IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v208y2019icp1-16.html
   My bibliography  Save this article

Multi-objective optimization of complex scheduling problems in low-volume low-variety production systems

Author

Listed:
  • Russell, Arya
  • Taghipour, Sharareh

Abstract

In this paper, a new approach for solving scheduling problems in low-volume low-variety production systems is proposed. Products assembled in such production systems follow a pre-defined processing order through a series of unique work centers, each budgeted with multiple classifications of resources, responsible to complete a pre-defined statement of work, over the span of an imposed takt-time. Aircraft, heavy aero-structures, and heavy mining and military equipment are examples of products assembled in such production systems. Despite prominent scholarly advancements in sequencing and scheduling optimization of a wide range of production systems, limited research has been reported on mathematical programming approaches for scheduling optimization of activities in low-volume low-variety production systems. This paper fills the gap in the current literature, through the formulation of a set of multi-objective mixed-integer linear mathematical programming models, developed for solving discrete-time work center scheduling problems in low-volume low-variety production systems. Three mathematical models are proposed in this paper, two of which are formulated for scheduling optimization of activities within a work center, differentiated by their objectives and underlying assumptions, reflective of two distinct industrial approaches to scheduling. Additionally, an alternative optimization model is proposed for evaluating a work center's maximum capacity given the complete saturation of resources, recommended for capacity studies and early detection of bottlenecks. The models proposed in this paper are validated and verified for compatibility and reliability through a real-world case study with a global leader in the aerospace industry.

Suggested Citation

  • Russell, Arya & Taghipour, Sharareh, 2019. "Multi-objective optimization of complex scheduling problems in low-volume low-variety production systems," International Journal of Production Economics, Elsevier, vol. 208(C), pages 1-16.
    • Handle: RePEc:eee:proeco:v:208:y:2019:i:c:p:1-16
    DOI: 10.1016/j.ijpe.2018.11.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527318304444
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2018.11.005?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.

    References listed on IDEAS

    as
    1. A. Alan B. Pritsker & Lawrence J. Waiters & Philip M. Wolfe, 1969. "Multiproject Scheduling with Limited Resources: A Zero-One Programming Approach," Management Science, INFORMS, vol. 16(1), pages 93-108, September.
    2. Zhao, Chuanli & Tang, Hengyong, 2012. "Two-machine flow shop scheduling with deteriorating jobs and chain precedence constraints," International Journal of Production Economics, Elsevier, vol. 136(1), pages 131-136.
    3. Drezet, L.-E. & Billaut, J.-C., 2008. "A project scheduling problem with labour constraints and time-dependent activities requirements," International Journal of Production Economics, Elsevier, vol. 112(1), pages 217-225, March.
    4. A. Charnes & W. W. Cooper, 1957. "Management Models and Industrial Applications of Linear Programming," Management Science, INFORMS, vol. 4(1), pages 38-91, October.
    5. Browning, Tyson R. & Yassine, Ali A., 2010. "Resource-constrained multi-project scheduling: Priority rule performance revisited," International Journal of Production Economics, Elsevier, vol. 126(2), pages 212-228, August.
    6. Huang, Rong-Hwa, 2010. "Multi-objective job-shop scheduling with lot-splitting production," International Journal of Production Economics, Elsevier, vol. 124(1), pages 206-213, March.
    7. Alan S. Manne, 1960. "On the Job-Shop Scheduling Problem," Operations Research, INFORMS, vol. 8(2), pages 219-223, April.
    8. Edward H. Bowman, 1959. "The Schedule-Sequencing Problem," Operations Research, INFORMS, vol. 7(5), pages 621-624, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Jeunet, Jully & Bou Orm, Mayassa, 2020. "Optimizing temporary work and overtime in the Time Cost Quality Trade-off Problem," European Journal of Operational Research, Elsevier, vol. 284(2), pages 743-761.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bertsimas, Dimitris & Gupta, Shubham & Lulli, Guglielmo, 2014. "Dynamic resource allocation: A flexible and tractable modeling framework," European Journal of Operational Research, Elsevier, vol. 236(1), pages 14-26.
    2. Jens Brunner & Jonathan Bard & Rainer Kolisch, 2009. "Flexible shift scheduling of physicians," Health Care Management Science, Springer, vol. 12(3), pages 285-305, September.
    3. Ullrich, Christian A., 2013. "Integrated machine scheduling and vehicle routing with time windows," European Journal of Operational Research, Elsevier, vol. 227(1), pages 152-165.
    4. Schirmer, Andreas & Riesenberg, Sven, 1997. "Parameterized heuristics for project scheduling: Biased random sampling methods," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 456, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    5. Kramer, Arthur & Dell’Amico, Mauro & Iori, Manuel, 2019. "Enhanced arc-flow formulations to minimize weighted completion time on identical parallel machines," European Journal of Operational Research, Elsevier, vol. 275(1), pages 67-79.
    6. Ripon K. Chakrabortty & Ruhul A. Sarker & Daryl L. Essam, 2020. "Single mode resource constrained project scheduling with unreliable resources," Operational Research, Springer, vol. 20(3), pages 1369-1403, September.
    7. Hartmann, Sönke & Briskorn, Dirk, 2010. "A survey of variants and extensions of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 207(1), pages 1-14, November.
    8. Kolisch, Rainer, 1994. "Serial and parallel resource-constrained projekt scheduling methodes revisited: Theory and computation," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 344, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    9. Yukang He & Tao Jia & Weibo Zheng, 2024. "Simulated annealing for centralised resource-constrained multiproject scheduling to minimise the maximal cash flow gap under different payment patterns," Annals of Operations Research, Springer, vol. 338(1), pages 115-149, July.
    10. Gómez Sánchez, Mariam & Lalla-Ruiz, Eduardo & Fernández Gil, Alejandro & Castro, Carlos & Voß, Stefan, 2023. "Resource-constrained multi-project scheduling problem: A survey," European Journal of Operational Research, Elsevier, vol. 309(3), pages 958-976.
    11. Van Eynde, Rob & Vanhoucke, Mario, 2022. "New summary measures and datasets for the multi-project scheduling problem," European Journal of Operational Research, Elsevier, vol. 299(3), pages 853-868.
    12. Xinyu Yao & Karmel S. Shehadeh & Rema Padman, 2024. "Multi-resource allocation and care sequence assignment in patient management: a stochastic programming approach," Health Care Management Science, Springer, vol. 27(3), pages 352-369, September.
    13. Shima Javanmard & Behrouz Afshar-Nadjafi & Seyed Taghi Akhavan Niaki, 2022. "A bi-objective model for scheduling of multiple projects under multi-skilled workforce for distributed load energy usage," Operational Research, Springer, vol. 22(3), pages 2245-2280, July.
    14. Kolisch, Rainer, 1994. "Efficient priority rules for the resource-constrained project scheduling problem," Manuskripte aus den Instituten für Betriebswirtschaftslehre der Universität Kiel 350, Christian-Albrechts-Universität zu Kiel, Institut für Betriebswirtschaftslehre.
    15. Bahman Naderi & Rubén Ruiz & Vahid Roshanaei, 2023. "Mixed-Integer Programming vs. Constraint Programming for Shop Scheduling Problems: New Results and Outlook," INFORMS Journal on Computing, INFORMS, vol. 35(4), pages 817-843, July.
    16. Rahman Torba & Stéphane Dauzère-Pérès & Claude Yugma & Cédric Gallais & Juliette Pouzet, 2024. "Solving a real-life multi-skill resource-constrained multi-project scheduling problem," Annals of Operations Research, Springer, vol. 338(1), pages 69-114, July.
    17. Ben Issa, Samer & Patterson, Raymond A. & Tu, Yiliu, 2021. "Solving resource-constrained multi-project environment under different activity assumptions," International Journal of Production Economics, Elsevier, vol. 232(C).
    18. Da Col, Giacomo & Teppan, Erich C., 2022. "Industrial-size job shop scheduling with constraint programming," Operations Research Perspectives, Elsevier, vol. 9(C).
    19. Nagar, Amit & Haddock, Jorge & Heragu, Sunderesh, 1995. "Multiple and bicriteria scheduling: A literature survey," European Journal of Operational Research, Elsevier, vol. 81(1), pages 88-104, February.
    20. Philippe Baptiste & Ruslan Sadykov, 2009. "On scheduling a single machine to minimize a piecewise linear objective function: A compact MIP formulation," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(6), pages 487-502, September.

    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:208:y:2019:i:c:p:1-16. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.