Affiliations: [a] School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China | [b] R&D Center, Xingfa Aluminum Holdings Limited, Foshan, Guangdong 528061, China | [c] School of Information Management Engineering, Guangdong University of Technology, Guangzhou, Guangdong 510006, China
Correspondence:
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Corresponding author: Peng Liang, School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China. E-mail: [email protected].
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
