The Dynamic Flow Shop Scheduling Problem with JIT Philosophy and Common Due Date

Abstract

Most studies on common due date problems discuss the topic in the context of solving the minimum penalty value for the single-machine or parallel machine scheduling environment. This study extends the problem of the common due date to the dynamic flow shop environment and proposes the enhanced heuristic algorithm to solve the minimum penalty value. The enhanced heuristic algorithm is characterized by designating mutant genomes of the child as the genomes located at the central location before mutation. The advantage is to integrate the successful experiences of the conventional common due date algorithm to improve the efficiency of the proposed heuristic algorithm. The performance of both algorithms is analyzed in terms of uniformly distributed job numbers, processing time, early penalty, and late penalty. The resulting outcomes indicate that the enhanced heuristic algorithm outperforms the conventional CDDA and EDD proposed by previous studies in the average penalties.