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Constraint satisfaction problems: algorithms and applications

Constraint satisfaction problems: algorithms and applications
Constraint satisfaction problems: algorithms and applications
A constraint satisfaction problem (CSP) requires a value, selected from a given finite domain, to be assigned to each variable in the problem, so that all constraints relating the variables are satisfied. Many combinatorial problems in operational research, such as scheduling and timetabling, can be formulated as CSPs. Researchers in artificial intelligence (AI) usually adopt a constraint satisfaction approach as their preferred method when tackling such problems. However, constraint satisfaction approaches are not widely known amongst operational researchers. The aim of this paper is to introduce constraint satisfaction to the operational researcher. We start by defining CSPs, and describing the basic techniques for solving them. We then show how various combinatorial optimization problems are solved using a constraint satisfaction approach. Based on computational experience in the literature, constraint satisfaction approaches are compared with well-known operational research (OR) techniques such as integer programming, branch and bound, and simulated annealing.
constraint satisfaction, combinatorial optimization, integer programming, local search
0377-2217
557-581
Brailsford, Sally C.
634585ff-c828-46ca-b33d-7ac017dda04f
Potts, Chris N.
501e3d0d-c857-460b-ae50-11c9f7f6d06b
Smith, Barbara M.
0f76d087-959e-4f75-b618-1b4e39e7df9f
Brailsford, Sally C.
634585ff-c828-46ca-b33d-7ac017dda04f
Potts, Chris N.
501e3d0d-c857-460b-ae50-11c9f7f6d06b
Smith, Barbara M.
0f76d087-959e-4f75-b618-1b4e39e7df9f

Brailsford, Sally C., Potts, Chris N. and Smith, Barbara M. (1999) Constraint satisfaction problems: algorithms and applications. European Journal of Operational Research, 119 (3), 557-581. (doi:10.1016/S0377-2217(98)00364-6).

Record type: Article

Abstract

A constraint satisfaction problem (CSP) requires a value, selected from a given finite domain, to be assigned to each variable in the problem, so that all constraints relating the variables are satisfied. Many combinatorial problems in operational research, such as scheduling and timetabling, can be formulated as CSPs. Researchers in artificial intelligence (AI) usually adopt a constraint satisfaction approach as their preferred method when tackling such problems. However, constraint satisfaction approaches are not widely known amongst operational researchers. The aim of this paper is to introduce constraint satisfaction to the operational researcher. We start by defining CSPs, and describing the basic techniques for solving them. We then show how various combinatorial optimization problems are solved using a constraint satisfaction approach. Based on computational experience in the literature, constraint satisfaction approaches are compared with well-known operational research (OR) techniques such as integer programming, branch and bound, and simulated annealing.

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More information

Published date: 1999
Keywords: constraint satisfaction, combinatorial optimization, integer programming, local search

Identifiers

Local EPrints ID: 36240
URI: http://eprints.soton.ac.uk/id/eprint/36240
ISSN: 0377-2217
PURE UUID: 638b1635-11ca-40eb-b38f-85b62df5df03
ORCID for Sally C. Brailsford: ORCID iD orcid.org/0000-0002-6665-8230

Catalogue record

Date deposited: 01 Aug 2006
Last modified: 07 Oct 2020 03:26

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