Cost Minimization with Project Crashing: Comparison of the Traditional Solution and Genetic Algorithm Approach

Year 2024, Volume: 28 Issue: 5, 959 - 977, 25.10.2024

Semih Caglayan , Sadik Yıgıt

https://doi.org/10.16984/saufenbilder.1467829

Abstract

Existence of delays and cost overruns frequently puts the project viability in jeopardy. The integrated nature of these threats brings forward project scheduling as the primary determinant of project management success. The quality of project scheduling depends highly on the way resources are assigned to activities. In the project management literature, the efficiency of resource allocation is examined closely by the phenomenon called project crashing. This study introduces traditional and genetic algorithm approaches for the project crashing events and explains their steps in achieving the most efficient resource allocation. Within this context, the project crashing event is visualized, the insights of alternative approaches are described, and their implementations are illustrated with a case study. Besides, the procedures required for adopting the genetic algorithm approach to a typical problem are expressed. The case study illustration reveals the advantages and disadvantages of the genetic algorithm approach over the traditional approach. It is observed that the genetic algorithm approach can reach the solution in a single phase while the traditional approach requires multiple phases. On the other hand, the genetic algorithm approach may not reach the optimum solution unless the toolbox options are appropriately selected. This study presents the contribution of operational research to the project management body of knowledge by demonstrating the applicability and efficiency of genetic algorithm in the project crashing events. Researchers and industry practitioners may benefit from the proposed approach by following the indicated procedures to incorporate genetic algorithm into optimization issues in different fields.

Keywords

Project crashing, Resource allocation, Project scheduling, Cost minimization, Metaheuristic algorithms, Genetic algorithm

References

• H. L. Chen, “Performance measurement and the prediction of capital project failure,” International Journal of Project Management, vol. 33, no. 6, pp. 1393-1404, 2015.
• E. Durna, B. Ozorhon, S. Caglayan, “Identifying critical success factors of public private partnership projects in Türkiye,” Sakarya University Journal of Science, vol. 28, no. 1, pp. 30-50, 2024.
• T. Huo, H. Ren, W. Cai, G. Q. Shen, B. Liu, M. Zhu, H. Wu, “Measurement and dependence analysis of cost overruns in megatransport infrastructure projects: Case study in Hong Kong,” Journal of Construction Engineering and Management, vol. 144, no. 3, pp. 05018001, 2018.
• P. Ballesteros-Perez, E. Sanz-Ablanedo, R. Soetanto, M. C. González-Cruz, G. D. Larsen, A. Cerezo-Narváez, “Duration and cost variability of construction activities: An empirical study,” Journal of Construction Engineering and Management, vol. 146, no. 1, pp. 04019093, 2020.
• C. Callegari, A. Szklo, R. Schaeffer, “Cost overruns and delays in energy megaprojects: How big is big enough?” Energy Policy, vol. 114, pp. 211-220, 2018.
• G. Heravi, M. Mohammadian, “Investigating cost overruns and delay in urban construction projects in Iran,” International Journal of Construction Management, vol. 21, no. 9, pp. 958-968, 2021.
• S. Durdyev, “Review of construction journals on causes of project cost overruns,” Engineering, Construction and Architectural Management, vol. 28, no. 4, pp. 1241-1260, 2020.
• S. Zareei, “Project scheduling for constructing biogas plant using critical path method,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 756-759, 2018.
• H. F. Rahman, R. K. Chakrabortty, M. J. Ryan, “Memetic algorithm for solving resource constrained project scheduling problems,” Automation in Construction, vol. 111, pp. 103052, 2020.
• R. Pellerin, N. Perrier, F. Berthaut, “A survey of hybrid metaheuristics for the resource-constrained project scheduling problem,” European Journal of Operational Research, vol. 280, no. 2, pp. 395-416, 2020.
• M. Á. Vega-Velázquez, A. García-Nájera, H. Cervantes, “A survey on the software project scheduling problem,” International Journal of Production Economics, vol. 202, pp. 145-161, 2018.
• E. Osaba, E. Villar-Rodriguez, J. Del Ser, A. J. Nebro, D. Molina, A. LaTorre, P. N. Suganthan, C. A. C. Coello, F. Herrera, “A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems,” Swarm and Evolutionary Computation, vol. 64, pp. 100888, 2021.
• S. Katoch, S. S. Chauhan, V. Kumar, “A review on genetic algorithm: past, present, and future,” Multimedia Tools and Applications, vol. 80, no. 5, pp. 8091-8126, 2021.
• Y. Fang, S. T. Ng, “Genetic algorithm for determining the construction logistics of precast components,” Engineering, Construction and Architectural Management, vol. 26, no. 10, pp. 2289-2306, 2019.
• K. Kim, J. Walewski, Y. K. Cho, “Multiobjective construction schedule optimization using modified niched pareto genetic algorithm,” Journal of Management in Engineering, vol. 32, no. 2, pp. 04015038, 2016.
• S. Mirjalili, J. S. Dong, A. S. Sadiq, H. Faris, “Genetic algorithm: Theory, literature review, and application in image reconstruction,” Nature-Inspired Optimizers, pp. 69-85, 2020.
• M. S. El-Abbasy, A. Elazouni, T. Zayed, “MOSCOPEA: Multi-objective construction scheduling optimization using elitist non-dominated sorting genetic algorithm,” Automation in Construction, vol. 71, pp. 153-170, 2016.
• S. Sabharwal, P. Bansal, N. Mittal, S. Malik, “Construction of mixed covering arrays for pair-wise testing using probabilistic approach in genetic algorithm,” Arabian Journal for Science and Engineering, vol. 41, no. 8, pp. 2821-2835, 2016.
• A. Elkelesh, M. Ebada, S. Cammerer, S. ten Brink, “Decoder-tailored polar code design using the genetic algorithm,” IEEE Transactions on Communications, vol. 67, no. 7, pp. 4521-4534, 2019.
• Z. Tong, “A genetic algorithm approach to optimizing the distribution of buildings in urban green space,” Automation in Construction, vol. 72, pp. 46-51, 2016.
• S. M. Lim, A. B. M. Sultan, M. N. Sulaiman, A. Mustapha, K. Y. Leong, “Crossover and mutation operators of genetic algorithms,” International Journal of Machine Learning and Computing, vol. 7, no. 1, pp. 9-12, 2017.
• Z. Shen, A. Hassani, Q. Shi, “Multi-objective time-cost optimization using Cobb-Douglas production function and hybrid genetic algorithm,” Journal of Civil Engineering and Management, vol. 22, no. 2, pp. 187-198, 2016.
• M. Mangal, J. C. Cheng, “Automated optimization of steel reinforcement in RC building frames using building information modeling and hybrid genetic algorithm,” Automation in Construction, vol. 90, pp. 39-57, 2018.
• S. RazaviAlavi, S. AbouRizk, “Genetic algorithm–simulation framework for decision making in construction site layout planning,” Journal of Construction Engineering and Management, vol. 143, no. 1, pp. 04016084, 2017.
• L. Zhang, T. N. Wong, “An object-coding genetic algorithm for integrated process planning and scheduling,” European Journal of Operational Research, vol. 244, no. 2, pp. 434-444, 2015.
• M. Artar, A. Daloglu, “The optimization of multi-storey composite steel frames with genetic algorithm including dynamic constraints,” Technical Journal, vol. 26, no. 2, pp. 7077-7098, 2015.
• M. Rita, E. Fairbairn, F. Ribeiro, H. Andrade, H. Barbosa, “Optimization of mass concrete construction using a twofold parallel genetic algorithm,” Applied Sciences, vol. 8, no. 3, pp. 399, 2018.
• H. Sebaaly, S. Varma, J. W. Maina, “Optimizing asphalt mix design process using artificial neural network and genetic algorithm,” Construction and Building Materials, vol. 168, pp. 660-670, 2018.
• E. Hazir, T. Ozcan, K. H. Koç, “Prediction of adhesion strength using extreme learning machine and support vector regression optimized with genetic algorithm,” Arabian Journal for Science and Engineering, vol. 45, pp. 6985-7004, 2020.
• A. A. Chiniforush, M. Gharehchaei, A. A. Nezhad, A. Castel, F. Moghaddam, L. Keyte, D. Hocking, S. Foster, “Minimising risk of early-age thermal cracking and delayed ettringite formation in concrete–A hybrid numerical simulation and genetic algorithm mix optimisation approach,” Construction and Building Materials, vol. 299, pp. 124280, 2021.
• A. Kumar, N. Grover, A. Manna, R. Kumar, J. S. Chohan, S. Singh, S. Singh, C. I. Pruncu, “Multi-objective optimization of WEDM of aluminum hybrid composites using AHP and genetic algorithm,” Arabian Journal for Science and Engineering, vol. 47, no. 7, pp. 8031-8043, 2022.
• E. Uray, O. Tan, S. Carbas, I. H. Erkan, “Metaheuristics-based pre-design guide for cantilever retaining walls,” Technical Journal, vol. 32, no. 4, pp. 10967-10993, 2021.
• A. M. Rayeni, H. G. Arab, M. R. Ghasemi, “An effective improved multi-objective evolutionary algorithm (IMOEA) for solving constraint civil engineering optimization problems,” Technical Journal, vol. 32, no. 2, pp. 10645-10674, 2021.
• I. Costa-Carrapiço, R. Raslan, J. N. González, “A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency,” Energy and Buildings, vol. 210, pp. 109690, 2020.
• Q. Li, L. Zhang, L. Zhang, X. Wu, “Optimizing energy efficiency and thermal comfort in building green retrofit,” Energy, vol. 237, pp. 121509, 2021.
• Y. Fan, X. Xia, “Energy-efficiency building retrofit planning for green building compliance,” Building and Environment, vol. 136, pp. 312-321, 2018.
• Y. He, N. Liao, J. Bi, L. Guo, “Investment decision-making optimization of energy efficiency retrofit measures in multiple buildings under financing budgetary restraint,” Journal of Cleaner Production, vol. 215, pp. 1078-1094, 2019.
• L. T. Le, H. Nguyen, J. Dou, J. Zhou, “A comparative study of PSO-ANN, GA-ANN, ICA-ANN, and ABC-ANN in estimating the heating load of buildings’ energy efficiency for smart city planning,” Applied Sciences, vol. 9, no. 13, pp. 2630, 2019.
• S. N. Al-Saadi, K. S. Al-Jabri, “Optimization of envelope design for housing in hot climates using a genetic algorithm (GA) computational approach,” Journal of Building Engineering, vol. 32, pp. 101712, 2020.
• P. Pérez-Gosende, J. Mula, M. Díaz-Madroñero, “Facility layout planning. An extended literature review,” International Journal of Production Research, vol. 59, no. 12, pp. 3777-3816, 2021.
• M. Oral, S. Bazaati, S. Aydinli, E. Oral, “Construction site layout planning: Application of multi-objective particle swarm optimization,” Technical Journal, vol. 29, no. 6, pp. 8691-8713, 2018.
• M. A. Brahami, M. Dahane, M. Souier, “Sustainable capacitated facility location/network design problem: a Non-dominated Sorting Genetic Algorithm based multiobjective approach,” Annals of Operations Research, vol. 311, pp. 821-852, 2020.
• A. Taghavi, R. Ghanbari, K. Ghorbani-Moghadam, A. Davoodi, A. Emrouznejad, “A genetic algorithm for solving bus terminal location problem using data envelopment analysis with multi-objective programming,” Annals of Operations Research, vol. 309, pp. 259-276, 2022.
• S. Kumar, A. Sikander, “Optimum mobile robot path planning using improved artificial bee colony algorithm and evolutionary programming,” Arabian Journal for Science and Engineering, vol. 47, no. 3, pp. 3519-3539, 2022.
• A. Mahdavian, A. Shojaei, “Hybrid genetic algorithm and constraint-based simulation framework for building construction project planning and control,” Journal of Construction Engineering and Management, vol. 146, no. 12, pp. 04020140, 2020.
• J. Liu, Y. Liu, Y. Shi, J. Li, “Solving resource-constrained project scheduling problem via genetic algorithm,” Journal of Computing in Civil Engineering, vol. 34, no. 2, pp. 04019055, 2020.
• R. L. Kadri, F. F. Boctor, “An efficient genetic algorithm to solve the resource-constrained project scheduling problem with transfer times: The single mode case,” European Journal of Operational Research, vol. 265, no. 2, pp. 454-462, 2018.
• H. Li, L. Xiong, Y. Liu, H. Li, “An effective genetic algorithm for the resource levelling problem with generalised precedence relations,” International Journal of Production Research, vol. 56, no. 5, pp. 2054-2075, 2018.
• W. Peng, J. Zhang, L. Chen, “A bi-objective hierarchical program scheduling problem and its solution based on NSGA-III,” Annals of Operations Research, vol. 308, no. 1, pp. 389-414, 2022.
• Z. Wu, G. Ma, “Automatic generation of BIM-based construction schedule: Combining an ontology constraint rule and a genetic algorithm,” Engineering, Construction and Architectural Management, vol. 30, no. 10, pp. 5253-5279, 2023.
• I. Behera, S. Sobhanayak, “Task scheduling optimization in heterogeneous cloud computing environments: A hybrid GA-GWO approach,” Journal of Parallel and Distributed Computing, vol. 183, pp. 104766, 2024.
• S. Caglayan, S. Yigit, B. Ozorhon, G. Ozcan-Deniz, “A genetic algorithm-based envelope design optimisation for residential buildings,” Proceedings of the Institution of Civil Engineers - Engineering Sustainability, vol. 173, no. 6, pp. 280-290, 2020.
• H. G. Lee, C. Y. Yi, D. E. Lee, D. Arditi, “An advanced stochastic time‐cost tradeoff analysis based on a CPM‐guided genetic algorithm,” Computer‐Aided Civil and Infrastructure Engineering, vol. 30, no. 10, pp. 824-842, 2015.
• S. Tao, C. Wu, Z. Sheng, X. Wang, “Stochastic project scheduling with hierarchical alternatives,” Applied Mathematical Modelling,” vol. 58, pp. 181-202, 2018.