A new approach to solve hybrid flow shop scheduling problems by artificial immune system

Abstract

Artificial immune system (AIS) is an intelligent problem-solving technique that has been used in scheduling problems for about 10 years. AIS are computational systems inspired by theoretical immunology, observed immune functions, principles and mechanisms in order to solve problems. In this research, a computational method based on clonal selection principle and affinity maturation mechanism of the immune response is used. The n-job, k-stage hybrid flow shop problem is one of the general production scheduling problems. Hybrid flow shop (HFS) problems are NP-Hard when the objective is to minimize the makespan [Two-stage hybrid flowshop scheduling problem, Oper. Res. Soc. 39 (1988) 359]. The research deals with the criterion of makespan minimization for the HFS scheduling problems. The operating parameters of meta-heuristics have an important role on the quality of the solution. In this paper we present a generic systematic procedure which is based on a multi-step experimental design approach for determining the optimum system parameters of AIS. AIS algorithm is tested with benchmark problems. Experimental results show that the artificial immune system algorithm is an effective and efficient method for solving HFS problems.