The Improved Genetic Algorithm for Multi-Objective Flexible Job Shop Scheduling Problem
To solve the multi-objective flexible job shop scheduling problem, an improved non-dominated sorting genetic algorithm is proposed. Multi-objective mathematical model is established, four objectives, makespan, maximal workload, total workload and total tardiness are considered together. In this paper a dual coding method is employed, and infeasible solutions were avoided by new crossover and mutation methods. Pareto optimal set was taken to deal with multi-objective optimization problem, in order to reduce computational complexity, the non-dominated sorting method was improved. The niche technology is adopted to increase the diversity of solutions, and a new self adaptive mutation rate computing method is designed. The proposed algorithm is tested on some instances, and the computation results demonstrate the superiority of the algorithm.
J. J. Yang et al., "The Improved Genetic Algorithm for Multi-Objective Flexible Job Shop Scheduling Problem", Applied Mechanics and Materials, Vols. 66-68, pp. 870-875, 2011