Integrating constraint logic programming and operations research techniques for the Crew Rostering Problem

In this paper, we investigate the possibility of integrating Artificial Intelligence (AI) and Operations Research (OR) techniques for solving the Crew Rostering Problem (CRP). CRP calls for the optimal sequencing of a given set of duties into rosters satisfying a set of constraints. The optimality c...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Software, practice & experience Ročník 28; číslo 1; s. 49 - 76
Hlavní autori: Caprara, A., Focacci, F., Lamma, E., Mello, P., Milano, M., Toth, P., Vigo, D.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York John Wiley & Sons, Ltd 01.01.1998
Predmet:
artificial intelligence
combinatorial optimization problems
constraint logic programming
Crew Rostering Problem
operations research
ISSN:0038-0644, 1097-024X
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:In this paper, we investigate the possibility of integrating Artificial Intelligence (AI) and Operations Research (OR) techniques for solving the Crew Rostering Problem (CRP). CRP calls for the optimal sequencing of a given set of duties into rosters satisfying a set of constraints. The optimality criterion requires the minimization of the number of crews needed to cover the duties. This kind of problem has been traditionally solved by OR techniques. In recent years, a new programming paradigm based on Logic Programming, named Constraint Logic Programming (CLP), has been successfully used for solving hard combinatorial optimization problems. CLP maintains all the advantages of logic programming such as declarativeness, non‐determinism and an incremental style of programming, while overcoming its limitations, mainly due to the inefficiency in exploring the search space. CLP achieves good results on hard combinatorial optimization problems which, however, are not comparable with those achieved by OR approaches. Therefore, we integrate both techniques in order to design an effective heuristic algorithm for CRP which fully exploits the advantages of the two methodologies: on the one hand, we maintain the declarativeness of CLP, its ease of representing knowledge and its rapid prototyping; on the other hand, we inherit from OR some efficient procedures based on a mathematical approach to the problem. Finally, we compare the results we achieved by means of the integration with those obtained by a pure OR approach, showing that AI and OR techniques for hard combinatorial optimization problems can be effectively integrated. © 1998 John Wiley & Sons, Ltd.
Bibliografia:ark:/67375/WNG-DQ9QKN4V-4
istex:6A76F622301ED7E64FB6DE963994B199633B07D2
ArticleID:SPE147
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0038-0644
1097-024X
DOI:10.1002/(SICI)1097-024X(199801)28:1<49::AID-SPE147>3.0.CO;2-R