An empirical study on the material distribution decision making

This paper addresses a mathematical approach to decision making in a real-world material distribution situation. The problem is characterized by a low-volume and highly-varied mix of products, therefore there is a lot of material movement between the facilities. This study focuses especially on the transportation scheduler with a tool that can be used to quantitatively analyze the volume of material moved, the type of truck to be used, production schedules, and due dates. In this research, we have developed a mixed integer programming problem using the minimum cost, multiperiod, multi-commodity network flow approach that minimizes the overall material movement costs. The results suggest that the optimization approach provides a set of feasible solution routes with the objective of reducing the overall fleet cost.