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Service charge and capacity selection of an inland river port with location-dependent shipping cost and service congestion

Institution:	1. School of Management, Huazhong University of Science & Technology, Luoyu Road 1037, Wuhan, PR China;2. School of Economics and Management, Tongji University, Siping Road 1239, Shanghai, PR China;3. Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China;1. Department of Logistics Engineering and Management, Lingnan (University) College, Sun Yat-Sen University, PR China;2. Department of Industrial Engineering and Logistics Management, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong SAR, PR China;1. Department of Transport and Regional Economics, Faculty of Business and Economics, University of Antwerp, Prinsstraat 13, 2000 Antwerp, Belgium;2. Tilburg School of Economics and Management Econometrics and Operations Research, Tilburg University, PO Box 90153, 5000 LE Tilburg, the Netherlands;3. Department of Economics, Faculty of Business and Economics, University of Antwerp, Prinsstraat 13, 2000 Antwerp, Belgium;1. Zachry Department of Civil and Environmental Engineering, Texas A&M University, 3136 TAMU, College Station, TX 77843, United States;2. Engineer Research and Development Center, U.S. Army Corps of Engineers, 3909 Halls Ferry Road, Vicksburg, MS 39180, United States;1. School of Transportation Management, Dalian Maritime University, 1 Linghai Road, Dalian 116026, PR China;2. Department of Maritime Administration, Texas A&M University, Galveston, USA;1. Centre of Maritime Studies, National University of Singapore, Singapore 118411, Singapore;2. Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore;3. School of Economics and Management, Tongji University, Shanghai 200092, China;1. Universiteit Hasselt, Campus Diepenbeek, Agoralaan Gebouw D, 3590 Diepenbeek, Belgium;2. Research Foundation Flanders (FWO), Egmontstraat 5, 1000 Brussels, Belgium;3. University of Liege (ULg), HEC Management School, QuantOM, Rue Louvrex 14 (N1), 4000 Liège, Belgium;4. Vrije Universiteit Brussel, MOBI, Pleinlaan 2, 1050 Brussels, Belgium;5. Université de Liège, LEMA, 1 Chemin des Chevreuils B52/3, 4000 Liège, Belgium

Abstract:	The inland waterway transportation has attracted a lot of attention worldwide in the last fifteen years. This paper studies the location, service charge and capacity decision of an inland river port to maximize its revenue or profit. The cargo shippers are assumed to be uniformly distributed along the inland river and can be shipped to the junction port via pure road transportation service or transshipment service with the inland river port. The natural heterogeneity of the river’ navigational condition is modeled by a location-dependent waterway transportation cost and the service congestion on the port is captured by the M/M/1 queueing model. We analytically investigate the properties of the optimal solutions for various decision problems associated with the inland river port. The effects of the natural heterogeneity and port service congestion of those optimal solutions are investigated. Those theoretical results are carefully examined in the case study of the Yangtze River.

Keywords:	Inland river Port service charge Service congestion Location-dependent cost
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