IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v31y2020i8d10.1007_s10845-020-01547-4.html
   My bibliography  Save this article

A research survey: heuristic approaches for solving multi objective flexible job shop problems

Author

Listed:
  • Alper Türkyılmaz

    (Marmara University)

  • Özlem Şenvar

    (Marmara University)

  • İrem Ünal

    (Marmara University)

  • Serol Bulkan

    (Marmara University)

Abstract

Flexible job shop scheduling problem is a relaxation of the job shop scheduling problem and is one of the well-known combinatorial optimization problems that has wide applications in the industrial fields such as production management, supply chain, transport systems, manufacturing systems. In recent years, many researches have been carried out with different approaches—ranging from mathematical models to heuristic methods—to solve multi objective flexible job shop scheduling problems (FJSSP). This study aims to present the forms of scrutiny of multi-objective FJSSPs and various heuristic techniques used to solve problems in the last decade. This review will allow the reader to select specific methods and follow the guidelines set forth in their future research.

Suggested Citation

  • Alper Türkyılmaz & Özlem Şenvar & İrem Ünal & Serol Bulkan, 2020. "A research survey: heuristic approaches for solving multi objective flexible job shop problems," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1949-1983, December.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01547-4
    DOI: 10.1007/s10845-020-01547-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-020-01547-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-020-01547-4?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. Mariano Frutos & Ana Olivera & Fernando Tohmé, 2010. "A memetic algorithm based on a NSGAII scheme for the flexible job-shop scheduling problem," Annals of Operations Research, Springer, vol. 181(1), pages 745-765, December.
    2. Baykasoglu, Adil & ÖzbakIr, Lale, 2010. "Analyzing the effect of dispatching rules on the scheduling performance through grammar based flexible scheduling system," International Journal of Production Economics, Elsevier, vol. 124(2), pages 369-381, April.
    3. Bryan A. Norman & James C. Bean, 1999. "A genetic algorithm methodology for complex scheduling problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 46(2), pages 199-211, March.
    4. Kacem, Imed & Hammadi, Slim & Borne, Pierre, 2002. "Pareto-optimality approach for flexible job-shop scheduling problems: hybridization of evolutionary algorithms and fuzzy logic," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 60(3), pages 245-276.
    5. Thomalla, Christoph S., 2001. "Job shop scheduling with alternative process plans," International Journal of Production Economics, Elsevier, vol. 74(1-3), pages 125-134, December.
    6. Xiong, Jian & Xing, Li-ning & Chen, Ying-wu, 2013. "Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns," International Journal of Production Economics, Elsevier, vol. 141(1), pages 112-126.
    7. Miguel A. Fernández Pérez & Fernanda M. P. Raupp, 2016. "A Newton-based heuristic algorithm for multi-objective flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 409-416, April.
    8. Huang, Rong-Hwa & Yang, Chang-Lin & Cheng, Wei-Che, 2013. "Flexible job shop scheduling with due window—a two-pheromone ant colony approach," International Journal of Production Economics, Elsevier, vol. 141(2), pages 685-697.
    9. Ho, Nhu Binh & Tay, Joc Cing & Lai, Edmund M.-K., 2007. "An effective architecture for learning and evolving flexible job-shop schedules," European Journal of Operational Research, Elsevier, vol. 179(2), pages 316-333, June.
    10. Stéphane Dauzère-Pérès & Jan Paulli, 1997. "An integrated approach for modeling and solving the general multiprocessor job-shop scheduling problem using tabu search," Annals of Operations Research, Springer, vol. 70(0), pages 281-306, April.
    11. Xuran Gong & Qianwang Deng & Guiliang Gong & Wei Liu & Qinghua Ren, 2018. "A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility," International Journal of Production Research, Taylor & Francis Journals, vol. 56(7), pages 2506-2522, April.
    12. K. Z. Gao & P. N. Suganthan & Q. K. Pan & T. J. Chua & T. X. Cai & C. S. Chong, 2016. "Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 363-374, April.
    13. Cenk Sahin & Melek Demirtas & Rizvan Erol & Adil Baykasoğlu & Vahit Kaplanoğlu, 2017. "A multi-agent based approach to dynamic scheduling with flexible processing capabilities," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1827-1845, December.
    14. Chiang, Tsung-Che & Lin, Hsiao-Jou, 2013. "A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling," International Journal of Production Economics, Elsevier, vol. 141(1), pages 87-98.
    15. Garcia-Martinez, C. & Cordon, O. & Herrera, F., 2007. "A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP," European Journal of Operational Research, Elsevier, vol. 180(1), pages 116-148, July.
    16. 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.
    17. Homburg, Carsten, 1998. "Hierarchical multi-objective decision making," European Journal of Operational Research, Elsevier, vol. 105(1), pages 155-161, February.
    18. Jacomine Grobler & Andries Engelbrecht & Schalk Kok & Sarma Yadavalli, 2010. "Metaheuristics for the multi-objective FJSP with sequence-dependent set-up times, auxiliary resources and machine down time," Annals of Operations Research, Springer, vol. 180(1), pages 165-196, November.
    19. Moslehi, Ghasem & Mahnam, Mehdi, 2011. "A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search," International Journal of Production Economics, Elsevier, vol. 129(1), pages 14-22, 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. Junqi Liu & Zeqiang Zhang & Feng Chen & Silu Liu & Lixia Zhu, 2022. "A novel hybrid immune clonal selection algorithm for the constrained corridor allocation problem," Journal of Intelligent Manufacturing, Springer, vol. 33(4), pages 953-972, April.
    2. Shun Jia & Yang Yang & Shuyu Li & Shang Wang & Anbang Li & Wei Cai & Yang Liu & Jian Hao & Luoke Hu, 2024. "The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing," Sustainability, MDPI, vol. 16(6), pages 1-22, March.
    3. 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.
    4. Bezoui, Madani & Olteanu, Alexandru-Liviu & Sevaux, Marc, 2023. "Integrating preferences within multiobjective flexible job shop scheduling," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1079-1086.
    5. Zeynep Ceylan & Hakan Tozan & Serol Bulkan, 2021. "A coordinated scheduling problem for the supply chain in a flexible job shop machine environment," Operational Research, Springer, vol. 21(2), pages 875-900, June.
    6. Fernando José Gómez Paredes & Moacir Godinho Filho & Matthias Thürer & Nuno O. Fernandes & Charbel José Chiappeta Jabbour, 2022. "Factors for choosing production control systems in make-to-order shops: a systematic literature review," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 639-674, March.
    7. Alejandro Vital-Soto & Mohammed Fazle Baki & Ahmed Azab, 2023. "A multi-objective mathematical model and evolutionary algorithm for the dual-resource flexible job-shop scheduling problem with sequencing flexibility," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 626-668, September.
    8. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.

    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. Li, Xinyu & Gao, Liang, 2016. "An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem," International Journal of Production Economics, Elsevier, vol. 174(C), pages 93-110.
    2. Dauzère-Pérès, Stéphane & Ding, Junwen & Shen, Liji & Tamssaouet, Karim, 2024. "The flexible job shop scheduling problem: A review," European Journal of Operational Research, Elsevier, vol. 314(2), pages 409-432.
    3. Yiyi Xu & M’hammed Sahnoun & Fouad Ben Abdelaziz & David Baudry, 2022. "A simulated multi-objective model for flexible job shop transportation scheduling," Annals of Operations Research, Springer, vol. 311(2), pages 899-920, April.
    4. Jose L. Andrade-Pineda & David Canca & Pedro L. Gonzalez-R & M. Calle, 2020. "Scheduling a dual-resource flexible job shop with makespan and due date-related criteria," Annals of Operations Research, Springer, vol. 291(1), pages 5-35, August.
    5. Po-Hsiang Lu & Muh-Cherng Wu & Hao Tan & Yong-Han Peng & Chen-Fu Chen, 2018. "A genetic algorithm embedded with a concise chromosome representation for distributed and flexible job-shop scheduling problems," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 19-34, January.
    6. Wei Xiong & Dongmei Fu, 2018. "A new immune multi-agent system for the flexible job shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 29(4), pages 857-873, April.
    7. Nicolás Álvarez-Gil & Rafael Rosillo & David de la Fuente & Raúl Pino, 2021. "A discrete firefly algorithm for solving the flexible job-shop scheduling problem in a make-to-order manufacturing system," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1353-1374, December.
    8. Julien Autuori & Faicel Hnaien & Farouk Yalaoui, 2016. "A mapping technique for better solution exploration: NSGA-II adaptation," Journal of Heuristics, Springer, vol. 22(1), pages 89-123, February.
    9. Alejandro Vital-Soto & Mohammed Fazle Baki & Ahmed Azab, 2023. "A multi-objective mathematical model and evolutionary algorithm for the dual-resource flexible job-shop scheduling problem with sequencing flexibility," Flexible Services and Manufacturing Journal, Springer, vol. 35(3), pages 626-668, September.
    10. Miguel A. Fernández Pérez & Fernanda M. P. Raupp, 2016. "A Newton-based heuristic algorithm for multi-objective flexible job-shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 27(2), pages 409-416, April.
    11. Vilcot, Geoffrey & Billaut, Jean-Charles, 2008. "A tabu search and a genetic algorithm for solving a bicriteria general job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 190(2), pages 398-411, October.
    12. Shen, Liji & Dauzère-Pérès, Stéphane & Neufeld, Janis S., 2018. "Solving the flexible job shop scheduling problem with sequence-dependent setup times," European Journal of Operational Research, Elsevier, vol. 265(2), pages 503-516.
    13. Chiang, Tsung-Che & Lin, Hsiao-Jou, 2013. "A simple and effective evolutionary algorithm for multiobjective flexible job shop scheduling," International Journal of Production Economics, Elsevier, vol. 141(1), pages 87-98.
    14. González, Miguel A. & Vela, Camino R. & Varela, Ramiro, 2015. "Scatter search with path relinking for the flexible job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 245(1), pages 35-45.
    15. Zhang, Sicheng & Li, Xiang & Zhang, Bowen & Wang, Shouyang, 2020. "Multi-objective optimisation in flexible assembly job shop scheduling using a distributed ant colony system," European Journal of Operational Research, Elsevier, vol. 283(2), pages 441-460.
    16. Kumar, V.N.S.A. & Kumar, V. & Brady, M. & Garza-Reyes, Jose Arturo & Simpson, M., 2017. "Resolving forward-reverse logistics multi-period model using evolutionary algorithms," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 458-469.
    17. Lunardi, Willian T. & Birgin, Ernesto G. & Ronconi, Débora P. & Voos, Holger, 2021. "Metaheuristics for the online printing shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 293(2), pages 419-441.
    18. Seyed Habib A. Rahmati & Abbas Ahmadi & Kannan Govindan, 2018. "A novel integrated condition-based maintenance and stochastic flexible job shop scheduling problem: simulation-based optimization approach," Annals of Operations Research, Springer, vol. 269(1), pages 583-621, October.
    19. Shen, Liji & Buscher, Udo, 2012. "Solving the serial batching problem in job shop manufacturing systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 14-26.
    20. Rossi, Andrea, 2014. "Flexible job shop scheduling with sequence-dependent setup and transportation times by ant colony with reinforced pheromone relationships," International Journal of Production Economics, Elsevier, vol. 153(C), pages 253-267.

    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:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01547-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.