An adaptive genetic algorithm for cost optimization of multi-stage supply chain

The distribution process of finished product is one of the important processes in the company. To maximize the profit, companies need to optimize the distribution network process by minimizing the costs. Genetic algorithm is proposed to find a solution of the problem. In this study, Genetic Algorithm use a one-cut-point (OCP) crossover, swap mutation, and elitism for the selection. Results obtained using GAs in this study is better than those achieved by baseline algorithm. This indicated that OCP crossover, swap mutation, and elitism selection are effective. To increase the quality of the solution, an adaptive genetic algorithm is proposed. The advantage of the adaptive process is producing more different chromosomes. Thus, the adaptive solution gives more efficient solution in minimizing the cost compared to the traditional genetic algorithms.