Schedule generation scheme for solving multi-mode resource availability cost problem by modified particle swarm optimization
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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.
KeywordsProject scheduling Multi-mode resource availability cost problem Schedule generation Forward activity list Modified particle swarm optimization
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.
- 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.Google Scholar
- 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).Google Scholar
- 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).Google Scholar
- 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.Google Scholar
- Neumann, K., Nübel, H., & Schwindt, C. (2000). Active and stable project scheduling. Mathematical Methods of Operations Research, 52(3), 441–465. Google Scholar
- Pardalos, P. M., & Resende, M. G. (Eds.). (2002). Handbook of applied optimization. Oxford: Oxford University Press.Google Scholar
- Shou, Y.-Y. (2010). Resource-constrained multi-project scheduling models and methods. Hangzhou: Zhejiang University Press.Google Scholar
- Sprecher, A., Hartmann, S., & Drexl, A. (1997). An exact algorithm for project scheduling with multiple modes. Operations-Research-Spektrum, 19(3), 195–203.Google Scholar