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Learning process on priority rules to solve the RCMPSP

Abstract

The priority rules are extensively used as a useful decision making technique for managers in single and multiple projects with limited resources, mainly because of their speed and simplicity. However, the question of which priority rule to use has been discussed without conclusive guidance. There is not any rule which performs better than any other in all instances. In this paper we propose an easy and quick learning process to determine which priority rule is the best for each instance. The analysis was carried out with 34 popular priority rules in 26 benchmarking problems. However, the process is capable of using any set of priority rules. As expected, every instance has its own best priority rule. It is also demonstrated that the selection of the most appropriate priority rule is extremely relevant, even when any meta-heuristic is used to solve the problem. In particular, we focus on genetic algorithms because of their high performance in scheduling problems. Our results show that a wrong choice of priority rule is not compensated by the high performance of the meta-heuristic.

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Notes

  1. The extension to the dynamic RCMPSP is immediate.

  2. The parents are selected according to a spin of a roulette wheel which is weighted according to the fitness values. High-fit string has more area assigned to it on the wheel and hence, a higher probability of ending up as the choice when the biased roulette wheel is spun.

  3. Two-points indicate the positions in the string to create a parents’ substring. Parent 1 substrings are directly inherited by children 1. The remaining genes of children 1 are inherited from parent 2. For more details about operators: http://www.eii.uva.es/elena/JSSP.htm.

  4. Two values exchange their positions in the string.

References

  • Archimede, B., Letouzey, A., Memon, M. A., & Xu, J. (2013). Towards a distributed multi-agent framework for shared resources scheduling. Journal of Intelligent Manufacturing. doi:10.1007/s10845-013-0748-8 (Published on line).

  • Ash, R. (1999). Activity scheduling in the dynamic, multi-project setting: Choosing heuristics through deterministic simulation. In Proceedings of the 1999 winter simulation conference (pp. 937–941). Pheoenix, USA.

  • Baker, K. (1974). Introduction to sequencing and scheduling. New York: Wiley.

    Google Scholar 

  • Browning, T., & Yassine, A. (2010a). Resource-constrained multi-project scheduling: Priority rule performance revisited. International Journal of Production Economics, 126, 212–228. doi:10.1016/j.ijpe.2010.03.009.

    Article  Google Scholar 

  • Browning, T., & Yassine, A. (2010b). A test bank generator for resource-constrained multi-project scheduling problems. Journal of Scheduling. doi:10.1007/s10951-009-0131-y.

  • Canbolat, Y., & Gundogar, E. (2004). Fuzzy priority rule for job shop scheduling. Journal of Intelligent Manufacturing, 15(4), 527–533. doi:10.1023/B:JIMS.0000034116.50789.df.

    Article  Google Scholar 

  • Chen, P., & Shahandashti, S. (2007). Simulated annealing algorithm for optimizing multi-project linear scheduling with multiple resource constratints. In 24th International symposium on automation & robotics in constructions.

  • Chen, P., & Shahandashti, S. (2009). Hybrid of genetic algorithm and simulated annealing for multiple project scheduling with multiple resource constraints. Automation in Construction. doi:10.1016/j.autcon.2008.10.007.

  • Chen, J., Jaong, W., Sun, C., Lee, H., Wu, J., & Ku, C. (2010). Applying genetic algorithm to resource constrained multi-project scheduling problems. Key Engineering Materials, 633, 419–420. www.scientific.net/KEM.419-420.633.

    Google Scholar 

  • Chiu, H. N., & Tsai, D. M. (2002). An efficient search procedure for the resource-constrained multi-project scheduling problem with discounted cash flows. Construction Management & Economics, 20(1), 55–66. doi:10.1080/01446190110089718.

    Article  Google Scholar 

  • Confessore, G., Giordani, S., & Rismondo, S. (2002). An auction based approach in decentralized project scheduling. In Proceedings of PMS 2002—International workshop on project management and scheduling (pp. 110–113). Valencia.

  • Confessore, G., Giordani, S., & Rismondo, S. (2007). A market-based multi-agent system model for decentralized multi-project scheduling. Annals of Operations Research, 150, 115–135. doi:10.1007/s10479-006-0158-9.

    Article  Google Scholar 

  • Davis, L. (1989). Adapting operators probabilities in genetic algorithms. In J. D. Schaffer (Ed.). Proceedings of the 3th international conference on genetic algorithms (pp. 375–378). San Mateo: Kaufmann

  • Deckro, R. F., Winkofsky, E. P., Hebert, J. E., & Gagnon, R. (1991). A decomposition approach to multi-project scheduling. European Journal of Operational Research, 51, 110–118. doi:10.1016/0377-2217(91)90150-T.

    Article  Google Scholar 

  • Demeulemeester, E., & Herroelen, W. (2002). Project scheduling: A research handbook. Boston: Kluwer Academic Publishers.

    Google Scholar 

  • Dodin, B., Elimam, A. A., & Rolland, E. (1998). Tabu search in audit scheduling. European Journal of Operational Research, 106(2–3), 373–392. doi:10.1016/S0377-2217(97)00280-4.

    Article  Google Scholar 

  • Goldberg, D. E. (1989). Genetic algorithms in search optimization and machine learning. Reading: Addison-Wesley.

    Google Scholar 

  • Gonçalves, J. F., Mendes, J. J. M., & Resende, M. G. C. (2008). A genetic algorithm for the resource constrained multi-project scheduling problem. European Journal of Operational Research, 189, 1171–1190. doi:10.1016/j.ejor.2006.06.074.

    Article  Google Scholar 

  • Hartmann, S., & Kolisch, R. (2000). Experimental state-of-the-art heuristics for the resource-constrained project scheduling problem. European Journal of Operational Research, 127(2), 394–407. doi:10.1016/S0377-2217(99)00485-3.

    Article  Google Scholar 

  • Herroelen, W. S. (2005). Project scheduling—Theory and practice. Production and Operations Management, 14(4), 413–432. doi:10.5555/ijop.2005.14.4.413.

  • Homberger, J. (2007). A multi-agent system for the decentralized resource-constrained multi-project scheduling problem. International Transactions in Operational Research, 14(6), 565–589. doi:10.1111/j.1475-3995.2007.00614.x.

    Article  Google Scholar 

  • Jinghua, L., & Wenjian, L. (2005). An agent-based system for multi-project planning and scheduling. In: Proceedings of the IEEE international conference on mechatronics & automation.

  • Kolisch, R. (1996). Efficient priority rules for the resource constrained project scheduling problem. Journal of Operations Management, 14(3), 179–192. doi:10.1016/0272-6963(95)00032-1.

    Article  Google Scholar 

  • Kolisch, R., & Hartmann, S. (1999). Heuristic algorithms for solving the resource-constrained project scheduling problem: Classification and computational analysis. In J. Weglarz (Ed.), Handbook on recent advances in project scheduling (pp. 147–178). Boston: Kluwer Academic Publishers.

    Google Scholar 

  • Kolisch, R., & Hartmann, S. (2006). Experimental investigation of heuristics for resource-constrained project scheduling: An update. European Journal of Operational Research, 174(1), 23–37. doi:10.1016/j.ejor.2005.01.065.

    Article  Google Scholar 

  • Kolisch, R., & Sprecher, A. (1996). PSPLIB—A project scheduling library. European Journal of Operational Research, 96, 205–216. doi:10.1016/S0377-2217(96)00170-1.

    Article  Google Scholar 

  • Kolisch, R., Schwindt, C. & Sprecher, A. (1999) Benchmark instances for project scheduling problems. J. Weglarz (Ed.), Handbook on recent advances in project scheduling (pp. 197–212). Kluwer

  • Kotwani, K., Yassine, A., & Zhao, Y. (2006). Scheduling resource constrained multi project DSM using modified simple GA and OmeGA. Working paper. Department of IESE, UIUC.

  • Kumanan, S., Jegan, G., & Raja, K. (2006). Multi-project scheduling using a heuristic and a genetic algorithm. International Journal of Advanced Manufacturing Technology, 31, 360–366. doi:10.1007/s00170-005-0199-.

    Article  Google Scholar 

  • Kurtulus, I., & Davis, E. W. (1982). Multi-project scheduling: Categorization of heuristic rules performance. Management Science, 28(2), 161–172. doi:10.1287/mnsc.28.2.161.

    Article  Google Scholar 

  • Lenstra, J. K., & Kan, A. H. (1978). Complexity of scheduling under precedence constraints. Operations Research, 26(1), 22–35. doi:10.1287/opre.26.1.22.

    Article  Google Scholar 

  • Linyi, D., & Yan, L. (2007). A particle swarm optimization for resource-constrained multi-project scheduling problem. . In International conference on computational intelligence and security (CIS 2007) (pp. 1010–1014).

  • Lova, A., Maroto, C., & Tormos, P. (2000). A multicriteria heuristic method to improve resource allocation in multi-project scheduling. European Journal of Operational Research, 127, 408–424. doi:10.1016/S0377-2217(99)00490-7.

    Article  Google Scholar 

  • Lova, A., & Tormos, P. (2001). Analysis of scheduling schemes and heuristic rules performance in resource-constrained multiproject scheduling. Annals of Operations Research, 102, 263–286. doi:10.1023/A:1010966401888.

    Article  Google Scholar 

  • Man, Z., Wei, T., Xiang, L., & Lishan, K. (2008). Research on multi-project scheduling problem based on hybrid genetic algorithm. In Computer science and software engineering, 2008 international conference on (Vol. 1, pp. 390–394).

  • Mattfeld, D. C. (1995). Evolutionary search and the job shop. Investigations on genetic algorithms for production scheduling. Berlin: Springer.

    Google Scholar 

  • Meeran, S., & Morshed, M. S. (2012). A hybrid genetic tabu search algorithm for solving job shop scheduling problems: A case study. Journal of Intelligent Manufacturing, 23, 1063–1078. doi:10.1007/s10845-011-0520-x.

    Article  Google Scholar 

  • Payne, J. H. (1995). Management of multiple simultaneous projects: A state-of-the-art review. International Journal of Project Management, 13(3), 163–168. doi:10.1016/0263-7863(94)00019-9.

    Article  Google Scholar 

  • Pérez, E., Herrera, F., & Hernández, C. (2003). Finding multiple solutions in job shop scheduling by niching genetic algorithms. Journal of Intelligent manufacturing, 14(3/4), 323–341. doi:10.1023/A:1024649709582.

    Article  Google Scholar 

  • Pérez, E., Posada, M., & Herrera, F. (2012). Analysis of new niching genetic algorithms for finding multiple solutions in the job shop scheduling. Journal of Intelligent manufacturing, 23(3), 341–356. doi:10.1007/s10845-010-0385-4.

    Google Scholar 

  • Pérez, E., Posada, M., & Lorenzana, A. (2010). Taking advantage of solving the resource constrained multi-project scheduling problems using multi-modal genetic algorithms. Soft Computing (in review process).

  • Pritsker, B., Watters, L. J., & Wolfe, P. M. (1969). Multi-project scheduling with limited resources: A zero-one programming approach. Management Science, 16(1), 93–108. doi:10.1287/mnsc.16.1.93.

    Article  Google Scholar 

  • Schwindt, C. (1995). ProGen/max: A new problem generator for different resource constrained project scheduling problems with minimal and maximal time lags. Report WIOR 449. Institut für Wirtschaftstheorie und Operations Research, Universit at Karlsruhe.

  • Sprecher, A., Kolisch, R., & Drexl, A. (1995). Semi-active, active, and non-delay schedules for the resource-constrained project scheduling problem. European Journal of Operational Research, 80, 94–102. doi:10.1016/0377-2217(93)E0294-8.

    Article  Google Scholar 

  • Steward, D. (1981). The design structure system: A method for managing the design of complex systems. IEEE Transactions on Engineering Management, 28, 71–74.

    Article  Google Scholar 

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Correspondence to E. Pérez Vázquez.

Appendix

Appendix

See Appendix Tables 3, 4, 5, 6 and 7.

Table 3 Priority rules
Table 4 Tie breakers
Table 5 Main characteristic of the instances
Table 6 Instances, optimum, PRs and TBs for the solution of minimizing \(C_{max}\) when using PR, when using learning on PR and when using learning on PR + GA
Table 7 Instances, optimum,PRs and TBs for the solution of minimizing average percent delay when using PR, when using learning on PR and when using learning on PR + GA

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Vázquez, E.P., Calvo, M.P. & Ordóñez, P.M. Learning process on priority rules to solve the RCMPSP. J Intell Manuf 26, 123–138 (2015). https://doi.org/10.1007/s10845-013-0767-5

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Keywords

  • Project scheduling
  • Priority rules
  • Resource constrained multi-project scheduling
  • Learning process
  • Genetic algorithm