Bi-objective Hybrid Flow Shop Scheduling with Family Setup Times Using Hybrid Genetic and Migrating Birds Optimization Algorithms

Abstract

This paper presents a hybrid metaheuristic algorithm to solve the hybrid flow shop scheduling problem (HFSP) with family setup times. Many conditions of HFSP have been extensively studied in recently years and metaheuristics and local search algorithms have also been developed to yield better solutions for multi-objective HFSP. HFSP in this work is based on a harddisk drive manufacturer. An effective NSGA-II integrated with migrating birds optimization (MBO) called MBNSGA-II is proposed to improve the quality of solutions for bi-objective HFSP. Makespan and total tardiness time are the objectives of this HFSP. MBO is added to mutation operation of genetic algorithm to improve the Pareto front. Next, various sizes of benchmark problem are utilized to evaluate the performance of NSGA-II and MBNSGA-II. The comparisons of two algorithms consisting of NSGA-II and MBNSGA-II are provided by using the numerical examples. It is obvious the Pareto fronts obtained from MBNSGA-II are adjacent to the approximated true Pareto front. In terms of inverted generational distance (IGD) which is the index of convergence and diversity of the solution set, the performance of proposed MBNSGA-II outperforms NSGA-II.