Multi-Objective Energy-Saving Job-Shop Scheduling Based on Improved NSGA-II
Dengya Huo, Xianghui Xiao, Ye Pan
Abstract
To pursue sustainable development, the manufacturing industry must meet strict requirements on energy-saving. However, the traditional manufacturing mode is not sufficiently green to satisfy such requirements. To solve the problem, this paper attempts to optimize the multi-objective energy-saving job-shop scheduling process. Firstly, a multi-objective optimization model was established to minimize the maximum makespan, total carbon emissions, and total tardiness. Then, the non-dominated sorting genetic algorithm II (NSGA-II) was improved to provide a solution to the multi-objective energy-saving job-shop scheduling problem (JSP). Finally, the effectiveness of the improved NSGA-II for solving the said problem was verified through simulation. The research provides a good reference for improving the greenness of manufacturing mode.