Abstract
Renewable energy is an alternative to non-renewable energy to reduce the carbon footprint of manufacturing systems. Finding out how to make an alternative energy-efficient scheduling solution when renewable and non-renewable energy drives production is of great importance. In this paper, a multi-objective flexible flow shop scheduling problem that considers variable processing time due to renewable energy (MFFSP-VPTRE) is studied. First, the optimization model of the MFFSP-VPTRE is formulated considering the periodicity of renewable energy and the limitations of energy storage capacity. Then, a hybrid non-dominated sorting genetic algorithm with variable local search (HNSGA-II) is proposed to solve the MFFSP-VPTRE. An operation and machine-based encoding method is employed. A low-carbon scheduling algorithm is presented. Besides the crossover and mutation, a variable local search is used to improve the offspring’s Pareto set. The offspring and the parents are combined and those that dominate more are selected to continue evolving. Finally, two groups of experiments are carried out. The results show that the low-carbon scheduling algorithm can effectively reduce the carbon footprint under the premise of makespan optimization and the HNSGA-II outperforms the traditional NSGA-II and can solve the MFFSP-VPTRE effectively and efficiently.
Highlights
The world’s electricity consumption is increasing with industrial development
Global warming is caused by the increase in greenhouse gas emissions, especially carbon dioxide released from fossil fuel combustion
Based on the flow shop scheduling problem, this paper considers a scenario in which the processing time changes
Summary
The world’s electricity consumption is increasing with industrial development. Most electricity is generated from fossil fuels, such as coal and oil, which cause greenhouse gas emissions as well as global warming [1]. China is adopting and strengthening a series of measures for energy-saving and emissions reduction, such as actively establishing low-carbon manufacturing systems, rapidly developing new energy storage technology, and increasing the use of renewable energy in industrial processes [2]. With the development of renewable energy power generation technology and large-scale energy storage technology, using renewable energy in the production process can effectively reduce non-renewable energy consumption and the carbon footprint. The generation of micro-grid technology enables renewable energy to reduce the carbon footprint in industrial production. Based on the characteristics of micro-grids and taking wind turbine blade production as an instance, this paper studies a multi-objective flexible flow shop scheduling problem considering variable processing time due to renewable energy (MFFSP-VPTRE). The rest of this paper is organized as follows: Section 2 reviews the literature; Section 3 formulates the problem; Section 4 proposes the HNSGA-II algorithm for the MFFSP-VPTRE; Section 5 reports the experimental results; and Section 6 concludes our study
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