The Green Flexible Job-Shop Scheduling Problem Considering Cost, Carbon Emissions, and Customer Satisfaction under Time-of-Use Electricity Pricing

Shun Jia (), Yang Yang, Shuyu Li, Shang Wang, Anbang Li, Wei Cai, Yang Liu, Jian Hao and Luoke Hu
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Shun Jia: Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Yang Yang: Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Shuyu Li: Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Shang Wang: Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Anbang Li: Engineering Training Center, Shandong University of Science and Technology, Qingdao 266590, China
Wei Cai: College of Engineering and Technology, Southwest University, Chongqing 400715, China
Yang Liu: Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Jian Hao: Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China
Luoke Hu: School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China

Sustainability, 2024, vol. 16, issue 6, 1-22

Abstract: Exploration of the green flexible job-shop scheduling problem is essential for enterprises aiming for sustainable practices, including energy conservation, emissions reduction, and enhanced economic and social benefits. While existing research has predominantly focused on carbon emissions or energy consumption as green scheduling objectives, this paper addresses the broader scope by incorporating the impact of variable energy prices on energy cost. Through the introduction of an energy cost model based on time-of-use electricity pricing, the study formulates a multi-objective optimization model for green flexible job-shop scheduling. The objectives include minimizing cost, reducing carbon emissions, and maximizing customer satisfaction. To prevent premature convergence and maintain population diversity, an enhanced genetic algorithm is employed for solving. The validation of the algorithm’s effectiveness is demonstrated through specific examples, providing decision results for optimal scheduling under various weight combinations. The research outcomes hold substantial practical value as they can significantly reduce energy expenses, lower carbon emissions, and elevate customer satisfaction while safeguarding production efficiency. This contributes to enhancing the market competitiveness and green brand image of businesses.

Keywords: flexible job-shop scheduling problem; time-of-use electricity pricing; energy cost; carbon emissions; customer satisfaction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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