Title: Multi-objective flexible flow shop batch scheduling problem with renewable energy

Authors: Xiuli Wu; Xiao Xiao; Qi Cui

Addresses: Department of Logistics Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Room 514B, Beijing, 100083, China ' Department of Logistics Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China ' Department of Logistics Engineering, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, 100083, China

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.

Keywords: flexible flow shop scheduling problem; FFSP; batch scheduling; handling time; HNSGA-II; renewable energy; low-carbon scheduling.

DOI: 10.1504/IJAAC.2020.110071

International Journal of Automation and Control, 2020 Vol.14 No.5/6, pp.519 - 553

Received: 30 Oct 2018
Accepted: 21 Mar 2019
Published online: 05 Oct 2020 *

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