Multi Mode Resource Constraint Construction Project Scheduling Problem (MRCPSP) Considering the Uncertainty in the Activities Duration and Delays

Document Type : Original Article

Authors

1 Civil Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran

2 Department of Civil Engineering, Faculty of Engineering, Kharazmi University of Tehran

3 Department of Civil Engineering, Faculty of Engineering, Kharazmi University, Tehran, Iran

Abstract

Choosing construction methods and how resources are allocated are important in the project control. The multi-mode resource constrained project scheduling problem (MRCPSP) is a significant subject in project management. The development of the above model for construction projects is an important issue because of uncertainty. Fuzzy logic has been used to display the uncertainty in the duration of activities and the delay between them. This paper examines this problem and schedules project with providing an intelligent algorithm combining fuzzy sets and genetic algorithms (GAs). The Fuzzy MRCPSP problem can be considered as the scheduling of a set of activities with the aim of finding an activity operation mode and the activity operation priority; so that the resource constraints (renewable resources and non-renewable resources) as well as the precedence constraints are met simultaneously and the time for completion of the project is minimal. In the first step, the above problem is modeled mathematically, and then the model is coded using GA-based algorithm in Matlab software and finally the model is solved. The results of the implementation of this algorithm on the standard instances of the PSPLIB site, in comparison with the GAMS software, indicate the successful performance of the combined GA algorithm with fuzzy sets. The approach used in this research can be easily used by project managers. This prevents human errors caused by people who are responsible for controlling the project at resource leveling phase and is a way to the optimized project scheduling.

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[1] Sabzeparvar, M. (2012). Project control step-by-step. Fourteenth ed. Tehran: Terme.
[2] Giran, O. Temur R. and Bekdaş, G. (2017). Resource constrained project scheduling by harmony search algorithm. KSCE Journal of Civil Engineering, 21(2), 479-487.
[3] Blazewicz, J. Lenstra, J.K. and A Kan, A.H.G.R. (1983). Scheduling subject to resource constraints: classification and complexity. Discrete Applied Mathematics, 5(1), 11-24.
[4] Artigues, C. Demassey, S. and Néron, E. (2008). Resource-constrained project scheduling : models, algorithms, extensions and applications. ed. F. Sourd, USA: John Wiley & Sons, Inc.
[5] Shirmohamadi, A.H. (2009). Project management and control. 2nd ed. Isfahan: Jihad University of Isfahan University of Technology.
[6] Neumann, K. and Zimmermann, J. (2003). Project Scheduling with Time Windows and Scarce Resources. 2 ed. Berlin: Springer-Verlag Berlin Heidelberg.
[7] Hartmann, S. and 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] Weglarz, J. (2009). Project Scheduling Recent Models, Algorithms And Applications. 1 ed. Springer US.
[9] Liu, B. (2009). Theory and Practice of Uncertain Programming. Studies in Fuzziness and Soft Computing. Springer-Verlag Berlin Heidelberg.
[10] Geiger, M.J. (2017) A multi-threaded local search algorithm and computer implementation for the multi-mode, resource-constrained multi-project scheduling problem. European Journal of Operational Research, 256(3), 729-741.
[11] Prade, H. (1979). Using fuzzy set theory in a scheduling problem: A case study. Fuzzy Sets and Systems, 2(2), 153-165.
[12] Hapke, M. Jaszkiewicz, A. and Słowiński, R. (2000). Pareto Simulated Annealing for Fuzzy Multi-Objective Combinatorial Optimization. Journal of Heuristics, 6(3), 329-345.
[13] Leu, S.S. and Hung, T.H. (2002). A genetic algorithm-based optimal resource-constrained scheduling simulation model. Construction Management and Economics, 20(2), 131-141.
[14] Zhang, H. et al. (2005). Particle swarm optimization-based schemes for resource-constrained project scheduling. Automation in Construction, 14(3), 393-404.
[15] Mendes, J.J.M. Gonçalves, J.F. and Resende, M.G.C. (2009). A random key based genetic algorithm for the resource constrained project scheduling problem. Computers & Operations Research, 36(1), 92-109.
[16] Huang, Y. Shou, Y. and Zhang, L. (2011). Genetic algorithm for the project scheduling problem with fuzzy time parameters. In: 2011 IEEE International Conference on Industrial Engineering and Engineering Management.
[17] Masmoudi, M. and Haït, A. (2013). Project scheduling under uncertainty using fuzzy modelling and solving techniques. Engineering Applications of Artificial Intelligence, 26(1), 135-149.
[18] Afshar-Nadjafi, B. and Majlesi, M. (2014). Resource constrained project scheduling problem with setup times after preemptive processes. Computers & Chemical Engineering, 69(3), 16-25.
[19] Knyazeva, M., Bozhenyuk, A. and Rozenberg, I. (2015). Resource-constrained Project Scheduling Approach Under Fuzzy Conditions. Procedia Computer Science, 77(1), 56-64.
[20] Joy, J. Rajeev, S. and Narayanan, V. (2016). Particle Swarm Optimization for Resource Constrained-project Scheduling Problem with Varying Resource Levels. Procedia Technology, 25(1), 948-954.
[21] Badri, A., Nadeau S. and Gbodossou, A. (2012). Proposal of a risk-factor-based analytical approach for integrating occupational health and safety into project risk evaluation. Accident Analysis & Prevention, 48(1), 223-234.
[22] Mulholland, B. and Christian, J. (1999). Risk Assessment in Construction Schedules. Journal of Construction Engineering and Management, 125(1), 8-15.
[23] Pinto, A., Nunes, I.L. and Ribeiro, R.A. Occupational risk assessment in construction industry – Overview and reflection. Safety Science. Safety Science, 49(5), 616-624.
[24] Daneshpayeh, H. Haswanpoor, H. (2013). Project scheduling with fuzzy data using refrigeration simulation algorithm. Production management, 9(2) 57-74.
[25] Gavareshki, M.K. (2004). New fuzzy GERT method for research projects scheduling. In: Engineering Management Conference, IEEE.
[26] Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353.
[27] Zadeh, L.A. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy sets and systems, 1(1), 3-28.
[28] Hamzedanesh P. and Hossienali P. (2013). Project scheduling with fuzzy data using refrigeration simulation algorithm. Production and Operations Management, 5(2), 57-74.
[29] Liu, S. Yung, K.L. (2007). Genetic local search for resource-constrained project scheduling under uncertainty. International Journal of Information and Management Sciences, 18(4), 347-356.
[30] Cheng, C.H. (1998). A new approach for ranking fuzzy numbers by distance method. Fuzzy sets and systems, 95(3), 307-317.
[31] Holland, J.H. (1975). Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor.
[32] Coelho, J. and Vanhoucke, M. (2011). Multi-mode resource-constrained project scheduling using RCPSP and SAT solvers. European Journal of Operational Research, 213(1), 73-82.
[33] Xu, J. and Feng, C. (2014). Multimode Resource-Constrained Multiple Project Scheduling Problem under Fuzzy Random Environment and Its Application to a Large Scale Hydropower Construction Project. The Scientific World Journal, 2014(1), 1-20.
[34] Menesi, W. and Hegazy, T. (2015). Multimode Resource-Constrained Scheduling and Leveling for Practical-Size Projects. Journal of Management in Engineering, 31(6), 1-7.
[35] Atli, O. and Kahraman, C. (2012). Fuzzy resource-constrained project scheduling using taboo search algorithm. International Journal of Intelligent Systems, 27(10), 873-907.
[36] Józefowska, J. et al. (2001). Simulated Annealing for Multi-Mode Resource-Constrained Project Scheduling. Annals of Operations Research, 102(1), 137-155.
[37] Lova, A. et al. (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.
[38] Kelley, J.E. (1963). The critical-path method: Resources planning and scheduling. Industrial scheduling, 347-365.
[39] Bedworth, D.D. and Bailey, J.E. (1987). Integrated Production Control Systems: Management. Analysis, Design, 2nd ed. New York: John Wiley & Sons, Inc.
[40] Safari, H. (2015). solving Resource Constrained-project Scheduling Problem (RCPSP) using the modified Imperialist Competitive Algorithm (DICA). Industrial Management, 17(7), 333-364.