Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

Recently, a multi-facility, multi-product and multi-period industrial production-distribution planning problem has been widely investigated in Supply Chain Management (SCM). One of the key issues in the current SCM research area involves reducing both production and distribution costs. We have developed an optimization model to tackle the above problems under the restricted conditions such as transportation time and a zero inventory. Computational experiments using commercial tool Ms-Excel Solver show that the real size problems we encountered can be solved in reasonable time. The model can be used to decide an appropriate production-distribution planning problem in SCM research area.