  In today's construction industry, poor performance often arises due to
various factors related to time, finances, and quality. These factors
frequently lead to project delays and resource losses, particularly in terms of
financial resources. This research addresses the Multimode Resource-Constrained
Project Scheduling Problem (MRCPSP), a real-world challenge that takes into
account the time value of money and project payment planning. In this context,
project activities exhibit discrete cost profiles under different execution
conditions and can be carried out in multiple ways. This paper aims to achieve
two primary objectives: minimizing the net present value of project costs and
project completion times while simultaneously improving the project's
productivity index. To accomplish this, a mathematical programming model based
on certain assumptions is proposed. Several test cases are designed, and they
are rigorously evaluated using the methodology outlined in this paper to
validate the modeling approach. Recognizing the NP-hard nature of this problem,
a multi-objective genetic algorithm capable of solving large-scale instances is
developed. Finally, the effectiveness of the proposed solution is assessed by
comparing it to the performance of the NSGA-II algorithm using well-established
efficiency metrics. Results demonstrate the superior performance of the
algorithm introduced in this study.
