A semi-dynamic time window constrained routeing strategy in an AGV system

Rajotia, Sunil; Shanker, Kripa; Batra, Jatin

doi:10.1080/002075498193921

Cited by 49 publications

(15 citation statements)

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“…Koo and Jang [23] presented stochastic vehicle travel time models for AGVs with emphasis on the empty travel times of vehicles. Rajotia et al [24] addressed the issue of vehicle route planning in an AGV system. Their simulation results showed that the proposed routing strategy was able to reduce vehicle blocking time and improve the system's throughput.…”

Section: Literature Reviewmentioning

confidence: 99%

A multiple-attribute method for concurrently solving the pickup-dispatching problem and the load-selection problem of multiple-load AGVs

Liu

Yih

2012

Journal of Manufacturing Systems

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Section: Literature Reviewmentioning

confidence: 99%

A multiple-attribute method for concurrently solving the pickup-dispatching problem and the load-selection problem of multiple-load AGVs

Liu

Yih

2012

Journal of Manufacturing Systems

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“…Kim and Tanchoco [4] used the concept of time-window graph, which is a directed graph of the free time-windows, for finding the shortest-time route on bidirectional guide-path networks. Rajotia et al [10] proposed a semi-dynamic time-window routing strategy, the principle of which is quite similar to the path-planning method of Kim and Tanchoco [4]. Time-windows that model the traffic-flow direction are placed on bidirectional arcs, which can be crossed only in one direction at a time.…”

Section: Introductionmentioning

confidence: 99%

Routing automated guided vehicles in container terminals through the Q-learning technique

Jeon¹,

Kim

Kopfer

2010

Logist. Res.

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This paper suggests a routing method for automated guided vehicles in port terminals that uses the Q-learning technique. One of the most important issues for the efficient operation of an automated guided vehicle system is to find shortest routes for the vehicles. In this paper, we determine shortest-time routes inclusive of the expected waiting times instead of simple shortest-distance routes, which are usually used in practice. For the determination of the total travel time, the waiting time must be estimated accurately. This study proposes a method for estimating for each vehicle the waiting time that results from the interferences among vehicles during travelling. The estimation of the waiting times is achieved by using the Q-learning technique and by constructing the shortesttime routing matrix for each given set of positions of quay cranes. An experiment was performed to evaluate the performance of the learning algorithm and to compare the performance of the learning-based routes with that of the shortest-distance routes by a simulation study.

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“…Taghaboni and Tanchoco (1995) introduce an incremental route planning and scheduling algorithm. Other approaches are introduced in Rajotia et al (1998b) and Qiu and Hsu (2001). More details about the vehicle routing issue can be found in Qiu et al (2002).…”

Section: Vehicle Routingmentioning

confidence: 99%

A review of design and control of automated guided vehicle systems

LeAnh

Koster

2006

European Journal of Operational Research

364

120

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This paper presents a review on design and control of automated guided vehicle systems. We address most key related issues including guide-path design, estimating the number of vehicles, vehicle scheduling, idle-vehicle positioning, battery management, vehicle routing, and conflict resolution. We discuss and classify important models and results from key publications in literature on automated guided vehicle systems, including often-neglected areas, such as idlevehicle positioning and battery management. In addition, we propose a decision framework for design and implementation of automated guided vehicle systems, and suggest some fruitful research directions. 5001-6182 Business AbstractThis paper presents a review on design and control of automated guided vehicle systems. We address most key related issues including guide-path design, estimating the number of vehicles, vehicle scheduling, idle-vehicle positioning, battery management, vehicle routing, and conflict resolution. We discuss and classify important models and results from key publications in literature on automated guided vehicle systems, including often-neglected areas, such as idle-vehicle positioning and battery management. In addition, we propose a decision framework for design and implementation of automated guided vehicle systems, and suggest some fruitful research directions.

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A semi-dynamic time window constrained routeing strategy in an AGV system

Cited by 49 publications

References 0 publications

A multiple-attribute method for concurrently solving the pickup-dispatching problem and the load-selection problem of multiple-load AGVs

A multiple-attribute method for concurrently solving the pickup-dispatching problem and the load-selection problem of multiple-load AGVs

Routing automated guided vehicles in container terminals through the Q-learning technique

A review of design and control of automated guided vehicle systems

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