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.
Highlights
We can see that previous studies mostly discussed the common due date of the single-machine or parallel machine scheduling; this study extends the common due date problem to the dynamic flow shop scheduling environment
By the simulation results derived in this study, we can see that employing the modified genetic algorithm can improve the optimum solution quality of common due date for the minimum total penalty value in the dynamic flow shop scheduling
Through iterative experiments, the modified genetic algorithm is proved to be superior to the common due date algorithm (CDDA) approach proposed by Suel [4]. is article considers the advantage of genetic algorithm to design the termination rule, crossover, and mutation to generate diversified children at the initial stage; the problems can be solved in a simple, timesaving manner
Summary
Most studies on common due date problems discuss the topic in the context of solving the minimum penalty value for the singlemachine or parallel machine scheduling environment. is 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. e enhanced heuristic algorithm is characterized by designating mutant genomes of the child as the genomes located at the central location before mutation. e advantage is to integrate the successful experiences of the conventional common due date algorithm to improve the efficiency of the proposed heuristic algorithm. e performance of both algorithms is analyzed in terms of uniformly distributed job numbers, processing time, early penalty, and late penalty. e resulting outcomes indicate that the enhanced heuristic algorithm outperforms the conventional CDDA and EDD proposed by previous studies in the average penalties. Is 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.
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