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|Title:||Cyclic deadlock prediction and avoidance for zone-controlled AGV system||Authors:||Lochana Moorthy, R.
|Keywords:||Automated guided vehicle
Cycle detection method
|Issue Date:||2003||Citation:||Lochana Moorthy, R., Hock-Guan, W., Wing-Cheong, N., Chung-Piaw, T. (2003). Cyclic deadlock prediction and avoidance for zone-controlled AGV system. International Journal of Production Economics 83 (3) : 309-324. ScholarBank@NUS Repository. https://doi.org/10.1016/S0925-5273(02)00370-5||Abstract:||Automated guided vehicles (AGVs) are now becoming popular in automated materials handling systems, flexible manufacturing systems and even containers handling applications at seaports. Its performance affects the efficiency of the entire system. Deadlock formation is a serious problem as it stalls the AGVS. The objective of this paper is to develop an efficient AGV deadlock prediction and avoidance algorithm for container port operations. Deadlock in a broad sense is a situation in which at least a part of the system stalls. There are a lot of situations in which the system may stall and most of these situations can be avoided through the control and navigation system. This paper discusses the development of an efficient strategy for predicting and avoiding the deadlocks in a large scale AGVS A variety of deadlock-detecting algorithms are available, but these methods work mainly for manufacturing system where the network layout is simple and uses only a small number of AGVs. The AGVS under study has complex layout and involves close to 80 AGVs. The proposed solution is implemented using the Automod simulation software, and the performance of the technique is presented. Simulation shows that all the potential deadlock situations can be detected and avoided via this methodology. Also, the proposed strategy is computationally efficient and is able to provide the movement decision to the vehicles within the 1.5-s sampling time. © 2002 Elsevier Science B.V. All rights reserved.||Source Title:||International Journal of Production Economics||URI:||http://scholarbank.nus.edu.sg/handle/10635/43974||ISSN:||09255273||DOI:||10.1016/S0925-5273(02)00370-5|
|Appears in Collections:||Staff Publications|
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