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

Jirasak Ji, Navee Chiadamrong

Source Title: International Journal of Information Systems and Supply Chain Management (IJISSCM)15(1)

ISSN: 1935-5726|EISSN: 1935-5734|EISBN13: 9781683180241|DOI: 10.4018/IJISSCM.2022010105

MLA

Ji, Jirasak, and Navee Chiadamrong. "Integrating Mathematical and Simulation Approach for Optimizing Production and Distribution Planning With Lateral Transshipment in a Supply Chain." IJISSCM vol.15, no.1 2022: pp.1-30. http://doi.org/10.4018/IJISSCM.2022010105

APA

Ji, J. & Chiadamrong, N. (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), 15(1), 1-30. http://doi.org/10.4018/IJISSCM.2022010105

Chicago

Ji, Jirasak, and Navee Chiadamrong. "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) 15, no.1: 1-30. http://doi.org/10.4018/IJISSCM.2022010105

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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.