Scholarlink

<p>A single-loop automated guided vehicle (AGV) system, in which multiple vehicles run on a single closed loop, is widely used in smart factories for material transport. The advantages of the system include its cost-effectiveness, ease of installation, and expansion capabilities. However, scheduling vehicles in a single-loop system is challenging because of potential interference among vehicles. For example, when a vehicle is picking up or dropping off loads at a station, the following vehicles must wait until the station becomes free. This paper presents a novel mixed-integer programming (MIP) approach to optimize vehicle scheduling while considering such interference. Building upon the core concept of modeling cycles in a looped path as consecutive linear paths, we develop a MIP formulation that incorporates both an objective function minimizing the total completion time for a set of transportation jobs and the constraints addressing interference among vehicles. We validate our approach through experiments using real-world data from a smart factory of a major pharmaceutical company. The results demonstrate that our proposed method reduces the completion time for a set of jobs by 10% compared to a baseline method based on a nearest neighbor heuristic. This research contributes to the productivity and efficiency of smart factories by providing an effective solution for vehicle scheduling in single-loop AGV systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Vehicle scheduling in single-loop automated guided vehicle system via mixed-integer programming

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月16日

查看全文
  • Masahiko Sugimura,
  • Hiroki Ishikura,
  • Akihiro Yoshida,
  • Keiichiro Yamamura,
  • Hiroyuki Koshiro,
  • Katsuki Fujisawa

摘要

A single-loop automated guided vehicle (AGV) system, in which multiple vehicles run on a single closed loop, is widely used in smart factories for material transport. The advantages of the system include its cost-effectiveness, ease of installation, and expansion capabilities. However, scheduling vehicles in a single-loop system is challenging because of potential interference among vehicles. For example, when a vehicle is picking up or dropping off loads at a station, the following vehicles must wait until the station becomes free. This paper presents a novel mixed-integer programming (MIP) approach to optimize vehicle scheduling while considering such interference. Building upon the core concept of modeling cycles in a looped path as consecutive linear paths, we develop a MIP formulation that incorporates both an objective function minimizing the total completion time for a set of transportation jobs and the constraints addressing interference among vehicles. We validate our approach through experiments using real-world data from a smart factory of a major pharmaceutical company. The results demonstrate that our proposed method reduces the completion time for a set of jobs by 10% compared to a baseline method based on a nearest neighbor heuristic. This research contributes to the productivity and efficiency of smart factories by providing an effective solution for vehicle scheduling in single-loop AGV systems.