A Deep Reinforcement Learning Framework with Convolution Augmented Attention and Gate Mechanism for Job Shop Scheduling DOI
Goytom Gebreyesus, Getu Fellek, Ahmed Farid

и другие.

Опубликована: Март 16, 2024

Язык: Английский

Evolution strategies-based optimized graph reinforcement learning for solving dynamic job shop scheduling problem DOI
Chupeng Su, Cong Zhang, Dan Xia

и другие.

Applied Soft Computing, Год журнала: 2023, Номер 145, С. 110596 - 110596

Опубликована: Июль 5, 2023

Язык: Английский

Процитировано

27
Machine Learning to Solve Vehicle Routing Problems: A Survey DOI

Aigerim Bogyrbayeva,

Meraryslan Meraliyev,

Taukekhan Mustakhov

и другие.

IEEE Transactions on Intelligent Transportation Systems, Год журнала: 2024, Номер 25(6), С. 4754 - 4772

Опубликована: Янв. 2, 2024

This paper provides a systematic overview of machine learning methods applied to solve NP-hard Vehicle Routing Problems (VRPs). Recently, there has been great interest from both the and operations research communities in solving VRPs either through pure or by combining them with traditional handcrafted heuristics. We present taxonomy studies on paradigms, solution structures, underlying models, algorithms. Detailed results state-of-the-art are presented, demonstrating their competitiveness approaches. The survey highlights advantages learning-based models that aim exploit symmetry VRP solutions. outlines future directions incorporate solutions address challenges modern transportation systems.

Язык: Английский

Процитировано

16
Residual Scheduling: A New Reinforcement Learning Approach to Solving Job Shop Scheduling Problem DOI Creative Commons

Kuo‐Hao Ho,

Jui-Yu Cheng,

Ji-Han Wu

и другие.

IEEE Access, Год журнала: 2024, Номер 12, С. 14703 - 14718

Опубликована: Янв. 1, 2024

Job-shop scheduling problem (JSP) is a mathematical optimization widely used in industries like manufacturing, and flexible JSP (FJSP) also common variant. Since they are NP-hard, it intractable to find the optimal solution for all cases within reasonable times. Thus, becomes important develop efficient heuristics solve JSP/FJSP. A kind of method solving problems construction heuristics, which constructs solutions via heuristics. Recently, many methods leverage deep reinforcement learning (DRL) with graph neural networks (GNN). In this paper, we propose new approach, named residual scheduling, remove irrelevant machines jobs such as those finished, that states include remaining (or relevant) only. Our experiments show our approach reaches state-of-the-art (SOTA) among known on most well-known open FJSP benchmarks. addition, observe even though model trained smaller sizes, still performs well large sizes terms makespan. Interestingly experiments, zero makespan gap 49 60 instances whose job numbers more than 100 15 machines.

Язык: Английский

Процитировано

9
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions DOI

Maziyar Khadivi,

Todd Charter, Marjan Yaghoubi

и другие.

Computers & Industrial Engineering, Год журнала: 2025, Номер unknown, С. 110856 - 110856

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
Optimization of Material Flow and Product Allocation in Inter-Unit Operations: A Case Study of a Refrigerator Manufacturing Facility DOI Creative Commons
Selman Karagöz, Yasin Karagöz

Logistics, Год журнала: 2025, Номер 9(1), С. 13 - 13

Опубликована: Янв. 16, 2025

Background: Logistics operations are integral to manufacturing systems, particularly in the transportation processes that occur not only between facilities and stakeholders but also warehouses workstations within a facility. The design of functional areas allocating goods appropriate zones warehouse management system (WMS) critical activities substantially influence efficiency logistics operations. Methods: This study develops mixed-integer programming (MIP) model optimize material flow product routing manufacturing. identifies efficient pathways, assigns products routes, determines required material-handling equipment. It is implemented Python (3.11.5) using Pyomo (6.7.3) package CBC solver (2.10.11), with sensitivity analysis performed on constraints decision variables evaluate robustness. Results: findings indicate Material Flow 3 Material-Handling Equipment 1 represent optimal configurations for managing majority system. Conclusions: proposed mathematical supports decision-making process by enabling adjustments proportions system, ensuring operational flexibility response changing demands. Furthermore, offers managerial insights suggests directions future research.

Язык: Английский

Процитировано

1
Fast Pareto set approximation for multi-objective flexible job shop scheduling via parallel preference-conditioned graph reinforcement learning DOI
Chupeng Su, Cong Zhang, Chuang Wang

и другие.

Swarm and Evolutionary Computation, Год журнала: 2024, Номер 88, С. 101605 - 101605

Опубликована: Май 28, 2024

Язык: Английский

Процитировано

7
Graph neural networks for job shop scheduling problems: A survey DOI Creative Commons
Igor G. Smit, Jianan Zhou, Robbert Reijnen

и другие.

Computers & Operations Research, Год журнала: 2024, Номер unknown, С. 106914 - 106914

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

5
Learn to optimise for job shop scheduling: a survey with comparison between genetic programming and reinforcement learning DOI Creative Commons
Meng Xu, Yi Mei, Fangfang Zhang

и другие.

Artificial Intelligence Review, Год журнала: 2025, Номер 58(6)

Опубликована: Март 15, 2025

Язык: Английский

Процитировано

0
Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning DOI

Qi Li,

Zhiguang Cao, Yining Ma

и другие.

Опубликована: Апрель 4, 2025

Язык: Английский

Процитировано

0
Solving two-stage stochastic integer programs via representation learning DOI
Yaoxin Wu, Zhiguang Cao, Wen Song

и другие.

Neural Networks, Год журнала: 2025, Номер unknown, С. 107446 - 107446

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0