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Hybrid Differential Evolution-Variable Neighborhood Search to Solve Multiobjective Hybrid Flowshop Scheduling with Job-Sequence Dependent Setup Time

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Abstract

This paper proposes a hybrid algorithm which combines the differential evolution algorithm (DE) with variable neighborhood search (VNS) to solve multi-objective hybrid flexible flowshop with job-sequence dependent setup time (HFFS/SDST). The objective is to minimize makespan and lateness maximization on a hybrid flexible flowshop. Each stage has at least two units operating in parallel machines as well as considering skipping the stage, where not all jobs must be processed at each stage of operation. The model also considers the machine’s setup time that depends on the sequence of jobs that are processed directly on the machine. Pareto solution is used as the process of collecting the points of non-dominated solutions. To evaluate the performance of our algorithm, we compare the results with those of DE-Insert and Particle Swarm Optimization (PSO)-VNS. Computational results and comparisons indicate that DE-VNS is more efective than DE-Insert and PSO-VNS.

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Acknowledgements

This research was supported by Minsitry of Research and Technology and Higher Education Indonesia.

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Authors and Affiliations

  1. Industrial Engineering Department, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia

    Budi Santosa

  2. Universitas Wijaya Putra, Surabaya, Indonesia

    Ong Andre Wahyu Riyanto

Authors
  1. Budi Santosa
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  2. Ong Andre Wahyu Riyanto
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Corresponding author

Correspondence to Budi Santosa .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Shenzhen University, Shenzhen, China

    Ben Niu

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Santosa, B., Riyanto, O.A.W. (2016). Hybrid Differential Evolution-Variable Neighborhood Search to Solve Multiobjective Hybrid Flowshop Scheduling with Job-Sequence Dependent Setup Time. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_59

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  • DOI: https://doi.org/10.1007/978-3-319-41000-5_59

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40999-3

  • Online ISBN: 978-3-319-41000-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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