Bi-objective carbon-efficient distributed flow-shop scheduling with multistep electricity pricing

Abstract

A bi-objective distributed flow-shop scheduling problem with multistep electricity pricing and carbon emissions is studied. One objective is to minimise the completion time of production, and the other is to minimise the total cost of multistep electricity pricing and carbon emissions. A mixed integer programming model and a two-stage knowledge based cooperative algorithm with a local reinforcement strategy are proposed for the problem. The extensive numerical experiments show that the two-stage algorithm was effective statistical significantly in generating non-dominated solution sets and Pareto frontiers. Simulations on Electricity Prices are applied to examine different multistep electricity pricing schemes, and management implications were drawn for both government and companies.

KEYWORDS:

• Industry
• innovation and infrastructure
• distributed flow-shop scheduling
• multistep electricity pricing
• energy efficient

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the first author, X. Yu, upon reasonable request.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under grant numbers 72274094, 72371129, and 71834003.

Notes on contributors

Xianyu Yu

Xianyu Yu is currently a professor of Nanjing University of Aeronautics and Astronautics. He received Ph.D. degree in management science and engineering from Southeast University, Nanjing, China, in 2015. His research interests include intelligent algorithms and green scheduling, energy economic simulation and policy analysis. He has published more than 40 papers in academic journals such as International Journal of Production Research, Energy Economics, Journal of Scheduling, Information Sciences, Computer & Industrial Engineering, Sustainable Cities and Society, Energy, and Energy Policy.

Hengte Du

Hengte Du is currently pursuing a master's degree in electronic information at Beihang University. He previously obtained a bachelor's degree in management at Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2022. His research interests mainly include combinatorial optimisation problems, systems engineering, and human-machine systems. He has published paper in the academic journal International Journal of Production Research.

Dequn Zhou

Dequn Zhou is currently a professor of Nanjing University of Aeronautics and Astronautics. He received Ph.D. degree in management science and engineering from China University of Mining and Technology, Xuzhou, China, in 1997. His research interests include energy economics, energy soft science theory, and energy transformation. He has published more than 200 papers in academic journals such as Nature Communications, International Journal of Production Research, Energy Economics, Applied Energy, and Energy Policy. His papers are selected as the ‘ESI Highly Cited Paper’ and ‘ESI Hot Paper’. He is selected on the Global Highly Cited Scientist List by Clarivate from 2019 to 2022.

Qunwei Wang

Qunwei Wang is currently a professor of Nanjing University of Aeronautics and Astronautics. He received Ph.D. degree in management science and engineering from Nanjing University of Aeronautics and Astronautics in 2011. His research interests include energy economics and management, environmental management and policy, and energy finance and carbon markets. He has published more than 100 papers in academic journals such as iScience, International Journal of Production Research, Naval Research Logistics, Energy Economics, Applied Energy, Energy, Renewable and Sustainable Energy Reviews, Ecological Economics, and Energy Policy. He is selected on the Global Highly Cited Scientist List by Clarivate in 2022. He currently serves as an Associate Editor for the international journal: Energy Economics.

Guohui Lin

Guohui Lin is currently a professor of Computing Science at the University of Alberta. He received his PhD degree in Computer Science from the Chinese Academy of Sciences in 1998. His research interests include combinatorial optimisation and bioinformatics. He has published more than 200 papers in academic journals such as International Journal of Production Research, European Journal of Operational Research, Algorithmica, Journal of Scheduling, Computers & Industrial Engineering, Journal of Combinatorial Optimisation, Discrete Applied Mathematics, and Theoretical Computer Science. He currently serves as an Associate Editor for the international journal: Journal of Combinatorial Optimisation and Quantitative Biology.