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Multistage stochastic programming approach for joint optimization of job scheduling and material ordering under endogenous uncertainties

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  • Sha, Yue
  • Zhang, Junlong
  • Cao, Hui

Abstract

Job scheduling incorporated with material ordering can better meet practical needs and lead to overall cost reduction. In this paper, we present a stochastic approach for this joint optimization problem, considering uncertainties in job processing times and resource consumptions. We formulate this integrated problem as a multistage stochastic mixed-integer program involving endogenous uncertainties. Several theoretical properties that can reduce the model size are studied. Based on this, a branch-and-bound exact algorithm and a sampling-based approximate method are designed as solution algorithms. The effectiveness of the integrated scheduling approaches and the efficiency of the proposed solution algorithms are evaluated via numerical experiments. It is shown that our approach can greatly reduce the overall cost compared with the traditional separate production planning approach, especially when production resources are not very restricted.

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  • Sha, Yue & Zhang, Junlong & Cao, Hui, 2021. "Multistage stochastic programming approach for joint optimization of job scheduling and material ordering under endogenous uncertainties," European Journal of Operational Research, Elsevier, vol. 290(3), pages 886-900.
    • Handle: RePEc:eee:ejores:v:290:y:2021:i:3:p:886-900
    DOI: 10.1016/j.ejor.2020.08.057
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    1. Oded Berman & Eungab Kim, 2004. "Dynamic inventory strategies for profit maximization in a service facility with stochastic service, demand and lead time," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 60(3), pages 497-521, December.
    2. A. Krishnamoorthy & Dhanya Shajin & B. Lakshmy, 2016. "On a queueing-inventory with reservation, cancellation, common life time and retrial," Annals of Operations Research, Springer, vol. 247(1), pages 365-389, December.
    3. He, Q. -M. & Jewkes, E. M. & Buzacott, J., 2002. "Optimal and near-optimal inventory control policies for a make-to-order inventory-production system," European Journal of Operational Research, Elsevier, vol. 141(1), pages 113-132, August.
    4. Maier, Sebastian & Pflug, Georg C. & Polak, John W., 2020. "Valuing portfolios of interdependent real options under exogenous and endogenous uncertainties," European Journal of Operational Research, Elsevier, vol. 285(1), pages 133-147.
    5. Stefan Creemers, 2019. "The preemptive stochastic resource-constrained project scheduling problem," Post-Print hal-02992618, HAL.
    6. Xiaobo Zhao & Yun Zhou & Jinxing Xie, 2017. "An inventory system with quasi-hyperbolic discounting rate," IISE Transactions, Taylor & Francis Journals, vol. 49(6), pages 593-602, June.
    7. Alexander Shapiro, 2003. "Inference of statistical bounds for multistage stochastic programming problems," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 58(1), pages 57-68, September.
    8. Bora Tarhan & Ignacio Grossmann & Vikas Goel, 2013. "Computational strategies for non-convex multistage MINLP models with decision-dependent uncertainty and gradual uncertainty resolution," Annals of Operations Research, Springer, vol. 203(1), pages 141-166, March.
    9. Zhi-Long Chen, 2010. "Integrated Production and Outbound Distribution Scheduling: Review and Extensions," Operations Research, INFORMS, vol. 58(1), pages 130-148, February.
    10. Zhao, Ning & Lian, Zhaotong, 2011. "A queueing-inventory system with two classes of customers," International Journal of Production Economics, Elsevier, vol. 129(1), pages 225-231, January.
    11. Li, Haitao & Womer, Norman K., 2015. "Solving stochastic resource-constrained project scheduling problems by closed-loop approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 246(1), pages 20-33.
    12. Van de Vonder, Stijn & Demeulemeester, Erik & Herroelen, Willy & Leus, Roel, 2005. "The use of buffers in project management: The trade-off between stability and makespan," International Journal of Production Economics, Elsevier, vol. 97(2), pages 227-240, August.
    13. Creemers, Stefan, 2019. "The preemptive stochastic resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 277(1), pages 238-247.
    14. Julia L. Higle & Suvrajeet Sen, 1991. "Stochastic Decomposition: An Algorithm for Two-Stage Linear Programs with Recourse," Mathematics of Operations Research, INFORMS, vol. 16(3), pages 650-669, August.
    15. Anoop Nair & M. Jacob & A. Krishnamoorthy, 2015. "The multi server M/M/(s,S) queueing inventory system," Annals of Operations Research, Springer, vol. 233(1), pages 321-333, October.
    16. Peter Brucker, 2007. "Scheduling Algorithms," Springer Books, Springer, edition 0, number 978-3-540-69516-5, January.
    17. Solak, Senay & Clarke, John-Paul B. & Johnson, Ellis L. & Barnes, Earl R., 2010. "Optimization of R&D project portfolios under endogenous uncertainty," European Journal of Operational Research, Elsevier, vol. 207(1), pages 420-433, November.
    18. Colvin, Matthew & Maravelias, Christos T., 2010. "Modeling methods and a branch and cut algorithm for pharmaceutical clinical trial planning using stochastic programming," European Journal of Operational Research, Elsevier, vol. 203(1), pages 205-215, May.
    19. Dieter Debels & Mario Vanhoucke, 2007. "A Decomposition-Based Genetic Algorithm for the Resource-Constrained Project-Scheduling Problem," Operations Research, INFORMS, vol. 55(3), pages 457-469, June.
    20. P. Girardeau & V. Leclere & A. B. Philpott, 2015. "On the Convergence of Decomposition Methods for Multistage Stochastic Convex Programs," Mathematics of Operations Research, INFORMS, vol. 40(1), pages 130-145, February.
    21. James H. Patterson, 1984. "A Comparison of Exact Approaches for Solving the Multiple Constrained Resource, Project Scheduling Problem," Management Science, INFORMS, vol. 30(7), pages 854-867, July.
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    3. Pei, Zhi & Lu, Haimin & Jin, Qingwei & Zhang, Lianmin, 2022. "Target-based distributionally robust optimization for single machine scheduling," European Journal of Operational Research, Elsevier, vol. 299(2), pages 420-431.

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