Abstract
To decrease the load of electricity grid during the peak period, time-of-use electricity price has been implemented in industries to shift the production activities from the peak to the off-peak period. This paper addresses the flexible job shop scheduling under time-of-use electricity prices (FJSPTOUEP) to minimize both makespan and total electricity cost (TEC) simultaneously. We present the mixed integer programming model and propose a hybrid multi-objective evolutionary algorithm based on decomposition (HMOEA/D) to solve the problem. To generate an initial population with certain quality and diversity, several rules are used together. In the framework of MOEA/D, a cooperative search operator is designed to generate new solutions by exchanging information of neighbours. To improve the quality of solutions, two local intensification operators are designed by analysing the critical path of the schedule. An adaptive selection strategy is designed based on the reference point for well using the local search operators to enhance exploitation ability. Moreover, according to the characteristics of time-of-use electricity prices, an adjustment strategy is proposed to reduce electricity cost for further improving solutions. Computational results and statistical comparisons show that both the local intensification and adjustment strategy are effective. It also shows that the proposed HMOEA/D is more effective than other optimization algorithms in solving the FJSPTOUEP.
Published Version
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