Flexible Job Shop Scheduling with Microgrid Assistance

Authors

  • Yong Chen Institute of Industrial Engineering, Zhejiang University of Technology, Hangzhou, China
  • Junhao Yang Institute of Industrial Engineering, Zhejiang University of Technology, Hangzhou, China
  • Zhi Pei Institute of Industrial Engineering, Zhejiang University of Technology, Hangzhou, China
  • Zhiwen Cheng Institute of Industrial Engineering, Zhejiang University of Technology, Hangzhou, China
  • Zuzhen Ji Institute of Industrial Engineering, Zhejiang University of Technology, Hangzhou, China
  • Wenchao Yi Institute of Industrial Engineering, Zhejiang University of Technology, Hangzhou, China

DOI:

https://doi.org/10.23055/ijietap.2025.32.5.11127

Keywords:

Flexible job shop scheduling; Microgrid; Energy distribution; Renewable energy;

Abstract

In the context of global energy shortage and climate warming, the application of renewable energy in industrial production is an effective strategy to solve the problem. In this paper, a microgrid system composed of photovoltaic energy storage is constructed, which is connected to the flexible workshop together with the power grid. Taking the completion time, economic cost, and carbon emission as the optimization objectives, a multi-objective flexible job shop scheduling and energy allocation model is established, and the improved NSGA-II algorithm is used for optimization and solution. The optimization objective is improved by prioritizing time periods, scheduling the floating processes in the time period with high PV production capacity and low electricity price. Adaptive probability is adopted in crossover and mutation, and critical path mutation is adopted in mutation operation to accelerate the convergence speed and diversity of algorithms; for the energy allocation strategy, branching exact algorithms under the heuristic rules are introduced to obtain the optimal results of the branching paths and integrated into the main algorithm for continuous Solving. The effectiveness of the algorithm is verified by the example dataset of a flexible job shop, and the comparison with the traditional shop scheduling reveals that the optimization effectiveness of the microgrid and flexible job shop depends on the “scale - elasticity - energy consumption” multifaceted characteristics of the production scenarios. By focusing on the operation of different optimization objectives, a coordinated solution is provided for enterprises to cope with different production demands, and the reliability of the algorithm is also verified by comparing with the ordinary NSGA-II algorithm and the GWO algorithm. The study shows that the application of renewable energy to industrial manufacturing scenarios can effectively reduce the production cost and environmental pollution of enterprises, and promote the development of green manufacturing.

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Published

2025-10-10

How to Cite

Chen, Y., Yang, J., Pei, Z., Cheng, Z., Ji, Z., & Yi, W. (2025). Flexible Job Shop Scheduling with Microgrid Assistance. International Journal of Industrial Engineering: Theory, Applications and Practice, 32(5). https://doi.org/10.23055/ijietap.2025.32.5.11127

Issue

Vol. 32 No. 5 (2025)

Section

Production Planning and Control

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