A neural network approach to the vessel dispatching problem |
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| Affiliation: | 1. University of Coimbra, CQC, Department of Chemistry, 3004–535, Coimbra, Portugal;2. MTA-ELTE Lendület Laboratory Astrochemistry Research Group, Institute of Chemistry, ELTE Eötvös Loránd University, P.O. Box 32, H–1518, Budapest, Hungary;3. Laboratory of Molecular Spectroscopy, Institute of Chemistry, ELTE Eötvös Loránd University, P.O. Box 32, H–1518, Budapest, Hungary;1. Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, Republic of Korea;2. Department of Health Science and Technology, Samsung Advanced Institute for Health Science and Technology, Sungkyunkwan University, 2066, Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, Republic of Korea;3. Applied Electromagnetic Wave Research Center, Korea Electrotechnology Research Ins., 111, Hanggaul-ro, Ansan, Gyeonggi-do, Republic of Korea;4. Department of Energy and Power Conversion Engineering, University of Science & Technology, 217, Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea;5. Chungwoo Co., Ltd., 2, Gasan digital 1-ro, Geumcheon-gu, Seoul, Republic of Korea |
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| Abstract: | The paper discusses the process of loading, transport and unloading of gravel by inland water transportation. At the loading port, the problem that needs to be solved is the assignment of load barges to pusher tugs for the planned period of one day. However, disturbances of planned schedules are very common. Whenever a disturbance in a daily schedule appears, the dispatcher urgently attempts to mitigate negative effects resulting from the disturbance. Real-time operations limit the amount of time that dispatchers in charge of traffic control have to make decisions and increase the level of stress associated with quick and adequate response. This paper aims to demonstrate the feasibility of a dispatch decision support system that could decrease the work load for the dispatcher and improve the quality of decisions. The proposed neural network with the ability to adapt or learn from examples of decisions can simulate the dispatcher's decision process. |
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