Hybrid Differential Evolution Particle Swarm Optimization for Machine Multi-States Aware Energy Saving Flexible Job Shop Scheduling Problem

With the continuous deepening of low-carbon emission reduction policies, the manufacturing industries urgently need reasonable energy-saving scheduling plans to achieve the balance between improving production efficiency and reducing energy consumption. In energy-saving scheduling, reasonable machin...

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Bibliographic Details
Published in:Chinese Control and Decision Conference pp. 884 - 889
Main Authors: Wang, Da, Zhang, Yu, Qian, Lina, Zhang, Kai
Format: Conference Proceeding
Language:English
Published: IEEE 16.05.2025
Subjects:
Differential evolution
Energy conservation
Energy consumption
Energy saving
Flexible job shop scheduling
Job shop scheduling
Machine multi states
Manufacturing industries
Particle swarm optimization
Production
Switches
ISSN:1948-9447
Online Access:Get full text
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Summary:With the continuous deepening of low-carbon emission reduction policies, the manufacturing industries urgently need reasonable energy-saving scheduling plans to achieve the balance between improving production efficiency and reducing energy consumption. In energy-saving scheduling, reasonable machine state switching is the key to achieving expected goals, that is, whether to switch speed between different operations, and whether to increase additional setup time between different Jobs. To address this issue, this study proposes an energy-saving flexible job scheduling problem based on machine multi state (EFJSP-M), which simultaneously considers the machine's multi-speeds and setup time. In order to solve the proposed EFJSP-M problem, a differential evolution particle swarm optimization algorithm (DEPSO) is designed. Based on the datasets MK, the experimental results are compared with three state-of-the-art algorithms, demonstrating the feasibility of EFJSP-M and the superiority of DEPSO.
ISSN:1948-9447
DOI:10.1109/CCDC65474.2025.11090916