{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:53:58Z","timestamp":1777697638075,"version":"3.51.4"},"reference-count":22,"publisher":"SAGE Publications","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDT"],"published-print":{"date-parts":[[2024,2,20]]},"abstract":"<jats:p>Logistics distribution is an indispensable part of the modern economy and is crucial for ensuring the efficient operation of the supply chain. With the continuous progress of technology, the application of intelligent scheduling systems in the field of logistics distribution is becoming increasingly widespread. Reinforcement learning, as one of the hot technologies in the field of artificial intelligence, is gradually receiving attention in its application in intelligent scheduling. Reinforcement learning can continuously learn and predict the same thing to enhance memory, while intelligent scheduling requires continuous prediction and optimization of logistics distribution paths. In response to the current problems of slow logistics distribution efficiency and low customer satisfaction, this article analyzed the application of intelligent scheduling in logistics distribution from the aspects of basic data maintenance, basic data review, intelligent scheduling, scheduling result review, distribution information management, and vehicle tracking. By using reinforcement learning, the traffic network weight in logistics distribution was studied to improve logistics distribution efficiency and customer satisfaction. This article analyzed the efficiency of logistics distribution, vehicle tracking accuracy, vehicle scheduling ability, and logistics distribution costs under different logistics distributions. The results showed that the logistics distribution under the integration of reinforcement learning and intelligent scheduling reduced 12.047\u00a0km compared to traditional distribution paths, and its distribution cost decreased by 129.718 yuan compared to traditional logistics distribution costs. The efficiency of logistics distribution that integrates reinforcement learning and intelligent scheduling has significantly improved, with optimized distribution costs and paths. It also has a positive effect on improving the utilization rate of logistics distribution vehicles.<\/jats:p>","DOI":"10.3233\/idt-230528","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T10:46:30Z","timestamp":1706870790000},"page":"57-74","source":"Crossref","is-referenced-by-count":1,"title":["Application of integrating reinforcement learning and intelligent scheduling in logistics distribution"],"prefix":"10.1177","volume":"18","author":[{"given":"Zhenhua","family":"He","sequence":"first","affiliation":[{"name":"School of Logistics and Transportation and Tourism, Jiangsu Vocational College of Finance and Economics, Huaian, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Sichuan Vocational College of Chemical Technology, Luzhou, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Management, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"4","key":"10.3233\/IDT-230528_ref1","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1049\/iet-its.2017.0357","article-title":"Simulation-optimisation framework for City Logistics: An application on multimodal last-mile delivery","volume":"12","author":"Guido","year":"2018","journal-title":"IET Intelligent Transport Systems."},{"issue":"4","key":"10.3233\/IDT-230528_ref2","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1080\/13675567.2020.1757053","article-title":"Smart logistics based on the internet of things technology: an overview","volume":"24","author":"Ding","year":"2021","journal-title":"International Journal of Logistics Research and 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Industrial Marketing Management."},{"issue":"8","key":"10.3233\/IDT-230528_ref15","first-page":"2101","article-title":"Vehicle Path Planning for Multiple Distribution Centers Based on Deep Reinforcement Learning","volume":"37","author":"Wang","year":"2022","journal-title":"Control and Decision."},{"issue":"2","key":"10.3233\/IDT-230528_ref16","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.ejor.2022.08.041","article-title":"Designing a multi-modal and variable-echelon delivery system for last-mile logistics","volume":"307","author":"Bayliss","year":"2023","journal-title":"European Journal of Operational Research."},{"issue":"1","key":"10.3233\/IDT-230528_ref17","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1287\/serv.2022.0310","article-title":"Personal shopper systems in last-mile logistics","volume":"15","author":"Van der","year":"2023","journal-title":"Service Science."},{"key":"10.3233\/IDT-230528_ref18","doi-asserted-by":"crossref","unstructured":"Mark G. Angolia Leslie R. Pagliari. Experiential learning for logistics and supply chain management using an SAP ERP software simulation. 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