Affiliation:
1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China
Abstract
Focusing on the problems of uncertainty and carbon emissions in the manufacturing process, this paper studies the low-carbon-emission scheduling optimization problem. Firstly, the variations in workpiece processing time and delivery date are selected as the uncertainty factors. A low-carbon-emission scheduling model for uncertain job shops is constructed with the optimization objectives of the time index, carbon emission index, and robustness index. Secondly, an improved third-generation non-dominated sorting genetic algorithm (NSGA-III) is proposed. Based on the original NSGA-III algorithm, this algorithm introduces the state transition algorithm to perform state transformation, neighborhood sampling, selection update, and alternate rotation operations on the parent population, generating new candidate solutions. Finally, the scheduling model and the improved algorithm are applied to a workshop example. Through case study computation and result analysis, the feasibility and effectiveness of the model and algorithm in addressing the low-carbon-emission job shop scheduling problem under uncertainty are further verified.
Funder
Natural Science Foundation of Zhejiang Province of China
National High-Tech R&D Program of China
Enterprise Project
National Natural Science Foundation
Reference37 articles.
1. Energy-efficient scheduling for a permutation flow shop with variable transportation time using an improved discrete whale swarm optimization;Xin;J. Clean. Prod.,2021
2. MILP models for energy-aware flexible job shop scheduling problem;Meng;J. Clean. Prod.,2019
3. Energy consumption, carbon emissions, product cost, and process time in incremental sheet forming process: A holistic review from sustainability perspective;Riaz;Proc. Inst. Mech. Eng. Part B J. Eng. Manuf.,2022
4. Low-carbon scheduling and estimating for a flexible job shop based on carbon footprint and carbon efficiency of multi-job processing;Zhang;Proc. Inst. Mech. Eng. Part B J. Eng. Manuf.,2015
5. Li, N., Wang, X., and Bai, Y. (2021). An Improved Genetic Algorithm for Low Carbon Dynamic Scheduling in a Discrete Manufacturing Workshop, IOP Publishing.