Main Article Content
Supply chain planning consists of designing an optimal and feasible production and distribution plan for the whole supply chain. Traditionally, two common methods of optimization are analytical and simulation-based optimization, and each of them has pros and cons. In this paper, both methods are combined to consolidate the strengths of each, also known as the hybrid analytical and simulation approach. A case study of a multi-period, multi-echelon, and multi-product production and distribution problem that maximizes the whole supply chain’s profit is introduced, to demonstrate the proposed hybrid approach. The analytical model is solved to find the optimal production-distribution plan, and then the plan is inputted into a simulation model, where uncertainties are incorporated. The proposed algorithm is then applied to identify a feasible plan that meets makespan limitation and service level requirements. Safety stock is incorporated to satisfy the service level requirements and maximize the supply chain’s profit. This procedure continues iteratively until the production-distribution plan is feasible and optimized. The results show that the proposed approach can solve for a near or possibly optimal as well as feasible solution with relatively fast computational time.
 Susarla N, Karimi IA, Integrated supply chain planning for multinational pharmaceutical enterprises, Comput Chem Eng. 42(2012);168-177.
 Fu MC, Feature Article: Optimization for simulation: Theory vs. Practice, INFORMS J Comput. 14(2002); 192-215.
 Glover F, Kelly JP, Laguna M. New advances for wedding optimization and simulation. In: Winter Simulation Conference 1999; 6 December 1999; Phoenix, USA: IEEE; 2000. p. 255-260.
 Layeb SB, Jaoua A, Jbira A, Makhlouf Y3. A simulation-optimization approach for scheduling in stochastic freight transportation.Comput Ind Eng. 126(2018);99-110.
 Chiadamrong N, Kawtummachai R. A methodology to support decision-making on sugar distribution for export channel: A case study of Thai sugar industry. Comput Electron Agric. 64(2008);248-261.
 Acar Y, Kadipasaoglu SN, Day JM, Incorporating uncertainty in optimal decision making: Integrating mixed integer programming and simulation to solve combinatorial problems. Comput Ind Eng. 56(2009); 106-112.
 Thammatadatrakul P, Chiadamrong N. Optimal inventory control policy of a hybrid manufacturing- remanufacturing system using a hybrid simulation optimisation algorithm. J Simul. 13(2017);14-27.
 Chiadamrong V, Piyathanavong N. Optimal design of supply chain network under uncertainty environment using hybrid analytical and simulation modeling approach. J Ind Eng Int. 13(2017);465-478.
 Ko HJ, Ko CS, Kim T. A hybrid optimization/simulation approach for a distribution network design of 3PLS. Comput Ind Eng. 50(2006);440-449.
 Suyabatmaz AC, Altekin FT, Sahin G. Hybrid simulation-analytical modeling approaches for the reverse logistics network design of a third-party logistics provider. Comput Ind Eng. 70(2014);74-89.
 Byrne M, Bakir M. Production planning using a hybrid simulation-analytical approach. Int J Prod Econ. 59(1999);305-311.
 Lee YH, Kim SH, Production-distribution planning in supply chain considering capacity constraints. Comput Ind Eng. 43(2002);169-190.
 Martins S, Amorim P, Figueira G, Almada-Lobo B. An optimization-simulation approach to the network redesign problem of pharmaceutical wholesalers. Comput Ind Eng. 106(2017);315-328.
 de Keizer M, Haijema R, Bloemhof JM, van der Vorst JGAJ. Hybrid optimization and simulation to design a logistics network for distributing perishable products. Comput Ind Eng. 88(2015);26-38.
 Chen CP. Complete asymptotic expansions for the density function of t-distribution. Stat Probab Lett. 141(2018);1-6.