sets. Firstly, a fuzzy set of the fuzzy solutions, which can be focused on providing complete information for the final decision, can be obtained by the proposed tolerance analysis of a non-dominated set. Secondly, the satisfying solution for the decisionmaker can be derived from Pareto optimal solutions by updating the current reference membership levels on the basis of the current levels of the membership functions together with the trade-off rates between the membership functions.
An improved multiple linear regression and data analysis computer program package
Sidik, S. M.
1972-01-01
NEWRAP, an improved version of a previous multiple linear regression program called RAPIER, CREDUC, and CRSPLT, allows for a complete regression analysis including cross plots of the independent and dependent variables, correlation coefficients, regression coefficients, analysis of variance tables, t-statistics and their probability levels, rejection of independent variables, plots of residuals against the independent and dependent variables, and a canonical reduction of quadratic response functions useful in optimum seeking experimentation. A major improvement over RAPIER is that all regression calculations are done in double precision arithmetic.
A FORTRAN program for the analysis of linear continuous and sample-data systems
Edwards, J. W.
1976-01-01
A FORTRAN digital computer program which performs the general analysis of linearized control systems is described. State variable techniques are used to analyze continuous, discrete, and sampled data systems. Analysis options include the calculation of system eigenvalues, transfer functions, root loci, root contours, frequency responses, power spectra, and transient responses for open- and closed-loop systems. A flexible data input format allows the user to define systems in a variety of representations. Data may be entered by inputing explicit data matrices or matrices constructed in user written subroutines, by specifying transfer function block diagrams, or by using a combination of these methods.
Zheng, Yuanjie; Hunter, Allan A; Wu, Jue; Wang, Hongzhi; Gao, Jianbin; Maguire, Maureen G; Gee, James C
2011-01-01
In this paper, we address the problem of landmark matching based retinal image registration. Two major contributions render our registration algorithm distinguished from many previous methods. One is a novel landmark-matching formulation which enables not only a joint estimation of the correspondences and transformation model but also the optimization with linear programming. The other contribution lies in the introduction of a reinforced self-similarities descriptor in characterizing the local appearance of landmarks. Theoretical analysis and a series of preliminary experimental results show both the effectiveness of our optimization scheme and the high differentiating ability of our features.
Bilevel linear programming model of charging for effluent based on price control
Institute of Scientific and Technical Information of China (English)
LI Yu-hua; LI Lei; HU Yun-quan; SHAO Hai-hong
2007-01-01
For the optimum price problem of charging for effluent, this paper analyzes the optimal Pigovian Tax and the serious information asymmetry problem existing in the application process of optimal Pigovian Tax,which is predominant in theory. Then the bilevel system optimizing decision-making theory is applied to give bilevel linear programming decision-making model of charging for effluent, in which the government (environmental protection agency) acts as the upper level decision-making unit and the polluting enterprises act as the lower level decision-making unit. To some extent, the model avoids the serious information asymmetry between the government and the polluting enterprises on charging for effluent.
Observations on the linear programming formulation of the single reflector design problem.
Canavesi, Cristina; Cassarly, William J; Rolland, Jannick P
2012-02-13
We implemented the linear programming approach proposed by Oliker and by Wang to solve the single reflector problem for a point source and a far-field target. The algorithm was shown to produce solutions that aim the input rays at the intersections between neighboring reflectors. This feature makes it possible to obtain the same reflector with a low number of rays - of the order of the number of targets - as with a high number of rays, greatly reducing the computation complexity of the problem.
A Polynomial Time Algorithm for a Special Case of Linear Integer Programming
Ghasemiesfeh, Golnaz; Tabesh, Yahya
2011-01-01
According to the wide use of integer programming in many fields, affords toward finding and solving sub classes of these problems which are solvable in polynomial time seems to be important and useful. Integer linear programming (ILP) problems have the general form: $Min \\{C^{T}x: Ax=b, x\\geq 0, x\\in Z^{n}\\}$ where $Z^{n}$ is the set of n-dimensional integer vectors. Algorithmic solution of ILP is at great interest, in this paper we have presented a polynomial algorithm for a special case of the ILP problems; we have used a graph theoretical formulation of the problem which leads to an $O[mn(m+n)]$ solution where $m$ and $n$ are dimensions of coefficient matrix $X$.
Directory of Open Access Journals (Sweden)
Muwafaq M. Alkubaisi
2017-03-01
Full Text Available This paper has presented an overview of theoretical and methodological issues in simplex based sensitivity analysis (SA. The paper focuses somewhat on developing shortcut methods to perform Linear Programming (L.P. sensitivity analysis manually and in particular to dual prices and its meaning and changes in the parameter of the L.P model. Shortcut methods for conducting sensitivity analysis have been suggested. To perform sensitivity analysis in real life, one needs computer packages (software to achieve the sensitivity analysis report for higher accuracy and to save time. Some of these computer packages are very professional, but, unfortunately, some other packages suffer from logical errors in the programming of sensitivity analysis.
Proximity search heuristics for wind farm optimal layout
DEFF Research Database (Denmark)
Fischetti, Martina; Monaci, Michele
2016-01-01
A heuristic framework for turbine layout optimization in a wind farm is proposed that combines ad-hoc heuristics and mixed-integer linear programming. In our framework, large-scale mixed-integer programming models are used to iteratively refine the current best solution according to the recently-......-proposed proximity search paradigm. Computational results on very large scale instances involving up to 20,000 potential turbine sites prove the practical viability of the overall approach....
Final Report-Optimization Under Uncertainty and Nonconvexity: Algorithms and Software
Energy Technology Data Exchange (ETDEWEB)
Jeff Linderoth
2008-10-10
The goal of this research was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems.
Saligrama, V
2008-01-01
We consider the classical Compressed Sensing problem. We have a large under-determined set of noisy measurements Y=GX+N, where X is a sparse signal and G is drawn from a random ensemble. In this paper we focus on a quantized linear programming solution for support recovery. Our solution of the problem amounts to solving $\\min \\|Z\\|_1 ~ s.t. ~ Y=G Z$, and quantizing/thresholding the resulting solution $Z$. We show that this scheme is guaranteed to perfectly reconstruct a discrete signal or control the element-wise reconstruction error for a continuous signal for specific values of sparsity. We show that in the linear regime when the sparsity, $k$, increases linearly with signal dimension, $n$, the sign pattern of $X$ can be recovered with $SNR=O(\\log n)$ and $m= O(k)$ measurements. Our proof technique is based on perturbation of the noiseless $\\ell_1$ problem. Consequently, the achievable sparsity level in the noisy problem is comparable to that of the noiseless problem. Our result offers a sharp characterizat...
A novel approach based on preference-based index for interval bilevel linear programming problem.
Ren, Aihong; Wang, Yuping; Xue, Xingsi
2017-01-01
This paper proposes a new methodology for solving the interval bilevel linear programming problem in which all coefficients of both objective functions and constraints are considered as interval numbers. In order to keep as much uncertainty of the original constraint region as possible, the original problem is first converted into an interval bilevel programming problem with interval coefficients in both objective functions only through normal variation of interval number and chance-constrained programming. With the consideration of different preferences of different decision makers, the concept of the preference level that the interval objective function is preferred to a target interval is defined based on the preference-based index. Then a preference-based deterministic bilevel programming problem is constructed in terms of the preference level and the order relation [Formula: see text]. Furthermore, the concept of a preference δ-optimal solution is given. Subsequently, the constructed deterministic nonlinear bilevel problem is solved with the help of estimation of distribution algorithm. Finally, several numerical examples are provided to demonstrate the effectiveness of the proposed approach.
Averaging and Linear Programming in Some Singularly Perturbed Problems of Optimal Control
Energy Technology Data Exchange (ETDEWEB)
Gaitsgory, Vladimir, E-mail: vladimir.gaitsgory@mq.edu.au [Macquarie University, Department of Mathematics (Australia); Rossomakhine, Sergey, E-mail: serguei.rossomakhine@flinders.edu.au [Flinders University, Flinders Mathematical Sciences Laboratory, School of Computer Science, Engineering and Mathematics (Australia)
2015-04-15
The paper aims at the development of an apparatus for analysis and construction of near optimal solutions of singularly perturbed (SP) optimal controls problems (that is, problems of optimal control of SP systems) considered on the infinite time horizon. We mostly focus on problems with time discounting criteria but a possibility of the extension of results to periodic optimization problems is discussed as well. Our consideration is based on earlier results on averaging of SP control systems and on linear programming formulations of optimal control problems. The idea that we exploit is to first asymptotically approximate a given problem of optimal control of the SP system by a certain averaged optimal control problem, then reformulate this averaged problem as an infinite-dimensional linear programming (LP) problem, and then approximate the latter by semi-infinite LP problems. We show that the optimal solution of these semi-infinite LP problems and their duals (that can be found with the help of a modification of an available LP software) allow one to construct near optimal controls of the SP system. We demonstrate the construction with two numerical examples.
A wavelet-linear genetic programming model for sodium (Na+) concentration forecasting in rivers
Ravansalar, Masoud; Rajaee, Taher; Zounemat-Kermani, Mohammad
2016-06-01
The prediction of water quality parameters in water resources such as rivers is of importance issue that needs to be considered in better management of irrigation systems and water supplies. In this respect, this study proposes a new hybrid wavelet-linear genetic programming (WLGP) model for prediction of monthly sodium (Na+) concentration. The 23-year monthly data used in this study, were measured from the Asi River at the Demirköprü gauging station located in Antakya, Turkey. At first, the measured discharge (Q) and Na+ datasets are initially decomposed into several sub-series using discrete wavelet transform (DWT). Then, these new sub-series are imposed to the ad hoc linear genetic programming (LGP) model as input patterns to predict monthly Na+ one month ahead. The results of the new proposed WLGP model are compared with LGP, WANN and ANN models. Comparison of the models represents the superiority of the WLGP model over the LGP, WANN and ANN models such that the Nash-Sutcliffe efficiencies (NSE) for WLGP, WANN, LGP and ANN models were 0.984, 0.904, 0.484 and 0.351, respectively. The achieved results even points to the superiority of the single LGP model than the ANN model. Continuously, the capability of the proposed WLGP model in terms of prediction of the Na+ peak values is also presented in this study.
Directory of Open Access Journals (Sweden)
Warid Warid
Full Text Available 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.
Indian Academy of Sciences (India)
ALI EBRAHIMNEJAD
2016-03-01
Transportation problem (TP) is an important network structured linear programming problem that arises in several contexts and has deservedly received a great deal of attention in the literature. The central concept in this problem is to find the least total transportation cost of a commodity in order to satisfy demands at destinations using available supplies at origins in a crisp environment. In real life situations, the decision maker may not be sure about the precise values of the coefficients belonging to the transportation problem. The aim of this paper is to introduce a formulation of TP involving interval-valued trapezoidal fuzzy numbers for the transportation costs and values of supplies and demands. We propose a fuzzy linear programming approach for solvinginterval-valued trapezoidal fuzzy numbers transportation problem based on comparison of interval-valued fuzzy numbers by the help of signed distance ranking. To illustrate the proposed approach an application example issolved. It is demonstrated that study of interval-valued trapezoidal fuzzy numbers transportation problem gives rise to the same expected results as those obtained for TP with trapezoidal fuzzy numbers.
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.
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.
Glocker, Ben; Paragios, Nikos; Komodakis, Nikos; Tziritas, Georgios; Navab, Nassir
2007-01-01
In this paper we propose a novel non-rigid volume registration based on discrete labeling and linear programming. The proposed framework reformulates registration as a minimal path extraction in a weighted graph. The space of solutions is represented using a set of a labels which are assigned to predefined displacements. The graph topology corresponds to a superimposed regular grid onto the volume. Links between neighborhood control points introduce smoothness, while links between the graph nodes and the labels (end-nodes) measure the cost induced to the objective function through the selection of a particular deformation for a given control point once projected to the entire volume domain, Higher order polynomials are used to express the volume deformation from the ones of the control points. Efficient linear programming that can guarantee the optimal solution up to (a user-defined) bound is considered to recover the optimal registration parameters. Therefore, the method is gradient free, can encode various similarity metrics (simple changes on the graph construction), can guarantee a globally sub-optimal solution and is computational tractable. Experimental validation using simulated data with known deformation, as well as manually segmented data demonstrate the extreme potentials of our approach.
Nguyen, Dzung; Tran, Trac; Kwan, Chiman; Ayhan, Bulent
2010-04-01
Endmember extraction in Hyperspectral Images (HSI) is a critical step for target detection and abundance estimation. In this paper, we propose a new approach to endmember extraction, which takes advantage of the sparsity property of the linear representation of HSI's spectral vector. Sparsity is measured by the l0 norm of the abundance vector. It is also well known that l1 norm well resembles l0 in boosting sparsity while keeping the minimization problem convex and tractable. By adding the l1 norm term to the objective function, we result in a constrained quadratic programming which can be solved effectively using the Linear Complementary Programming (LCP). Unlike existing methods which require expensive computations in each iteration, LCP only requires pivoting steps, which are extremely simple and efficient for the un-mixing problem, since the number of signatures in the reconstructing basis is reasonably small. Preliminary experiments of the proposed methods for both supervised and unsupervised abundance decomposition showed competitive results as compared to LS-based method like Fully Constrained Least Square (FCLS). Furthermore, combination of our unsupervised decomposition with anomaly detection makes a decent target detection algorithm as compared to methods which require prior information of target and background signatures.
Linear programming analysis of the R-parity violation within EDM-constraints
Yamanaka, Nodoka; Sato, Toru; Kubota, Takahiro
2014-12-01
The constraint on the R-parity violating supersymmetric interactions is discussed in the light of current experimental data of the electric dipole moment of neutron, 129Xe , 205Tl, and 199Hg atoms, and YbF and ThO molecules. To investigate the constraints without relying upon the assumption of the dominance of a particular combination of couplings over all the rest, an extensive use is made of the linear programming method in the scan of the parameter space. We give maximally possible values for the EDMs of the proton, deuteron, 3He nucleus, 211Rn, 225Ra, 210Fr, and the R-correlation of the neutron beta decay within the constraints from the current experimental data of the EDMs of neutron, 129Xe, 205Tl, and 199Hg atoms, and YbF and ThO molecules using the linear programming method. It is found that the R-correlation of the neutron beta decay and hadronic EDMs are very useful observables to constrain definite regions of the parameter space of the R-parity violating supersymmetry.
Uncovering signal transduction networks from high-throughput data by integer linear programming.
Zhao, Xing-Ming; Wang, Rui-Sheng; Chen, Luonan; Aihara, Kazuyuki
2008-05-01
Signal transduction is an important process that transmits signals from the outside of a cell to the inside to mediate sophisticated biological responses. Effective computational models to unravel such a process by taking advantage of high-throughput genomic and proteomic data are needed to understand the essential mechanisms underlying the signaling pathways. In this article, we propose a novel method for uncovering signal transduction networks (STNs) by integrating protein interaction with gene expression data. Specifically, we formulate STN identification problem as an integer linear programming (ILP) model, which can be actually solved by a relaxed linear programming algorithm and is flexible for handling various prior information without any restriction on the network structures. The numerical results on yeast MAPK signaling pathways demonstrate that the proposed ILP model is able to uncover STNs or pathways in an efficient and accurate manner. In particular, the prediction results are found to be in high agreement with current biological knowledge and available information in literature. In addition, the proposed model is simple to be interpreted and easy to be implemented even for a large-scale system.
Radial-interval linear programming for environmental management under varied protection levels.
Tan, Qian; Huang, Guo H; Cai, Yanpeng
2010-09-01
In this study, a radial-interval linear programming (RILP) approach was developed for supporting waste management under uncertainty. RILP improved interval-parameter linear programming and its extensions in terms of input reasonableness and output robustness. From the perspective of modeling inputs, RILP could tackle highly uncertain information at the bounds of interval parameters through introducing the concept of fluctuation radius. Regarding modeling outputs, RILP allows controlling the degree of conservatism associated with interval solutions and is capable of quantifying corresponding system risks and benefits. This could facilitate the reflection of interactive relationship between the feasibility of system and the uncertainty of parameters. A computationally tractable algorithm was provided to solve RILP. Then, a long-term waste management case was studied to demonstrate the applicability of the developed methodology. A series of interval solutions obtained under varied protection levels were compared, helping gain insights into the interactions among protection level, violation risk, and system cost. Potential waste allocation alternatives could be generated from these interval solutions, which would be screened in real-world practices according to various projected system conditions as well as decision-makers' willingness to pay and risk tolerance levels. Sensitivity analysis further revealed the significant impact of fluctuation radii of interval parameters on the system. The results indicated that RILP is applicable to a wide spectrum of environmental management problems that are subject to compound uncertainties.
Chen, Ruoying; Zhang, Zhiwang; Wu, Di; Zhang, Peng; Zhang, Xinyang; Wang, Yong; Shi, Yong
2011-01-21
Protein-protein interactions are fundamentally important in many biological processes and it is in pressing need to understand the principles of protein-protein interactions. Mutagenesis studies have found that only a small fraction of surface residues, known as hot spots, are responsible for the physical binding in protein complexes. However, revealing hot spots by mutagenesis experiments are usually time consuming and expensive. In order to complement the experimental efforts, we propose a new computational approach in this paper to predict hot spots. Our method, Rough Set-based Multiple Criteria Linear Programming (RS-MCLP), integrates rough sets theory and multiple criteria linear programming to choose dominant features and computationally predict hot spots. Our approach is benchmarked by a dataset of 904 alanine-mutated residues and the results show that our RS-MCLP method performs better than other methods, e.g., MCLP, Decision Tree, Bayes Net, and the existing HotSprint database. In addition, we reveal several biological insights based on our analysis. We find that four features (the change of accessible surface area, percentage of the change of accessible surface area, size of a residue, and atomic contacts) are critical in predicting hot spots. Furthermore, we find that three residues (Tyr, Trp, and Phe) are abundant in hot spots through analyzing the distribution of amino acids.
Fan, Yurui; Huang, Guohe; Veawab, Amornvadee
2012-01-01
In this study, a generalized fuzzy linear programming (GFLP) method was developed to deal with uncertainties expressed as fuzzy sets that exist in the constraints and objective function. A stepwise interactive algorithm (SIA) was advanced to solve GFLP model and generate solutions expressed as fuzzy sets. To demonstrate its application, the developed GFLP method was applied to a regional sulfur dioxide (SO2) control planning model to identify effective SO2 mitigation polices with a minimized system performance cost under uncertainty. The results were obtained to represent the amount of SO2 allocated to different control measures from different sources. Compared with the conventional interval-parameter linear programming (ILP) approach, the solutions obtained through GFLP were expressed as fuzzy sets, which can provide intervals for the decision variables and objective function, as well as related possibilities. Therefore, the decision makers can make a tradeoff between model stability and the plausibility based on solutions obtained through GFLP and then identify desired policies for SO2-emission control under uncertainty.
Drag reduction of a car model by linear genetic programming control
Li, Ruiying; Cordier, Laurent; Borée, Jacques; Harambat, Fabien; Kaiser, Eurika; Duriez, Thomas
2016-01-01
We investigate open- and closed-loop active control for aerodynamic drag reduction of a car model. Turbulent flow around a blunt-edged Ahmed body is examined at $Re_{H}\\approx3\\times10^{5}$ based on body height. The actuation is performed with pulsed jets at all trailing edges combined with a Coanda deflection surface. The flow is monitored with pressure sensors distributed at the rear side. We apply a model-free control strategy building on Dracopoulos & Kent (Neural Comput. & Applic., vol. 6, 1997, pp. 214-228) and Gautier et al. (J. Fluid Mech., vol. 770, 2015, pp. 442-457). The optimized control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback including also time-history information feedback and combination thereof. Key enabler is linear genetic programming as simple and efficient framework for multiple inputs (actuators) and multiple outputs (sensors). The proposed linear genetic programming control can select the best open- or closed-loop control in an unsupervis...
Nidumolu, Uday Bhaskar; Keulen, Herman van; Lubbers, Marcel; Mapfumo, Andrew
2007-01-01
An Interactive Multiple Goal Linear Programming (IMGLP) model is developed that considers objectives of multiple stakeholders, i.e. different farmer groups, district agricultural officers and agricultural scientists for agricultural land use analysis. The analysis focuses on crop selection;
Nidumolu, Uday Bhaskar; Keulen, Herman van; Lubbers, Marcel; Mapfumo, Andrew
2007-01-01
An Interactive Multiple Goal Linear Programming (IMGLP) model is developed that considers objectives of multiple stakeholders, i.e. different farmer groups, district agricultural officers and agricultural scientists for agricultural land use analysis. The analysis focuses on crop selection; consider
Tonkin, Matthew J.; Tiedeman, Claire R.; Ely, D. Matthew; Hill, Mary C.
2007-01-01
The OPR-PPR program calculates the Observation-Prediction (OPR) and Parameter-Prediction (PPR) statistics that can be used to evaluate the relative importance of various kinds of data to simulated predictions. The data considered fall into three categories: (1) existing observations, (2) potential observations, and (3) potential information about parameters. The first two are addressed by the OPR statistic; the third is addressed by the PPR statistic. The statistics are based on linear theory and measure the leverage of the data, which depends on the location, the type, and possibly the time of the data being considered. For example, in a ground-water system the type of data might be a head measurement at a particular location and time. As a measure of leverage, the statistics do not take into account the value of the measurement. As linear measures, the OPR and PPR statistics require minimal computational effort once sensitivities have been calculated. Sensitivities need to be calculated for only one set of parameter values; commonly these are the values estimated through model calibration. OPR-PPR can calculate the OPR and PPR statistics for any mathematical model that produces the necessary OPR-PPR input files. In this report, OPR-PPR capabilities are presented in the context of using the ground-water model MODFLOW-2000 and the universal inverse program UCODE_2005. The method used to calculate the OPR and PPR statistics is based on the linear equation for prediction standard deviation. Using sensitivities and other information, OPR-PPR calculates (a) the percent increase in the prediction standard deviation that results when one or more existing observations are omitted from the calibration data set; (b) the percent decrease in the prediction standard deviation that results when one or more potential observations are added to the calibration data set; or (c) the percent decrease in the prediction standard deviation that results when potential information on one
Gizaw, S.; Komen, J.; Arendonk, van J.A.M.
2008-01-01
Ease of measurement of linear size traits is of particular significance in livestock breeding, particularly under village breeding programs where measuring live weight is difficult. In this study, we estimated genetic parameters and realized responses for live weight (LW) and linear size traits
Nakhanu, Shikuku Beatrice; Musasia, Amadalo Maurice
2015-01-01
The topic Linear Programming is included in the compulsory Kenyan secondary school mathematics curriculum at form four. The topic provides skills for determining best outcomes in a given mathematical model involving some linear relationship. This technique has found application in business, economics as well as various engineering fields. Yet many…
A O(n^8) X O(n^7) Linear Programming Model of the Quadratic Assignment Problem
Diaby, Moustapha
2008-01-01
This paper has been withdrawn because Theorem 21 and Corollary 22 are in error; The modeling idea is OK, but it needs 9-dimensional variables instead of the 8-dimensional variables defined in notations 6.9. Examples of the correct model (with 9-index variables) are: (1) Diaby, M., "Linear Programming Formulation of the Set Partitioning Problem," International Journal of Operational Research 8:4 (August 2010) pp. 399-427; (2) Diaby, M., "Linear Programming Formulation of the Vertex Coloring Pr...
Lee, Chang Jun
2015-01-01
In the fields of researches associated with plant layout optimization, the main goal is to minimize the costs of pipelines and pumping between connecting equipment under various constraints. However, what is the lacking of considerations in previous researches is to transform various heuristics or safety regulations into mathematical equations. For example, proper safety distances between equipments have to be complied for preventing dangerous accidents on a complex plant. Moreover, most researches have handled single-floor plant. However, many multi-floor plants have been constructed for the last decade. Therefore, the proper algorithm handling various regulations and multi-floor plant should be developed. In this study, the Mixed Integer Non-Linear Programming (MINLP) problem including safety distances, maintenance spaces, etc. is suggested based on mathematical equations. The objective function is a summation of pipeline and pumping costs. Also, various safety and maintenance issues are transformed into inequality or equality constraints. However, it is really hard to solve this problem due to complex nonlinear constraints. Thus, it is impossible to use conventional MINLP solvers using derivatives of equations. In this study, the Particle Swarm Optimization (PSO) technique is employed. The ethylene oxide plant is illustrated to verify the efficacy of this study.
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.
DEFF Research Database (Denmark)
Parlesak, Alexandr; Tetens, Inge; Jensen, Jørgen Dejgård
2016-01-01
Background: Food-Based Dietary Guidelines (FBDGs) are developed to promote healthier eating patterns, but increasing food prices may make healthy eating less affordable. The aim of this study was to design a range of cost-minimized nutritionally adequate health-promoting food baskets (FBs......) that help prevent both micronutrient inadequacy and diet-related non-communicable diseases at lowest cost. Methods: Average prices for 312 foods were collected within the Greater Copenhagen area. The cost and nutrient content of five different cost-minimized FBs for a family of four were calculated per day......: Use of linear programming facilitates the generation of low-cost food baskets that are nutritionally adequate, health promoting, and culturally acceptable....
Fuzzy linear fractional bi-level multi-objective programming problems
Directory of Open Access Journals (Sweden)
nemat safaei
2012-08-01
Full Text Available The Kuhn-Tuker condition has become nowadays an important tool in the hands of investigation for checking the optimality in optimization literature. In the present paper with use of a Taylor series and Kuhn-Tucker conditions approach, we solve a fuzzy linear fractional bilevel multi-objective programming (FLFBL-MOP problem. The Taylor series is an expansion of a series that represents a function. In the proposed approach, membership functions associated with each level(s ofthe objective(s of FLFBL-MOP problems are transformed and unied by using a Taylor series approach. By using the Kuhn-Tucker conditions, the problem is reduced to a single objective and nally, numericalexample is given to illustrates the efficiency and superiority of the proposed approach.
An Integer Linear Programming Solution to the Telescope Network Scheduling Problem
Lampoudi, Sotiria; Eastman, Jason
2015-01-01
Telescope networks are gaining traction due to their promise of higher resource utilization than single telescopes and as enablers of novel astronomical observation modes. However, as telescope network sizes increase, the possibility of scheduling them completely or even semi-manually disappears. In an earlier paper, a step towards software telescope scheduling was made with the specification of the Reservation formalism, through the use of which astronomers can express their complex observation needs and preferences. In this paper we build on that work. We present a solution to the discretized version of the problem of scheduling a telescope network. We derive a solvable integer linear programming (ILP) model based on the Reservation formalism. We show computational results verifying its correctness, and confirm that our Gurobi-based implementation can address problems of realistic size. Finally, we extend the ILP model to also handle the novel observation requests that can be specified using the more advanc...
Optimization Of Irrigation Area of Ukai Right Bank Main Canal-A Linear Programming Approach
Bhuvandas, Nishi; Mirajkar, A. B.; Timbadiya, P. V.; Patel, P. L.
2010-11-01
This paper presents a Linear Programming (LP) model for obtaining optimized cropping area in the command of Ukai reservoir. The objective is to maximize the sum of the relative yields from all crops in the irrigated area for specific range of water availability like 100%, 90%, 80% and 70%. The present study is aimed to get the optimal allocation of irrigation water depending upon the availability of water from the source. The net revenue from agricultural production will be maximized for available irrigation water taking into account the sets of constraints like crop area, cropping pattern and water requirement. The model is applied to a part of Ukai reservoir system namely Ukai Right Bank Main Canal (URBMC), in Gujarat state, India.
Directory of Open Access Journals (Sweden)
Clemens Posten
2013-10-01
Full Text Available The unicellular microalga Phaeodactylum tricornutum exhibits the ability to accumulate triacylglycerols to a high specific content when nutrients are limited in the culture medium. Therefore, the organism is a promising candidate for biodiesel production. Mathematical modeling can substantially contribute to process development and optimization of algae cultivation on different levels. In our work we describe a linear programming approach to model and simulate the growth and storage molecule accumulation of P. tricornutum. The model is based on mass and energy balances and shows that the organism realizes the inherent drive for maximization of energy to biomass conversion and growth. The model predicts that under nutrient limiting conditions both storage carbohydrates and lipids are synthesized simultaneously but at different rates. The model was validated with data gained from batch growth experiments.
Directory of Open Access Journals (Sweden)
Tumpal Sihombing
2013-01-01
Full Text Available The world is entering the era of recession when the trend is bearish and market is not so favorable. The capital markets in every major country were experiencing great amount of loss and people suffered in their investment. The Jakarta Composite Index (JCI has shown a great downturn for the past one year but the trend bearish year of the JCI. Therefore, rational investors should consider restructuring their portfolio to set bigger proportion in bonds and cash instead of stocks. Investors can apply modern portfolio theory by Harry Markowitz to find the optimum asset allocation for their portfolio. Higher return is always associated with higher risk. This study shows investors how to find out the lowest risk of a portfolio investment by providing them with several structures of portfolio weighting. By this way, investor can compare and make the decision based on risk-return consideration and opportunity cost as well. Keywords: Modern portfolio theory, Monte Carlo, linear programming
Techniques that strive to combat the influence of degeneracy in linear programming problems
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J.H. Nel
2003-12-01
Full Text Available Degeneracy can cause enormous problems when solving large scale linear programming problems. This is not only because there is a possibility that the problem can cycle, but also because a large number of iterations can be executed that do not improve the objective. In this article a procedure which utilises derived reduced costs is discussed. The derived reduced cost of a non- basic variable is defined in such a way that it makes the introduction to the non-basic variable into the basis unattractive if such a decision fails to improve the objective. The procedure deliberately strives to combat degeneracy using derived reduced costs, but it also utilises the advantageous properties of the classical gradient methods.
Linear programming to build food-based dietary guidelines: Romanian food baskets
DEFF Research Database (Denmark)
Parlesak, Alexandr; Robertson, Aileen; Hondru, Gabriela
As in many Member States of the WHO European Region, Romania is seeing an increase in the prevalence of overweight and obesity, particularly among children and adolescents. This is a major risk factor for the development of noncommunicable diseases (NCDs), and innovative approaches using...... approach using linear programming methodology to design national dietary recommendations which aim to prevent both NCDs and micronutrient deficiencies and still be affordable by low income groups. This new approach is applied within the context of food availability in Romania in 2014. Eating the same food...... basket that incorporates only WHO food-based dietary guidelines does not meet all the recommended nutrient intake values for, for example, vitamins A, D, K, iodine and calcium. • The version of a Romanian fully nutritious, health-promoting food basket for a family (two adults, two children) costs 19...
Approximating high-dimensional dynamics by barycentric coordinates with linear programming
Energy Technology Data Exchange (ETDEWEB)
Hirata, Yoshito, E-mail: yoshito@sat.t.u-tokyo.ac.jp; Aihara, Kazuyuki; Suzuki, Hideyuki [Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8505 (Japan); Department of Mathematical Informatics, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656 (Japan); CREST, JST, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012 (Japan); Shiro, Masanori [Department of Mathematical Informatics, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656 (Japan); Mathematical Neuroinformatics Group, Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-8568 (Japan); Takahashi, Nozomu; Mas, Paloma [Center for Research in Agricultural Genomics (CRAG), Consorci CSIC-IRTA-UAB-UB, Barcelona 08193 (Spain)
2015-01-15
The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics of the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.
Linear programming method for computing the gamut of object color solid.
Li, Changjun; Luo, M Ronnier; Cho, Maeng-Sub; Kim, Jin-Seo
2010-05-01
Recently there has been great interest in establishing the color gamut of solid colors or the optimum colors. The optimum colors are widely used for quantifying the quality of light sources and evaluating reproduction devices. An enumeration method was developed by Martinez-Verdu et al. [J. Opt. Soc. Am. A 24, 1501 (2007)] for finding optimum colors. However, it was found that the method is too time-costly. In this paper, a linear programming approach is proposed. The proposed method is simple and faster and has the advantage of keeping the characteristics of the true boundary. Comparison of the present method with the method of Martinez-Verdu et al. is also given.
A linear programming model to optimize the decision-making to managing cogeneration system
Energy Technology Data Exchange (ETDEWEB)
Tibi, Naser A. [United Arab Emirates University, P.O. Box 93330, Dubai (United Arab Emirates); Arman, Husam [University of Nottingham, Nottingham (United Kingdom)
2007-08-15
A mathematical linear programming (LP) model was developed to optimize the decision-making for managing a cogeneration facility as a potential clean-development mechanism project in a hospital in Palestine. The model was developed to optimize the cost of energy and the cost of installation of a small cogeneration plant under constraints on electricity-and-heat supply and demand balances. In the model, the sources of electricity are either from cogeneration or public utilities and it was calculated the least cost to supply electricity and heat to the hospital. The hospital is using heat for their operation and that made the application for the cogeneration to be attractive and feasible. In this study, we will develop the LP model and will show the results and the time schedule for the cogeneration. This developed LP model can be used and run to any cogeneration application with little modification. (orig.)
Polytope of Correct (Linear Programming) Decoding and Low-Weight Pseudo-Codewords
Chertkov, Michael
2011-01-01
We analyze Linear Programming (LP) decoding of graphical binary codes operating over soft-output, symmetric and log-concave channels. We show that the error-surface, separating domain of the correct decoding from domain of the erroneous decoding, is a polytope. We formulate the problem of finding the lowest-weight pseudo-codeword as a non-convex optimization (maximization of a convex function) over a polytope, with the cost function defined by the channel and the polytope defined by the structure of the code. This formulation suggests new heuristics for finding the lowest weight pseudo-codewords, which is advantageous in comparison with the one discussed previously, in particular because of being provably monotonic and also resulting in more frequent discoveries of pseudo-codewords with the lowest weights. The algorithm performance is tested on the example of the Tanner [155, 64, 20] code over the Additive White Gaussian Noise (AWGN) channel.
Optimalisasi Produksi Tebu dengan Program Linear pada Pabrik Gula Takalar Sulawesi Selatan
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Amran Sulaiman
2007-12-01
Full Text Available The aim of the optimization of sugar cane production at PG Takalar is allocate all resources proportionally to get the maximal production according to factory design attached witt relatively low cost. The optimizetion was conducted with linear programming. The result showed that optimal planting period for the plant cane is June, July. October, and November, while for retoon is June up to November. The varieties those give the optimal production is Camp, F.154, Others, PS.61, PS.85-1. Q.81, and ROC. 10. The optimal portion of plant category was 25% plant cane, 30% retoon I. 25% retoon II and 20% of ratoon III. By optimization, the total area of 3.213 ha potentially can produce 192.036. 13 ton/year, which is increase about 115% from the regular practice.
Highlights of the SLD Physics Program at the SLAC Linear Collider
Energy Technology Data Exchange (ETDEWEB)
Willocq, Stephane
2001-09-07
Starting in 1989, and continuing through the 1990s, high-energy physics witnessed a flowering of precision measurements in general and tests of the standard model in particular, led by e{sup +}e{sup -} collider experiments operating at the Z{sup 0} resonance. Key contributions to this work came from the SLD collaboration at the SLAC Linear Collider. By exploiting the unique capabilities of this pioneering accelerator and the SLD detector, including a polarized electron beam, exceptionally small beam dimensions, and a CCD pixel vertex detector, SLD produced a broad array of electroweak, heavy-flavor, and QCD measurements. Many of these results are one of a kind or represent the world's standard in precision. This article reviews the highlights of the SLD physics program, with an eye toward associated advances in experimental technique, and the contribution of these measurements to our dramatically improved present understanding of the standard model and its possible extensions.
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Ibnu Fazar
2016-02-01
Full Text Available Kegiatan pembelajaran merupakan kegiatan yang paling pokok dalam keseluruhan proses pendidikan. Penggunaan media teknologi dalam pembelajaran memungkinkan berlangsungnya proses pembelajaran secara individual (individual learning dengan menumbuhkan kemandirian dalam proses belajar, sehingga siswa akan mengalami proses yang jauh lebih bermakna dibandingkan dengan pembelajaran konvensional. Salah satu solusinya adalah pembelajaran menggunakan aplikasi GeoGebra berbantuan Android. Dari hasil penelitian diperoleh data observasi keaktifan siswa rata-rata 86,09 dengan kategori sangat baik. Dari minat siswa 90,32% kategori sangat berminat dan 9,68% kategori berminat. Dari hasil tes diperoleh 38,72% kategori sangat baik, 48,38% kategori baik, dan 12,90% kategori cukup. Dengan demikian pengembangan Lembar Aktivitas Siswa (LAS pada pokok bahasan program linear menggunakan aplikasi GeoGebra berbantuan android yang dikembangkan memiliki efek potensial terhadap peningkatan kemampuan pemahaman siswa dan bahan ajar yang dihasilkan telah dinyatakan valid, praktis, dan mempunyai efek potensial terhadap pemaham konsep matematika.
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......, the crisp equivalent of the fuzzy constraints should be less relaxed than that purely based on the concept of soft constraints. Based on the application context we adopt a strengthened version of soft constraints to interpret fuzzy constraints and form a crisp model with consistent and compact constraints...... for solution. Simulation results based on realistic data show that the failure risk can be managed by proper combination of aspiration levels and confidence factors for defining fuzzy numbers. There is a tradeoff between failure risk and material cost. The presented approach applies also for other scrap...
Neji, Radhouène; Besbes, Ahmed; Komodakis, Nikos; Deux, Jean-François; Maatouk, Mezri; Rahmouni, Alain; Bassez, Guillaume; Fleury, Gilles; Paragios, Nikos
2009-01-01
In this paper, we present a manifold clustering method fo the classification of fibers obtained from diffusion tensor images (DTI) of the human skeletal muscle. Using a linear programming formulation of prototype-based clustering, we propose a novel fiber classification algorithm over manifolds that circumvents the necessity to embed the data in low dimensional spaces and determines automatically the number of clusters. Furthermore, we propose the use of angular Hilbertian metrics between multivariate normal distributions to define a family of distances between tensors that we generalize to fibers. These metrics are used to approximate the geodesic distances over the fiber manifold. We also discuss the case where only geodesic distances to a reduced set of landmark fibers are available. The experimental validation of the method is done using a manually annotated significant dataset of DTI of the calf muscle for healthy and diseased subjects.
Approximating high-dimensional dynamics by barycentric coordinates with linear programming.
Hirata, Yoshito; Shiro, Masanori; Takahashi, Nozomu; Aihara, Kazuyuki; Suzuki, Hideyuki; Mas, Paloma
2015-01-01
The increasing development of novel methods and techniques facilitates the measurement of high-dimensional time series but challenges our ability for accurate modeling and predictions. The use of a general mathematical model requires the inclusion of many parameters, which are difficult to be fitted for relatively short high-dimensional time series observed. Here, we propose a novel method to accurately model a high-dimensional time series. Our method extends the barycentric coordinates to high-dimensional phase space by employing linear programming, and allowing the approximation errors explicitly. The extension helps to produce free-running time-series predictions that preserve typical topological, dynamical, and/or geometric characteristics of the underlying attractors more accurately than the radial basis function model that is widely used. The method can be broadly applied, from helping to improve weather forecasting, to creating electronic instruments that sound more natural, and to comprehensively understanding complex biological data.
Directory of Open Access Journals (Sweden)
Guan Yu
Full Text Available Accurately identifying mild cognitive impairment (MCI individuals who will progress to Alzheimer's disease (AD is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI and fluorodeoxyglucose positron emission tomography (FDG-PET. However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI subjects and 226 stable MCI (sMCI subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images and also the single-task classification method (using only MRI or only subjects with both MRI and
PAPR reduction in FBMC using an ACE-based linear programming optimization
van der Neut, Nuan; Maharaj, Bodhaswar TJ; de Lange, Frederick; González, Gustavo J.; Gregorio, Fernando; Cousseau, Juan
2014-12-01
This paper presents four novel techniques for peak-to-average power ratio (PAPR) reduction in filter bank multicarrier (FBMC) modulation systems. The approach extends on current PAPR reduction active constellation extension (ACE) methods, as used in orthogonal frequency division multiplexing (OFDM), to an FBMC implementation as the main contribution. The four techniques introduced can be split up into two: linear programming optimization ACE-based techniques and smart gradient-project (SGP) ACE techniques. The linear programming (LP)-based techniques compensate for the symbol overlaps by utilizing a frame-based approach and provide a theoretical upper bound on achievable performance for the overlapping ACE techniques. The overlapping ACE techniques on the other hand can handle symbol by symbol processing. Furthermore, as a result of FBMC properties, the proposed techniques do not require side information transmission. The PAPR performance of the techniques is shown to match, or in some cases improve, on current PAPR techniques for FBMC. Initial analysis of the computational complexity of the SGP techniques indicates that the complexity issues with PAPR reduction in FBMC implementations can be addressed. The out-of-band interference introduced by the techniques is investigated. As a result, it is shown that the interference can be compensated for, whilst still maintaining decent PAPR performance. Additional results are also provided by means of a study of the PAPR reduction of the proposed techniques at a fixed clipping probability. The bit error rate (BER) degradation is investigated to ensure that the trade-off in terms of BER degradation is not too severe. As illustrated by exhaustive simulations, the SGP ACE-based technique proposed are ideal candidates for practical implementation in systems employing the low-complexity polyphase implementation of FBMC modulators. The methods are shown to offer significant PAPR reduction and increase the feasibility of FBMC as
Tetens, Inge; Dejgård Jensen, Jørgen; Smed, Sinne; Gabrijelčič Blenkuš, Mojca; Rayner, Mike; Darmon, Nicole; Robertson, Aileen
2016-01-01
Background Food-Based Dietary Guidelines (FBDGs) are developed to promote healthier eating patterns, but increasing food prices may make healthy eating less affordable. The aim of this study was to design a range of cost-minimized nutritionally adequate health-promoting food baskets (FBs) that help prevent both micronutrient inadequacy and diet-related non-communicable diseases at lowest cost. Methods Average prices for 312 foods were collected within the Greater Copenhagen area. The cost and nutrient content of five different cost-minimized FBs for a family of four were calculated per day using linear programming. The FBs were defined using five different constraints: cultural acceptability (CA), or dietary guidelines (DG), or nutrient recommendations (N), or cultural acceptability and nutrient recommendations (CAN), or dietary guidelines and nutrient recommendations (DGN). The variety and number of foods in each of the resulting five baskets was increased through limiting the relative share of individual foods. Results The one-day version of N contained only 12 foods at the minimum cost of DKK 27 (€ 3.6). The CA, DG, and DGN were about twice of this and the CAN cost ~DKK 81 (€ 10.8). The baskets with the greater variety of foods contained from 70 (CAN) to 134 (DGN) foods and cost between DKK 60 (€ 8.1, N) and DKK 125 (€ 16.8, DGN). Ensuring that the food baskets cover both dietary guidelines and nutrient recommendations doubled the cost while cultural acceptability (CAN) tripled it. Conclusion Use of linear programming facilitates the generation of low-cost food baskets that are nutritionally adequate, health promoting, and culturally acceptable. PMID:27760131
Yu, Guan; Liu, Yufeng; Thung, Kim-Han; Shen, Dinggang
2014-01-01
Accurately identifying mild cognitive impairment (MCI) individuals who will progress to Alzheimer's disease (AD) is very important for making early interventions. Many classification methods focus on integrating multiple imaging modalities such as magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET). However, the main challenge for MCI classification using multiple imaging modalities is the existence of a lot of missing data in many subjects. For example, in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, almost half of the subjects do not have PET images. In this paper, we propose a new and flexible binary classification method, namely Multi-task Linear Programming Discriminant (MLPD) analysis, for the incomplete multi-source feature learning. Specifically, we decompose the classification problem into different classification tasks, i.e., one for each combination of available data sources. To solve all different classification tasks jointly, our proposed MLPD method links them together by constraining them to achieve the similar estimated mean difference between the two classes (under classification) for those shared features. Compared with the state-of-the-art incomplete Multi-Source Feature (iMSF) learning method, instead of constraining different classification tasks to choose a common feature subset for those shared features, MLPD can flexibly and adaptively choose different feature subsets for different classification tasks. Furthermore, our proposed MLPD method can be efficiently implemented by linear programming. To validate our MLPD method, we perform experiments on the ADNI baseline dataset with the incomplete MRI and PET images from 167 progressive MCI (pMCI) subjects and 226 stable MCI (sMCI) subjects. We further compared our method with the iMSF method (using incomplete MRI and PET images) and also the single-task classification method (using only MRI or only subjects with both MRI and PET images
1980-05-31
34 AIIE Transac- tions, Vol. 11, Nc, 1, March 1979, pp. 61-69. (8] Taylor, Bernard and Keown , Arthur J., "A Goal Programming Application of Capital...Programming," OMEGA, Vol. 1, No. 2, April 1973, pp. 193-205. [29] Lee, S. M., and Keown , Arthur J., "Integer Goal Programming Model for Capital...Hadley, G., Linear Algebra, Addison-Wesley Publishing Co., Inc., Reading, MA, 1961. [60] Zuckerman, Martin M., Sets and Transfinite Numbers, Macmillan
Institute of Scientific and Technical Information of China (English)
Wan Zhongping; Wang Guangrain; Lv Yibing
2011-01-01
The penalty function method, presented many years ago, is an important nu- merical method for the mathematical programming problems. In this article, we propose a dual-relax penalty function approach, which is significantly different from penalty func- tion approach existing for solving the bilevel programming, to solve the nonlinear bilevel programming with linear lower level problem. Our algorithm will redound to the error analysis for computing an approximate solution to the bilevel programming. The error estimate is obtained among the optimal objective function value of the dual-relax penalty problem and of the original bilevel programming problem. An example is illustrated to show the feasibility of the proposed approach.
Wei, Peng; Sridhar, Banavar; Chen, Neil Yi-Nan; Sun, Dengfent
2012-01-01
A class of strategies has been proposed to reduce contrail formation in the United States airspace. A 3D grid based on weather data and the cruising altitude level of aircraft is adjusted to avoid the persistent contrail potential area with the consideration to fuel-efficiency. In this paper, the authors introduce a contrail avoidance strategy on 3D grid by considering additional operationally feasible constraints from an air traffic controller's aspect. First, shifting too many aircraft to the same cruising level will make the miles-in-trail at this level smaller than the safety separation threshold. Furthermore, the high density of aircraft at one cruising level may exceed the workload for the traffic controller. Therefore, in our new model we restrict the number of total aircraft at each level. Second, the aircraft count variation for successive intervals cannot be too drastic since the workload to manage climbing/descending aircraft is much larger than managing cruising aircraft. The contrail reduction is formulated as an integer-programming problem and the problem is shown to have the property of total unimodularity. Solving the corresponding relaxed linear programming with the simplex method provides an optimal and integral solution to the problem. Simulation results are provided to illustrate the methodology.
Drag reduction of a car model by linear genetic programming control
Li, Ruiying; Noack, Bernd R.; Cordier, Laurent; Borée, Jacques; Harambat, Fabien
2017-08-01
We investigate open- and closed-loop active control for aerodynamic drag reduction of a car model. Turbulent flow around a blunt-edged Ahmed body is examined at ReH≈ 3× 105 based on body height. The actuation is performed with pulsed jets at all trailing edges (multiple inputs) combined with a Coanda deflection surface. The flow is monitored with 16 pressure sensors distributed at the rear side (multiple outputs). We apply a recently developed model-free control strategy building on genetic programming in Dracopoulos and Kent (Neural Comput Appl 6:214-228, 1997) and Gautier et al. (J Fluid Mech 770:424-441, 2015). The optimized control laws comprise periodic forcing, multi-frequency forcing and sensor-based feedback including also time-history information feedback and combinations thereof. Key enabler is linear genetic programming (LGP) as powerful regression technique for optimizing the multiple-input multiple-output control laws. The proposed LGP control can select the best open- or closed-loop control in an unsupervised manner. Approximately 33% base pressure recovery associated with 22% drag reduction is achieved in all considered classes of control laws. Intriguingly, the feedback actuation emulates periodic high-frequency forcing. In addition, the control identified automatically the only sensor which listens to high-frequency flow components with good signal to noise ratio. Our control strategy is, in principle, applicable to all multiple actuators and sensors experiments.
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.
Linear genetic programming for time-series modelling of daily flow rate
Indian Academy of Sciences (India)
Aytac Guven
2009-04-01
In this study linear genetic programming (LGP),which is a variant of Genetic Programming,and two versions of Neural Networks (NNs)are used in predicting time-series of daily ﬂow rates at a station on Schuylkill River at Berne,PA,USA.Daily ﬂow rate at present is being predicted based on different time-series scenarios.For this purpose,various LGP and NN models are calibrated with training sets and validated by testing sets.Additionally,the robustness of the proposed LGP and NN models are evaluated by application data,which are used neither in training nor at testing stage.The results showed that both techniques predicted the ﬂow rate data in quite good agreement with the observed ones,and the predictions of LGP and NN are challenging.The performance of LGP,which was moderately better than NN,is very promising and hence supports the use of LGP in predicting of river ﬂow data.
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.
Directory of Open Access Journals (Sweden)
Mohammad Ali Afshari
2012-10-01
Full Text Available The aim of this paper is to present mathematical models optimizing all materials flows in supply chain. In this research a fuzzy multi-objective nonlinear mixed- integer programming model with piecewise linear membership function is applied to design a multi echelon supply chain network (SCN by considering total transportation costs and capacities of all echelons with fuzzy objectives. The model that is proposed in this study has 4 fuzzy functions. The first function is minimizing the total transportation costs between all echelons (suppliers, factories, distribution centers (DCs and customers. The second one is minimizing holding and ordering cost on DCs. The third objective is minimizing the unnecessary and unused capacity of factories and DCs via decreasing variance of transported amounts between echelons. The forth is minimizing the number of total vehicles that ship the materials and products along with SCN. For solving such a problem, as nodes increases in SCN, the traditional method does not have ability to solve large scale problem. So, we applied a Meta heuristic method called Genetic Algorithm. The numerical example is real world applied and compared the results with each other demonstrate the feasibility of applying the proposed model to given problem, and also its advantages are discussed.
Directory of Open Access Journals (Sweden)
David Pérez Perales
2016-07-01
Full Text Available It is known that capacity issues in tactical production plans in a hierarchical context are relevant since its inaccurate determination may lead to unrealistic or simply non-feasible plans at the operational level. Semi-continuous industrial processes, such as ceramic ones, often imply large setups and their consideration is crucial for accurate capacity estimation. However, in most of production planning models developed in a hierarchical context at this tactical (aggregated level, setup changes are not explicitly considered. Their consideration includes not only decisions about lot sizing of production, but also allocation, known as Capacitated Lot Sizing and Loading Problem (CLSLP. However, CLSLP does not account for set-up continuity, specially important in contexts with lengthy and costly set-ups and where product families minimum run length are similar to planning periods. In this work, a mixed integer linear programming (MILP model for a two stage ceramic firm which accounts for lot sizing and loading decisions including minimum lot-sizes and set-up continuity between two consecutive periods is proposed. Set-up continuity inclusion is modelled just considering which product families are produced at the beginning and at the end of each period of time, and not the complete sequence. The model is solved over a simplified two-stage real-case within a Spanish ceramic firm. Obtained results confirm its validity.
Chen, Vivian Yi-Ju; Yang, Tse-Chuan
2012-08-01
An increasing interest in exploring spatial non-stationarity has generated several specialized analytic software programs; however, few of these programs can be integrated natively into a well-developed statistical environment such as SAS. We not only developed a set of SAS macro programs to fill this gap, but also expanded the geographically weighted generalized linear modeling (GWGLM) by integrating the strengths of SAS into the GWGLM framework. Three features distinguish our work. First, the macro programs of this study provide more kernel weighting functions than the existing programs. Second, with our codes the users are able to better specify the bandwidth selection process compared to the capabilities of existing programs. Third, the development of the macro programs is fully embedded in the SAS environment, providing great potential for future exploration of complicated spatially varying coefficient models in other disciplines. We provided three empirical examples to illustrate the use of the SAS macro programs and demonstrated the advantages explained above.
Aspect-object alignment with Integer Linear Programming in opinion mining.
Directory of Open Access Journals (Sweden)
Yanyan Zhao
Full Text Available Target extraction is an important task in opinion mining. In this task, a complete target consists of an aspect and its corresponding object. However, previous work has always simply regarded the aspect as the target itself and has ignored the important "object" element. Thus, these studies have addressed incomplete targets, which are of limited use for practical applications. This paper proposes a novel and important sentiment analysis task, termed aspect-object alignment, to solve the "object neglect" problem. The objective of this task is to obtain the correct corresponding object for each aspect. We design a two-step framework for this task. We first provide an aspect-object alignment classifier that incorporates three sets of features, namely, the basic, relational, and special target features. However, the objects that are assigned to aspects in a sentence often contradict each other and possess many complicated features that are difficult to incorporate into a classifier. To resolve these conflicts, we impose two types of constraints in the second step: intra-sentence constraints and inter-sentence constraints. These constraints are encoded as linear formulations, and Integer Linear Programming (ILP is used as an inference procedure to obtain a final global decision that is consistent with the constraints. Experiments on a corpus in the camera domain demonstrate that the three feature sets used in the aspect-object alignment classifier are effective in improving its performance. Moreover, the classifier with ILP inference performs better than the classifier without it, thereby illustrating that the two types of constraints that we impose are beneficial.
Aspect-object alignment with Integer Linear Programming in opinion mining.
Zhao, Yanyan; Qin, Bing; Liu, Ting; Yang, Wei
2015-01-01
Target extraction is an important task in opinion mining. In this task, a complete target consists of an aspect and its corresponding object. However, previous work has always simply regarded the aspect as the target itself and has ignored the important "object" element. Thus, these studies have addressed incomplete targets, which are of limited use for practical applications. This paper proposes a novel and important sentiment analysis task, termed aspect-object alignment, to solve the "object neglect" problem. The objective of this task is to obtain the correct corresponding object for each aspect. We design a two-step framework for this task. We first provide an aspect-object alignment classifier that incorporates three sets of features, namely, the basic, relational, and special target features. However, the objects that are assigned to aspects in a sentence often contradict each other and possess many complicated features that are difficult to incorporate into a classifier. To resolve these conflicts, we impose two types of constraints in the second step: intra-sentence constraints and inter-sentence constraints. These constraints are encoded as linear formulations, and Integer Linear Programming (ILP) is used as an inference procedure to obtain a final global decision that is consistent with the constraints. Experiments on a corpus in the camera domain demonstrate that the three feature sets used in the aspect-object alignment classifier are effective in improving its performance. Moreover, the classifier with ILP inference performs better than the classifier without it, thereby illustrating that the two types of constraints that we impose are beneficial.
A linear programming model to optimize diets in environmental policy scenarios.
Moraes, L E; Wilen, J E; Robinson, P H; Fadel, J G
2012-03-01
The objective was to develop a linear programming model to formulate diets for dairy cattle when environmental policies are present and to examine effects of these policies on diet formulation and dairy cattle nitrogen and mineral excretions as well as methane emissions. The model was developed as a minimum cost diet model. Two types of environmental policies were examined: a tax and a constraint on methane emissions. A tax was incorporated to simulate a greenhouse gas emissions tax policy, and prices of carbon credits in the current carbon markets were attributed to the methane production variable. Three independent runs were made, using carbon dioxide equivalent prices of $5, $17, and $250/t. A constraint was incorporated into the model to simulate the second type of environmental policy, reducing methane emissions by predetermined amounts. The linear programming formulation of this second alternative enabled the calculation of marginal costs of reducing methane emissions. Methane emission and manure production by dairy cows were calculated according to published equations, and nitrogen and mineral excretions were calculated by mass conservation laws. Results were compared with respect to the values generated by a base least-cost model. Current prices of the carbon credit market did not appear onerous enough to have a substantive incentive effect in reducing methane emissions and altering diet costs of our hypothetical dairy herd. However, when emissions of methane were assumed to be reduced by 5, 10, and 13.5% from the base model, total diet costs increased by 5, 19.1, and 48.5%, respectively. Either these increased costs would be passed onto the consumer or dairy producers would go out of business. Nitrogen and potassium excretions were increased by 16.5 and 16.7% with a 13.5% reduction in methane emissions from the base model. Imposing methane restrictions would further increase the demand for grains and other human-edible crops, which is not a progressive
Directory of Open Access Journals (Sweden)
Yen Sun
2010-05-01
Full Text Available It is observed that the number of Indonesia’s domestic investor who involved in the stock exchange is very less compare to its total number of population (only about 0.1%. As a result, Indonesia Stock Exchange (IDX is highly affected by foreign investor that can threat the economy. Domestic investor tends to invest in risk-free asset such as deposit in the bank since they are not familiar yet with the stock market and anxious about the risk (risk-averse type of investor. Therefore, it is important to educate domestic investor to involve in the stock exchange. Investing in portfolio of stock is one of the best choices for risk-averse investor (such as Indonesia domestic investor since it offers lower risk for a given level of return. This paper studies the optimization of Indonesian stock portfolio. The data is the historical return of 10 stocks of LQ 45 for 5 time series (January 2004 – December 2008. It will be focus on selecting stocks into a portfolio, setting 10 of stock portfolios using mean variance method combining with the linear programming (solver. Furthermore, based on Efficient Frontier concept and Sharpe measurement, there will be one stock portfolio picked as an optimum Portfolio (Namely Portfolio G. Then, Performance of portfolio G will be evaluated by using Sharpe, Treynor and Jensen Measurement to show whether the return of Portfolio G exceeds the market return. This paper also illustrates how the stock composition of the Optimum Portfolio (G succeeds to predict the portfolio return in the future (5th January – 3rd April 2009. The result of the study observed that optimization portfolio using Mean-Variance (consistent with Markowitz theory combine with linear programming can be applied into Indonesia stock’s portfolio. All the measurements (Sharpe, Jensen, and Treynor show that the portfolio G is a superior portfolio. It is also been found that the composition (weights stocks of optimum portfolio (G can be used to