Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Computers and Industrial Engineering
Computers and Industrial Engineering
Bat algorithm for multi-objective optimisation
International Journal of Bio-Inspired Computation
A new hybrid GA/SA algorithm for the job shop scheduling problem
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
International Journal of Bio-Inspired Computation
Use of a genetic algorithm for building efficient choice designs
International Journal of Bio-Inspired Computation
International Journal of Computer Applications in Technology
International Journal of Computer Applications in Technology
A new approach for integrating process planning with scheduling
International Journal of Computer Applications in Technology
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
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In many real-world applications, processing time may vary dynamically due to human factors or operating faults and there are some other uncertain factors in the scheduling problems. In this paper, fuzzy sets are used to model uncertain processing time and due date. In addition, an improved multi-objective genetic algorithm is presented to solve the multi-objective fuzzy Flexible Job-shop Scheduling Problem FJSP. About this improved multi-objective genetic algorithm, Pareto-optimality is applied, including the non-dominated sorting scheme and an improved elite reservation strategy based on NSGA-II. Meanwhile, the immune and entropy principle is used to preserve the diversity of individuals. Moreover, the advanced crossover and the mutation operators are used to adapt to the special chromosome structure. The computational results demonstrate the effectiveness of the proposed algorithm.