IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v266y2018i3p950-962.html
   My bibliography  Save this article

Heuristics for a flowshop scheduling problem with stepwise job objective function

Author

Listed:
  • Pessoa, Luciana S.
  • Andrade, Carlos E.

Abstract

In this work, we introduce the Flowshop Scheduling Problem with Delivery Dates and Cumulative Payoffs. This problem is a variation of the flowshop scheduling problem with job release dates that maximizes the total payoff with a stepwise job objective function. This paper contributes towards proposing a mathematical formulation for this new problem and an original constructive heuristic. Additionally, we develop a new benchmark of 300 hard instances which are available in a public repository. Besides, we provide primal and dual bounds for them. Extensive computational experiments were carried out taking into account classical constructive heuristics and hybrid local searches. Results show the merit of the FF heuristic when compared to other classical heuristics for flowshop scheduling problems. Additionally, we compare an Iterated Local Search (ILS), an Iterated Greedy Search (IGS), and a Biased Random-Key Genetic Algorithm (BRKGA), both customized for the studied problem, and a commercial mixed integer programming solver. The comparison between these methods showed that the BRKGA starting with a solution from FF heuristic is able to find the best solutions in a very short period of time. Iterated local Search and the commercial solver presented significantly worse results than the aforementioned methods.

Suggested Citation

  • Pessoa, Luciana S. & Andrade, Carlos E., 2018. "Heuristics for a flowshop scheduling problem with stepwise job objective function," European Journal of Operational Research, Elsevier, vol. 266(3), pages 950-962.
    • Handle: RePEc:eee:ejores:v:266:y:2018:i:3:p:950-962
    DOI: 10.1016/j.ejor.2017.10.045
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037722171730958X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2017.10.045?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. 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.
    2. Chen, Chuen-Lung & Vempati, Venkateswara S. & Aljaber, Nasser, 1995. "An application of genetic algorithms for flow shop problems," European Journal of Operational Research, Elsevier, vol. 80(2), pages 389-396, January.
    3. C. N. Potts, 1985. "Analysis of Heuristics for Two-Machine Flow-Shop Sequencing Subject to Release Dates," Mathematics of Operations Research, INFORMS, vol. 10(4), pages 576-584, November.
    4. 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.
    5. Ruiz, Ruben & Stutzle, Thomas, 2008. "An Iterated Greedy heuristic for the sequence dependent setup times flowshop problem with makespan and weighted tardiness objectives," European Journal of Operational Research, Elsevier, vol. 187(3), pages 1143-1159, June.
    6. Fanjul-Peyro, Luis & Ruiz, Rubén, 2010. "Iterated greedy local search methods for unrelated parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 207(1), pages 55-69, November.
    7. López-Ibáñez, Manuel & Dubois-Lacoste, Jérémie & Pérez Cáceres, Leslie & Birattari, Mauro & Stützle, Thomas, 2016. "The irace package: Iterated racing for automatic algorithm configuration," Operations Research Perspectives, Elsevier, vol. 3(C), pages 43-58.
    8. Ladhari, Talel & Rakrouki, Mohamed Ali, 2009. "Heuristics and lower bounds for minimizing the total completion time in a two-machine flowshop," International Journal of Production Economics, Elsevier, vol. 122(2), pages 678-691, December.
    9. Ruiz, Rubén & Maroto, Concepciøn & Alcaraz, Javier, 2006. "Two new robust genetic algorithms for the flowshop scheduling problem," Omega, Elsevier, vol. 34(5), pages 461-476, October.
    10. Liu, Jiyin & Reeves, Colin R, 2001. "Constructive and composite heuristic solutions to the P//[summation operator]Ci scheduling problem," European Journal of Operational Research, Elsevier, vol. 132(2), pages 439-452, July.
    11. Zhang, Yi & Li, Xiaoping & Wang, Qian, 2009. "Hybrid genetic algorithm for permutation flowshop scheduling problems with total flowtime minimization," European Journal of Operational Research, Elsevier, vol. 196(3), pages 869-876, August.
    12. Ruiz, Ruben & Stutzle, Thomas, 2007. "A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 2033-2049, March.
    13. Thiago Noronha & Mauricio Resende & Celso Ribeiro, 2011. "A biased random-key genetic algorithm for routing and wavelength assignment," Journal of Global Optimization, Springer, vol. 50(3), pages 503-518, July.
    14. Caserta, Marco & Reiners, Torsten, 2016. "A pool-based pattern generation algorithm for logical analysis of data with automatic fine-tuning," European Journal of Operational Research, Elsevier, vol. 248(2), pages 593-606.
    15. Della Croce, Federico & Gupta, Jatinder N. D. & Tadei, Roberto, 2000. "Minimizing tardy jobs in a flowshop with common due date," European Journal of Operational Research, Elsevier, vol. 120(2), pages 375-381, January.
    16. Nawaz, Muhammad & Enscore Jr, E Emory & Ham, Inyong, 1983. "A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem," Omega, Elsevier, vol. 11(1), pages 91-95.
    17. J M Framinan & J N D Gupta & R Leisten, 2004. "A review and classification of heuristics for permutation flow-shop scheduling with makespan objective," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 55(12), pages 1243-1255, December.
    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. Xiaoyu Yu & Jingyi Qian & Yajing Zhang & Min Kong, 2023. "Supply Chain Scheduling Method for the Coordination of Agile Production and Port Delivery Operation," Mathematics, MDPI, vol. 11(15), pages 1-24, July.
    2. Zhang, Haowei & Xie, Junwei & Ge, Jiaang & Zhang, Zhaojian & Zong, Binfeng, 2019. "A hybrid adaptively genetic algorithm for task scheduling problem in the phased array radar," European Journal of Operational Research, Elsevier, vol. 272(3), pages 868-878.
    3. Calmels, Dorothea, 2022. "An iterated local search procedure for the job sequencing and tool switching problem with non-identical parallel machines," European Journal of Operational Research, Elsevier, vol. 297(1), pages 66-85.
    4. Andrade, Carlos E. & Toso, Rodrigo F. & Gonçalves, José F. & Resende, Mauricio G.C., 2021. "The Multi-Parent Biased Random-Key Genetic Algorithm with Implicit Path-Relinking and its real-world applications," European Journal of Operational Research, Elsevier, vol. 289(1), pages 17-30.
    5. Elisa Negri & Vibhor Pandhare & Laura Cattaneo & Jaskaran Singh & Marco Macchi & Jay Lee, 2021. "Field-synchronized Digital Twin framework for production scheduling with uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1207-1228, April.
    6. Carrabs, Francesco, 2021. "A biased random-key genetic algorithm for the set orienteering problem," European Journal of Operational Research, Elsevier, vol. 292(3), pages 830-854.

    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. 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.
    2. Pan, Quan-Ke & Wang, Ling, 2012. "Effective heuristics for the blocking flowshop scheduling problem with makespan minimization," Omega, Elsevier, vol. 40(2), pages 218-229, April.
    3. 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.
    4. Pan, Quan-Ke & Ruiz, Rubén, 2012. "Local search methods for the flowshop scheduling problem with flowtime minimization," European Journal of Operational Research, Elsevier, vol. 222(1), pages 31-43.
    5. Vallada, Eva & Ruiz, Rubén, 2010. "Genetic algorithms with path relinking for the minimum tardiness permutation flowshop problem," Omega, Elsevier, vol. 38(1-2), pages 57-67, February.
    6. Rad, Shahriar Farahmand & Ruiz, Rubén & Boroojerdian, Naser, 2009. "New high performing heuristics for minimizing makespan in permutation flowshops," Omega, Elsevier, vol. 37(2), pages 331-345, April.
    7. Pagnozzi, Federico & Stützle, Thomas, 2021. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems with additional constraints," Operations Research Perspectives, Elsevier, vol. 8(C).
    8. Perez-Gonzalez, Paz & Framinan, Jose M., 2024. "A review and classification on distributed permutation flowshop scheduling problems," European Journal of Operational Research, Elsevier, vol. 312(1), pages 1-21.
    9. Pagnozzi, Federico & Stützle, Thomas, 2019. "Automatic design of hybrid stochastic local search algorithms for permutation flowshop problems," European Journal of Operational Research, Elsevier, vol. 276(2), pages 409-421.
    10. 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.
    11. Kalczynski, Pawel J. & Kamburowski, Jerzy, 2009. "An empirical analysis of the optimality rate of flow shop heuristics," European Journal of Operational Research, Elsevier, vol. 198(1), pages 93-101, October.
    12. Naderi, Bahman & Ruiz, Rubén, 2014. "A scatter search algorithm for the distributed permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 239(2), pages 323-334.
    13. Lobo, Fernando G. & Bazargani, Mosab & Burke, Edmund K., 2020. "A cutoff time strategy based on the coupon collector’s problem," European Journal of Operational Research, Elsevier, vol. 286(1), pages 101-114.
    14. Brammer, Janis & Lutz, Bernhard & Neumann, Dirk, 2022. "Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning," European Journal of Operational Research, Elsevier, vol. 299(1), pages 75-86.
    15. Maria Raquel C. Costa & Jorge M. S. Valente & Jeffrey E. Schaller, 2020. "Efficient procedures for the weighted squared tardiness permutation flowshop scheduling problem," Flexible Services and Manufacturing Journal, Springer, vol. 32(3), pages 487-522, September.
    16. Ruiz, Rubén & Pan, Quan-Ke & Naderi, Bahman, 2019. "Iterated Greedy methods for the distributed permutation flowshop scheduling problem," Omega, Elsevier, vol. 83(C), pages 213-222.
    17. 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.
    18. Ruiz, Ruben & Stutzle, Thomas, 2007. "A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem," European Journal of Operational Research, Elsevier, vol. 177(3), pages 2033-2049, March.
    19. Hatami, Sara & Ruiz, Rubén & Andrés-Romano, Carlos, 2015. "Heuristics and metaheuristics for the distributed assembly permutation flowshop scheduling problem with sequence dependent setup times," International Journal of Production Economics, Elsevier, vol. 169(C), pages 76-88.
    20. Xiong, Fuli & Xing, Keyi & Wang, Feng, 2015. "Scheduling a hybrid assembly-differentiation flowshop to minimize total flow time," European Journal of Operational Research, Elsevier, vol. 240(2), pages 338-354.

    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:266:y:2018:i:3:p:950-962. 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.