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A study of exponential neighborhoods for the Travelling Salesman Problem and for the Quadratic Assignment Problem

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UNSPECIFIED (2000) A study of exponential neighborhoods for the Travelling Salesman Problem and for the Quadratic Assignment Problem. MATHEMATICAL PROGRAMMING, 87 (3). pp. 519-542. ISSN 0025-5610

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Abstract

This paper deals with exponential neighborhoods for combinatorial optimization problems. Exponential neighborhoods are large sets of feasible solutions whose size grows exponentially with the input length. We are especially interested in exponential neighborhoods over which the TSP (respectively, the QAP) can be solved in polynomial time, and we investigate combinatorial and algorithmical questions related to such neighborhoods. First, we perform a careful study of exponential neighborhoods for the TSP. We investigate neighborhoods that can be declined in a simple way via assignments, matchings in bipartite graphs, partial orders, trees and other combinatorial structures. we identify several properties of these combinatorial structures that lead to polynomial time optimization algorithms, and we also provide variants that slightly violate these properties and lead to NP-complete optimization problems. Whereas it is relatively easy to find exponential neighborhoods over which the TSP can be solved in polynomial time, the corresponding situation for the QAP looks pretty hopeless: Every exponential neighborhood that is considered in this paper provably lends to an NP-complete optimization problem for the QAP.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management
Q Science > QA Mathematics
Journal or Publication Title: MATHEMATICAL PROGRAMMING
Publisher: SPRINGER VERLAG
ISSN: 0025-5610
Date: May 2000
Volume: 87
Number: 3
Number of Pages: 24
Page Range: pp. 519-542
Publication Status: Published
URI: http://wrap.warwick.ac.uk/id/eprint/13320

Data sourced from Thomson Reuters' Web of Knowledge

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