Fast Evolutionary Algorithm for Flow Shop Scheduling Problems

Being complex and combinatorial optimization problems, Permutation Flow Shop Scheduling Problems (PFSSP) are difficult to be solved optimally. PFSSP occurs in many manufacturing systems i.e. automobile industry, glass paper appliances pharmaceutical the generation of best schedule is very important for these systems. Evolution Strategy (ES) a subclass Evolutionary algorithms this paper, we propose an Improved reduce makespan PFSSP. Two variants proposed namely ES5 ES10. The initial solution generated using shortest processing time rule. In ES5, four offsprings from one parent while ES10, nine parent. selection pool consists both parents offsprings. Quad swap mutation operator has been minimize computational maximum search space. Also, variable rate used fine-tuning results, with increasing number iterations reduced. performances ES were tested on two test domains. First, it applied benchmark Carlier Reeves. Computational results matched other well-known techniques available literature, show effectiveness robustness techniques. Secondly, real-life problem batteries demonstrate its effectiveness. Data was taken Pakistan Accumulator NS30-40 Plates battery, company daily producing 1400 units battery. different batch sizes 35, 140, 1120 & 1400. Our that Min %GAP 1.25 found Hence can increase monthly 450 NS30 ES10 algorithm.