SciELO - Scientific Electronic Library Online

 
vol.23 issue2Step by step search for achievable intermediate targets in zero sum gains DEA modelA Genetic Based Approach to the Vehicle Routing Problem author indexsubject indexarticles search
Home Pagealphabetic serial listing  

Services on Demand

Journal

Article

Indicators

Related links

  • Have no similar articlesSimilars in SciELO

Share


Investigação Operacional

Print version ISSN 0874-5161

Inv. Op. vol.23 no.2 Lisboa Dec. 2003

 

Um algoritmo genético para o problema do sequenciamento de projectos com recursos limitados

Jorge José de Magalhães Mendes *

José Fernando Gonçalves †

* Departamento de Engenharia Informática - Instituto Superior de Engenharia - Instituto Politécnico do Porto

jjm@isep.ipp.pt

† Faculdade de Economia da Universidade do Porto

jfgoncal@fep.up.pt

 

 

Title: A Genetic Algorithm for the Resource Constrained Project Scheduling Problem

Abstract

This paper presents a genetic algorithm for the Resource Constrained Project Scheduling Problem (RCPSP). The chromosome representation of the problem is based on random keys. The schedule is constructed using a heuristic priority rule in which the priorities activities are defined by the genetic algorithm. The heuristic generates parameterized active schedules. The approach is tested on a set of standard problems taken from the literature and compared with other approaches. The computation results validate the effectiveness of the proposed algorithm.

Keywords: Project Management, Scheduling, Genetic Algorithms, RCPSP, Random Keys.

 

 

Resumo

Neste artigo apresenta-se um algoritmo genético para o Problema do Sequenciamento de Projectos com Recursos Limitados (RCPSP). A representação cromossómica utilizada baseia-se em chaves aleatórias. O sequenciamento das actividades é feito com recurso a uma heurística baseada em prioridades definidas pelo algoritmo genético. A heurística gera sequenciamentos activos parametrizados. O algoritmo é testado num conjunto de problemas padrão retirados da literatura da especialidade e é comparado com outras abordagens. Os resultados computacionais validam o bom desempenho do algoritmo em termos de qualidade da solução.

 

Texto completo disponível apenas em PDF.

Full text only available in PDF format.

 

Bibliografia

1. Alvarez-Valdez, R., Tamarit, J.M. (1989). Heuristic algoritms for resource-constrained project scheduling: A review and empirical analysis. In: Slowinski, R., Weglarz, J. (Eds.), Advances in Project Scheduling. Elsevier, Amsterdam, pp. 113-134.        [ Links ]

2. Baar, T., Brucker, P., Knust, S. (1998). Tabu-search Algorithms and lower bounds for the resource-constrained project scheduling problem. In: S.Voss, S. Martello, I.Osman, C. Roucairol (Eds.), Meta-heurisitics: Advances and Trends in Local Search Paradigms for Optimization, Kluwer, Boston, pp. 18.        [ Links ]

3. Bean, J. C. (1994). Genetics and Random Keys for Sequencing and Optimization, ORSA Journal on Computing 6, pp. 154-160.        [ Links ]

4. Blazewicz, J., Lenstra, J.K., Kan, A H.G. Rinnooy (1983). Scheduling subject to resource constraints: Classification and Complexity. Discrete Applied Mathematics, 5, pp. 11-24.        [ Links ]

5. Boctor, F. F. (1990). Some efficient multi-heuristic procedures for resource-constrained project scheduling. European Journal of Operational Research 49, pp. 3-13.        [ Links ]

6. Bouleimen, K., Lecocq, H. (1998). A new efficient simulated annealing Algorithms for the resource-constrained project scheduling problem. Techinical Report, Service de Robotique et Automatisation, Université de Liège.        [ Links ]

7. Bouleimen, K., Lecocq, H. (2002). A new efficient simulated annealing algorithm for the resource constrained project scheduling problem. European Journal of Operational Research. To appear.        [ Links ]

8. Brucker, P., Drexl, A., Mohring, R., Neumann, K., Pesch, E. (1999). Resource-constrained project scheduling : Notation, classification, models, and methods. European Journal of Operational Research 112, pp. 3-412.        [ Links ]

9. Brucker, P., Knust, S. Schoo, A., Thiele, O . (1998). A branch and bound Algorithms for the resource-constrained project scheduling problem. European Journal of Operational Research 107, pp.272-288.        [ Links ]

10. Christofides, N. Alvarez-Valdés, R. Tamarit, J.M. (1987). Problem scheduling with resource constraints : A branch and bound approach. European Journal of Operational Research, 29, pp. 262-273.        [ Links ]

11. Cooper, D. F. (1976). Heuristics for scheduling resource-constrained projects : An experimental investigation. Management Science, 22(11), pp. 1186-1194.        [ Links ]

12. Cooper, D. F. (1977). A note on Serial and Parallel heuristics for resource-constrained project scheduling. Foundations of Control Engineering, 2(4), pp. 131-133.        [ Links ]

13. Davis, E.W., Patterson, J.H. (1975). A compararison of heuristic and optimum solutions in resource-constrained project scheduling. Management Science, 21 (11), pp. 944-955.        [ Links ]

14. Demeulemeester, E., Herroelen, W. (1997). New benchmark results for the resource-constrained project scheduling problem. Management Science 43, pp. 1485-1492.        [ Links ]

15. Goldberg, D. E., (1989). Genetic Algorithms in Search Optimization and Machine Learning, Addison-Wesley.        [ Links ]

16. Gonçalves, J. F., Beirão, N. (1999). Um Algoritmo Genético Baseado em Chaves Aleatórias para Sequenciamento de Operações. Revista Portuguesa de Investigação Operacional 19, pp. 123-137.        [ Links ]

17. Hartmann, S. (1998). A competitive genetic Algorithms for resource-constrained project scheduling. Naval Research Logistics 45, pp. 279-302.        [ Links ]

18. Hartmann, S., Kolisch, R. (2000). Experimental evaluation of state-of-the-art heuristics for the respurce-constrained project scheduling problem. European Journal of Operational Research 127, pp. 394-407.        [ Links ]

19. Herroelen, W., DE Reyck, B., Demeulemeester, E. (1998). Resource-constrained project scheduling: a survey of recent developments, Computers and Operations Research, 25(4), 279-302.        [ Links ]

20. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems, Ann Arbor: The University of Michigan Press.        [ Links ]

21. Klein, R. (2000). Bidirectional planning : improving priority rule-based heuristics for scheduling resource-cpnstrained projects. European Journal of Operational Research 127, pp. 619-638.        [ Links ]

22. Klein, R. (1999). Scheduling of Resource-Constrained Projects. Kluwer, Dordrecht.        [ Links ]

23. Klein, R.,Scholl, A. (1998a). Progress: Optimally solving the generalized resource-constrained project scheduling problem. Working paper, University of Technology, Darmstadt.        [ Links ]

24. Klein, R., Scholl, A. (1998b) . Scattered branch and bound: An adaptative search strategy applied to resource-constrained project scheduling problem. Working paper, University of Technology, Darmstadt.        [ Links ]

25. Kolisch, R. (1996). Serial and Parallel resource-constrained project scheduling methods revisited: Theory and Computation. European Journal of Operational Research, 90, pp. 320-333.        [ Links ]

26. Kolisch. R. (1996). Efficient priority rules for the resource-constrained project scheduling problem. Journal of Operations Management, 14(3), pp. 179-192.        [ Links ]

27. Kolisch, R., Drexl, A. (1996). Adaptative search for solving hard project scheduling problems. Naval Research Logistics, 43, pp. 43-23.        [ Links ]

28. Kolisch, R., Padman, R. (2001). An integrated survey of deterministic project scheduling, The International Journal of Management Science, Vol. 29, pp. 249-272.        [ Links ]

29. Kolisch., R., Sprecher, A ., Drexl, A. (1995). Characterization and generation of a general class of resource-constrained project scheduling problems. Management Science, 41(10), pp. 1693-1703.        [ Links ]

30. Kolisch, R., Schwindt, C., Sprecher, A. (1998). Benchmark instances for scheduling problems. In J.Weglarz, ed. Handbook on recent advances in project scheduling, Kluwer, Amsterdam, pp. 197-212.        [ Links ]

31. Kolisch, R., Hartmann, S. (1999). Heuristic Algorithms for Solving the Resource-Constrained Project Scheduling Problem: Classification and Computational Analysis. In: Weglarz, J. (Ed.), Handbook on Recent Advances in Project Scheduling. Kluwer, Dordrecht, pp. 147-178.        [ Links ]

32. Lawrence, S. R. (1985). Resource constrained project scheduling - A computational comparison of heuristic scheduling techniques. Technical report, Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh.        [ Links ]

33. Mingozzi, A., Maniezzo, V., Ricciardelli, S., Bianco, L. (1998). An exact Algorithm for project scheduling with resource constraints based on a new mathematical formulation. Management Science 44, pp. 714-729.        [ Links ]

34. Spears, W. M., Dejong, K. A. (1991). On the Virtues of Parameterized Uniform Crossover, in Proceedings of the Fourth International Conference on Genetic Algorithms, pp. 230-236.        [ Links ]

35. Schirmer, A., Riesenberg, S. (1998). Case-based reasoning and parameterized random sampling for project scheduling. Technical report, forthcoming.        [ Links ]

36. Sprecher, A. (1997). Solving the RCPSP efficiently at modest memory requirements. Working paper, University of Kiel.        [ Links ]