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 |
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| Jazyk: | angličtina |
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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. |
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| 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. – reference: L. Bianco, M. Bielli, A. Mingozzi, S. Ricciarelli and M. Spadoni, 'A heuristic procedure for the crew rostering problem', Euro. J. Operational Research, 58, 272-283 (1992). – reference: R. Tadei, F. Della Croce, C. Alfieri, P. Baracco, F. Rizzante and M. Sbodio, 'A genetic algorithm for the crew rostering problem', J. Operational Research Society, 76, 37-48 (1995). – reference: J. Jaffar and M. J. Maher, 'Constraint Logic Programming: a survey', J. Logic Programming Special Issue on 10 years of Logic Programming, 19-20, 503-582 (1994). – reference: J. Jaffar, S. Michaylov, P. J. Stuckey and R. H. C. Yap, 'The CLP(R) language and system', ACM Trans. Programming Language and Systems, 14(3), 339-395 (1992). – 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). – reference: N. Beldiceanu and E. Contejean, 'Introducing global constraints in CHIP', J. Mathematical and Computer Modelling, 20(12), 97-123 (1994). – reference: P. Van Hentenryck, Constraint Satisfaction in Logic Programming, MIT Press, Cambridge, MA, 1989. – reference: J. W. Lloyd, Foundation of Logic Programming, Springer-Verlag, 1987. (Second Extended Edition) – reference: R. Kowalski, Logic for Problem Solving, North-Holland, Amsterdam, 1979. – reference: A. Colmerauer, 'An introduction to Prolog III', ACM Comm. 33(7), 70-90 (1990). – reference: A. K. Mackworth, 'Consistency in networks of relations', Artificial Intelligence, 8, 99-118 (1977). – reference: E. Lamma, P. Mello and M. Milano, 'A distributed constraint-based scheduler for railway traffic', Artificial Intelligence in Engineering, 11, 91-105 (1997). – reference: D. M. Ryan, 'The solution of massive generalized set partitioning problem in air-crew rostering', J. Operational Research Society, 43, 459-467 (1992). – volume: 19–20 start-page: 503 year: 1994 end-page: 582 article-title: Constraint Logic Programming: a survey publication-title: J. Logic Programming Special Issue on 10 years of Logic Programming – start-page: 481 year: 1995 end-page: 498 – volume: 58 start-page: 272 year: 1992 end-page: 283 article-title: A heuristic procedure for the crew rostering problem publication-title: Euro. J. Operational Research – start-page: 693 year: 1988 end-page: 702 – volume: 76 start-page: 37 year: 1995 end-page: 48 article-title: A genetic algorithm for the crew rostering problem publication-title: J. Operational Research Society – volume: 8 start-page: 75 issue: 1–2 year: 1990 end-page: 93 article-title: Solving large combinatorial problems in logic programming publication-title: J. Logic Programming – year: 1988 – year: 1987 – volume: 14 start-page: 339 issue: 3 year: 1992 end-page: 395 article-title: The CLP(R) language and system publication-title: ACM Trans. Programming Language and Systems – year: 1989 – year: 1995 – volume: 16 start-page: 163 year: 1984 end-page: 173 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 article-title: The solution of massive generalized set partitioning problem in air‐crew rostering publication-title: J. Operational Research Society – volume: 33 start-page: 70 issue: 7 year: 1990 end-page: 90 article-title: An introduction to Prolog III publication-title: ACM Comm. – year: 1979 – volume: 20 start-page: 97 issue: 12 year: 1994 end-page: 123 article-title: Introducing global constraints in CHIP publication-title: J. Mathematical and Computer Modelling – year: 1993 – year: 1992 – year: 1994 – volume: 8 start-page: 99 year: 1977 end-page: 118 article-title: Consistency in networks of relations 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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