Pup Matching: Model Formulations and Solution Approaches

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5076

Title:	Pup Matching: Model Formulations and Solution Approaches
Issue Date:	9-Oct-2013
Publisher:	Massachusetts Institute of Technology, Operations Research Center
Description:	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%.
URI:	http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers:	http://hdl.handle.net/1721.1/5076
Appears in Collections:	MIT Items

Files in This Item:

There are no files associated with this item.

DSpace JSPUI