An algorithm for solving the job-shop problem
Management Science
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
An effective hybrid optimization approach for multi-objective flexible job-shop scheduling problems
Computers and Industrial Engineering
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A novel multi-objective optimization scheme for grid resource allocation
Proceedings of the 6th international workshop on Middleware for grid computing
Adaptive representation for flexible job-shop scheduling and rescheduling
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Solving multiobjective flexible job-shop scheduling using an adaptive representation
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A multi-objective genetic algorithm for fuzzy flexible job-shop scheduling problem
International Journal of Computer Applications in Technology
| Hi-index | 0.00 |
Finding realistic schedules for Flexible Job Shop Problems has attracted many researchers recently due to its NP-hardness. In this paper, we present an efficient approach for solving the multi-objective flexible job shop by combining Evolutionary Algorithm and Guided Local Search. Instead of applying random local search to find neighborhood solutions, we introduce a guided local search procedure to accelerate the process of convergence to Pareto-optimal solutions. The main improvement of this combination is to help diversify the population towards the Pareto-front. Empirical studies show that 1) the gaps between the obtained results and known lower bounds are small, and 2) the multi-objective solutions of our algorithms dominate previous designs for solving the same benchmarks while incurring less computational time.