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Abstract

In order to solve the problem of minimizing power cost and makespan with time-of-use electricity. A genetic algorithm based on individual concentration and similarity vector distance strategy is proposed. The proposed genetic algorithm overcomes premature convergence problem by keeping the fittest individual through computing individual concentration and similarity vector distance. Production power cost reduction is achieved by using right-shift local search algorithm. The effectiveness of the proposed algorithm is illustrated by comparing the proposed algorithm with other scheduling algorithms. The comparative experiments indicate the proposed algorithm has better performance on minimizing power cost as well as makespan.

Keywords

Job-shop scheduling machine power cost time-of-use electricity genetic algorithm

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Job-shop scheduling problem considering power cost and makespan under time-of-use electricity price

Abstract

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