IDEAS home Printed from https://ideas.repec.org/a/igg/jisscm/v15y2022i1p1-30.html
   My bibliography  Save this article

Integrating Mathematical and Simulation Approach for Optimizing Production and Distribution Planning With Lateral Transshipment in a Supply Chain

Author

Listed:
  • Jirasak Ji

    (SIIT, Thammasat University, Thailand)

  • Navee Chiadamrong

    (SIIT, Thammasat University, Thailand)

Abstract

Supply chain planning aims to maximize the chain's profit and find an effective way to integrate production and distribution. A mathematical and simulation-based optimizations are two common disciplines in which this study integrates both of them together to consolidate their advantages. A mathematical model is formulated to find an optimal production-distribution plan. Then, the result is fed into a simulation model operating under uncertainty to verify the feasibility of the plan. Our integrated approach tries to find a feasible plan that satisfies both required customer service level and makespan limitation where safety stock is used to hedge against uncertainties, and lateral transshipment is used for emergency measures against excessive fluctuation of customer demand. A case study that optimizes the profit of an entire chain is used to demonstrate the algorithm. The outcomes of the study show that our proposed approach can yield feasible results (with near or even optimal solution) with much faster computational time as compared to the traditional simulation-based optimization.

Suggested Citation

  • Jirasak Ji & Navee Chiadamrong, 2022. "Integrating Mathematical and Simulation Approach for Optimizing Production and Distribution Planning With Lateral Transshipment in a Supply Chain," International Journal of Information Systems and Supply Chain Management (IJISSCM), IGI Global, vol. 15(1), pages 1-30, January.
  • Handle: RePEc:igg:jisscm:v:15:y:2022:i:1:p:1-30
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJISSCM.2022010105
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael C. Fu, 2002. "Feature Article: Optimization for simulation: Theory vs. Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 192-215, August.
    Full references (including those not matched with items on IDEAS)

    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. Noordhoek, Marije & Dullaert, Wout & Lai, David S.W. & de Leeuw, Sander, 2018. "A simulation–optimization approach for a service-constrained multi-echelon distribution network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 292-311.
    2. Zheng, Liang & Xue, Xinfeng & Xu, Chengcheng & Ran, Bin, 2019. "A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 287-308.
    3. Roberto Aringhieri & Giuliana Carello & Daniela Morale, 2016. "Supporting decision making to improve the performance of an Italian Emergency Medical Service," Annals of Operations Research, Springer, vol. 236(1), pages 131-148, January.
    4. Jianxin Fang & Brenda Cheang & Andrew Lim, 2023. "Problems and Solution Methods of Machine Scheduling in Semiconductor Manufacturing Operations: A Survey," Sustainability, MDPI, vol. 15(17), pages 1-44, August.
    5. Warren B. Powell, 2010. "Rejoinder ---The Languages of Stochastic Optimization," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 23-25, February.
    6. David J. Eckman & Shane G. Henderson & Sara Shashaani, 2023. "Diagnostic Tools for Evaluating and Comparing Simulation-Optimization Algorithms," INFORMS Journal on Computing, INFORMS, vol. 35(2), pages 350-367, March.
    7. Lam, Chiou-Peng & Masek, Martin & Kelly, Luke & Papasimeon, Michael & Benke, Lyndon, 2019. "A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics," Operations Research Perspectives, Elsevier, vol. 6(C).
    8. Warren B. Powell, 2009. "What you should know about approximate dynamic programming," Naval Research Logistics (NRL), John Wiley & Sons, vol. 56(3), pages 239-249, April.
    9. Jack P. C. Kleijnen & Susan M. Sanchez & Thomas W. Lucas & Thomas M. Cioppa, 2005. "State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments," INFORMS Journal on Computing, INFORMS, vol. 17(3), pages 263-289, August.
    10. Poorya Ghafoorpoor Yazdi & Aydin Azizi & Majid Hashemipour, 2019. "A Hybrid Methodology for Validation of Optimization Solutions Effects on Manufacturing Sustainability with Time Study and Simulation Approach for SMEs," Sustainability, MDPI, vol. 11(5), pages 1-26, March.
    11. Chuljin Park & Seong-Hee Kim, 2015. "Penalty Function with Memory for Discrete Optimization via Simulation with Stochastic Constraints," Operations Research, INFORMS, vol. 63(5), pages 1195-1212, October.
    12. Sigrún Andradóttir & Andrei A. Prudius, 2009. "Balanced Explorative and Exploitative Search with Estimation for Simulation Optimization," INFORMS Journal on Computing, INFORMS, vol. 21(2), pages 193-208, May.
    13. Stefan Vonolfen & Michael Affenzeller, 2016. "Distribution of waiting time for dynamic pickup and delivery problems," Annals of Operations Research, Springer, vol. 236(2), pages 359-382, January.
    14. Peter Frazier & Warren Powell & Savas Dayanik, 2009. "The Knowledge-Gradient Policy for Correlated Normal Beliefs," INFORMS Journal on Computing, INFORMS, vol. 21(4), pages 599-613, November.
    15. S Robinson, 2005. "Discrete-event simulation: from the pioneers to the present, what next?," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(6), pages 619-629, June.
    16. Kenneth J. Klassen & Reena Yoogalingam, 2019. "Appointment scheduling in multi-stage outpatient clinics," Health Care Management Science, Springer, vol. 22(2), pages 229-244, June.
    17. L. Jeff Hong & Barry L. Nelson, 2006. "Discrete Optimization via Simulation Using COMPASS," Operations Research, INFORMS, vol. 54(1), pages 115-129, February.
    18. Kleijnen, J.P.C. & Sanchez, S.M. & Lucas, T.W. & Cioppa, T.M., 2003. "A User's Guide to the Brave New World of Designing Simulation Experiments," Discussion Paper 2003-1, Tilburg University, Center for Economic Research.
    19. Satyajith Amaran & Nikolaos V. Sahinidis & Bikram Sharda & Scott J. Bury, 2016. "Simulation optimization: a review of algorithms and applications," Annals of Operations Research, Springer, vol. 240(1), pages 351-380, May.
    20. Sigrún Andradóttir, 2002. "Simulation Optimization: Integrating Research and Practice," INFORMS Journal on Computing, INFORMS, vol. 14(3), pages 216-219, August.

    More about this item

    Statistics

    Access and download statistics

    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:igg:jisscm:v:15:y:2022:i:1:p:1-30. 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: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    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.