Cited by A Q-learning improved differential evolution algorithm for human-centric dynamic distributed flexible job shop scheduling problem

A Bi-Learning Evolutionary Algorithm for Transportation-Constrained and Distributed Energy-Efficient Flexible Scheduling DOI

Zixiao Pan, Ling Wang, Jingjing Wang

et al.

IEEE Transactions on Evolutionary Computation, Journal Year: 2024, Volume and Issue: 29(1), P. 232 - 246

Published: Jan. 16, 2024

With the rise of globalization and environmental concerns, distributed scheduling energy-efficient have become crucial topics in informational manufacturing system. Additionally, growing consideration about realistic constraints, such as transportation time finite resources, has made problem increasingly complex. Facing these challenges, special mechanisms are required to improve efficiency solving algorithms. In this paper, a bi-learning evolutionary algorithm (BLEA) is proposed solve flexible job shop with constraints (DEFJSP-T). Firstly, we integrate statistical learning (SL) (EL) framework, while decomposition Pareto dominance methods employed different stages handle conflicting objectives. During SL stage, probability models established statistically search for advantageous substructures on each weight vector, an update mechanism devised exploration. EL genetic operators introduced improved local that takes into account properties realize sufficient exploitation. Finally, according performance SL, novel switching between designed ensure rational allocation computing resources. Extensive experiments conducted test performances BLEA. The comparison shows BLEA superior DEFJSP-T terms effectiveness.

Language: Английский

Citations

Shared manufacturing-based distributed flexible job shop scheduling with supply-demand matching DOI

Guangyan Wei,

Chunming Ye,

Jianning Xu

et al.

Computers & Industrial Engineering, Journal Year: 2024, Volume and Issue: 189, P. 109950 - 109950

Published: Feb. 5, 2024

Language: Английский

Citations

Integrated remanufacturing scheduling of disassembly, reprocessing and reassembly considering energy efficiency and stochasticity through group teaching optimization and simulation approaches DOI

Yaping Fu,

Zhengpei Zhang,

Pei Liang

et al.

Engineering Optimization, Journal Year: 2024, Volume and Issue: 56(12), P. 2018 - 2039

Published: Jan. 4, 2024

The energy crisis and environmental pollution are receiving increasing attention from governments communities. This study researches energy-aware remanufacturing systems. Remanufacturing aims to reuse valuable resources end-of-life products produce as-new products. Since systems involve a series of disassembly, processing assembly operations, schedule integrates shops. A multi-objective scheduling is proposed, considering workstation use, consumption customer satisfaction simultaneously. chance-constrained programming model established minimize makespan while satisfying total tardiness requirements. hybrid method developed, using group teaching optimization discrete event simulation system, which can seek evaluate potentially favourable solutions. approach validated on test instances well-known methods. results reveal that this find non-dominated solutions with well-converged well-diversified performance, verifying its advantages in providing informed decisions for managers engineers.

Language: Английский

Citations

Dynamic scheduling of hybrid flow shop problem with uncertain process time and flexible maintenance using NeuroEvolution of Augmenting Topologies DOI

Yarong Chen,

Junjie Zhang, Mudassar Rauf

et al.

IET Collaborative Intelligent Manufacturing, Journal Year: 2024, Volume and Issue: 6(3)

Published: Sept. 1, 2024

Abstract A hybrid flow shop is pivotal in modern manufacturing systems, where various emergencies and disturbances occur within the smart context. Efficiently solving dynamic scheduling problem (HFSP), characterised by release times, uncertain job processing flexible machine maintenance has become a significant research focus. NeuroEvolution of Augmenting Topologies (NEAT) algorithm proposed to minimise maximum completion time. To improve NEAT algorithm's efficiency effectiveness, several features were integrated: multi‐agent system with autonomous interaction centralised training develop parallel policy, maintenance‐related action for optimal decision learning, proactive avoid waiting jobs at moments, thereby exploring broader solution space. The performance trained model was experimentally compared Deep Q‐Network (DQN) five classical priority dispatching rules (PDRs) across scales. results show that achieves better solutions responds more quickly changes than DQN PDRs. Furthermore, generalisation test demonstrate NEAT's rapid problem‐solving ability on instances different from set.

Language: Английский

Citations

A Deep Reinforcement Learning Method Based on a Transformer Model for the Flexible Job Shop Scheduling Problem DOI

Shuai Xu, Yanwu Li, Qiuyang Li

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(18), P. 3696 - 3696

Published: Sept. 18, 2024

The flexible job shop scheduling problem (FJSSP), which can significantly enhance production efficiency, is a mathematical optimization widely applied in modern manufacturing industries. However, due to its NP-hard nature, finding an optimal solution for all scenarios within reasonable time frame faces serious challenges. This paper proposes that transforms the FJSSP into Markov Decision Process (MDP) and employs deep reinforcement learning (DRL) techniques resolution. First, we represent state features of environment using seven feature vectors utilize transformer encoder as extraction module effectively capture relationships between representation capability. Second, based on jobs machines, design 16 composite dispatching rules from multiple dimensions, including completion rate, processing time, waiting resource utilization, achieve efficient decisions. Furthermore, project intuitive dense reward function with objective minimizing total idle machines. Finally, verify performance feasibility algorithm, evaluate proposed policy model Brandimarte, Hurink, Dauzere datasets. Our experimental results demonstrate framework consistently outperforms traditional rules, surpasses metaheuristic methods larger-scale instances, exceeds existing DRL-based across most

Language: Английский

Citations

A knowledge-driven memetic algorithm for distributed green flexible job shop scheduling considering the endurance of machines DOI

Libao Deng, Yixuan Qiu,

Yuanzhu Di

et al.

Applied Soft Computing, Journal Year: 2025, Volume and Issue: 170, P. 112697 - 112697

Published: Jan. 6, 2025

Language: Английский

Citations

Structural entropy-based scheduler for job planning problems using multi-agent reinforcement learning DOI

Lixin Liang,

Shuo Sun,

Zhifeng Hao

et al.

International Journal of Machine Learning and Cybernetics, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 12, 2025

Language: Английский

Citations

A modularity-based improved small-world genetic algorithm for large-scale intercell scheduling with flexible routes DOI

Guangshuai Ning,

Qiong Liu, Mengbang Zou

et al.

Computers & Operations Research, Journal Year: 2025, Volume and Issue: unknown, P. 106979 - 106979

Published: Jan. 1, 2025

Language: Английский

Citations

A discrete water source cycle algorithm design for solving production scheduling problem in flexible manufacturing systems DOI

Wenxiang Xu,

Shimin Xu,

Junyong Liang

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 94, P. 101897 - 101897

Published: Feb. 21, 2025

Language: Английский

Citations

Deep reinforcement learning-based memetic algorithm for solving dynamic distributed green flexible job shop scheduling problem with finite transportation resources DOI

Xinxin Zhou, Feimeng Wang, Bin Wu

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 94, P. 101885 - 101885

Published: Feb. 21, 2025

Language: Английский

Citations