چكيده به لاتين
Nowadays, poor performance might derive from temporal, financial, and qualitative factors that have transformed into a widespread global problem in the construction industry. These factors typically incur project breakdown and loss of resources, especially financials. By considering these issues, the present research addresses the Multimode Resource-Constrained Project Scheduling Problem (MRCPSP), wherein the time value of money and project payment planning is practically considered in the real world’s problems. Activities have discrete and different cost slopes in every executive condition and can be performed through one of the possible ways during projects’ implementation. This paper seeks to minimize the net present value of projects’ costs and their completion times besides enhancing the project productivity index. For this purpose, a mathematical programming model has been proposed to formulate the problem based on the existing assumptions. Several samples have been designed and accurately examined by the method proposed in this paper to validate the modeling procedure. Due to the non-deterministic polynomial-time hardness of this problem (NP-Hard), a multi-objective genetic algorithm is developed for solving large-size problems. Finally, the efficiency of the proposed solution is evaluated by the performance of the classic NSGA-II algorithm based on some well-known efficient criteria. The results indicate the privilege of the algorithm performance presented by this study For example, the genetic algorithm of this research had an average of 20.87% better answers in the overall productivity objective function and also 5.73% for the objective function had a net present value of project costs compared to the NSGA-II algorithm. The index case had the number of Pareto answers and the distance from the ideal point of 95% and 21%, respectively..
