A hybrid discrete particle swarm optimization for dual-resource constrained job shop scheduling with resource flexibility

Jing Zhang (), Wanliang Wang () and Xinli Xu
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Jing Zhang: Zhejiang Police College
Wanliang Wang: Zhejiang University of Technology
Xinli Xu: Zhejiang University of Technology

Journal of Intelligent Manufacturing, 2017, vol. 28, issue 8, No 13, 1972 pages

Abstract: Abstract In this paper, a novel hybrid discrete particle swarm optimization algorithm is proposed to solve the dual-resource constrained job shop scheduling problem with resource flexibility. Particles are represented based on a three-dimension chromosome coding scheme of operation sequence and resources allocation. Firstly, a mixed population initialization method is used for the particles. Then a discrete particle swarm optimization is designed as the global search process by taking the dual-resources feature into account. Moreover, an improved simulated annealing with variable neighborhoods structure is introduced to improve the local searching ability for the proposed algorithm. Finally, experimental results are given to show the effectiveness of the proposed algorithm.

Keywords: Particle swarm optimization; Simulated annealing; Dual-resource constraint; Resource flexibility (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10845-015-1082-0

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