2014Unpublished venueRequires access

A method for the hub location problem of scale-distance tradeoffs

Gang Gang Niu, Li Zhang, Hongbo Li, Hong Wei Ding

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Abstract

Scale and distance factors determine the economies in less-than-truckload transportation. A 3-stage method is presented for the hub location problem of scale-distance tradeoffs in a context of hub-and-spoke network. A business-rule based preprocessing determines cargo quantities shipped directly and hub-transferred. A 0-1 integer programming model incorporating stepwise transport rates is proposed. Dynamic transport rates are utilized to maximize applicable scale and distance economies by best matching cargo flows with vehicle types. A case from one leading less-than-truckload company in China is studied and results demonstrate the effectiveness of the proposed method.

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What this paper is about

Scale and distance factors determine the economies in less-than-truckload transportation. A 3-stage method is presented for the hub location problem of scale-distance tradeoffs in a context of hub-and-spoke network. A business-rule based preprocessing determines cargo quantities shipped directly and hub-transferred. A 0-1 integer programming model incorporating stepwise transport rates is proposed. Dynamic transport rates are utilized to maximize applicable scale and distance economies by best matching cargo flows with vehicle types. A case from one leading less-than-truckload company in China is studied and results demonstrate the effectiveness of the proposed method.

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Available abstract

Scale and distance factors determine the economies in less-than-truckload transportation. A 3-stage method is presented for the hub location problem of scale-distance tradeoffs in a context of hub-and-spoke network. A business-rule based preprocessing determines cargo quantities shipped directly and hub-transferred. A 0-1 integer programming model incorporating stepwise transport rates is proposed. Dynamic transport rates are utilized to maximize applicable scale and distance economies by best matching cargo flows with vehicle types. A case from one leading less-than-truckload company in China is studied and results demonstrate the effectiveness of the proposed method.

Key concepts: Scale (ratio), Context (archaeology), Matching (statistics), Computer science, Economies of scale, Integer programming, Operations research, Mathematical optimization

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