A MULTI-OBJECTIVE RESOURCE-CONSTRAINED PROJECT SCHEDULING PROBLEM USING GENETIC ALGORITHM
Keywords:
Multi- Objective, Resource-Constrained, Project Scheduling Problem, Genetic Algorithm, Precedence relations.Abstract
Resource-Constrained Project Scheduling Problem (RCPSP) has been modeled as a single or multi-objective, using minimization of project make-span, lateness, total weighted start time, total project cost and maximization of project net present value. In this paper, a multi-objective RCPSP incorporated resource idleness into the list of RCPSP objectives. Here, the RCPSP is modeled as a Mixed Integer Non-Linear Programme to depict the various objective factors namely cost, time and resource idleness. Genetic algorithm (GA) meta-heuristic solution technique is used to promote solution diversity and determine the Pareto optimal for the multi-objective problem. The performance of the proposed RCPSP model was evaluated using a standard test problem that consist of 5 activities, 3 reusable resource types and a network diagram; a comprehensive computational experiment was performed and the results were analyzed with precedence relations considering the objectives as single objectives, bi-objectives and in combined form as multi-objectives simultaneously. The integration of resources idleness into the multi-objective policy gives more realistic result.References
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2. Habibi, F., Barzinpour, F., & Sadjadi, S. (2018). Resource-constrained project scheduling problem: review of past and recent developments. Journal of Project Management, 3(2), 55-88.
3. Salem, H., & Hassine, A. B. (2015). Meeting scheduling based on swarm intelligence. Procedia Computer Science, 60, 1081-1091.
4. Abdolshah, M. (2014). A Review of Resource-Constrained Project Scheduling Problems (RCPSP) Approaches and Solutions. International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies, 50-84.
5. Messelis, T., & De Causmaecker, P. (2014). An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem. European Journal of Operational Research, 233(3), 511-528.
6. Ma, W., Che, Y., Huang, H., & Ke, H. (2016). Resource-constrained project scheduling problem with uncertain durations and renewable resources. International journal of machine learning and cybernetics, 7(4), 613-621.
7. Hartmann, S., & Briskorn, D. (2010). A survey of variants and extensions of the resource-constrained project scheduling problem. European Journal of operational research, 207(1), 1-14.
8. Choi, Y. H., Jung, D., Lee, H. M., Yoo, D. G., & Kim, J. H. (2017). Improving the Quality of Pareto Optimal Solutions in Water Distribution Network Design. Journal of Water Resources Planning and Management, 143(8), 04017036.
9. MartÃnez-Iranzo, M., Herrero, J. M., Sanchis, J., Blasco, X., & GarcÃa-Nieto, S. (2009). Applied Pareto multi-objective optimization by stochastic solvers. Engineering applications of artificial intelligence, 22(3), 455 – 465.
10. Al-Fawzan, M. A., & Haouari, M. (2005). A bi-objective model for robust resource-constrained project scheduling. International Journal of production economics, 96(2), 175-187.
11. BallestÃn, F., & Blanco, R. (2015). Theoretical and practical fundamentals. In Handbook on Project Management and Scheduling Vol. 1 (pp. 411-427). Springer, Cham.
12. Blazewicz, J., Lenstra, J. K., & Kan, A. R. (1983). Scheduling subject to resource constraints: classification and complexity. Discrete applied mathematics, 5(1), 11-24.
13. Möhring, R. H., Schulz, A. S., Stork, F., & Uetz, M. (2003). Solving project scheduling problems by minimum cut computations. Management Science, 49(3), 330-350.
14. Mejia, O. P., Anselmet, M. C., Artigues, C., & Lopez, P. (2017, October). A new RCPSP variant for scheduling research activities in a nuclear laboratory. In 47th International Conference on Computers & Industrial Engineering (CIE47) (p. 8p).
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2019-04-29
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