An improved genetic algorithm for the flowshop scheduling problem

Abstract

This paper considers the permutation flowshop scheduling problem with the objective of minimizing makespan. Genetic algorithm (GA) is one of the search heuristics used to solve global optimization problems in complex search spaces. It is observed that, the efficiency of GA in solving a flowshop problem can be improved significantly by tailoring the various GA operators to suit the structure of the problem. In this paper, an effective Improved Genetic Algorithm (IGA) for flowshop scheduling, incorporating multi-crossover operators, multi-mutation operators and hypermutation is proposed. Computation results based on some permutation flowshop scheduling benchmark problems (OR-Library) show that the IGA gives a better solution when compared with the earlier reported results.