Constraint satisfaction problems: Algorithms and applications

• Published in: European Journal of Operational Research Vol. 119; no. 3; pp. 557 - 581
• Main Authors: Brailsford, Sally C., Potts, Chris N., Smith, Barbara M.
• Format: Book Review Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16-12-1999; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Algorithms; Artificial intelligence; Combinatorial optimization; Constraint satisfaction; Integer programming; Local search; Operations research; Optimization; Studies; Integer programming; Constraint satisfaction; Local search; Combinatorial optimization
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/S0377-2217(98)00364-6

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.