Multi-objective flexible flow shop batch scheduling problem with renewable energy Online publication date: Mon, 05-Oct-2020
by Xiuli Wu; Xiao Xiao; Qi Cui
International Journal of Automation and Control (IJAAC), Vol. 14, No. 5/6, 2020
Abstract: Renewable energy is an alternative for the non-renewable energy to reduce the carbon emission in manufacturing system. How to make an energy-efficient scheduling solution when renewable and non-renewable energy drive the production alternatively is of great importance. In this paper, a multi-objective flexible flow shop batch scheduling problem with renewable energy (MFBSP-RE) is studied, variable processing time and handling time are taken into account. To begin with, the mathematical model is formulated to minimise the carbon emission and makespan simultaneously. Then, a hybrid non-dominated sorting genetic algorithm-II (HNSGA-II) with variable local search is proposed to solve the MFBSP-RE. The operation-based encoding method is employed. A low-carbon scheduling algorithm is presented. Besides the crossover and mutation, a variable local search is employed to improve the Pareto set. Finally, the results of experiments show that the proposed HNSGA-II outperforms the standard NSGA-II algorithm and can solve the MFBSP-RE effectively and efficiently.
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