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Soft Computing

, Volume 23, Issue 10, pp 3465–3479 | Cite as

A generic heuristic for multi-project scheduling problems with global and local resource constraints (RCMPSP)

  • Félix Villafáñez
  • David PozaEmail author
  • Adolfo López-Paredes
  • Javier Pajares
  • Ricardo del Olmo
Methodologies and Application

Abstract

This paper presents a novel algorithm to solve the multi-project scheduling problem with resource constraints (RCMPSP). The algorithm was tested with all the problems proposed in the multi-project scheduling problem library, which is the main reference to benchmark RCMPSP algorithms. Our analysis of the results demonstrates that this algorithm, in spite of its simplicity, outperforms other algorithms published in the library in 16% of the cases and holds the best result in 27% of the cases. These results, along with the fact that this is a general-purpose algorithm, make it a good choice to deal with limited time and resources in portfolio management.

Keywords

Project management Project scheduling Portfolio management Multi-project scheduling RCMPSP MPSPLib 

Notes

Acknowledgements

This research has been partially financed by the project ABARNET (Agent-Based Algorithms for Railway NETworks optimization) financed by the Spanish Ministry of Economy, Industry and Competitiveness, with Grant DPI2016-78902-P, and the Computational Models for Industrial Management (CM4IM) project, funded by the Valladolid University General Foundation.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Human and animals participants

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Acebes F, Pereda M, Poza D et al (2015) Stochastic earned value analysis using Monte Carlo simulation and statistical learning techniques. Int J Proj Manag.  https://doi.org/10.1016/j.ijproman.2015.06.012 Google Scholar
  2. Algan M, Roy B, Simonard M (1962) Principes d’une méthode d’exploration de certains domaines et application à l’ordonnancement de la construction de grands ensemblesGoogle Scholar
  3. Alvarez-Valdés Olaguíbel R, Tamarit Goerlich JM (1993) The project scheduling polyhedron: dimension, facets and lifting theorems. Eur J Oper Res 67:204–220.  https://doi.org/10.1016/0377-2217(93)90062-R CrossRefzbMATHGoogle Scholar
  4. Araúzo Araúzo JA, Pajares J, López-Paredes A (2010) Simulating the dynamic scheduling of project portfolios. Simul Model Pract Theory 18:1428–1441.  https://doi.org/10.1016/j.simpat.2010.04.008 CrossRefGoogle Scholar
  5. Barr RS, Golden BL, Kelly JP et al (1995) Designing and reporting on computational experiments with heuristic methods. J Heuristics 1:9–32CrossRefzbMATHGoogle Scholar
  6. Bartusch M, Möhring RH, Radermacher FJ (1988) Scheduling project networks with resource constraints and time windows. Ann Oper Res 16:199–240MathSciNetCrossRefzbMATHGoogle Scholar
  7. Blazewicz J, Lenstra JK, Rinnooy Kan AHG (1983) Scheduling subject to resource constraints: classification and complexity. Discrete Appl Math 5:11–24MathSciNetCrossRefzbMATHGoogle Scholar
  8. Boctor FF (1990) Some efficient multi-heuristic procedures for resource-constrained project scheduling. Eur J Oper Res 49:3–13.  https://doi.org/10.1016/0377-2217(90)90116-S CrossRefzbMATHGoogle Scholar
  9. Brucker P, Drexl A, Möhring R et al (1999) Resource-constrained project scheduling: notation, classification, models, and methods. Eur J Oper Res 112:3–41CrossRefzbMATHGoogle Scholar
  10. Confessore G, Roma S, Giordani S, Rismondo S (2002) An auction based approach in decentralized project scheduling. In: Proceedings of the 8th international workshop on project management and scheduling, pp 110–113Google Scholar
  11. Confessore G, Giordani S, Rismondo S (2007) A market-based multi-agent system model for decentralized multi-project scheduling. Ann Oper Res 150::115–135.  https://doi.org/10.1007/s10479-006-0158-9 MathSciNetCrossRefzbMATHGoogle Scholar
  12. Dalfard VM, Ranjbar V (2012) Multi-projects scheduling with resource constraints & priority rules by the use of simulated annealing algorithm [Programiranje više projekata uz ograničena sredstva & Pravila prioriteta primjenom algoritma simuliranog žarenja]. Teh Vjesn 19:493–499Google Scholar
  13. Davis EW (1969) An exact algorithm for the multiple constrained project scheduling problem. Yale University, New HavenGoogle Scholar
  14. Demeulemeester EL, Herroelen WS (2002) Project scheduling: a research handbook. Kluwer, BostonGoogle Scholar
  15. Fendley LG (1968) Towards the development of a complete multi-project scheduling system. J Ind Eng 19:505–515Google Scholar
  16. Fink A, Homberger J (2013) An ant-based coordination mechanism for resource-constrained project scheduling with multiple agents and cash flow objectives. Flex Serv Manuf J 25:94–121.  https://doi.org/10.1007/s10696-012-9136-5 CrossRefGoogle Scholar
  17. Fink A, Homberger J (2015) Decentralized multi-project scheduling. In: Schwindt C, Zimmermann J (eds) Handbook on project management and scheduling, vol 2. Springer International Publishing, Cham, pp 685–706Google Scholar
  18. Hartmann S, Briskorn D (2010) A survey of variants and extensions of the resource-constrained project scheduling problem. Eur J Oper Res 207:1–14MathSciNetCrossRefzbMATHGoogle Scholar
  19. Hartmann S, Kolisch R (2000) Experimental evaluation of state-of-the-art heuristics for the resource-constrained project scheduling problem. Eur J Oper Res 127:394–407CrossRefzbMATHGoogle Scholar
  20. Herroelen W (2005) Project scheduling—theory and practice. Prod Oper Manag 14:413–432.  https://doi.org/10.1111/j.1937-5956.2005.tb00230.x CrossRefGoogle Scholar
  21. Homberger J (2007) A multi-agent system for the decentralized resource-constrained multi-project scheduling problem. Int Trans Oper Res 14:565–589.  https://doi.org/10.1111/j.1475-3995.2007.00614.x CrossRefzbMATHGoogle Scholar
  22. Kaplan LA (1996) Resource-constrained project scheduling with preemption of jobs. UMI, Ann ArborGoogle Scholar
  23. Kelley JE Jr (1963) The critical path method: resources planning and scheduling. Ind Sched 13:347–365Google Scholar
  24. Kelley JE, Walker MR (1959) Critical-path planning and scheduling. Paper presented at December 1–3, 1959, East Jt IRE-AIEE-ACM Computing Conference—IRE-AIEE-ACM ’59, vol 32, pp 160–173. https://doi.org/10.1145/1460299.1460318
  25. Klein R (2000) Scheduling of resource-constrained projects. Kluwer Academic Publishers, DordrechtCrossRefzbMATHGoogle Scholar
  26. Kolisch R (1996a) Efficient priority rules for the resource-constrained project scheduling problem. J Oper Manag 14:179–192.  https://doi.org/10.1016/0272-6963(95)00032-1 CrossRefGoogle Scholar
  27. Kolisch R (1996b) Serial and parallel resource-constrained project scheduling methods revisited: theory and computation. Eur J Oper Res 90:320–333.  https://doi.org/10.1016/0377-2217(95)00357-6 CrossRefzbMATHGoogle Scholar
  28. Kolisch R, Hartmann S (1999) Heuristic algorithms for the resource-constrained project scheduling problem: classification and computational analysis. Proj Sched 14:147–178.  https://doi.org/10.1007/978-1-4615-5533-9_7 CrossRefGoogle Scholar
  29. Kolisch R, Sprecher A (1996) PSPLIB—a project scheduling problem library. Eur J Oper Res 96:205–216CrossRefzbMATHGoogle Scholar
  30. Kolisch R, Sprecher A, Drexl A (1995) Characterization and generation of a general class of resource-constrained project scheduling problems. Manag Sci 41:1693–1703.  https://doi.org/10.1287/mnsc.41.10.1693 CrossRefzbMATHGoogle Scholar
  31. Kurtulus I, Davis EW (1982) Multi-project scheduling: categorization of heuristic rules performance. Manag Sci 28:161–172CrossRefzbMATHGoogle Scholar
  32. Lova A, Tormos P (2001) Analysis of scheduling schemes and heuristic rules performance in resource-constrained multiproject scheduling. Ann Oper Res 102:263–286.  https://doi.org/10.1023/A:1010966401888 MathSciNetCrossRefzbMATHGoogle Scholar
  33. Lova A, Tormos P, Barber F (2006) Multi-mode resource constrained project scheduling: scheduling schemes, priority rules and mode selection rules. Intell Artif 10:69–86.  https://doi.org/10.4114/ia.v10i30.947 Google Scholar
  34. Malcolm DG, Roseboom JH, Clark CE, Fazar W (1959) Application of a technique for research and development program evaluation. Oper Res 7:646–669.  https://doi.org/10.1287/opre.7.5.646 CrossRefzbMATHGoogle Scholar
  35. Mingozzi A, Maniezzo V, Ricciardelli S, Bianco L (1998) An exact algorithm for the resource-constrained project scheduling problem based on a new mathematical formulation. Manag Sci 44:714–729. https://doi.org/10.1287/mnsc.44.5.714
  36. Özdamar L, Ulusoy G (1995) A survey on the resource-constrained project scheduling problem. IIE Trans 27:574–586.  https://doi.org/10.1080/07408179508936773 CrossRefGoogle Scholar
  37. Patterson JH (1973) Alternate methods of project scheduling with limited resources. Nav Res Logist Q 20:767–784.  https://doi.org/10.1002/nav.3800200415 CrossRefGoogle Scholar
  38. Patterson JH (1984) A comparison of exact approaches for solving the multiple constrained resource, project scheduling problem. Manag Sci 30:854–867.  https://doi.org/10.1287/mnsc.30.7.854 CrossRefGoogle Scholar
  39. Pérez E, Posada M, Lorenzana A (2015) Taking advantage of solving the resource constrained multi-project scheduling problems using multi-modal genetic algorithms. Soft Comput.  https://doi.org/10.1007/s00500-015-1610-z Google Scholar
  40. Pritsker AAB, Watters LJ, Wolfe PM (1969) Multiproject scheduling with limited resources: a zero-one programming approach. Manag Sci 16:93–108CrossRefGoogle Scholar
  41. Schirmer A (1996) New insights on the complexity of resource-constrained project scheduling—a case of single-mode schedulingGoogle Scholar
  42. Semwal VB, Raj M, Nandi GC (2015) Biometric gait identification based on a multilayer perceptron. Robot Auton Syst 65:65–75.  https://doi.org/10.1016/j.robot.2014.11.010 CrossRefGoogle Scholar
  43. Semwal VB, Singha J, Sharma PK et al (2016) An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification. Multimed Tools Appl.  https://doi.org/10.1007/s11042-016-4110-y Google Scholar
  44. Semwal VB, Mondal K, Nandi GC (2017) Robust and accurate feature selection for humanoid push recovery and classification: deep learning approach. Neural Comput Appl 28:565–574.  https://doi.org/10.1007/s00521-015-2089-3 CrossRefGoogle Scholar
  45. Song W, Kang D, Zhang J, Xi H (2016) Decentralized multi-project scheduling via multi-unit combinatorial auction. Aamas 2016:836–844Google Scholar
  46. Vázquez EP, Calvo MP, Ordóñez PM (2013) Learning process on priority rules to solve the RCMPSP. J Intell Manuf 26:123–138.  https://doi.org/10.1007/s10845-013-0767-5 CrossRefGoogle Scholar
  47. Villafáñez F, Poza DJ (2010) Propuesta de Modelo MAS para la resolución del RCMPSP basado en Subastas Combinatorias. In: Gutiérrez Pajares J, López-Paredes A, Iglesias Hernández C (eds) Best practices in project management. Methodologies and case studies in construction and engineering. INSISOC, Valladolid, SpainGoogle Scholar
  48. Villafañez F, Lopez-Paredes A, Pajares J, de la Fuente D (2014a) From the RCPSP to the DRCMPSP?: Methodological foundations. In: Proceedings on the international conference on artificial intelligence (ICAI), Athens. The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), Athens, pp 1–6Google Scholar
  49. Villafañez F, Lopez-Paredes A, Pajares J, de la Fuente D (2014b) From the RCPSP to the DRCMPSP: methodological foundations. In: Proceedings on the international conference on artificial intelligence (ICAI), p 1Google Scholar
  50. Wauters M, Vanhoucke M (2017) A nearest neighbour extension to project duration forecasting with artificial intelligence. Eur J Oper Res 259:1097–1111.  https://doi.org/10.1016/j.ejor.2016.11.018 MathSciNetCrossRefzbMATHGoogle Scholar
  51. Wauters T, Verbeeck K, De Causmaecker P, Vanden Berghe G (2015) A learning-based optimization approach to multi-project scheduling. J Sched 18:61–74MathSciNetCrossRefzbMATHGoogle Scholar
  52. Zheng Z, Guo Z, Zhu Y, Zhang X (2014) A critical chains based distributed multi-project scheduling approach. Neurocomputing 143:282–293CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.INSISOC Research GroupUniversity of ValladolidValladolidSpain
  2. 2.INSISOC Research GroupUniversity of BurgosBurgosSpain

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