Optimizing Robotic Mobile Fulfillment Systems for Order Picking Based on Deep Reinforcement Learning DOI Creative Commons

Zhenyi Zhu,

Sai Wang, Tuan‐Tuan Wang

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(14), P. 4713 - 4713

Published: July 20, 2024

Robotic Mobile Fulfillment Systems (RMFSs) face challenges in handling large-scale orders and navigating complex environments, frequently encountering a series of intricate decision-making problems, such as order allocation, shelf selection, robot scheduling. To address these challenges, this paper integrates Deep Reinforcement Learning (DRL) technology into an RMFS, to meet the needs efficient processing system stability. This study focuses on three key stages RMFSs: allocation sorting, coordinated For each stage, mathematical models are established corresponding solutions proposed. Unlike traditional methods, DRL is introduced solve utilizing Genetic Algorithm Ant Colony Optimization handle decision making related orders. Through simulation experiments, performance indicators-such access frequency total time RMFS-are evaluated. The experimental results demonstrate that, compared our algorithms excel orders, showcasing exceptional superiority, capable completing approximately 110 tasks within hour. Future research should focus integrated modeling for stage RMFSs designing heuristic further enhance efficiency.

Language: Английский

Strength Model for Cement-Stabilized Marine Clay: SEM Image Analysis and Microstructural Insights DOI Creative Commons
Liyang Xu, Xipeng Wang, Yanzhi Qi

et al.

Journal of Marine Science and Engineering, Journal Year: 2025, Volume and Issue: 13(2), P. 388 - 388

Published: Feb. 19, 2025

This study investigates the strength development of cement-stabilized marine clay, which is influenced by a complex interplay microstructural factors. To optimize its performance for coastal and offshore engineering, we explored relationship between microstructure unconfined compressive (UCS). Using Scanning Electron Microscopy (SEM) Pore/Crack Analysis System (PCAS), analyzed samples with varying cement contents (10%, 15%, 20%) curing times (3, 7, 14, 28 days). Key parameters, including porosity, particle shape, size, arrangement, were quantified correlated UCS results. A novel comprehensive micro-parameter was introduced to encapsulate combined effects these factors, revealing an exponential development. The findings provide quantitative framework predicting contributing more efficient solutions in geotechnical engineering.

Language: Английский

Citations

0
A comprehensive evaluation framework for green ecological urban underground space using factor analysis and AHP DOI Creative Commons
Chao Jiang, Ting Jiang, Bin Zhu

et al.

Applied Water Science, Journal Year: 2025, Volume and Issue: 15(4)

Published: March 14, 2025

Language: Английский

Citations

0
Urban Underground Space Geological Suitability—A Theoretical Framework, Index System, and Evaluation Method DOI Creative Commons
Ji Tian, Yubo Xia, Jinhuan Zhang

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4326 - 4326

Published: April 14, 2025

With rapid urbanization, urban underground space (UUS) development has become crucial for sustainable growth. This paper systematically reviews geological suitability evaluation (GSE) methods UUS, integrating theoretical frameworks, indicator systems, and assessment techniques. We establish a comprehensive framework based on environmental strategic (ESA) principles, analyzing key factors, including rock/soil properties, hydrogeological conditions, hazards, existing structures. The study compares weighting (AHP, EWM, CRITIC) models (FCE, TOPSIS, BNM), highlighting their advantages application scenarios. A case of Xiong’an New Area demonstrates how multi-layer UUS planning integrates constraints with goals. results show that combining 3D modeling hybrid significantly improves decision-making accuracy. review provides practical guidance optimizing utilization while addressing current challenges in selection, weight rationalization, heterogeneity management.

Language: Английский

Citations

0
The application of grey statistical method and analytic hierarchy process in the evaluation of community park rehabilitation landscapes DOI Creative Commons
Qingtao Cheng, Qiuping Li

Humanities and Social Sciences Communications, Journal Year: 2025, Volume and Issue: 12(1)

Published: Feb. 7, 2025

Language: Английский

Citations

0
Multi-Level Gray Evaluation Method for Assessing Health Risks in Indoor Environments DOI Creative Commons
Yajing Wang, Yan Ding, Chunhua Liu

et al.

Buildings, Journal Year: 2025, Volume and Issue: 15(5), P. 789 - 789

Published: Feb. 27, 2025

Recently, health risk assessment and early warning systems for high-temperature events have become critical concerns. However, current primarily focus on temperature alone, which fails to accurately reflect the actual heat exposure levels associated risks. Therefore, this paper proposes an improved AHP (analytic hierarchy process) combined with a multi-level gray evaluation method assessing human risks during conditions. A comprehensive system is developed, incorporating various indicators, including status, building conditions, weather forecasts, making it more holistic than traditional temperature-based systems. case study shows that highest score young individuals 3.41, while elderly males receive of 2.5. Furthermore, females 3.1. The results indicate individuals, no alert issued; elderly, red triggered; middle-aged issues orange yellow alerts based varying risk. This can be used monitor provide message humans. Based proposed system, people able predict in time.

Language: Английский

Citations

0
Urban underground space value assessment and regeneration strategies in symbiosis with the urban block: A case study of large residential areas in Beijing DOI
Hongbin Yu, Zhilong Chen, Wanjie Hu

et al.

Tunnelling and Underground Space Technology, Journal Year: 2025, Volume and Issue: 163, P. 106728 - 106728

Published: May 12, 2025

Language: Английский

Citations

0
Spatiotemporal analysis of the food-related carbon emissions of China: Regional heterogeneity and the urban‒rural divide DOI
Jinyu Han,

Jiansheng Qu,

Tek Maraseni

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 370, P. 122441 - 122441

Published: Sept. 19, 2024

Language: Английский

Citations

2
Ensemble learning approach for accurate virtual borehole prediction in 3D geological modeling DOI Creative Commons

Bingning Guo

International Journal of Digital Earth, Journal Year: 2024, Volume and Issue: 17(1), P. 1 - 27

Published: Oct. 3, 2024

Language: Английский

Citations

1
Optimizing Robotic Mobile Fulfillment Systems for Order Picking Based on Deep Reinforcement Learning DOI Creative Commons

Zhenyi Zhu,

Sai Wang, Tuan‐Tuan Wang

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(14), P. 4713 - 4713

Published: July 20, 2024

Robotic Mobile Fulfillment Systems (RMFSs) face challenges in handling large-scale orders and navigating complex environments, frequently encountering a series of intricate decision-making problems, such as order allocation, shelf selection, robot scheduling. To address these challenges, this paper integrates Deep Reinforcement Learning (DRL) technology into an RMFS, to meet the needs efficient processing system stability. This study focuses on three key stages RMFSs: allocation sorting, coordinated For each stage, mathematical models are established corresponding solutions proposed. Unlike traditional methods, DRL is introduced solve utilizing Genetic Algorithm Ant Colony Optimization handle decision making related orders. Through simulation experiments, performance indicators-such access frequency total time RMFS-are evaluated. The experimental results demonstrate that, compared our algorithms excel orders, showcasing exceptional superiority, capable completing approximately 110 tasks within hour. Future research should focus integrated modeling for stage RMFSs designing heuristic further enhance efficiency.

Language: Английский

Citations

1