Locating urban and regional container terminals in a competitive environment: An entropy maximising approach. (November 2018)

• Record Type: Journal Article
• Authors: Teye, Collins; Bell, Michael GH; Bliemer, Michiel CJ
• Is Part Of: Transportation research. Volume 117(2018)Part B
• Page Start: 971
• Page End: 985
• Publication Date: 2018-11
• Subjects: Intermodal container terminals -- Entropy -- Intermodal transport -- Inland terminals -- Lagrangian relaxation; Transportation -- Research -- Periodicals; Transportation -- Mathematical models -- Periodicals
• DOI: 10.1016/j.trb.2017.08.017
• Languages: English
• ISSNs: 0191-2615

Abstract

Highlights: Locating multi-user freight facilities using welfare maximization approach. Established a link between entropy maximisation and welfare maximisation. Application of model to locate urban and regional inland intermodal terminals. Re -formulation of the relaxed problem into a three-level nesting structure logit model to facilitate forecasting and policy testing. New algorithm for solving large instances of the problem. Abstract: A flexible and policy-oriented model based on the principle of entropy maximisation is proposed for locating competitive multi-user freight facilities in general and inland multi-user intermodal container terminals (IMTs) in particular in a context where multiple users have choices which include whether or not to use the facilities. The overall problem is decomposed into a linked facility location problem (FLP) and a mode choice problem (MCP). The MCP is cast as a three-level nested probability model for determining modal and IMT demands. It was shown that for terminals with sufficiently large handling capacities, the objective function of the overall problem reduces to that of the MCP, the result is a single level mathematical program, which locates facilities to maximise shippers' expected utility or consumer surplus. The model is suitable for urban or regional planning, but may also be used by terminal operators to estimate shipper demand for given locations. Algorithms for solving the model, principal features of the algorithms and the model are also presented.