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Enhancing Heuristics for Order Acceptance and Scheduling Using Genetic Programming

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Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

Order acceptance and scheduling (OAS) in make-to-order manufacturing systems is a NP-hard problem for which finding optimal solutions for problem instances can be challenging. Because of this, several heuristic approaches have been proposed in the literature to find near-optimal solutions to OAS. Many previous heuristic approaches are very effective, but require careful design and developing new heuristics can be difficult. Genetic Programming (GP) has been used to generate reusable and efficient heuristics in OAS and shows promising results. However, in terms of solution quality, the evolved heuristics are still less competitive as compared to highly customised heuristics designed by human experts. To overcome these limitations, this paper proposes two new Particle Swarm Optimisation (PSO) approaches to OAS. Afterwards, GP evolved rules are combined with an existing Tabu Search (TS) heuristic and with the proposed PSO algorithms as hybrid approaches to OAS. The experimental results show that these PSO approaches are competitive with effective heuristics such as TS. In addition, TS heuristic greatly benefits from evolved rules, whereas PSO approaches do not benefit.

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Author information

Authors and Affiliations

  1. Evolutionary Computation Research Group, Victoria University of Wellington, PO Box 600, Wellington, New Zealand

    John Park, Su Nguyen, Mengjie Zhang & Mark Johnston

  2. International University - VNU HCMC, Ho Chi Minh City, Vietnam

    Su Nguyen

Authors
  1. John Park
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  2. Su Nguyen
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  3. Mengjie Zhang
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  4. Mark Johnston
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Park, J., Nguyen, S., Zhang, M., Johnston, M. (2014). Enhancing Heuristics for Order Acceptance and Scheduling Using Genetic Programming. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_61

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_61

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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