The article explores the integration of the Critical Path Method (CPM) and Linear Programming (LP) for optimizing task allocation in multi-team IT projects. The research aims to develop and implement a model that minimizes the overall project completion time, considering task dependencies and ensuring an even distribution of workload among teams.
The article describes a mathematical model that includes variables such as task duration, task dependencies, start and finish times, maximum completion time for all tasks, and binary variables for assigning tasks to teams. The model is solved using linear programming, which allows finding the optimal task distribution and the minimum project completion time.
The research results demonstrate the effectiveness of the proposed model using real data. An analysis of tasks and their dependencies was conducted, the critical path was calculated, the task distribution among teams was determined, and the project completion time was evaluated. The proposed model ensures a reduction in the overall project completion time and an even workload distribution among teams.
The article also provides recommendations for implementing the model in IT project management practice. These include training project teams, customizing the model to the specifics of each project, phased implementation, regular monitoring of the model’s effectiveness, and its continuous improvement. Implementing the model will significantly improve IT project management efficiency, minimize project execution time, and ensure optimal resource allocation.
Thus, the research has shown that the integration of CPM and LP is an effective approach for optimizing task allocation in multi-team IT projects, ensuring the achievement of project goals within optimal timeframes.
- Project Management Institute. (2021). A Guide to the Project Management Body of Knowledge (PMBOK® Guide). Seventh edition.
- Heagney, J. (2022). Fundamentals of Project Management. Sixth Edition. HarperCollins Leadership.
- Zhang, J., Shen, X., & Yao, C. (2023). Evolutionary Algorithm for Software Project Scheduling Considering Team Relationships. IEEE Access, 11, 43690-43706. doi: 10.1109/ACCESS.2023.3270163.
- Luofan, L., & Wei, Z. (2023). Optimization of Critical Chain Multi-project Schedule Based on Delay Loss. In Proceedings of the 2023 14th International Conference on E-Education, E-Business, E-Management and E- Learning (IC4E ‘23). Association for Computing Machinery, New York, NY, USA, 276–282. doi: 10.1145/3588243.3588277.
- Cai, T., & Shen, H. (2022). Bayesian Optimization-Based Task Scheduling Algorithm on Heterogeneous System. In H. Shen et al. (Eds.), Parallel and Distributed Computing, Applications and Technologies. PDCAT 2021. Lecture Notes in Computer Science (Vol. 13148). Springer, Cham. doi: 10.1007/978-3-030-96772-7_5.
- Al-Refaie, A., Qapaja, A., & Al-Hawadi, A. (2021). Optimal Fuzzy Scheduling and Sequencing of Work- Intensive Multiple Projects Under Normal and Unexpected Events. International Journal of Information Technology Project Management, IGI Global, vol. 12(3), pages 64–89.
- Smith, P., & Jones, R. (2019). Ant Colony Optimization for Task Scheduling. Computational Intelligence Journal, 32(4), 567–579. doi: 10.1049/ip-cdt:20050196.
- Alcaraz, J., & Maroto, C. (2001). A robust genetic algorithm for resource allocation in project scheduling.Annals of Operations Research, 102, 83–109. doi: 10.1023/A:1010949931021.
- Kucuksayacigil, F., & Ulusoy, G. (2018). A hybrid genetic algorithm application for a bi-objective, multi- project, multi-mode, resource-constrained project scheduling problem. Working Paper #36791, Sabancı University.
- Ning, M., He, Z., Jia, T., & Wang, N. (2017). Metaheuristics for multi-mode cash flow balanced project scheduling with stochastic duration of activities. Automation in Construction, 81, 224–233.
- Ismail, I.Y., & Barghash, M.A. (2019). Diversity guided genetic algorithm to solve the resource constrained project scheduling problem. International Journal of Planning and Scheduling, 1(3), 147–170.