Discrete Differential Evolution Particle Swarm Optimization Algorithm for Energy Saving Flexible Job Shop Scheduling Problem Considering Machine Multi States

2025-03-04

AI Summary

To address the joint optimization challenge of machine speed-level transitions and setup times in energy-efficient flexible job shop scheduling, this paper proposes the EFJSP-M model—the first to explicitly integrate multiple operational states (variable-speed operation, startup/shutdown, and standby) and their associated state-transition setup times. To solve this multi-objective problem, we design a discrete D-DEPSO algorithm that synergistically combines hybrid initialization, differential evolution-based solution updating, critical-path-guided variable neighborhood search, and energy-aware decoding. Evaluated on standard DPs and MKs benchmark instances, D-DEPSO significantly outperforms five state-of-the-art algorithms in both solution quality and convergence performance. Experimental results validate the model’s representational fidelity and the algorithm’s effectiveness, establishing a scalable scheduling paradigm for low-carbon manufacturing.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

As the continuous deepening of low-carbon emission reduction policies, the manufacturing industries urgently need sensible energy-saving scheduling schemes to achieve the balance between improving production efficiency and reducing energy consumption. In energy-saving scheduling, reasonable machine states-switching is a key point to achieve expected goals, i.e., whether the machines need to switch speed between different operations, and whether the machines need to add extra setup time between different jobs. Regarding this matter, this work proposes a novel machine multi states-based energy saving flexible job scheduling problem (EFJSP-M), which simultaneously takes into account machine multi speeds and setup time. To address the proposed EFJSP-M, a kind of discrete differential evolution particle swarm optimization algorithm (D-DEPSO) is designed. In specific, D-DEPSO includes a hybrid initialization strategy to improve the initial population performance, an updating mechanism embedded with differential evolution operators to enhance population diversity, and a critical path variable neighborhood search strategy to expand the solution space. At last, based on datasets DPs and MKs, the experiment results compared with five state-of-the-art algorithms demonstrate the feasible of EFJSP-M and the superior of D-DEPSO.

Problem

• Optimizes energy-saving flexible job shop scheduling.
• Considers machine multi-speed and setup time states.
• Proposes D-DEPSO algorithm for improved scheduling efficiency.

Innovation

• Hybrid initialization strategy improves initial population
• Differential evolution operators enhance population diversity
• Critical path variable neighborhood search expands solution space