.png){kind=icon}
.png){kind=icon}
Return
Bi-objective scheduling for energy-efficient distributed assembly blocking flow shop
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cirp.2024.04.010.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Energy-efficient scheduling plays a pivotal role in distributed sustainable manufacturing. This study provides the first attempt to use a knowledge-based bi-objective collaborative optimization algorithm with Q-learning (KBCQL) to address an energy-efficient distributed assembly blocking flow shop scheduling problem (EEDABFSP) considering the total assembly completion time and total assembly energy consumption. Constructive heuristics are introduced to establish a favorable initial population characterized by high quality and diversity. Experimental results on benchmark instances demonstrated that the proposed KBCQL can effectively balance these two objectives and outperform comparative algorithms for solving the EEDABFSP problem. (c) 2024 CIRP. Published by Elsevier Ltd. All rights reserved.
Keywords:
Distributed manufacturing
Scheduling
Optimization
Journal
C
IF:
3.6
Papers:
3.4K
Citations:
1.3W