Abstract
The resource availability cost problem (RACP) (Möhring, Operations Research, 32:89–120, 1984) is commonly encountered in project scheduling. RACP aims to minimize the resource availability cost of a project by a given project deadline. In this study, RACP is extended from a single mode to a multi-mode called multi-mode RACP (MMRACP), which is more complicated than RACP but more convenient in practice. To solve MMRACP efficiently, forward activity list (FAL), a schedule generation scheme, is proposed. Heuristic algorithms are designed according to the characteristics of FAL to repair infeasible solutions and to improve the fitness of the solution. Modified particle swarm optimization (MPSO), which combines the advantages of particle swarm optimization and scatter search, is proposed to make the search for the best solution efficient. Computational experiments involving 180 instances are performed to validate the performance of the proposed algorithm. The results reveal that MPSO using FAL is a very effective method to solve MMRACP.
Similar content being viewed by others
References
Alfandari, L., Plateau, A., & Tolla, P. (2001). A two-phase path-relinking algorithm for the generalized assignment problem. Proceedings of the Fourth Metaheuristics International Conference, Porto.
Alvarez, A. M., González-Velarde, J. L., & De-Alba, K. (2005). GRASP embedded scatter search for the multicommodity capacitated network design problem. Journal of Heuristics, 11(3), 233–257.
Barrios, A., Ballestín, F., & Valls, V. (2011). A double genetic algorithm for the MRCPSP/max. Computers & Operations Research, 38(1), 33–43.
Chen, T., Zhang, B., Hao, X., & Dai, Y. (2006, July). Task scheduling in grid based on particle swarm optimization. Fifth IEEE International Symposium on Parallel and Distributed Computing, ISPDC’06 (pp. 238–245).
Chen, R. M., Wu, C. L., Wang, C. M., & Lo, S. T. (2010). Using novel particle swarm optimization scheme to solve resource-constrained scheduling problem in PSPLIB. Expert Systems with Applications, 37(3), 1899–1910.
Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58–73.
Damak, N., Jarboui, B., Siarry, P., & Loukil, T. (2009). Differential evolution for solving multi-mode resource-constrained project scheduling problems. Computers & Operations Research, 36(9), 2653–2659.
Deblaere, F., Demeulemeester, E., & Herroelen, W. (2011). Reactive scheduling in the multi-mode RCPSP. Computers & Operations Research, 38(1), 63–74.
Glover, F. (1977). Heuristics for integer programming using surrogate constraints. Decision Sciences, 8(1), 156–166.
Glover, F. (1998). A template for scatter search and path relinking. In J.-K. Hao (Ed.), Artificial evolution (pp. 1–51). Berlin: Springer.
Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99.
Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of IEEE international conference on neural networks, 4, 1942–1948.
Khorasani, J. (2012). A new heuristic approach for unit commitment problem using particle swarm optimization. Arabian Journal for Science and Engineering, 37(4), 1033–1042.
Kolisch, R., & Sprecher, A. (1997). PSPLIB-a project scheduling problem library: OR software-ORSEP operations research software exchange program. European Journal of Operational Research, 96(1), 205–216.
Laguna, M., & Martín, R. (2003). Scatter search: methodology and implementations in C. Kluwer: Springer.
Lambrechts, O., Demeulemeester, E., & Herroelen, W. (2008). A tabu search procedure for developing robust predictive project schedules. International Journal of Production Economics, 111(2), 493–508.
Liu, Y. H. (2006). A scatter search based approach with approximate evaluation for the heterogeneous probabilistic traveling salesman problem. IEEE Congress on InEvolutionary Computation, CEC 2006 (pp. 1603–1609).
López, F. C. G., Torres, M. G., Pérez, J. A. M., & Vega, J. M. M. (2004). Scatter search for the feature selection problem. In R. Conejo (Ed.), Current topics in artificial intelligence (pp. 517–525). Berlin: Springer.
Lova, A., Tormos, P., Cervantes, M., & Barber, F. (2009). An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes. International Journal of Production Economics, 117(2), 302–316.
Luo, X., Wang, D., Tang, J., & Tu, Y. (2006). An improved PSO algorithm for resource-constrained project scheduling problem, intelligent control and automation, 2006. The Sixth World Congress on WCICA, 2006(1), 3514–3518.
Maghsoudi, M. J., Ibrahim, Z., Buyamin, S., & Rahmat, M. F. A. (2012). Data clustering for the DNA computing readout method implemented on LightCycler and based on particle swarm optimization. Arabian Journal for Science and Engineering, 37(3), 697–707.
Möhring, R. H. (1984). Minimizing costs of resource requirements in project networks subject to a fixed completion time. Operations Research, 32(1), 89–120.
Neumann, K., Nübel, H., & Schwindt, C. (2000). Active and stable project scheduling. Mathematical Methods of Operations Research, 52(3), 441–465.
Orosz, J. E., & Jacobson, S. H. (2002). Analysis of static simulated annealing algorithms. Journal of Optimization theory and Applications, 115(1), 165–182.
Ozdamar, L. (1999). A genetic algorithm approach to a general category project scheduling problem. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 29(1), 44–59.
Pardalos, P. M., & Resende, M. G. (Eds.). (2002). Handbook of applied optimization. Oxford: Oxford University Press.
Parsopoulos, K. E., & Vrahatis, M. N. (2004). On the computation of all global minimizers through particle swarm optimization. IEEE Transactions on Evolutionary Computation, 8(3), 211–224.
Van Peteghem, V., & Vanhoucke, M. (2010). A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem. European Journal of Operational Research, 201(2), 409–418.
Ranjbar, M., Kianfar, F., & Shadrokh, S. (2008). Solving the resource availability cost problem in project scheduling by path relinking and genetic algorithm. Applied Mathematics and Computation, 196(2), 879–888.
Rodrigues, S. B., & Yamashita, D. S. (2010). An exact algorithm for minimizing resource availability costs in project scheduling. European Journal of Operational Research, 206(3), 562–568.
Russell, R. A., & Chiang, W. C. (2006). Scatter search for the vehicle routing problem with time windows. European Journal of Operational Research, 169(2), 606–622.
Santana-Quintero, L. V., Ramírez, N., & Coello, C. C. (2006). A multi-objective particle swarm optimizer hybridized with scatter search. In A. Gelbukh & C. A. Reyes-Garcia (Eds.), MICAI 2006: advances in artificial intelligence (pp. 294–304). Berlin: Springer.
Shadrokh, S., & Kianfar, F. (2007). A genetic algorithm for resource investment project scheduling problem, tardiness permitted with penalty. European Journal of Operational Research, 181(1), 86–101.
Shou, Y.-Y. (2010). Resource-constrained multi-project scheduling models and methods. Hangzhou: Zhejiang University Press.
Speranza, M. G., & Vercellis, C. (1993). Hierarchical models for multi-project planning and scheduling. European Journal of Operational Research, 64(2), 312–325.
Sprecher, A., Hartmann, S., & Drexl, A. (1997). An exact algorithm for project scheduling with multiple modes. Operations-Research-Spektrum, 19(3), 195–203.
Thomas, P. R., & Salhi, S. (1998). A tabu search approach for the resource constrained project scheduling problem. Journal of Heuristics, 4(2), 123–139.
Toklu, Y. C. (2002). Application of genetic algorithms to construction scheduling with or without resource constraints. Canadian Journal of Civil Engineering, 29(3), 421–429.
Triki, E., Collette, Y., & Siarry, P. (2005). A theoretical study on the behavior of simulated annealing leading to a new cooling schedule. European Journal of Operational Research, 166(1), 77–92.
Valls, V., Laguna, M., Lino, P., Pérez, A., & Quintanilla, S. (1999). Project scheduling with stochastic activity interruptions. In J. Wȩglarz (Ed.), Project scheduling (pp. 333–353). Kluwer: Springer.
Yamashita, D. S., Armentano, V. A., & Laguna, M. (2006). Scatter search for project scheduling with resource availability cost. European Journal of Operational Research, 169(2), 623–637.
Zhang, C., Sun, J., Zhu, X., & Yang, Q. (2008). An improved particle swarm optimization algorithm for flowshop scheduling problem. Information Processing Letters, 108(4), 204–209.
Acknowledgments
The authors sincerely thank the associate editor and anonymous referees for their valuable comments. This research was partially supported by the National Natural Science Foundation of China under Grants 71371181, 71201166, and 71201170.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Qi, JJ., Liu, YJ., Jiang, P. et al. Schedule generation scheme for solving multi-mode resource availability cost problem by modified particle swarm optimization. J Sched 18, 285–298 (2015). https://doi.org/10.1007/s10951-014-0374-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10951-014-0374-0