Research on green single machine scheduling based on improved ant colony algorithm

Author:

Qiao Dongping12,Wang Yajing12ORCID,Pei Jie12,Bai Wentong12,Wen Xiaoyu12

Affiliation:

1. College of Mechanical and Electrical Engineering, Zhengzhou University of Light Industry, Zhengzhou, China

2. Henan Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou, China

Abstract

This paper studies the green single-machine scheduling problem that considers the delay cost and the energy consumption of manufacturing equipment and builds its integrated optimization model. The improved ant colony scheduling algorithm based on the Pareto solution set is used to solve this problem. By setting the heuristic information, state transition rules, and other core parameters reasonably, the performance of the algorithm is improved effectively. Finally, the model and the improved algorithm are verified by the simulation experiment of 10 benchmark cases.

Publisher

SAGE Publications

Subject

Applied Mathematics,Control and Optimization,Instrumentation

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. An improved deep Q-learning algorithm for a trade-off between energy consumption and productivity in batch scheduling;Computers & Industrial Engineering;2024-02

2. Application of Improved Ant Colony Algorithm in Automatic Recognition of Abnormal Signals in Wireless Communication Networks;2023 International Conference on Data Science and Network Security (ICDSNS);2023-07-28

3. Analysis of Multi-Objective Integrated Management System of Engineering Project Based on Ant Colony Algorithm;2023 International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE);2023-04-29

4. Ant Colony optimization application in bottleneck station scheduling;Advanced Engineering Informatics;2023-04

5. Green development mode of manufacturing industry based on AHP algorithm under the background of double carbon;2022 International Conference on Artificial Intelligence of Things and Crowdsensing (AIoTCs);2022-10

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