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Pup Matching: Model Formulations and Solution Approaches

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dc.creator Bossert, J.M.
dc.creator Magnanti, Thomas L.
dc.date 2003-12-23T02:45:18Z
dc.date 2003-12-23T02:45:18Z
dc.date 2002-01
dc.date.accessioned 2013-10-09T02:33:41Z
dc.date.available 2013-10-09T02:33:41Z
dc.date.issued 2013-10-09
dc.identifier http://hdl.handle.net/1721.1/4007
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description We model Pup Matching, the logistics problem of matching or pairing semitrailers known as pups to cabs able to tow one or two pups simultaneously, as an NP-complete version of the Network Loading Problem (NLP). We examine a branch and bound solution approach tailored to the NLP formulation through the use of three families of cutting planes and four heuristic procedures. Theoretically, we specify facet defining conditions for a cut family that we refer to as odd flow inequalities and show that each heuristic yields a 2-approximation. Computationally, the cheapest of the four heuristic values achieved an average error of 1.3% among solved test problems randomly generated from realistic data. The branch and bound method solved to optimality 67% of these problems. Application of the cutting plane families reduced the average relative difference between upper and lower bounds prior to branching from 18.8% to 6.4%.
dc.description Singapore-MIT Alliance (SMA)
dc.format 205898 bytes
dc.format application/pdf
dc.language en_US
dc.relation High Performance Computation for Engineered Systems (HPCES);
dc.subject network loading
dc.subject network design
dc.subject cutting planes
dc.title Pup Matching: Model Formulations and Solution Approaches
dc.type Article


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