A Q-learning improved differential evolution algorithm for human-centric dynamic distributed flexible job shop scheduling problem DOI
Xixing Li, Ao Guo, Xiyan Yin

и другие.

Journal of Manufacturing Systems, Год журнала: 2025, Номер 80, С. 794 - 823

Опубликована: Апрель 24, 2025

Язык: Английский

A Learning-Driven Multi-Objective cooperative artificial bee colony algorithm for distributed flexible job shop scheduling problems with preventive maintenance and transportation operations DOI

Zhengpei Zhang,

Yaping Fu, Kaizhou Gao

и другие.

Computers & Industrial Engineering, Год журнала: 2024, Номер 196, С. 110484 - 110484

Опубликована: Авг. 18, 2024

Язык: Английский

Процитировано

26

Hybrid quantum particle swarm optimization and variable neighborhood search for flexible job-shop scheduling problem DOI

Yuanxing Xu,

Mengjian Zhang,

Ming Yang

и другие.

Journal of Manufacturing Systems, Год журнала: 2024, Номер 73, С. 334 - 348

Опубликована: Фев. 26, 2024

Язык: Английский

Процитировано

24

Multi-objective fitness landscape-based estimation of distribution algorithm for distributed heterogeneous flexible job shop scheduling problem DOI
Fuqing Zhao, Mengjie Li, Ningning Zhu

и другие.

Applied Soft Computing, Год журнала: 2025, Номер unknown, С. 112780 - 112780

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2

Solving multi-objective energy-saving flexible job shop scheduling problem by hybrid search genetic algorithm DOI
L. Hao, Zhiyuan Zou, Xu Liang

и другие.

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 110829 - 110829

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1

Quantum particle swarm optimization with chaotic encoding schemes for flexible job-shop scheduling problem DOI

Yuanxing Xu,

Deguang Wang, Mengjian Zhang

и другие.

Swarm and Evolutionary Computation, Год журнала: 2025, Номер 93, С. 101836 - 101836

Опубликована: Янв. 7, 2025

Язык: Английский

Процитировано

1

Production scheduling with multi-robot task allocation in a real industry 4.0 setting DOI Creative Commons
Zohreh Shakeri, Khaled Benfriha, Mohsen Varmazyar

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 13, 2025

The demand for efficient Industry 4.0 systems has driven the need to optimize production systems, where effective scheduling is crucial. In smart manufacturing, robots handle material transfers, making precise essential seamless operations. However, research often oversimplifies Robotic Flexible Job Shop problem by focusing only on transportation time, ignoring resource allocation and robot diversity. This study addresses these gaps, tackling a Multi-Robot (MRFJS) with limited buffers. It involves non-identical parallel machines varying capabilities overseeing handling under blocking conditions. case based real scenario, layout restricts each robotic arm's access, requiring strategic buffer placement part transfers. A Mixed-Integer Programming (MILP) model aims minimize makespan, followed new Genetic Algorithm (GA) using Roy Sussman's Alternative Graph. Computational tests various scales data from manufacturing plant demonstrate GA's efficacy in solving complex problems real-world settings. Based data, Proposed (PGA), an average Relative Deviation (ARD) of 0.25%, performed approximately 34% better compared Basic (BGA), ARD 0.38%. percentage indicates that PGA significantly outperforms problems.

Язык: Английский

Процитировано

1

Tabu search based on novel neighborhood structures for solving job shop scheduling problem integrating finite transportation resources DOI

Youjie Yao,

Lin Gui, Xinyu Li

и другие.

Robotics and Computer-Integrated Manufacturing, Год журнала: 2024, Номер 89, С. 102782 - 102782

Опубликована: Май 14, 2024

Язык: Английский

Процитировано

8

A hybrid simheuristic algorithm for solving bi-objective stochastic flexible job shop scheduling problems DOI Creative Commons

Saman Nessari,

Reza Tavakkoli‐Moghaddam, Hessam Bakhshi-Khaniki

и другие.

Decision Analytics Journal, Год журнала: 2024, Номер 11, С. 100485 - 100485

Опубликована: Май 29, 2024

The flexible job shop scheduling problem (FJSSP) is a complex optimization challenge that plays crucial role in enhancing productivity and efficiency modern manufacturing systems, aimed at optimizing the allocation of jobs to variable set machines. This paper introduces an algorithm tackle FJSSP by minimizing makespan total weighted earliness tardiness under uncertainty. hybrid effectively addresses complexities stochastic multi-objective integrating equilibrium optimizer (EO) as initial solutions generator, Non-dominated sorting genetic II (NSGA-II), simulation techniques. algorithm's effectiveness validated showcasing specific instances delivering decision results for optimal across varying levels Results reveal consistent superiority managing parameters various scales, achieving lower improved Pareto front quality compared existing methods. Particularly notable faster convergence robust performance, statistical Wilcoxon test, which confirms its reliability efficacy handling dynamic situations. These findings underscore potential providing flexible, solutions. proposed unique balance exploitative explorative capabilities within framework enables effective uncertainty FJSSP, offering flexibility customization adaptable environments.

Язык: Английский

Процитировано

6

Multimanned disassembly line balancing optimization considering walking workers and task evaluation indicators DOI
Tuo Yang, Zeqiang Zhang, Tengfei Wu

и другие.

Journal of Manufacturing Systems, Год журнала: 2023, Номер 72, С. 263 - 286

Опубликована: Дек. 12, 2023

Язык: Английский

Процитировано

16

An enhanced memetic algorithm with hierarchical heuristic neighborhood search for type-2 green fuzzy flexible job shop scheduling DOI
Kanglin Huang, Wenyin Gong, Chao Lu

и другие.

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 130, С. 107762 - 107762

Опубликована: Дек. 26, 2023

Язык: Английский

Процитировано

14