A cooperative discrete artificial bee colony algorithm with Q-learning for solving the distributed permutation flowshop group scheduling problem with preventive maintenance DOI

Wanzhong Wu,

Hongyan Sang,

Quan Pan

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 95, P. 101910 - 101910

Published: March 19, 2025

Language: Английский

A Q-learning driven multi-objective evolutionary algorithm for worker fatigue dual-resource-constrained distributed hybrid flow shop DOI
Haonan Song, Junqing Li,

Zhaosheng Du

et al.

Computers & Operations Research, Journal Year: 2024, Volume and Issue: unknown, P. 106919 - 106919

Published: Nov. 1, 2024

Language: Английский

Citations

9
Problem feature based meta-heuristics with Q-learning for solving urban traffic light scheduling problems DOI
Wang Liang, Kaizhou Gao, Zhongjie Lin

et al.

Applied Soft Computing, Journal Year: 2023, Volume and Issue: 147, P. 110714 - 110714

Published: Aug. 11, 2023

Language: Английский

Citations

18
Solving Heterogeneous USV Scheduling Problems by Problem-Specific Knowledge Based Meta-Heuristics with Q-Learning DOI Creative Commons
Zhenfang Ma, Kaizhou Gao, Hui Yu

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(2), P. 339 - 339

Published: Jan. 19, 2024

This study focuses on the scheduling problem of heterogeneous unmanned surface vehicles (USVs) with obstacle avoidance pretreatment. The goal is to minimize overall maximum completion time USVs. First, we develop a mathematical model for problem. Second, obstacles, an A* algorithm employed generate path between two points where tasks need be performed. Third, three meta-heuristics, i.e., simulated annealing (SA), genetic (GA), and harmony search (HS), are improved solve problems. Based problem-specific knowledge, nine local operators designed improve performance proposed algorithms. In each iteration, Q-learning strategies used select high-quality operators. We aim meta-heuristics by using Q-learning-based Finally, 13 instances different scales adopted validate effectiveness strategies. compare classical existing meta-heuristics. better than compared ones. results comparisons show that HS second Q-learning, + QL2, exhibits strongest competitiveness (the smallest mean rank value 1.00) among 15

Language: Английский

Citations

7
A Q-Learning Based Hybrid Meta-Heuristic for Integrated Scheduling of Disassembly and Reprocessing Processes Considering Product Structures and Stochasticity DOI Creative Commons

Fuquan Wang,

Yaping Fu, Kaizhou Gao

et al.

Complex System Modeling and Simulation, Journal Year: 2024, Volume and Issue: 4(2), P. 184 - 209

Published: June 1, 2024

Remanufacturing is regarded as a sustainable manufacturing paradigm of energy conservation and environment protection. To improve the efficiency remanufacturing process, this work investigates an integrated scheduling problem for disassembly reprocessing in where product structures uncertainty are taken into account. First, stochastic programming model developed to minimize maximum completion time (makespan). Second, Q-learning based hybrid meta-heuristic (Q-HMH) specially devised. In each iteration, method employed adaptively choose premium algorithm from four candidate ones, including genetic (GA), artificial bee colony (ABC), shuffled frog-leaping (SFLA), simulated annealing (SA) methods. At last, simulation experiments carried out by using sixteen instances with different scales, three state-of-the-art algorithms literature exact solver CPLEX chosen comparisons. By analyzing results average relative percentage deviation (RPD) metric, we find that Q-HMH outperforms its rivals 9.79%-26.76%. The comparisons verify excellent competitiveness solving concerned problems.

Language: Английский

Citations

7
Collaborative Q-learning hyper-heuristic evolutionary algorithm for the production and transportation integrated scheduling of silicon electrodes DOI
Rong Hu, Yu-Fang Huang, Xing Wu

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 86, P. 101498 - 101498

Published: Feb. 8, 2024

Language: Английский

Citations

6
Problem-Specific Knowledge Based Multi-Objective Meta-Heuristics Combined Q-Learning for Scheduling Urban Traffic Lights With Carbon Emissions DOI
Zhongjie Lin, Kaizhou Gao, Naiqi Wu

et al.

IEEE Transactions on Intelligent Transportation Systems, Journal Year: 2024, Volume and Issue: 25(10), P. 15053 - 15064

Published: May 17, 2024

In complex and variable traffic environments, efficient multi-objective urban light scheduling is imperative. However, the carbon emission problem accompanying delays often neglected in most existing literature. This study focuses on problems (MOUTLSP), concerning emissions simultaneously. First, a mathematical model firstly developed to describe MOUTLSP minimize vehicle delays, pedestrian emissions. Second, three well-known meta-heuristics, namely genetic algorithm (GA), particle swarm optimization (PSO), differential evolution (DE), are improved solve MOUTLSP. Six problem-feature-based local search operators (LSO) designed based solution structure incorporated into iterative process of meta-heuristics. Third, nature utilized design two novel Q-learning-based strategies for LSO selection, respectively. The selection (QAS) strategy guides non-dominated solutions obtain good trade-off among objectives generates high-quality by selecting suitable algorithms. (QLSS) employed seek premium neighborhood throughout improving convergence speed. effectiveness improvement verified solving 11 instances with different scales. proposed algorithms compared classical some state-of-the-art problems. experimental results comparisons demonstrate that GA $+$ QLSS, variant GA, competitive one. research proposes new ideas Q-learning assisted evolutionary firstly. It provides strong support achieving more environmentally friendly management.

Language: Английский

Citations

5
A Q-learning based artificial bee colony algorithm for solving surgery scheduling problems with setup time DOI
Ruixue Zhang, Hui Yu, Kaizhou Gao

et al.

Swarm and Evolutionary Computation, Journal Year: 2024, Volume and Issue: 90, P. 101686 - 101686

Published: Aug. 9, 2024

Language: Английский

Citations

5
Ensemble meta-heuristics and Q-learning for staff dissatisfaction constrained surgery scheduling and rescheduling DOI
Hui Yu, Kaizhou Gao, Naiqi Wu

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 134, P. 108668 - 108668

Published: May 30, 2024

Language: Английский

Citations

4
A rollout heuristic-reinforcement learning hybrid algorithm for disassembly sequence planning with uncertain depreciation condition and diversified recovering strategies DOI
Yaping Ren,

Zhehao Xu,

Yanzi Zhang

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 64, P. 103082 - 103082

Published: Jan. 5, 2025

Language: Английский

Citations

0
Integrated scheduling problem of multi-load AGVs and parallel machines considering the recovery process DOI
Xin Fan, Hongyan Sang,

Mengxi Tian

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 94, P. 101861 - 101861

Published: Feb. 3, 2025

Language: Английский

Citations

0