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Minimizing total tardiness for the order scheduling problem

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  • Lee, Ik Sun

Abstract

This paper considers the order scheduling problem to minimize total tardiness. The system is composed of multiple machines, and each order consists of multiple components, with each component being manufactured on a dedicated machine (specified in advance). The completion time of each order is represented by the time at which all the components comprising the order are completed. The tardiness minimization is the performance objective of this paper. In the problem analysis, this paper first derives some dominance properties to determine the sequence of some orders, and then analyzes problem complexities for some special cases. Three heuristic algorithms are derived with a performance bound. Moreover, three lower bounds of objective are derived and tested along with the derived properties in a branch-and-bound scheme. The overall performances of the proposed property, branch-and-bound and heuristic algorithms are evaluated via numerical experiments.

Suggested Citation

  • Lee, Ik Sun, 2013. "Minimizing total tardiness for the order scheduling problem," International Journal of Production Economics, Elsevier, vol. 144(1), pages 128-134.
    • Handle: RePEc:eee:proeco:v:144:y:2013:i:1:p:128-134
    DOI: 10.1016/j.ijpe.2013.01.025
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    References listed on IDEAS

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    1. Chang Sup Sung & Sang Hum Yoon, 1998. "Minimizing total weighted completion time at a pre-assembly stage composed of two feeding machines," International Journal of Production Economics, Elsevier, vol. 54(3), pages 247-255, May.
    2. Leung, Joseph Y.-T. & Li, Haibing & Pinedo, Michael, 2006. "Scheduling orders for multiple product types with due date related objectives," European Journal of Operational Research, Elsevier, vol. 168(2), pages 370-389, January.
    3. Joseph Leung & Haibing Li & Michael Pinedo, 2008. "Scheduling orders on either dedicated or flexible machines in parallel to minimize total weighted completion time," Annals of Operations Research, Springer, vol. 159(1), pages 107-123, March.
    4. Cheng, T. C. E. & Ng, C. T. & Yuan, J. J. & Liu, Z. H., 2005. "Single machine scheduling to minimize total weighted tardiness," European Journal of Operational Research, Elsevier, vol. 165(2), pages 423-443, September.
    5. Leung, Joseph Y.-T. & Lee, C.Y. & Ng, C.W. & Young, G.H., 2008. "Preemptive multiprocessor order scheduling to minimize total weighted flowtime," European Journal of Operational Research, Elsevier, vol. 190(1), pages 40-51, October.
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    Cited by:

    1. 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.
    2. Husam Dauod & Nieqing Cao & Debiao Li & Jaehee Kim & Sang Won Yoon & Daehan Won, 2023. "An Order Scheduling Heuristic to Minimize the Total Collation Delays and the Makespan in High-Throughput Make-to-Order Manufacturing Systems," SN Operations Research Forum, Springer, vol. 4(2), pages 1-23, June.
    3. Framinan, Jose M. & Perez-Gonzalez, Paz, 2018. "Order scheduling with tardiness objective: Improved approximate solutions," European Journal of Operational Research, Elsevier, vol. 266(3), pages 840-850.
    4. Xiaoling Cao & Wen-Hsing Wu & Wen-Hung Wu & Chin-Chia Wu, 2018. "Some two-agent single-machine scheduling problems to minimize minmax and minsum of completion times," Operational Research, Springer, vol. 18(2), pages 293-314, July.
    5. Lung-Yu Li & Jian-You Xu & Shuenn-Ren Cheng & Xingong Zhang & Win-Chin Lin & Jia-Cheng Lin & Zong-Lin Wu & Chin-Chia Wu, 2022. "A Genetic Hyper-Heuristic for an Order Scheduling Problem with Two Scenario-Dependent Parameters in a Parallel-Machine Environment," Mathematics, MDPI, vol. 10(21), pages 1-22, November.
    6. Framinan, Jose M. & Perez-Gonzalez, Paz & Fernandez-Viagas, Victor, 2019. "Deterministic assembly scheduling problems: A review and classification of concurrent-type scheduling models and solution procedures," European Journal of Operational Research, Elsevier, vol. 273(2), pages 401-417.

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