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A Cooperative Intelligent Approach for Job-shop Scheduling Based on Bacterial Foraging Strategy and Particle Swarm Optimization

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Computational Intelligence Systems in Industrial Engineering

Part of the book series: Atlantis Computational Intelligence Systems ((ATLANTISCIS,volume 6))

Abstract

The optimization of job-shop scheduling is very important because of its theoretical and practical significance. In this chapter, a computationally effective approach of combining bacterial foraging strategy with particle swarm optimization for solving the minimum makespan problem of job shop scheduling is proposed. In the artificial bacterial foraging system, a novel chemotactic model is designed to address the job shop scheduling problem and a mechanism of quorum sensing and communication are presented to improve the foraging performance. In the particle swarm system, a novel concept for the distance and velocity of a particle is presented to pave the way for the job-shop scheduling problem. The proposed cooperative algorithm effectively exploits the capabilities of distributed and parallel computing of swarm intelligence approaches. The algorithm is examined using a set of benchmark instances with various sizes and levels of hardness and compared with other approaches reported in some existing literatures. The computational results validate the effectiveness of the proposed algorithm.

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Authors and Affiliations

  1. College of Computer Science and Technology, Dalian University of Technology, Dalian, 116024, China

    Hongwei Ge & Guozhen Tan

Authors
  1. Hongwei Ge
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  2. Guozhen Tan
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Corresponding author

Correspondence to Hongwei Ge .

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Editors and Affiliations

  1. Fac. Management, Dept. Industrial Engineering, Istanbul Technical University, Campus Macka, Istanbul, 34367, Turkey

    Cengiz Kahraman

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Ge, H., Tan, G. (2012). A Cooperative Intelligent Approach for Job-shop Scheduling Based on Bacterial Foraging Strategy and Particle Swarm Optimization. In: Kahraman, C. (eds) Computational Intelligence Systems in Industrial Engineering. Atlantis Computational Intelligence Systems, vol 6. Atlantis Press, Paris. https://doi.org/10.2991/978-94-91216-77-0_17

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  • DOI: https://doi.org/10.2991/978-94-91216-77-0_17

  • Publisher Name: Atlantis Press, Paris

  • Print ISBN: 978-94-91216-76-3

  • Online ISBN: 978-94-91216-77-0

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