Entropy-Enhanced Genetic Algorithm with Tabu Search for Job Shop Scheduling Problems
2012 ◽
Vol 590
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pp. 557-562
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Keyword(s):
Job Shop
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By combining Genetic algorithm with Tabu search algorithm and adjusting crossover rate and mutation rate based on information entropy, a hybrid genetic algorithm was proposed for larger-scale job shop scheduling problems, and the benchmark instances were used to verify the algorithm with simulation. Simulation results show that the proposed algorithm can solve larger-scale job shop scheduling problems, and it has obvious advantages over traditional scheduling algorithms.