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...

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Vydáno v:Software, practice & experience Ročník 28; číslo 1; s. 49 - 76
Hlavní autoři: Caprara, A., Focacci, F., Lamma, E., Mello, P., Milano, M., Toth, P., Vigo, D.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York John Wiley & Sons, Ltd 01.01.1998
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ISSN:0038-0644, 1097-024X
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Abstract 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.
AbstractList The paper investigates the possibility of integrating artificial intelligence 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 called 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. (Original abstract - amended)
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.
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.
Author Focacci, F.
Mello, P.
Caprara, A.
Toth, P.
Lamma, E.
Milano, M.
Vigo, D.
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References_xml – reference: G. L. Nemhauser and L. A. Wolsey, Integer and Combinatorial Optimization, John Wiley & Sons, New York, 1988.
– reference: S. Breitinger and H. C. R. Lock, Using Constraint Logic Programming for Industrial Scheduling Problem, Elsevier Science, Amsterdam, 1995.
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– reference: P. Carraresi and G. Gallo, 'A multilevel bottleneck assignment problem for the bus drivers' rostering problem', Euro. J. Operational Research, 16, 163-173 (1984).
– reference: M. Dincbas, P. Van Hentenryck and H. Simonis, 'Solving large combinatorial problems in logic programming', J. Logic Programming, 8(1-2), 75-93 (1990).
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  publication-title: J. Operational Research Society
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  publication-title: J. Logic Programming
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  article-title: A multilevel bottleneck assignment problem for the bus drivers' rostering problem
  publication-title: Euro. J. Operational Research
– volume: 11
  start-page: 91
  year: 1997
  end-page: 105
  article-title: A distributed constraint‐based scheduler for railway traffic
  publication-title: Artificial Intelligence in Engineering
– volume: 43
  start-page: 459
  year: 1992
  end-page: 467
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  article-title: An introduction to Prolog III
  publication-title: ACM Comm.
– year: 1979
– volume: 20
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  article-title: Introducing global constraints in CHIP
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  publication-title: Artificial Intelligence
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SubjectTerms artificial intelligence
combinatorial optimization problems
constraint logic programming
Crew Rostering Problem
operations research
Title Integrating constraint logic programming and operations research techniques for the Crew Rostering Problem
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