Integrating production and transportation scheduling in a two-stage supply chain considering order assignment

Abstract

This study is concerned with the integration of production and transportation scheduling in a two-stage supply chain environment while considering the assignment of orders to the suppliers. The first stage contains m suppliers distributed in various geographic zones, and the second stage is composed of l vehicles with different speeds and transportation capacities that transport n jobs from the supplier to a manufacturing company. In addition, it is assumed that each job occupies a different vehicle size and could be processed by some permissible suppliers. After modeling the problem as a mixed integer programming problem, a genetic algorithm named dynamic genetic algorithm (DGA) is proposed to solve it. Since this problem has not been mentioned in the literature, DGA performance was evaluated by comparing its outputs with optimum solutions for small-sized problems and to the random search approach for larger problems. Additionally, the performance of the DGA was compared with that of a similar problem from the literature. The results of these comparisons show that the DGA is an excellent approach. In addition, the impact of grouping technology initialization is examined, showing that the quality of the solution was not improved and that there was an increase in the CPU time.