Search results for " Programming"

showing 10 items of 1616 documents

Dynamic programming and Munkres algorithm for optimal photovoltaic arrays reconfiguration

2015

Abstract In this paper, an original formulation of the control problem for optimal PV array reconfiguration, following a Total Cross Tied layout, is proposed. The formulation follows the well-known subset sum problem, which is a special case of the knapsack problem. The reconfiguration is a measure devoted to mitigate the mismatch effect and maximize the output power of small photovoltaic plants under non-homogeneous working conditions. Therefore, reconfiguration means changing the connections of the solar panels adaptively by a dynamic switching matrix. The control system implements an easy dynamic programming algorithm to change the switches layout. The use of the Munkres assignment metho…

Mathematical optimizationRenewable Energy Sustainability and the EnvironmentComputer sciencePhotovoltaic systemMismatch Photovoltaic modules Optimization Reconfiguration.Control reconfigurationPower (physics)Settore ING-IND/33 - Sistemi Elettrici Per L'EnergiaDynamic programmingSettore ING-IND/31 - ElettrotecnicaHungarian algorithmKnapsack problemControl systemSubset sum problemGeneral Materials ScienceSolar Energy
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Linear Programming Based Methods for Solving Arc Routing Problems

2000

From the pioneering works of Dantzig, Edmonds and others, polyhedral (i.e. linear programming based) methods have been successfully applied to the resolution of many combinatorial optimization problems. See Junger, Reinelt & Rinaldi (1995) for an excellent survey on this topic. Roughly speaking, the method consists of trying to formulate the problem as a Linear Program and using the existing powerful methods of Linear Programming to solve it.

Mathematical optimizationRoute inspection problemLinear programmingComputer scienceCombinatorial optimization problemResolution (logic)Arc routing
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Optimal Switches in Multi–inventory Systems

2007

Given a switched multi-inventory system we wish to find the optimal schedule of the resets to maintain the system in a safe operating interval, while minimizing a function related to the cost of the resets. We discuss a family of instances that can be solved in polynomial time by linear programming. We do this by introducing a set-covering formulation with a totally unimodular constraint matrix.

Mathematical optimizationScheduleUnimodular matrixLinear programmingInterval (mathematics)Function (mathematics)Constraint matrixTime complexityMathematics
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A bilateral convergent bounding technique for plastic deformations

1990

For the class of elastic perfectly plastic discrete structures, subjected to a dynamic loading history, a bilateral bounding technique for plastic deformations has been studied. The computation of the bound is founded on the concept that to obtain it, any history of fictitious plastic deformations can be used, if only admissible. Such history is obtained by solving a sequence of linear programming problems (LPPs) with a multiple step compared to the step of the sequence of the quadratic programming problems (QPPs) adopting in the classic elasto-plastic analysis. The constraints of the LPPs coincide with the constraints of the QPPs, while the objective function is a linear combination of var…

Mathematical optimizationSequenceLinear programmingMechanics of MaterialsBounding overwatchDynamic loadingMechanical EngineeringComputationApplied mathematicsQuadratic programmingCondensed Matter PhysicsLinear combinationMathematicsMeccanica
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Stability Analysis of Large Scale Networks of Autonomous Work Systems with Delays

2011

This paper considers the problem of stability analysis for a class of production networks of autonomous work systems with delays in the capacity changes. The system under consideration does not share information between work systems and the work systems adjust capacity with the objective of maintaining a desired amount of local work in progress (WIP). Attention is focused to derive explicit sufficient delay-dependent stability conditions for the network using properties of matrix norm. Finally, numerical results are provided to demonstrate the proposed approach.

Mathematical optimizationStability conditionsClass (computer programming)Computer scienceScale (chemistry)Matrix normStability (learning theory)Production (economics)Work in processWork systems
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Solving the Discrete Multiple Criteria Problem using Convex Cones

1984

An interactive method employing pairwise comparisons of attainable solutions is developed for solving the discrete, deterministic multiple criteria problem assuming a single decision maker who has an implicit quasi-concave increasing utility (or value) function. The method chooses an arbitrary set of positive multipliers to generate a proxy composite linear objective function which is then maximized over the set of solutions. The maximizing solution is compared with several solutions using pairwise judgments asked of the decision maker. Responses are used to eliminate alternatives using convex cones based on expressed preferences, and then a new set of weights is found that satisfies the i…

Mathematical optimizationStrategy and ManagementRegular polygonMultiple criteriaPairwise comparisonManagement Science and Operations ResearchDecision makerProxy (statistics)Mathematical proofMathematicsDecision analysismultiattribute programming: multiple criteria convex cones [decision analysis utility/preference]Management Science
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The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data

2019

Traditional Learning Automata (LA) work with the understanding that the actions are chosen purely based on the “state” in which the machine is. This modus operandus completely ignores any estimation of the Random Environment’s (RE’s) (specified as \(\mathbb {E}\)) reward/penalty probabilities. To take these into consideration, Estimator/Pursuit LA utilize “cheap” estimates of the Environment’s reward probabilities to make them converge by an order of magnitude faster. This concept is quite simply the following: Inexpensive estimates of the reward probabilities can be used to rank the actions. Thereafter, when the action probability vector has to be updated, it is done not on the basis of th…

Mathematical optimizationTheoretical computer scienceLearning automataBasis (linear algebra)Computer scienceRank (computer programming)Object PartitioningPartitioning-based learningEstimatorLearning Automata02 engineering and technologyProbability vectorField (computer science)AutomatonRanking0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processing[INFO]Computer Science [cs]Object Migration Automaton
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Cut-First Branch-and-Price-Second for the Capacitated Arc-Routing Problem

2012

This paper presents the first full-fledged branch-and-price (bap) algorithm for the capacitated arc-routing problem (CARP). Prior exact solution techniques either rely on cutting planes or the transformation of the CARP into a node-routing problem. The drawbacks are either models with inherent symmetry, dense underlying networks, or a formulation where edge flows in a potential solution do not allow the reconstruction of unique CARP tours. The proposed algorithm circumvents all these drawbacks by taking the beneficial ingredients from existing CARP methods and combining them in a new way. The first step is the solution of the one-index formulation of the CARP in order to produce strong cut…

Mathematical optimizationbiologyComputer scienceBranch and priceFunction (mathematics)Management Science and Operations Researchbiology.organism_classificationUpper and lower boundsComputer Science ApplicationsTransformation (function)Vehicle routing problemCarpArc routingAlgorithmInteger programmingOperations Research
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Scatter Search and Path Relinking: Advances and Applications

2006

Scatter search (SS) is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, SS uses strategies for combining solution vectors that have proved effective in a variety of problem settings. Path relinking (PR) has been suggested as an approach to integrate intensification and diversification strategies in a search scheme. The approach may be viewed as an extreme (highly focused) instance of a strategy that seeks to incorporate attributes of high quality solutions, by creating inducements to favo…

Mathematical optimizationeducation.field_of_studyEngineeringbusiness.industryPopulationDecision ruleTabu searchNonlinear programmingVariety (cybernetics)Path (graph theory)Local search (optimization)Set (psychology)educationbusiness
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Best Proximity Points for Some Classes of Proximal Contractions

2013

Given a self-mapping g: A → A and a non-self-mapping T: A → B, the aim of this work is to provide sufficient conditions for the existence of a unique point x ∈ A, called g-best proximity point, which satisfies d g x, T x = d A, B. In so doing, we provide a useful answer for the resolution of the nonlinear programming problem of globally minimizing the real valued function x → d g x, T x, thereby getting an optimal approximate solution to the equation T x = g x. An iterative algorithm is also presented to compute a solution of such problems. Our results generalize a result due to Rhoades (2001) and hence such results provide an extension of Banach's contraction principle to the case of non-s…

Mathematical optimizationmetric spacesArticle SubjectIterative methodApplied Mathematicslcsh:MathematicsWork (physics)proximal contractionbest proximity pointExtension (predicate logic)Resolution (logic)lcsh:QA1-939Nonlinear programmingReal-valued functionPoint (geometry)Settore MAT/03 - GeometriaContraction principleAnalysisMathematicsAbstract and Applied Analysis
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