IDEAS home Printed from https://ideas.repec.org/a/eee/jomega/v105y2021ics0305048321001080.html
   My bibliography  Save this article

Real-time order acceptance and scheduling for data-enabled permutation flow shops: Bilevel interactive optimization with nonlinear integer programming

Author

Listed:
  • Chen, Wenchong
  • Gong, Xuejian
  • Rahman, Humyun Fuad
  • Liu, Hongwei
  • Qi, Ershi

Abstract

With the fourth-generation industrial revolution, manufacturing industries are focusing on dynamic, fully autonomous, and more customer-oriented production systems. This customer-oriented change converts classically static customer demand into that which is dynamic and real-time, as no prior information regarding customer demand is known in advance. This paper focuses on real-time order acceptance and scheduling (r-OAS) for a data-enabled permutation flow shop. To compensate for the shortage in prevailing approaches that make bottleneck-based decisions or assume that the intermediate buffers among workstations are infinite, an r-OAS scheme is generated based on a data-driven representation, which can concisely predict the dynamic production status of flow shops and the corresponding makespan of a job with finite intermediate buffer constraints. Using this representation, real-time job release planning (r-JRP) can be coupled with r-OAS to minimize various operational costs of flow shops (i.e., the costs of the work-in-process, earliness, and tardiness). In terms of the inherent interactive mechanism between r-OAS and r-JRP, in which r-OAS generates a decision space for r-JRP and r-JRP then feeds the lowest operational costs back for use in r-OAS decision-making, a bilevel interactive optimization (BIO) is formulated to simultaneously address the two subproblems based on the Stackelberg game. The r-OAS acts as the leader, while r-JRP acts as the follower. The BIO is a type of nonlinear integer programming, and a bilevel tabu-enumeration heuristic algorithm is developed to solve it. The efficiency of the BIO is verified through a practical case study. The results show that the BIO can increase the net revenue of flow shops by 2.97%, compared to the bottleneck-based approach, and by 2.45% and 0.92%, respectively, compared to step-by-step methodologies.

Suggested Citation

  • Chen, Wenchong & Gong, Xuejian & Rahman, Humyun Fuad & Liu, Hongwei & Qi, Ershi, 2021. "Real-time order acceptance and scheduling for data-enabled permutation flow shops: Bilevel interactive optimization with nonlinear integer programming," Omega, Elsevier, vol. 105(C).
    • Handle: RePEc:eee:jomega:v:105:y:2021:i:c:s0305048321001080
    DOI: 10.1016/j.omega.2021.102499
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0305048321001080
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.omega.2021.102499?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. Yenisey, Mehmet Mutlu & Yagmahan, Betul, 2014. "Multi-objective permutation flow shop scheduling problem: Literature review, classification and current trends," Omega, Elsevier, vol. 45(C), pages 119-135.
    2. Xiong, Yixuan & Du, Gang & Jiao, Roger J., 2018. "Modular product platforming with supply chain postponement decisions by leader-follower interactive optimization," International Journal of Production Economics, Elsevier, vol. 205(C), pages 272-286.
    3. Yang Liu & Jingshan Li, 2010. "Split and merge production systems: performance analysis and structural properties," IISE Transactions, Taylor & Francis Journals, vol. 42(6), pages 422-434.
    4. 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.
    5. Navid Hashemian & Claver Diallo & Béla Vizvári, 2014. "Makespan minimization for parallel machines scheduling with multiple availability constraints," Annals of Operations Research, Springer, vol. 213(1), pages 173-186, February.
    6. Joseph D. Blackburn & Dean H. Kropp & Robert A. Millen, 1986. "A Comparison of Strategies to Dampen Nervousness in MRP Systems," Management Science, INFORMS, vol. 32(4), pages 413-429, April.
    7. Framinan, Jose M. & Ruiz, Rubén, 2010. "Architecture of manufacturing scheduling systems: Literature review and an integrated proposal," European Journal of Operational Research, Elsevier, vol. 205(2), pages 237-246, September.
    8. Lei, Deming & Guo, Xiuping, 2015. "A parallel neighborhood search for order acceptance and scheduling in flow shop environment," International Journal of Production Economics, Elsevier, vol. 165(C), pages 12-18.
    9. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
    10. Wang, Haibo & Alidaee, Bahram, 2019. "Effective heuristic for large-scale unrelated parallel machines scheduling problems," Omega, Elsevier, vol. 83(C), pages 261-274.
    11. Carlos Herrera & Sana Belmokhtar-Berraf & André Thomas & Víctor Parada, 2016. "A reactive decision-making approach to reduce instability in a master production schedule," International Journal of Production Research, Taylor & Francis Journals, vol. 54(8), pages 2394-2404, April.
    12. Bilge, Umit & Kurtulan, Mujde & Kirac, Furkan, 2007. "A tabu search algorithm for the single machine total weighted tardiness problem," European Journal of Operational Research, Elsevier, vol. 176(3), pages 1423-1435, February.
    13. G. M. Komaki & Shaya Sheikh & Behnam Malakooti, 2019. "Flow shop scheduling problems with assembly operations: a review and new trends," International Journal of Production Research, Taylor & Francis Journals, vol. 57(10), pages 2926-2955, May.
    14. Xiao, Yiyong & Yuan, Yingying & Zhang, Ren-Qian & Konak, Abdullah, 2015. "Non-permutation flow shop scheduling with order acceptance and weighted tardiness," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 312-333.
    15. Xiuli Wang & Guodong Huang & Xiuwu Hu & T C Edwin Cheng, 2015. "Order acceptance and scheduling on two identical parallel machines," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(10), pages 1755-1767, October.
    16. Robert H. Hayes & Kim B. Clark, 1985. "Explaining Observed Productivity Differentials Between Plants: Implications for Operations Research," Interfaces, INFORMS, vol. 15(6), pages 3-14, December.
    17. Maravillo, Héctor & Camacho-Vallejo, José-Fernando & Puerto, Justo & Labbé, Martine, 2020. "A market regulation bilevel problem: A case study of the Mexican petrochemical industry," Omega, Elsevier, vol. 97(C).
    18. Lei Xu & Qian Wang & Simin Huang, 2015. "Dynamic order acceptance and scheduling problem with sequence-dependent setup time," International Journal of Production Research, Taylor & Francis Journals, vol. 53(19), pages 5797-5808, October.
    19. Slotnick, Susan A., 2011. "Order acceptance and scheduling: A taxonomy and review," European Journal of Operational Research, Elsevier, vol. 212(1), pages 1-11, July.
    20. Wang, Xiuli & Xie, Xingzi & Cheng, T.C.E., 2013. "Order acceptance and scheduling in a two-machine flowshop," International Journal of Production Economics, Elsevier, vol. 141(1), pages 366-376.
    21. Andrew Kusiak, 2017. "Smart manufacturing must embrace big data," Nature, Nature, vol. 544(7648), pages 23-25, April.
    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. Kasper, T.A. Arno & Land, Martin J. & Teunter, Ruud H., 2023. "Towards System State Dispatching in High‐Variety Manufacturing," Omega, Elsevier, vol. 114(C).
    2. Ferreira, Cristiane & Figueira, Gonçalo & Amorim, Pedro, 2022. "Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning," Omega, Elsevier, vol. 111(C).

    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. Mohamadreza Dabiri & Mehdi Yazdani & Bahman Naderi & Hassan Haleh, 2022. "Modeling and solution methods for hybrid flow shop scheduling problem with job rejection," Operational Research, Springer, vol. 22(3), pages 2721-2765, July.
    2. Wang, Xiuli & Zhu, Qianqian & Cheng, T.C.E., 2015. "Subcontracting price schemes for order acceptance and scheduling," Omega, Elsevier, vol. 54(C), pages 1-10.
    3. Li, Xin & Ventura, Jose A., 2020. "Exact algorithms for a joint order acceptance and scheduling problem," International Journal of Production Economics, Elsevier, vol. 223(C).
    4. Perea, Federico & Yepes-Borrero, Juan C. & Menezes, Mozart B.C., 2023. "Acceptance Ordering Scheduling Problem: The impact of an order-portfolio on a make-to-order firm’s profitability," International Journal of Production Economics, Elsevier, vol. 264(C).
    5. Hanane Krim & Nicolas Zufferey & Jean-Yves Potvin & Rachid Benmansour & David Duvivier, 2022. "Tabu search for a parallel-machine scheduling problem with periodic maintenance, job rejection and weighted sum of completion times," Journal of Scheduling, Springer, vol. 25(1), pages 89-105, February.
    6. Esmaeilbeigi, Rasul & Charkhgard, Parisa & Charkhgard, Hadi, 2016. "Order acceptance and scheduling problems in two-machine flow shops: New mixed integer programming formulations," European Journal of Operational Research, Elsevier, vol. 251(2), pages 419-431.
    7. Shih-Hsin Chen & Yeong-Cheng Liou & Yi-Hui Chen & Kun-Ching Wang, 2019. "Order Acceptance and Scheduling Problem with Carbon Emission Reduction and Electricity Tariffs on a Single Machine," Sustainability, MDPI, vol. 11(19), pages 1-16, September.
    8. Amirhosein Gholami & Nasim Nezamoddini & Mohammad T. Khasawneh, 2023. "Customized orders management in connected make-to-order supply chains," Operations Management Research, Springer, vol. 16(3), pages 1428-1443, September.
    9. Naderi, Bahman & Roshanaei, Vahid, 2020. "Branch-Relax-and-Check: A tractable decomposition method for order acceptance and identical parallel machine scheduling," European Journal of Operational Research, Elsevier, vol. 286(3), pages 811-827.
    10. Wang, Xiuli & Cheng, T.C.E., 2015. "A heuristic for scheduling jobs on two identical parallel machines with a machine availability constraint," International Journal of Production Economics, Elsevier, vol. 161(C), pages 74-82.
    11. Yung-Chia Chang & Kuei-Hu Chang & Ching-Ping Zheng, 2022. "Application of a Non-Dominated Sorting Genetic Algorithm to Solve a Bi-Objective Scheduling Problem Regarding Printed Circuit Boards," Mathematics, MDPI, vol. 10(13), pages 1-21, July.
    12. Weiwei Cui & Biao Lu, 2020. "A Bi-Objective Approach to Minimize Makespan and Energy Consumption in Flow Shops with Peak Demand Constraint," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    13. Tarhan, İstenç & Oğuz, Ceyda, 2022. "A matheuristic for the generalized order acceptance and scheduling problem," European Journal of Operational Research, Elsevier, vol. 299(1), pages 87-103.
    14. Chun-Lung Chen, 2023. "An Iterated Population-Based Metaheuristic for Order Acceptance and Scheduling in Unrelated Parallel Machines with Several Practical Constraints," Mathematics, MDPI, vol. 11(6), pages 1-14, March.
    15. Xiao, Yiyong & Yuan, Yingying & Zhang, Ren-Qian & Konak, Abdullah, 2015. "Non-permutation flow shop scheduling with order acceptance and weighted tardiness," Applied Mathematics and Computation, Elsevier, vol. 270(C), pages 312-333.
    16. Wang, Xiuli & Geng, Sujie & Cheng, T.C.E., 2018. "Negotiation mechanisms for an order subcontracting and scheduling problem," Omega, Elsevier, vol. 77(C), pages 154-167.
    17. Ren-Xia Chen & Shi-Sheng Li, 2020. "Minimizing maximum delivery completion time for order scheduling with rejection," Journal of Combinatorial Optimization, Springer, vol. 40(4), pages 1044-1064, November.
    18. Zhang, Zhe & Gong, Xue & Song, Xiaoling & Yin, Yong & Lev, Benjamin & Chen, Jie, 2022. "A column generation-based exact solution method for seru scheduling problems," Omega, Elsevier, vol. 108(C).
    19. Lei, Deming & Guo, Xiuping, 2015. "A parallel neighborhood search for order acceptance and scheduling in flow shop environment," International Journal of Production Economics, Elsevier, vol. 165(C), pages 12-18.
    20. Javad Seif & Mohammad Dehghanimohammadabadi & Andrew Junfang Yu, 2020. "Integrated preventive maintenance and flow shop scheduling under uncertainty," Flexible Services and Manufacturing Journal, Springer, vol. 32(4), pages 852-887, December.

    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:jomega:v:105:y:2021:i:c:s0305048321001080. 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/wps/find/journaldescription.cws_home/375/description#description .

    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.