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Order acceptance and scheduling with consideration of service level

Author

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  • Jinwen Ou

    (Jinan University)

  • Xueling Zhong

    (Guangdong University of Finance)

Abstract

In traditional order acceptance and scheduling (OAS) research, there is no constraint on how many orders are allowed to be rejected in total. We study OAS with consideration of service level in this paper. In our OAS model, there are n orders and m machines available at time zero. To maintain a predefined high service level, the number of orders to be rejected is limited to be no greater than a given value k. The objective is to minimize the completion time of the last accepted order plus the total penalty of all rejected orders. The problem is NP-hard in the strong sense in general. For the special case with a single machine (i.e., when $$m=1$$ m = 1 ), we present an exact algorithm with a complexity of $$O(n\log k)$$ O ( n log k ) . For the general case, we first propose an $$O(n\log n)$$ O ( n log n ) heuristic with a worst-case bound of $$2-\frac{1}{m}$$ 2 - 1 m , followed by a sophisticated heuristic by making use of LP-relaxation and bin-packing techniques. The second heuristic has a worst-case bound of $$1.5+\epsilon $$ 1.5 + ϵ and a time complexity of $$O(n\log n + nm^2/\epsilon )$$ O ( n log n + n m 2 / ϵ ) , where $$\epsilon >0$$ ϵ > 0 can be any small given constant.

Suggested Citation

  • Jinwen Ou & Xueling Zhong, 2017. "Order acceptance and scheduling with consideration of service level," Annals of Operations Research, Springer, vol. 248(1), pages 429-447, January.
  • Handle: RePEc:spr:annopr:v:248:y:2017:i:1:d:10.1007_s10479-016-2277-2
    DOI: 10.1007/s10479-016-2277-2
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Ou, Jinwen & Zhong, Xueling, 2017. "Bicriteria order acceptance and scheduling with consideration of fill rate," European Journal of Operational Research, Elsevier, vol. 262(3), pages 904-907.
    3. Jinwen Ou, 2020. "Near-linear-time approximation algorithms for scheduling a batch-processing machine with setups and job rejection," Journal of Scheduling, Springer, vol. 23(5), pages 525-538, October.
    4. 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.
    5. Li, Weidong & Ou, Jinwen, 2024. "Machine scheduling with restricted rejection: An Application to task offloading in cloud–edge collaborative computing," European Journal of Operational Research, Elsevier, vol. 314(3), pages 912-919.
    6. Wenchang Luo & Rylan Chin & Alexander Cai & Guohui Lin & Bing Su & An Zhang, 2022. "A tardiness-augmented approximation scheme for rejection-allowed multiprocessor rescheduling," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 690-722, August.
    7. Qi Feng & Shisheng Li, 2022. "Algorithms for Multi-Customer Scheduling with Outsourcing," Mathematics, MDPI, vol. 10(9), pages 1-12, May.
    8. 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.
    9. Lingfa Lu & Liqi Zhang & Jinwen Ou, 2021. "In-house production and outsourcing under different discount schemes on the total outsourcing cost," Annals of Operations Research, Springer, vol. 298(1), pages 361-374, March.
    10. Xiaofei Liu & Weidong Li & Yaoyu Zhu, 2021. "Single Machine Vector Scheduling with General Penalties," Mathematics, MDPI, vol. 9(16), pages 1-16, August.

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