A Simple and Effective Approach for Tackling the Permutation Flow Shop Scheduling Problem

In this research, a new approach for tackling the permutation flow shop scheduling problem (PFSSP) is proposed. This algorithm based on steps of elitism continuous genetic improved by two strategies and used largest rank value (LRV) rule to transform values into discrete ones enabling solving combinatorial PFSSP. The first strategy combining arithmetic crossover with uniform give high capability exploitation in addition reducing stuck local minima. second one re-initializing an individual selected randomly from population increase exploration avoiding Afterward, those are combined proposed produce known as efficient (IEGA). To IEGA, it hybridized search version abbreviated HIEGA. HIEGA IEGA validated three common benchmarks compared number well-known robust evolutionary meta-heuristic algorithms check their efficacy. experimental results show that competitive others datasets incorporated comparison, such Carlier, Reeves, Heller.