A cooperative adaptive genetic algorithm for reentrant hybrid flow shop scheduling with sequence-dependent setup time and limited buffers

Abstract This paper deals with a reentrant hybrid flow shop problem sequence-dependent setup time and limited buffers where there are multiple unrelated parallel machines at each stage. A mathematical model the minimization of total weighted completion is constructed for this problem. Considering complexity hand, an effective cooperative adaptive genetic algorithm (CAGA) proposed. In algorithm, dual chain coding scheme staged-hierarchical decoding approach are, respectively, designed to encode decode solution. Six dispatch heuristics dynamic adjustment method introduced define initial population. To balance exploration exploitation abilities, three operations implemented: (1) two new crossover mutation operators collaborative mechanism imposed on algorithm; (2) strategy re-optimize better solutions after operations, ant colony search modified greedy heuristic intelligently switched; (3) reset variable step embedded re-generate some non-improved solutions. Taguchi design experiment adopted calibrate parameter values in presented algorithm. Comparison experiments executed test instances different scale problems. Computational results show that proposed CAGA more efficient than several well-known algorithms solving studied