A guide path network design method for automated guided vehicle systems using Q-learning

Jae Kook Lim, Joon Mook Lim, Kazuho Yoshimoto, Kap Hwan Kim, Teruo Takahashi

    Research output: Contribution to journalArticle

    Abstract

    In this paper, a guide path design method is suggested for automated guided vehicle systems using Q-learning technique. Numerous manufacturing companies have recognized various advantages of Automated Guided Vehicle System (AGVS) for material handling. These advantages include the flexibility in transportation, the improved space utilization, and the lead-time reduction. With the rapid advance of the state of art technology for AGVS, the application of AGVS to automated manufacturing systems has been more popular than ever before. However, the design of the guide-path network has been considered as one of difficulties in the application of AGVS. By applying the Q-learning technique, it is possible to consider the traffic congestion at intersections or at pickup/delivery stations, and interference among vehicles on bi-directional path segments. It is discussed how the Q-learning technique can be applied to the guide path design problem. A numerical experiment was performed to evaluate the performance of the rules obtained from the learning process for the network design. The result of this research is compared with those by previous studies.

    Original languageEnglish
    Pages (from-to)1319-1328
    Number of pages10
    JournalNippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C
    Volume68
    Issue number4
    Publication statusPublished - 2002 Apr

    Fingerprint

    Traffic congestion
    Pickups
    Materials handling
    Lead
    Industry
    Experiments

    Keywords

    • Automated Guided Vehicle System (AGVS)
    • Guide Path Design
    • Q-Learning

    ASJC Scopus subject areas

    • Mechanical Engineering

    Cite this

    A guide path network design method for automated guided vehicle systems using Q-learning. / Lim, Jae Kook; Lim, Joon Mook; Yoshimoto, Kazuho; Kim, Kap Hwan; Takahashi, Teruo.

    In: Nippon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C, Vol. 68, No. 4, 04.2002, p. 1319-1328.

    Research output: Contribution to journalArticle

    @article{ed1266fea2a94f02a2f01ec301e08d2a,
    title = "A guide path network design method for automated guided vehicle systems using Q-learning",
    abstract = "In this paper, a guide path design method is suggested for automated guided vehicle systems using Q-learning technique. Numerous manufacturing companies have recognized various advantages of Automated Guided Vehicle System (AGVS) for material handling. These advantages include the flexibility in transportation, the improved space utilization, and the lead-time reduction. With the rapid advance of the state of art technology for AGVS, the application of AGVS to automated manufacturing systems has been more popular than ever before. However, the design of the guide-path network has been considered as one of difficulties in the application of AGVS. By applying the Q-learning technique, it is possible to consider the traffic congestion at intersections or at pickup/delivery stations, and interference among vehicles on bi-directional path segments. It is discussed how the Q-learning technique can be applied to the guide path design problem. A numerical experiment was performed to evaluate the performance of the rules obtained from the learning process for the network design. The result of this research is compared with those by previous studies.",
    keywords = "Automated Guided Vehicle System (AGVS), Guide Path Design, Q-Learning",
    author = "Lim, {Jae Kook} and Lim, {Joon Mook} and Kazuho Yoshimoto and Kim, {Kap Hwan} and Teruo Takahashi",
    year = "2002",
    month = "4",
    language = "English",
    volume = "68",
    pages = "1319--1328",
    journal = "Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C",
    issn = "0387-5024",
    publisher = "Japan Society of Mechanical Engineers",
    number = "4",

    }

    TY - JOUR

    T1 - A guide path network design method for automated guided vehicle systems using Q-learning

    AU - Lim, Jae Kook

    AU - Lim, Joon Mook

    AU - Yoshimoto, Kazuho

    AU - Kim, Kap Hwan

    AU - Takahashi, Teruo

    PY - 2002/4

    Y1 - 2002/4

    N2 - In this paper, a guide path design method is suggested for automated guided vehicle systems using Q-learning technique. Numerous manufacturing companies have recognized various advantages of Automated Guided Vehicle System (AGVS) for material handling. These advantages include the flexibility in transportation, the improved space utilization, and the lead-time reduction. With the rapid advance of the state of art technology for AGVS, the application of AGVS to automated manufacturing systems has been more popular than ever before. However, the design of the guide-path network has been considered as one of difficulties in the application of AGVS. By applying the Q-learning technique, it is possible to consider the traffic congestion at intersections or at pickup/delivery stations, and interference among vehicles on bi-directional path segments. It is discussed how the Q-learning technique can be applied to the guide path design problem. A numerical experiment was performed to evaluate the performance of the rules obtained from the learning process for the network design. The result of this research is compared with those by previous studies.

    AB - In this paper, a guide path design method is suggested for automated guided vehicle systems using Q-learning technique. Numerous manufacturing companies have recognized various advantages of Automated Guided Vehicle System (AGVS) for material handling. These advantages include the flexibility in transportation, the improved space utilization, and the lead-time reduction. With the rapid advance of the state of art technology for AGVS, the application of AGVS to automated manufacturing systems has been more popular than ever before. However, the design of the guide-path network has been considered as one of difficulties in the application of AGVS. By applying the Q-learning technique, it is possible to consider the traffic congestion at intersections or at pickup/delivery stations, and interference among vehicles on bi-directional path segments. It is discussed how the Q-learning technique can be applied to the guide path design problem. A numerical experiment was performed to evaluate the performance of the rules obtained from the learning process for the network design. The result of this research is compared with those by previous studies.

    KW - Automated Guided Vehicle System (AGVS)

    KW - Guide Path Design

    KW - Q-Learning

    UR - http://www.scopus.com/inward/record.url?scp=0036555254&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=0036555254&partnerID=8YFLogxK

    M3 - Article

    VL - 68

    SP - 1319

    EP - 1328

    JO - Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C

    JF - Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C

    SN - 0387-5024

    IS - 4

    ER -