Bi-objective scheduling for reentrant hybrid flow shop using Pareto genetic algorithm

This paper deals with a scheduling problem for reentrant hybrid flowshop with serial stages where each stage consists of identical parallel machines. In a reentrant flowshop, a job may revisit any stage several times. Local-search based Pareto genetic algorithms with Minkowski distance-based crossover operator is proposed to approximate the Pareto optimal solutions for the minimization of makespan and total tardiness in a reentrant hybrid flowshop. The Pareto genetic algorithms are compared with existing multi-objective genetic algorithm, NSGA-II in terms of the convergence to optimal solution, the diversity of solution and the dominance of solution. Experimental results show that the proposed crossover operator and local search are effective and the proposed algorithm outperforms NSGA-II by statistical analysis.

► We consider a reentrant hybrid flowshop with serial stages. ► Local-search based Pareto genetic algorithms with Minkowski distance-based crossover operator is proposed. ► The objective is the minimization of makespan and total tardiness. ► The Pareto genetic algorithms are compared with NSGA-II. ► The proposed algorithm outperforms NSGA-II by statistical analysis.