A study of permutation crossover operators on the traveling salesman problem
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Routing and scheduling in a flexible job shop by tabu search
Annals of Operations Research - Special issue on Tabu search
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Computers and Industrial Engineering
Computers and Industrial Engineering
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Flexible job shop scheduling using a multiobjective memetic algorithm
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Solving Multiple-Objective Flexible Job Shop Problems by Evolution and Local Search
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
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In this paper, a new memetic algorithm (MA) is proposed for the muti-objective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload and critical workload. By using well-designed chromosome encoding/decoding scheme and genetic operators, the non-dominated sorting genetic algorithm II (NSGA-II) is first adapted for the MO-FJSP. Then the MA is developed by incorporating a novel local search algorithm into the adapted NSGA-II, where several mechanisms to balance the genetic search and local search are employed. In the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. Experimental results on well-known benchmark instances show that the proposed MA outperforms significantly two off-the-shelf multi-objective evolutionary algorithms and four state-of-the-art algorithms specially proposed for the MO-FJSP.