Pup Matching: Model Formulations and Solution Approaches
Name
OR-366-03.pdf
Size
1.7 MB
Format
Adobe PDF
Checksum (MD5)
1fad049d61b0a2e726bce736e2a71c40
Author(s)
Bossert, John M.
Magnanti, Thomas L.
Date Issued
February 2003
Publisher
Massachusetts Institute of Technology, Operations Research Center
Series/Report no.
Operations Research Center Working Paper;OR 366-03
Abstract
We model Pup Matching, the logistics problem of matching or pairing semitrailers known as pups to cabs that are able to tow one or two of the pups simultaneously, as an AfP-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. Branch and bound 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%.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
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