A multilayer feed-forward neural network (MLFNN) for the resource-constrained project scheduling problem (RCPSP)

Golab, A.; Gooya, E.; Falou, A.; Cabon, M.

Growing Science » Decision Science Letters » A multilayer feed-forward neural network (MLFNN) for the resource-constrained project scheduling problem (RCPSP)

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Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print) Quarterly Publication Volume 11 Issue 4 pp. 407-418 , 2022

A multilayer feed-forward neural network (MLFNN) for the resource-constrained project scheduling problem (RCPSP) Pages 407-418 Download PDF

Authors: Amir Golab, Ehsan Sedgh Gooya, Ayman Al Falou, Mikael Cabon

DOI: 10.5267/j.dsl.2022.7.004

Keywords: Project scheduling, Project management, Artificial neural network, Priority rules, RCPSP, Resource constraint

Abstract: Project management has a fundamental role in national development, industrial development, and economic growth. Schedule management is also one of the knowledge areas of project management, which includes the processes employed to manage the timely completion of the project. This paper deals with the Resource-Constrained Project Scheduling Problem (RCPSP), which is a part of schedule management. The objective of the problem is to optimize and minimize the project duration while constraining the resource quantities during project scheduling. There are two important constraints in this problem, namely resource constraints and precedence relationships of activities during project scheduling. Many methods such as exact, heuristic, and meta-heuristic have been developed by researchers to solve the problem, but there is a lack of investigation of the problem using methods such as neural networks and machine learning. In this article, we develop a multi-layer feed-forward neural network (MLFNN) to solve the standard single- mode RCPSP. The advantage of this method over evolutionary methods or metaheuristics is that it is not necessary to generate numerous solutions or populations. The developed MLFNN learns based on eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, percentage of remaining work, etc., which are calculated at each step of project scheduling, and identified priority rules, which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter out an unscheduled activity from the list of eligible activities and schedule all activities of the project according to the given project constraints. Finally, we investigate the performance of the presented approach using the standard benchmark problems from PSPLIB.

How to cite this paper

 Golab, A., Gooya, E., Falou, A & Cabon, M. (2022). A multilayer feed-forward neural network (MLFNN) for the resource-constrained project scheduling problem (RCPSP).Decision Science Letters , 11(4), 407-418.

Refrences

Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11).
Agarwal, A., Colak, S., & Erenguc, S. (2011). A neurogenetic approch for the resource-constrained project schedulig problem. Computers & operations research, 38(1), 44 - 50.
Agarwal, A., Pirkul, H., & Jacob, V. S. (2003). Augmented neural networks for task scheduling. European Journal of Operational Research, 151(3), 481-502.
Aggarwal, C. C. (2018). Neural networks and deep learning. springer.
Alcaraz, J., & Concepción, M. (2001). A robust genetic algorithm for resource allocation in project scheduling. Annals of operations Research, 102(1), 83-109.
Bebis, G., & Georgiopoulos, M. (1994). Feed-forward neural networks. IEEE Potentials, 13(4), 27-31.
Bouleimen, K., & Lecocq, H. (2003). A new efficient simulated annealing algorithm for the resource-constrained project scheduling problem and its multiple mode version. European journal of operational research, 149(2), 268-281.
Chen, R.-M. (2011). Particle swarm optimization with justification and designed mechanisms for resource-constrained project scheduling problem. Expert Systems with Applications, 38(6), 7102-7111.
Chen, W., Shi, Y.-j., Teng, H.-f., Lan, X.-p., & Hu, L.-c. (2010). An efficient hybrid algorithm for resource-constrained project scheduling. Information Sciences, 180(6), 1031-1039.
Choi, R. Y., Coyner, A. S., Kalpathy-Cramer, J., Chiang, M. F., & Campbell, J. P. (2020). introduction to machine learning, neural networks, and deep learning. Translational Vision Science & Technology, 9(2), 14 - 14.
Golab, A., Sedgh Gooya, E., Al Falou, A., & Cabon, M. (2022). Investigating the performance of an artificial neural network for solving the resource constrained project scheduling problem (RCPSP). In Pattern Recognition and Tracking XXXIII. 12101, pp. 78-83. Florida: SPIE. doi:https://doi.org/10.1117/12.2618499
Golab, A., Gooya, E., Falou, A & Cabon, M. (2022). Review of conventional metaheuristic techniques for resource-constrained project scheduling problem.Journal of Project Management, 7(2), 95-110. doi:10.5267/j.jpm.2021.10.002
Gonçalves, J. F., Resende, M. G., & Mendes, J. J. (2011). A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem. Journal of Heuristics, 17(5), 467-486.
Habibi, F., Barzinpour, F., & Sadjadi, S. J. (2018). Resource-constrained project scheduling problem: review of past and recent developments. Journal of Project Management, 3(2), 55-88.
Hartmann, S. (2002). A self‐adapting genetic algorithm for project scheduling under resource constraints. Naval Research Logistics (NRL), 49(5), 433-448.
Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. Neural networks for perception, 65-93.
Kolisch, R. (1996). Efficient priority rules for the resource‐constrained project scheduling problem. Journal of Operations Management, 14(3), 179-192.
Kolisch, R., & Hartmann, S. (1999). heuristic algorithms for solving the resource-constrained project scheduling problem: classification and computational analysis. Project scheduling (pp. 147 - 178). Boston: Springer.
Kolisch, R., & Hartmann, S. (2006). Experimental investigation of heuristics for resource-constrained project scheduling: An update. European Journal of Operational Research, 174(1), 23-37.
Kolisch, R., Sprecher, A., & Drexl, A. (1995). characterisation and generation of a general class of resource-constrained project scheduling problem. management science, 41(10), 1693 - 1703.
Koulinas, G., Kotsikas, L., & Anagnostopoulos, K. (2014). A particle swarm optimization based hyper-heuristic algorithm for the classic resource constrained project scheduling problem. Information Sciences, 277(1), 680-693.
Lim, A., Ma, H., Rodrigues, B., & Tan, S. T. (2013). New meta-heuristics for the resource-constrained project scheduling problem. Flexible Services and Manufacturing Journal, 25(1), 48-73.
Liu, J., Liu, Y., Shi, Y., & Li, J. (2020). Solving resource-constrained project scheduling problem via genetic algorithm. Journal of Computing in Civil Engineering, 34(2).
Mendes, J. J., Gonçalves, J. F., & Resende, M. G. (2009). Mendes, Jorge JM, José Fernando Gonçalves, and Mauricio GC Resende. "A random key based genetic algorithm for the resource constrained project scheduling problem. Computers & operations research, 31(1), 92-109.
Mobini, M., Mobini, Z., & Rabbani, M. (2011). An Artificial Immune Algorithm for the project scheduling problem under resource constraints. Applied Soft Computing, 11(2).
Nonobe, K., & Ibaraki, T. (2002). Formulation and tabu search algorithm for the resource constrained project scheduling problem. In Essays and surveys in metaheuristics (pp. 557-588). Boston: Springer.
Olaguíbel, R. A., & Goerlich, J. M. (1989). Heuristic algorithms for resource-constrained project scheduling: A review and an empirical analysis. Advances in project scheduling, 113-134.
Özkan, Ö., & Gulçlçek, Ü. (2015). A neural network for resource constrained project scheduling programming. Civil Engineering and Management, 21(2), 193 - 200.
PMI. (2017). Project management body of knowledge (PMP Guide) SIXTH Edition.
Proon, S., & Jin, M. (2011). A genetic algorithm with neighborhood search for the resource‐constrained project scheduling problem. Naval Research Logistics (NRL), 58(2), 73-82.
Roy, B., & Sen, A. K. (2019). Meta-heuristic Techniques to Solve Resource-Constrained Project Scheduling Problem. In International conference on innovative computing and communications (pp. 93-99). Springer.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural networks, 85-117.
Sharma, S., Sharma, S., & Athaiya, A. (2017). Activation functions in neural networks. towards data science, 6(12), 310 - 316.
Shou, Y. (2005). A neural network based heuristic for resource-constrained project scheduling. International Symposium on Neural Networks (pp. 794- 799). Berlin, Heidelberg: Springer.
Sprecher, A., & Kolisch, R. (1997). PSPLIB - A project scheduling problem library: OR Software - ORSEP Operations Research Software Exchange Program. European Journal of Operational Research, 6(1), 205-216.
Svozil, D., Kvasnicka, v., & Pospichal, J. (1997). Introduction to multi-layer feed-forward neural networks. Chemometrics and Intelligent Laboratory Systems, 39(1), 43-62.
Ulusoy, G., & Özdamar, L. (1989). Heuristic performance and network/resource characteristics in resource-constrained project scheduling. Journal of the operational research society, 40(12), 1145 - 1152.
Valls, V., Ballestín, F., & Quintanilla, S. (2008). A hybrid genetic algorithm for the resource-constrained project scheduling problem. European Journal of Operational Research, 185(2), 495-508.
Wang, L., & Fang, C. (2012). A hybrid estimation of distribution algorithm for solving the resource-constrained project scheduling problem. Expert Systems with Applications, 39(3), 2451-2460.
Wang, S.-C. (2003). Artificial neural network. In Interdisciplinary computing in java programming (pp. 81-100). Boston: springer.
Zamani, R. (2017). An evolutionary implicit enumeration procedure for solving the resource‐constrained project scheduling problem. International Transactions in Operational Research, 24(6), 1525-1547.
Ziarati, K., & Akbari, R. ,. (2011). On the performance of bee algorithms for resource-constrained project scheduling problem. Applied Soft Computing, 11(4), 3720-3733.