IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v195y2018icp227-248.html
   My bibliography  Save this article

A comparison of two stage-based hybrid algorithms for a batch scheduling problem in hybrid flow shop with learning effect

Author

Listed:
  • Shahvari, Omid
  • Logendran, Rasaratnam

Abstract

This paper addresses the hybrid flow shop batch scheduling problem with sequence- and machine-dependent family setup times where the objective is to simultaneously minimize the weighted sum of the total weighted completion time and total weighted tardiness, being mindful of the producer and customers, respectively. In order to reflect the industry requirements, machine availability times, job release times, machine capability and eligibility for processing jobs, stage skipping, and learning effect are considered. Unlike group scheduling, batch scheduling disregards the group technology assumptions by splitting pre-determined groups of jobs into inconsistent batches to perform timely processing of jobs with higher priority and utilize the maximum available capacity of the machines. One of the contributions of this research is to realize the benefits of integrating the batching decision into the group scheduling approach. Another contribution is to develop robust meta-heuristics based on hybridization of local search and population-based structures along with the stage-based interdependency strategy to solve the research problem. An initial solution finding mechanism and a comprehensive data generation mechanism are developed. The efficiency and effectiveness of the meta-heuristic algorithms are verified by lower bounds obtained by two mixed-integer linear programming models. The benefits of considering the batching decision with respect to desired lower bounds on batch sizes will hopefully encourage practitioners to apply the batch scheduling approach instead of the group scheduling approach.

Suggested Citation

  • Shahvari, Omid & Logendran, Rasaratnam, 2018. "A comparison of two stage-based hybrid algorithms for a batch scheduling problem in hybrid flow shop with learning effect," International Journal of Production Economics, Elsevier, vol. 195(C), pages 227-248.
    • Handle: RePEc:eee:proeco:v:195:y:2018:i:c:p:227-248
    DOI: 10.1016/j.ijpe.2017.10.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527317303304
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2017.10.015?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ozturk, Onur, 2020. "A truncated column generation algorithm for the parallel batch scheduling problem to minimize total flow time," European Journal of Operational Research, Elsevier, vol. 286(2), pages 432-443.
    2. Liu, Ming & Yang, Xuenan & Chu, Feng & Zhang, Jiantong & Chu, Chengbin, 2020. "Energy-oriented bi-objective optimization for the tempered glass scheduling," Omega, Elsevier, vol. 90(C).
    3. Fan Yang & Roel Leus, 2021. "Scheduling hybrid flow shops with time windows," Journal of Heuristics, Springer, vol. 27(1), pages 133-158, April.
    4. Shaojun Lu & Jun Pei & Xinbao Liu & Xiaofei Qian & Nenad Mladenovic & Panos M. Pardalos, 2020. "Less is more: variable neighborhood search for integrated production and assembly in smart manufacturing," Journal of Scheduling, Springer, vol. 23(6), pages 649-664, December.
    5. Omid Shahvari & Rasaratnam Logendran & Madjid Tavana, 2022. "An efficient model-based branch-and-price algorithm for unrelated-parallel machine batching and scheduling problems," Journal of Scheduling, Springer, vol. 25(5), pages 589-621, October.
    6. Wu, Wei & Hayashi, Takito & Haruyasu, Kato & Tang, Liang, 2023. "Exact algorithms based on a constrained shortest path model for robust serial-batch and parallel-batch scheduling problems," European Journal of Operational Research, Elsevier, vol. 307(1), pages 82-102.
    7. Neufeld, Janis S. & Schulz, Sven & Buscher, Udo, 2023. "A systematic review of multi-objective hybrid flow shop scheduling," European Journal of Operational Research, Elsevier, vol. 309(1), pages 1-23.
    8. Cheng, Bayi & Zhu, Huijun & Li, Kai & Li, Yongjun, 2019. "Optimization of batch operations with a truncated batch-position-based learning effect," Omega, Elsevier, vol. 85(C), pages 134-143.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:195:y:2018:i:c:p:227-248. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.