Applying Genetic Algorithms to Optimize Hybrid Flow Shop Scheduling in Disaster Response Logistics

Abstract

This paper explains the hybrid flow shop problem and its occurrence in logistics. The paper introduced an example to explain the situation. The example problem is a condition involving different jobs, machines, and stages. The background is in a disaster area where people need supplies. The paper divided the whole process into two stages. The paper applies the genetic algorithm to solve this problem. Each schedule was represented in a form of an individual of the genetic algorithm. The paper narrows the range of the individuals by reintroducing a better individual to improve the fitness value of the individual. The result of the problem when the fitness value was nearly constant. The process of solving the problem is explained in detail. The final schedule was drawn in a graph approximately to scale. The paper explains the flaws of the genetic algorithm. In the code, the values were set to increase the efficiency to get the answer and reduce the flaw. At last, the paper discusses the future use of the hybrid flow shop problem in logistics.