IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v289y2021i3p841-854.html
   My bibliography  Save this article

A combinatorial analysis of the permutation and non-permutation flow shop scheduling problems

Author

Listed:
  • Rossit, Daniel A.
  • Vásquez, Óscar C.
  • Tohmé, Fernando
  • Frutos, Mariano
  • Safe, Martín D.

Abstract

In this paper we introduce a novel approach to the combinatorial analysis of flow shop scheduling problems for the case of two jobs, assuming that processing times are unknown. The goal is to determine the dominance properties between permutation flow shop (PFS) and non-permutation flow shop (NPFS) schedules. In order to address this issue we develop a graph-theoretical approach to describe the sets of operations that define the makespan of feasible PFS and NPFS schedules (critical paths). The cardinality of these sets is related to the number of switching machines at which the sequence of the previous operations of the two jobs becomes reversed. This, in turn, allows us to uncover structural and dominance properties between the PFS and NPFS versions of the scheduling problem. We also study the case in which the ratio between the shortest and longest processing times, denoted ρ, is the only information known about those processing times. A combinatorial argument based on ρ leads to the identification of the NPFS schedules that are dominated by PFS ones, restricting the space of feasible solutions to the NPFS problem. We also extend our analysis to the comparison of NPFS schedules (with different number of switching machines). Again, based on the value of ρ, we are able to identify NPFS schedules dominated by other NPFS schedules.

Suggested Citation

  • Rossit, Daniel A. & Vásquez, Óscar C. & Tohmé, Fernando & Frutos, Mariano & Safe, Martín D., 2021. "A combinatorial analysis of the permutation and non-permutation flow shop scheduling problems," European Journal of Operational Research, Elsevier, vol. 289(3), pages 841-854.
  • Handle: RePEc:eee:ejores:v:289:y:2021:i:3:p:841-854
    DOI: 10.1016/j.ejor.2019.07.055
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221719306344
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2019.07.055?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. Waldherr, Stefan & Knust, Sigrid, 2017. "Decomposition algorithms for synchronous flow shop problems with additional resources and setup times," European Journal of Operational Research, Elsevier, vol. 259(3), pages 847-863.
    2. Anis Gharbi & Mohamed Labidi & Mohamed Aly Louly, 2014. "The Nonpermutation Flowshop Scheduling Problem: Adjustment and Bounding Procedures," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-14, November.
    3. Sheldon B. Akers, 1956. "Letter to the Editor---A Graphical Approach to Production Scheduling Problems," Operations Research, INFORMS, vol. 4(2), pages 244-245, April.
    4. Viswanath Nagarajan & Maxim Sviridenko, 2009. "Tight Bounds for Permutation Flow Shop Scheduling," Mathematics of Operations Research, INFORMS, vol. 34(2), pages 417-427, May.
    5. Sheldon B. Akers & Joyce Friedman, 1955. "A Non-Numerical Approach to Production Scheduling Problems," Operations Research, INFORMS, vol. 3(4), pages 429-442, November.
    6. S. M. Johnson, 1954. "Optimal two‐ and three‐stage production schedules with setup times included," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 1(1), pages 61-68, March.
    7. M. R. Garey & D. S. Johnson & Ravi Sethi, 1976. "The Complexity of Flowshop and Jobshop Scheduling," Mathematics of Operations Research, INFORMS, vol. 1(2), pages 117-129, May.
    8. Shabtay, Dvir & Zofi, Moshe, 2018. "Single machine scheduling with controllable processing times and an unavailability period to minimize the makespan," International Journal of Production Economics, Elsevier, vol. 198(C), pages 191-200.
    9. Bultmann, Matthias & Knust, Sigrid & Waldherr, Stefan, 2018. "Synchronous flow shop scheduling with pliable jobs," European Journal of Operational Research, Elsevier, vol. 270(3), pages 943-956.
    10. Nowicki, Eugeniusz & Smutnicki, Czeslaw, 1996. "A fast tabu search algorithm for the permutation flow-shop problem," European Journal of Operational Research, Elsevier, vol. 91(1), pages 160-175, May.
    11. Rossit, Daniel Alejandro & Tohmé, Fernando & Frutos, Mariano, 2018. "The Non-Permutation Flow-Shop scheduling problem: A literature review," Omega, Elsevier, vol. 77(C), pages 143-153.
    12. Vallada, Eva & Ruiz, Rubén & Framinan, Jose M., 2015. "New hard benchmark for flowshop scheduling problems minimising makespan," European Journal of Operational Research, Elsevier, vol. 240(3), pages 666-677.
    13. James E. Kelley, 1961. "Critical-Path Planning and Scheduling: Mathematical Basis," Operations Research, INFORMS, vol. 9(3), pages 296-320, June.
    14. Choi, Byung-Cheon & Yoon, Suk-Hun & Chung, Sung-Jin, 2007. "Minimizing maximum completion time in a proportionate flow shop with one machine of different speed," European Journal of Operational Research, Elsevier, vol. 176(2), pages 964-974, January.
    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. Geser, Philine & Le, Hoang Thanh & Hartmann, Tom & Middendorf, Martin, 2022. "On permutation schedules for two-machine flow shops with buffer constraints and constant processing times on one machine," European Journal of Operational Research, Elsevier, vol. 303(2), pages 593-601.

    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. Jan Gmys, 2022. "Exactly Solving Hard Permutation Flowshop Scheduling Problems on Peta-Scale GPU-Accelerated Supercomputers," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2502-2522, September.
    2. S. S. Panwalkar & Christos Koulamas, 2019. "The evolution of schematic representations of flow shop scheduling problems," Journal of Scheduling, Springer, vol. 22(4), pages 379-391, August.
    3. C N Potts & V A Strusevich, 2009. "Fifty years of scheduling: a survey of milestones," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 41-68, May.
    4. Byung-Cheon Choi & Joseph Y.-T. Leung & Michael L. Pinedo, 2011. "Minimizing makespan in an ordered flow shop with machine-dependent processing times," Journal of Combinatorial Optimization, Springer, vol. 22(4), pages 797-818, November.
    5. S. Knust & N. V. Shakhlevich & S. Waldherr & C. Weiß, 2019. "Shop scheduling problems with pliable jobs," Journal of Scheduling, Springer, vol. 22(6), pages 635-661, December.
    6. Gmys, Jan & Mezmaz, Mohand & Melab, Nouredine & Tuyttens, Daniel, 2020. "A computationally efficient Branch-and-Bound algorithm for the permutation flow-shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 284(3), pages 814-833.
    7. Jean-Paul Watson & Laura Barbulescu & L. Darrell Whitley & Adele E. Howe, 2002. "Contrasting Structured and Random Permutation Flow-Shop Scheduling Problems: Search-Space Topology and Algorithm Performance," INFORMS Journal on Computing, INFORMS, vol. 14(2), pages 98-123, May.
    8. Monaci, Marta & Agasucci, Valerio & Grani, Giorgio, 2024. "An actor-critic algorithm with policy gradients to solve the job shop scheduling problem using deep double recurrent agents," European Journal of Operational Research, Elsevier, vol. 312(3), pages 910-926.
    9. Tseng, Lin-Yu & Lin, Ya-Tai, 2010. "A genetic local search algorithm for minimizing total flowtime in the permutation flowshop scheduling problem," International Journal of Production Economics, Elsevier, vol. 127(1), pages 121-128, September.
    10. M Haouari & T Ladhari, 2003. "A branch-and-bound-based local search method for the flow shop problem," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(10), pages 1076-1084, October.
    11. Christoph Hertrich & Christian Weiß & Heiner Ackermann & Sandy Heydrich & Sven O. Krumke, 2020. "Scheduling a proportionate flow shop of batching machines," Journal of Scheduling, Springer, vol. 23(5), pages 575-593, October.
    12. Fernandez-Viagas, Victor & Ruiz, Rubén & Framinan, Jose M., 2017. "A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation," European Journal of Operational Research, Elsevier, vol. 257(3), pages 707-721.
    13. Pan, Quan-Ke & Ruiz, Rubén, 2014. "An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem," Omega, Elsevier, vol. 44(C), pages 41-50.
    14. Bo Liu & Ling Wang & Ying Liu & Shouyang Wang, 2011. "A unified framework for population-based metaheuristics," Annals of Operations Research, Springer, vol. 186(1), pages 231-262, June.
    15. Kameng Nip & Zhenbo Wang & Fabrice Talla Nobibon & Roel Leus, 2015. "A combination of flow shop scheduling and the shortest path problem," Journal of Combinatorial Optimization, Springer, vol. 29(1), pages 36-52, January.
    16. Ruiz, Ruben & Maroto, Concepcion, 2005. "A comprehensive review and evaluation of permutation flowshop heuristics," European Journal of Operational Research, Elsevier, vol. 165(2), pages 479-494, September.
    17. Zongxu Mu & Minming Li, 2015. "DVS scheduling in a line or a star network of processors," Journal of Combinatorial Optimization, Springer, vol. 29(1), pages 16-35, January.
    18. A.A. Gladky & Y.M. Shafransky & V.A. Strusevich, 2004. "Flow Shop Scheduling Problems Under Machine–Dependent Precedence Constraints," Journal of Combinatorial Optimization, Springer, vol. 8(1), pages 13-28, March.
    19. Yong Chen & Yinhui Cai & Longcheng Liu & Guangting Chen & Randy Goebel & Guohui Lin & Bing Su & An Zhang, 2022. "Path cover with minimum nontrivial paths and its application in two-machine flow-shop scheduling with a conflict graph," Journal of Combinatorial Optimization, Springer, vol. 43(3), pages 571-588, April.
    20. Fernandez-Viagas, Victor & Talens, Carla & Framinan, Jose M., 2022. "Assembly flowshop scheduling problem: Speed-up procedure and computational evaluation," European Journal of Operational Research, Elsevier, vol. 299(3), pages 869-882.

    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:ejores:v:289:y:2021:i:3:p:841-854. 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/eor .

    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.