Solving the simple plant location problem by genetic algorithm

Kratica, Jozef; Tošić, Dejan V.; Filipović, Vladimir; Ljubić, Ivana

doi:10.1051/ro:2001107

Cited by 139 publications

(73 citation statements)

References 21 publications

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“…Sun [19] examined tabu search procedure against the Lagrangean method and heuristic procedures reported by Ghosh [2]. Genetic algorithms (GA) are also applied by Kratica and Jaramillo [20,21]. Finally, there are also artificial neural network approaches to solve UFL problems in Gen et al [22] and Vaithyanathan et al [23].…”

Section: Literature Reviewmentioning

confidence: 99%

A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

Güner

Sevkli

2008

Journal of Artificial Evolution and Applications

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A discrete version of particle swarm optimization (DPSO) is employed to solve uncapacitated facility location (UFL) problem which is one of the most widely studied in combinatorial optimization. In addition, a hybrid version with a local search is defined to get more efficient results. The results are compared with a continuous particle swarm optimization (CPSO) algorithm and two other metaheuristics studies, namely, genetic algorithm (GA) and evolutionary simulated annealing (ESA). To make a reasonable comparison, we applied to same benchmark suites that are collected from OR-library. In conclusion, the results showed that DPSO algorithm is slightly better than CPSO algorithm and competitive with GA and ESA.

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Section: Literature Reviewmentioning

confidence: 99%

A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

Güner

Sevkli

2008

Journal of Artificial Evolution and Applications

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“…In the context of performing economic activities efficiently, various objects have been considered as facilities, such as manufacturing plants, storage facilities, warehouses, libraries, fire stations, hospitals or wireless service stations. Several techniques have been applied to the UFLP such as the swarm intelligence algorithms or a meta-heuristics algorithm; particle swarm optimization (PSO) [3], ant colony optimization (ACO) [5], artificial bee colony algorithm (ABC) [9], and genetic algorithm [7].…”

Section: Introductionmentioning

confidence: 99%

Fitness Function in ABC Algorithm for Uncapacitated Facility Location Problem

Watanabe

Takaya

Yamamura

2015

Information and Communication Technology

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Part 5: Applied Modeling and SimulationInternational audienceWe study the fitness function of the artificial bee colony algorithm applying to solve the uncapacitated facility location problem. Our hypothesis is that the fitness function in the artificial bee colony algorithm is not necessarily suitable for specific optimization problems. We carry out experiments to examine several fitness functions for the artificial bee colony algorithm to solve the uncapacitated facility location problem and show the conventional fitness function is not necessarily suitable

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“…The CPL is a well-known combinatorial optimization problem and a number of decision problems can be obtained as special cases of CPL. There are substantial numbers of heuristic solution algorithms proposed in the literature (See Rolland et al, 1996;Holmberg & Ling, 1997;Delmaire et al, 1999;Kratica et al, 2001;He et al, 2003;Uno et al, 2005). As well, exact solution methods have been studied by many authors.…”

Section: Introductionmentioning

confidence: 99%