A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application

Allahverdi, Ali; Al-Anzi, Fawaz S.

doi:10.1016/j.cor.2004.09.002

Cited by 152 publications

(60 citation statements)

References 21 publications

(34 reference statements)

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“…Since PSO is developed for continuous optimization problem initially, most existing PSO applications are resorting to continuous function value optimization [4][5][6]29]. Recently, a few researches applied PSO for discrete combinatorial optimization problems [26][27][28][33][34][35][36][37]. For the problem addressed in this paper we explore the use of a hybrid discrete PSO algorithm to overcome the feasibility issues of the traditional binary algorithm.…”

Section: The Discrete Particle Swarm Optimization Algorithmmentioning

confidence: 99%

“…In PSO, the potential solutions (particles), move around in a multidimensional search space with a velocity, which is constantly updated by a combination of the particle's own experience, the experience of the particle's neighbors and the experience of the entire swarm. PSO has been successfully applied to a wide range of applications such as neural network training [25], task assignment [26], and scheduling problem [27,28]. See [6] for a further discussion on applications.…”

Section: The Discrete Particle Swarm Optimization Algorithmmentioning

confidence: 99%

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An Hybrid Discrete Particle Swarm Optimization Algorithm For Solving A Sustainable Remanufacturing Suply Chain Problem

González¹,

Mateus²,

Luna³

et al. 2016

Anais Do 10. Congresso Brasileiro De Inteligência Computacional

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-We propose a hybrid evolutionary algorithm based on discrete Particle Swarm Optimization for solving a novel remanufacturing sustainable supply chain design problem. There is little research being done in mathematical modeling and solutions methods for these problems. The paper describes a NP-hard mixed-integer 0-1 model (MIP) for this remanufacturing sustainable problem in which given a network of three layers, we have to determine the number of remanufacturing facilities to be located at sites chosen from among a given set of candidate sites, to allocate sourcing facilities to remanufacturing facilities and remanufacturing facilities to demand facilities with a given demand. The objective is to minimize transportation, distribution and operation costs of the facilities. The heuristics combine the traditional binary PSO algorithm with greedyexchange strategies in order to improve the quality of the solutions. We report computational results for instances generated from known data test available in the literature.

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Section: The Discrete Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Section: The Discrete Particle Swarm Optimization Algorithmmentioning

confidence: 99%

An Hybrid Discrete Particle Swarm Optimization Algorithm For Solving A Sustainable Remanufacturing Suply Chain Problem

González¹,

Mateus²,

Luna³

et al. 2016

Anais Do 10. Congresso Brasileiro De Inteligência Computacional

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show abstract

“…There have been a significant number of research works about optimisation of the assembly scheduling for traditional production systems. This class of optimisation problems has been solved by various approaches, such as heuristic (Andrés et al 2008;Kim et al 1996;Al-Anzi and Allahverdi 2007;Allahverdi and Al-Anzi 2009;Sung and Kim 2008;Koulamas and Kyparisis 2001), particle swarm optimisation (Dong et al 2012;Wang and Liu 2010;Hamta et al 2013;Allahverdi and Al-Anzi 2006), mixed integer programming (Ozturk et al 2010;Lin and Liao 2012;Terekhov et al 2012;Sawik 2004), genetic algorithm (Wong et al 2009;Marian et al 2003Marian et al , 2006Yolmeh and Kianfar 2012;Celano et al 1999;Dini et al 1999), Taguchi method (Chen et al 2010), dynamic programming (Jiang et al 1997;Zhang et al 2005;Yee and Ventura 1999), neural networks (Chen et al 2008;Hong and Cho 1995), multi-agent evolutionary algorithm (Zeng et al 2011), simulated annealing (Milner et al 1994), etc. In general, all of the works done so far deal with two main optimisation issues: assembly sequence and assembly resource location.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimisation of assembly scheduling in VCIM systems using genetic algorithm

2017

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Assembly plays an important role in any production system as it constitutes a significant portion of the lead time and cost of a product. Virtual computer-integrated manufacturing (VCIM) system is a modern production system being conceptually developed to extend the application of traditional computer-integrated manufacturing (CIM) system to global level. Assembly scheduling in VCIM systems is quite different from one in traditional production systems because of the difference in the working principles of the two systems. In this article, the assembly scheduling problem in VCIM systems is modeled and then an integrated approach based on genetic algorithm (GA) is proposed to search for a global optimised solution to the problem. Because of dynamic nature of the scheduling problem, a novel GA with unique chromosome representation and modified genetic operations is developed herein. Robustness of the proposed approach is verified by a numerical example.

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“…Recently, a few researches applied PSO for discrete combinatorial optimization problems [26][27][28][33][34][35][36][37].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In PSO, the potential solutions, the so-called particles, move around in a multidimensional search space with a velocity, which is constantly updated by the particle's own experience and the experience of the particle's neighbors or the experience of the whole swarm. PSO has been successfully applied to a wide range of applications such as function optimization, neural network training [25], task assignment [26], and scheduling problem [27,28].…”

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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A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application

Cited by 152 publications

References 21 publications

An Hybrid Discrete Particle Swarm Optimization Algorithm For Solving A Sustainable Remanufacturing Suply Chain Problem

An Hybrid Discrete Particle Swarm Optimization Algorithm For Solving A Sustainable Remanufacturing Suply Chain Problem

Optimisation of assembly scheduling in VCIM systems using genetic algorithm

A Discrete Particle Swarm Optimization Algorithm for Uncapacitated Facility Location Problem

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