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

Authors :
Qiao, Dongping
Wang, Yajing
Pei, Jie
Bai, Wentong
Wen, Xiaoyu
Source :
Measurement & Control (0020-2940). Jan2022, Vol. 55 Issue 1/2, p35-48. 14p.
Publication Year :
2022

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00202940
Volume :
55
Issue :
1/2
Database :
Academic Search Index
Journal :
Measurement & Control (0020-2940)
Publication Type :
Academic Journal
Accession number :
157265144
Full Text :
https://doi.org/10.1177/00202940211064243