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Applying available-to-promise (ATP) concept in mixed-model assembly line sequencing problems in a Make-To-Order (MTO) environment: problem extension, model formulation and Lagrangian relaxation algorithm

Author

Listed:
  • F. Tanhaie

    (University of Tehran)

  • M. Rabbani

    (University of Tehran)

  • N. Manavizadeh

    (Khatam University)

Abstract

Mixed-model assembly line is known to be a special case of production lines where variety of product models similar to product characteristics are assembled. This article addresses available-to-promise (ATP) in mixed-model assembly line sequencing problems in a Make-To-Order environment in two stages. First, the customers are prioritized based on their corresponding profit values and a decision support system for order acceptance/rejection based on ATP is developed. By implementing this concept and developing a mathematical model, delivery quantity and date in a planning horizon are determined based on the inventories in the stock. The second stage is solving a mixed binary mathematical model to sequence accepted orders suitably according to demands due dates that guarantees the orders are not released too late or too early. The problem simultaneously considers following objectives: minimizing the total tardiness and earliness costs based on the determined priority of orders and minimizing the utility work and idle time of workers in the production line. An algorithm based on Lagrangian relaxation is developed for the problem, and tested in terms of solution quality and computational efficiency. To validate the performance of the proposed algorithm, various test problems in small size are solved using the CPLEX solver, and compared with the Lagrangian relaxation method. Finally, the proposed model is solved in large size problems to analyze the model performance. The drawback of the CPLEX is that it could not solve large problem instances in reasonable time. For the small sized problem, there is approximately 1% duality gap for the Lagrangian relaxation method. The maximum duality gap in the Lagrangian relaxation method for the large sized problem is always kept below 4% while the average computing time is very reasonable. Therefore, according to the results obtained from test problems, the developed Lagrangian relaxation method proved to be the suitable method for this problem.

Suggested Citation

  • F. Tanhaie & M. Rabbani & N. Manavizadeh, 2020. "Applying available-to-promise (ATP) concept in mixed-model assembly line sequencing problems in a Make-To-Order (MTO) environment: problem extension, model formulation and Lagrangian relaxation algori," OPSEARCH, Springer;Operational Research Society of India, vol. 57(2), pages 320-346, June.
  • Handle: RePEc:spr:opsear:v:57:y:2020:i:2:d:10.1007_s12597-019-00436-6
    DOI: 10.1007/s12597-019-00436-6
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    References listed on IDEAS

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    1. Marshall L. Fisher, 1981. "The Lagrangian Relaxation Method for Solving Integer Programming Problems," Management Science, INFORMS, vol. 27(1), pages 1-18, January.
    2. Tamura, Takayoshi & Long, Hong & Ohno, Katsuhisa, 1999. "A sequencing problem to level part usage rates and work loads for a mixed-model assembly line with a bypass subline," International Journal of Production Economics, Elsevier, vol. 60(1), pages 557-564, April.
    3. Nick T. Thomopoulos, 1967. "Line Balancing-Sequencing for Mixed-Model Assembly," Management Science, INFORMS, vol. 14(2), pages 59-75, October.
    4. Öner-Közen, Miray & Minner, Stefan, 2017. "Impact of priority sequencing decisions on on-time probability and expected tardiness of orders in make-to-order production systems with external due-dates," European Journal of Operational Research, Elsevier, vol. 263(2), pages 524-539.
    5. John Miltenburg, 1989. "Level Schedules for Mixed-Model Assembly Lines in Just-In-Time Production Systems," Management Science, INFORMS, vol. 35(2), pages 192-207, February.
    6. Dobson, Gregory & Arai Yano, Candace, 1994. "Cyclic scheduling to minimize inventory in a batch flow line," European Journal of Operational Research, Elsevier, vol. 75(2), pages 441-461, June.
    7. Bautista, J. & Companys, R. & Corominas, A., 1996. "Heuristics and exact algorithms for solving the Monden problem," European Journal of Operational Research, Elsevier, vol. 88(1), pages 101-113, January.
    8. Boysen, Nils & Fliedner, Malte & Scholl, Armin, 2009. "Sequencing mixed-model assembly lines: Survey, classification and model critique," European Journal of Operational Research, Elsevier, vol. 192(2), pages 349-373, January.
    9. Jordi Pereira & Mariona Vilà, 2015. "An exact algorithm for the mixed-model level scheduling problem," International Journal of Production Research, Taylor & Francis Journals, vol. 53(19), pages 5809-5825, October.
    10. Xiaobo, Zhao & Ohno, Katsuhisa, 2000. "Properties of a sequencing problem for a mixed model assembly line with conveyor stoppages," European Journal of Operational Research, Elsevier, vol. 124(3), pages 560-570, August.
    11. J. L. C. Macaskill, 1973. "Computer Simulation for Mixed-Model Production Lines," Management Science, INFORMS, vol. 20(3), pages 341-348, November.
    12. Iman Ghalehkhondabi & Dusan Sormaz & Gary Weckman, 2016. "Multiple customer order decoupling points within a hybrid MTS/MTO manufacturing supply chain with uncertain demands in two consecutive echelons," OPSEARCH, Springer;Operational Research Society of India, vol. 53(4), pages 976-997, December.
    13. Abolfazl Jafari Asl & Maghsud Solimanpur & Ravi Shankar, 2019. "Multi-objective multi-model assembly line balancing problem: a quantitative study in engine manufacturing industry," OPSEARCH, Springer;Operational Research Society of India, vol. 56(3), pages 603-627, September.
    14. Korkmazel, Tugrul & Meral, Sedef, 2001. "Bicriteria sequencing methods for the mixed-model assembly line in just-in-time production systems," European Journal of Operational Research, Elsevier, vol. 131(1), pages 188-207, May.
    15. Bautista, Joaquín & Alfaro, Rocío & Batalla, Cristina, 2015. "Modeling and solving the mixed-model sequencing problem to improve productivity," International Journal of Production Economics, Elsevier, vol. 161(C), pages 83-95.
    16. Kinable, Joris & Cire, Andre A. & van Hoeve, Willem-Jan, 2017. "Hybrid optimization methods for time-dependent sequencing problems," European Journal of Operational Research, Elsevier, vol. 259(3), pages 887-897.
    17. G.J. Miltenburg & T. Goldstein, 1991. "Developing production schedules which balance part usage and smooth production loads for just‐in‐time production systems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 38(6), pages 893-910, December.
    18. Mansouri, S. Afshin, 2005. "A Multi-Objective Genetic Algorithm for mixed-model sequencing on JIT assembly lines," European Journal of Operational Research, Elsevier, vol. 167(3), pages 696-716, December.
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