Optimization of Project Scheduling Problem with Limited Resources and uncertainty using the Intelligent Water Drop and Particle Swarm Optimization Algorithms
نویسندگان
1 Department of Industrial Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 Department of Industrial Engineering, Abhar Branch, Islamic Azad University, Abhar, Iran
3 Department of Industrial Engineering, Faculty of Engineering, Kharazmi University, Tehran,
4 Department of Industrial Engineering, Bonab Branch, Islamic Azad University, Bonab, Iran
5 Department of Industrial Engineering, Eyvanekey University, Semnan, Iran
doi
چکیده
Nowadays, the project scheduling problem with limited resources has become one of the most important optimization issues. Given the enormous costs spent on projects as well as materials, resources, and forces, projects are expected to be successful and finish at the planned time. This schedule in functional mode will have many uncertainties due to the needs of the moment. Such a goal requires careful planning and advanced algorithms to solve these complex issues in a short and reasonable time. In this research, a new method is presented using the Intelligent Water Drops (IWD) algorithm for the resource-constrained project scheduling problem. For this reason and because of the importance of these projects, in this research, an optimization model has been developed for project scheduling in the state of uncertainty, which can solve many implementation obstacles. For this purpose, first, the problem is formulated as a mixed-inter linear programming (MILP) model. Next, the model is optimized using the IDW algorithm. To evaluate the performance of the proposed method, the standard data set was used in previous research and articles, and four datasets with different scales were selected from the PSLIB library. The results show that the proposed method is capable of obtaining the best precision in terms of the least critical deviation from the optimal solution. Moreover, the results of the proposed method were compared with metaheuristic algorithms, such as the particle congestion algorithm (PSO) , which was able to get the best solution among these algorithms.
