Performance evaluation of the scatter search method for permutation flowshop sequencing problems

Abstract

Many optimization problems from the industrial engineering world, in particular manufacturing systems, are very complex in nature and are quite hard to solve by conventional optimization techniques. There has been increasing interest to apply metaheuristic methods to solve such kinds of hard optimization problems. In this work, a novel metaheuristic approach called scatter search (SS) is applied for the n/m/P/C max problem, an NP-hard sequencing problem, which is used to find a processing order of n different jobs to be processed on m machines in the same sequence with minimizing the makespan. SS contrasts with other evolutionary procedures by providing a wide exploration of the search space through intensification and diversification. In addition, it has a unifying principle for joining solutions and they exploit the adaptive memory principle to avoid generating or incorporating duplicate solutions at various stages of the problem. In this paper, various metaheuristic methods and best heuristics from the literature are used for solving the well-known benchmark problem set of Taillard (Eur J Oper Res 64:278–285, 1993). The results available for the various existing metaheuristic and heuristic methods are compared with the results obtained by the SS method. The proposed framework achieves better results for 4 of 12 benchmark problems and also achieves an average deviation of 1.003% from the benchmark problem set of Taillard (Eur J Oper Res 64:278–285, 1993). The computational results show that SS is a more effective metaheuristic for the n/m/P/C max problem.