A genetic algorithm-based method for solving multi-mode resource-constrained project scheduling problem in uncertain environment

Khalilzadeh, M.; Hosseini, S.; Ghaeli, R.

Growing Science » Journal of Project Management » A genetic algorithm-based method for solving multi-mode resource-constrained project scheduling problem in uncertain environment

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Journal of Project Management

ISSN 2371-8374 (Online) - ISSN 2371-8366 (Print) Quarterly Publication Volume 5 Issue 2 pp. 79-86 , 2020

A genetic algorithm-based method for solving multi-mode resource-constrained project scheduling problem in uncertain environment Pages 79-86 Download PDF

Authors: Mohammad Khalilzadeh, Seyyed Hajir Hosseini, Reza Ghaeli

DOI: 10.5267/j.jpm.2020.1.002

Keywords: MRCPSP, Simulation, Uncertainty, Genetic Algorithm

Abstract: Project scheduling models with resource constraints and multi-mode activities aims to create a schedule for carrying out activities considering precedence constraints and available resources in order to minimize the project duration. In the real world, we face uncertainty related to projects, where there are no historical data, hence, we should rely on the experts' judgements to estimate activity durations. For this purpose, in this paper, the 99-simulation method is used to deal with uncertainty. The exact mathematical programming model is presented in this paper and the hybrid algorithm based on Genetic Algorithm is used to solve this type of project scheduling problem which finds the near-optimal solution in a short computational time. Finally, the effectiveness of the proposed model is examined with a numerical example.

How to cite this paper

 Khalilzadeh, M., Hosseini, S & Ghaeli, R. (2020). A genetic algorithm-based method for solving multi-mode resource-constrained project scheduling problem in uncertain environment.Journal of Project Management, 5(2), 79-86.

Refrences

Afshar-Nadjafi, B., Rahimi, A., & Karimi, H. (2013). A genetic algorithm for mode identity and the resource constrained project scheduling problem. Scientia Iranica, 20(3), 824-831.
Afshar-Nadjafi, B. (2014). A solution procedure for preemptive multi-mode project scheduling problem with mode changeability to resumption. Applied Computing and Informatics.
Aziz, R. F. (2013). Optimizing strategy software for repetitive construction projects within multi-mode re-sources. Alexandria Engineering Journal, 52(3), 373-385.
Bakhshi, T., Sinharay, A., Sarkar, B., & Sanyal, S. K. (2015). A Semi Definite Programming Based Optimiza-tion Algorithm for Project Scheduling Problem. Journal of The Institution of Engineers (India): Series C, 96(2), 89-98.
Beşikci, U., Bilge, Ü., & Ulusoy, G. (2015). Multi-mode resource constrained multi-project scheduling and re-source portfolio problem. European Journal of Operational Research, 240(1), 22-31.
Bianco, L., Caramia, M., & Giordani, S. (2016). Resource levelling in project scheduling with generalized prec-edence relationships and variable execution intensities. OR Spectrum, 38(2), 405-425.
Blazewicz, J., Lenstra, J. K., & Kan, A. R. (1983). Scheduling subject to resource constraints: classification and complexity. Discrete Applied Mathematics, 5(1), 11-24.
Cheng, J., Fowler, J., Kempf, K., & Mason, S. (2015). Multi-mode resource-constrained project scheduling problems with non-preemptive activity splitting. Computers & Operations Research, 53, 275-287.
Chen, W., & Ni, X. (2014). Chaotic differential evolution algorithm for resource constrained project scheduling problem. International Journal of Computing Science and Mathematics, 5(1), 81-93.
Chen, L., & Zhang, Z. (2016). Preemption resource-constrained project scheduling problems with fuzzy random duration and resource availabilities. Journal of Industrial and Production Engineering, 33(6), 373-382.
Jie, H., Huaiyan, C., & Yun, C. (2011, August). Uncertainty evaluation using Monte Carlo method with MATLAB. In Electronic Measurement & Instruments (ICEMI), 2011 10th International Conference on (Vol. 2, pp. 282-286). IEEE.
Rahmani, N., Zeighami, V., & Akbari, R. (2015). A study on the performance of differential search algorithm for single mode resource constrained project scheduling problem. Decision Science Letters, 4(4), 537-550.
Rezaeian, J., Soleimani, F., Mohaselafshary, S., & Arab, A. (2015). Using a meta-heuristic algorithm for solving the multi-mode resource-constrained project scheduling problem. International Journal of Operational Re-search, 24(1), 1-16.
Singh, A. (2014). Resource Constrained Multi-Project Scheduling with Priority Rules & Analytic Hierarchy Process. Procedia Engineering, 69, 725-734.
Tavana, M., Abtahi, A. R., & Khalili-Damghani, K. (2014). A new multi-objective multi-mode model for solv-ing preemptive time–cost–quality trade-off project scheduling problems. Expert Systems with Applications, 41(4), 1830-1846.
Van Peteghem, V., & Vanhoucke, M. (2014). An experimental investigation of metaheuristics for the multi-mode resource-constrained project scheduling problem on new dataset instances. European Journal of Op-erational Research, 235(1), 62-72.