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Scheduling Deteriorating Jobs and Module Changes with Incompatible Job Families on Parallel Machines Using a Hybrid SADE-AFSA Algorithm

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Learning and Intelligent Optimization (LION 12 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11353))

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Abstract

This research is motivated by a scheduling problem found in the special steel industry of continuous casting processing, where the special steel is produced on the parallel machines, i.e., the continuous casting machine, and each machine can produce more than one types of special steel. Usually, different types of special steel have diversity alloy content, which generates distinct cooling requirements. Consequently, the job families are incompatible, different types of special steel cannot be continuous process. This indicates that the machine will pause for a period of time to execute the module change activity between two adjacent job families. In this context, we attempt to investigate a parallel machine scheduling problem with the objective of minimizing the makespan, i.e., the completion time of the last job. The effect of deterioration, incompatible job families, and the module change activity are taken into consideration simultaneously, and the actual processing time of each job depends on its starting time and normal processing time. A hybrid SADE-AFSA algorithm combining Self-Adaptive Differential Evolution (SADE) and Artificial fish swarm algorithm (AFSA) is proposed to tackle this problem. Finally, the computational experiments are conducted to evaluate the performance of the proposed algorithm.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 71601065, 71231004, 71501058, 71690235, 71690230), and Innovative Research Groups of the National Natural Science Foundation of China (71521001), the Humanities and Social Sciences Foundation of the Chinese Ministry of Education (No. 15YJC630097), Anhui Province Natural Science Foundation (No. 1608085QG167). Panos M. Pardalos is partially supported by the project of “Distinguished International Professor by the Chinese Ministry of Education” (MS2014HFGY026).

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, 230009, China

    Yuwei Sun, Xiaofei Qian & Siwen Liu

Authors
  1. Yuwei Sun
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  2. Xiaofei Qian
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  3. Siwen Liu
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Corresponding authors

Correspondence to Xiaofei Qian or Siwen Liu .

Editor information

Editors and Affiliations

  1. University of Trento, Trento, Italy

    Roberto Battiti

  2. University of Trento, Trento, Italy

    Mauro Brunato

  3. Wilfrid Laurier University, Waterloo, ON, Canada

    Ilias Kotsireas

  4. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

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Sun, Y., Qian, X., Liu, S. (2019). Scheduling Deteriorating Jobs and Module Changes with Incompatible Job Families on Parallel Machines Using a Hybrid SADE-AFSA Algorithm. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_40

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  • DOI: https://doi.org/10.1007/978-3-030-05348-2_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05347-5

  • Online ISBN: 978-3-030-05348-2

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