A cross entropy-based metaheuristic algorithm for large-scale capacitated facility location problems

In this paper, we present a metaheuristic-based algorithm for the capacitated facility location problem. The proposed scheme is made up by three phases: (i) solution construction phase, in which a cross entropy-based scheme is used to 'intelligently' guess which facilities should be opened...

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Published inThe Journal of the Operational Research Society Vol. 60; no. 10; pp. 1439 - 1448
Main Authors Caserta, M, Quiñonez Rico, E
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 01.10.2009
Palgrave Macmillan
Palgrave Macmillan UK
Taylor & Francis Ltd
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Abstract In this paper, we present a metaheuristic-based algorithm for the capacitated facility location problem. The proposed scheme is made up by three phases: (i) solution construction phase, in which a cross entropy-based scheme is used to 'intelligently' guess which facilities should be opened; (ii) local search phase, aimed at exploring the neighbourhood of 'elite' solutions of the previous phase; and (iii) learning phase, aimed at fine-tuning the stochastic parameters of the algorithm. The algorithm has been thoroughly tested on large-scale random generated instances as well as on benchmark problems and computational results show the effectiveness and robustness of the algorithm.
AbstractList In this paper, we present a metaheuristic-based algorithm for the capacitated facility location problem. The proposed scheme is made up by three phases: (i) solution construction phase, in which a cross entropy-based scheme is used to 'intelligently' guess which facilities should be opened; (ii) local search phase, aimed at exploring the neighbourhood of 'elite' solutions of the previous phase; and (iii) learning phase, aimed at fine-tuning the stochastic parameters of the algorithm. The algorithm has been thoroughly tested on large-scale random generated instances as well as on benchmark problems and computational results show the effectiveness and robustness of the algorithm.
In this paper, we present a metaheuristic-based algorithm for the capacitated facility location problem. The proposed scheme is made up by three phases: (i) solution construction phase, in which a cross entropy-based scheme is used to 'intelligently' guess which facilities should be opened; (ii) local search phase, aimed at exploring the neighbourhood of 'elite' solutions of the previous phase; and (iii) learning phase, aimed at fine-tuning the stochastic parameters of the algorithm. The algorithm has been thoroughly tested on large-scale random generated instances as well as on benchmark problems and computational results show the effectiveness and robustness of the algorithm. [PUBLICATION ABSTRACT]
Author Caserta, M
Quiñonez Rico, E
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Issue 10
Keywords location
heuristics
integer programming
cross entropy
local search
Local search
Entropy
Location problem
Facility location
Integer programming
Heuristic method
Capacity constraint
Robustness
Large scale
Metamodel
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Snippet In this paper, we present a metaheuristic-based algorithm for the capacitated facility location problem. The proposed scheme is made up by three phases: (i)...
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SubjectTerms Algorithms
Applied sciences
Approximation
Business and Management
Cost functions
cross entropy
Entropy
Exact sciences and technology
Fixed costs
Flows in networks. Combinatorial problems
Heuristic
Heuristics
Integer programming
local search
location
Location analysis
Logistics
Management
Mathematical vectors
Objective functions
Operational research and scientific management
Operational research. Management science
Operations research
Operations Research/Decision Theory
Optimal solutions
Savings
Studies
Theoretical Paper
Theoretical Papers
Transportation
Title A cross entropy-based metaheuristic algorithm for large-scale capacitated facility location problems
URI https://www.tandfonline.com/doi/abs/10.1057/jors.2008.77
https://www.jstor.org/stable/40295703
http://dx.doi.org/10.1057/jors.2008.77
https://link.springer.com/article/10.1057/jors.2008.77
https://www.proquest.com/docview/231388728
Volume 60
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