A multi-objective PSO for job-shop scheduling problems

Authors:
D. Y. Sha;Hsing-Hung Lin
Affiliations:
Department of Industrial Engineering and System Management, Chung Hua University, Hsin Chu, Taiwan, ROC;Department of Industrial Engineering and Management, National Chiao Tung University, Hsin Chu, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 9
Cited 8

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A new particle swarm optimization for the open shop scheduling problem

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A Pareto archive particle swarm optimization for multi-objective job shop scheduling

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An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems

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Handling multiple objectives with particle swarm optimization

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Comparison and application of four versions of particle swarm optimization algorithms in the sequence optimization

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An adaptive annealing genetic algorithm for the job-shop planning and scheduling problem

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A new hybrid multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem

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An improved group search optimizer with operation of quantum-behaved swarm and its application

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Inventory based two-objective job shop scheduling model and its hybrid genetic algorithm

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A Swarm Intelligence Method Combined to Evolutionary Game Theory Applied to the Resources Allocation Problem

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An improved intelligent water drops algorithm for solving multi-objective job shop scheduling

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An improved particle swarm optimization with decline disturbance index (DDPSO) for multi-objective job-shop scheduling problem

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Abstract

Most previous research into the job-shop scheduling problem has concentrated on finding a single optimal solution (e.g., makespan), even though the actual requirement of most production systems requires multi-objective optimization. The aim of this paper is to construct a particle swarm optimization (PSO) for an elaborate multi-objective job-shop scheduling problem. The original PSO was used to solve continuous optimization problems. Due to the discrete solution spaces of scheduling optimization problems, the authors modified the particle position representation, particle movement, and particle velocity in this study. The modified PSO was used to solve various benchmark problems. Test results demonstrated that the modified PSO performed better in search quality and efficiency than traditional evolutionary heuristics.