A GA-SA Multiobjective Hybrid Search Algorithm for Integrating Lot Sizing and Sequencing in Flow-Line Scheduling

It is very important that the sequencing and lot sizing in the flow-line environment are not independent. They should be integrated. In this paper, a multiobjective hybrid evolutionary search algorithm which combines a genetic algorithm and a simulated annealing algorithm is proposed and the performance of the proposed algorithm is compared with the existing genetic algorithm and the simulated annealing algorithm. The algorithms are coded independently and the performance is compared with randomly generated test problems. The objective functions considered for evaluation are the minimisation of makespan, minimisation of overtime and minimisation of holding cost. The scalar fitness function combining all the three objective functions, which minimises total cost, is used. The results are presented in tables and figures. The results show that the proposed hybrid algorithm performs better than the genetic algorithm and the simulated annealing algorithm.