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The final agreement on the timing of project completion is one of the obvious problems between project managers and their clients. There have been numerous reports of customers requesting shorter completion times than previously announced. This request will definitely affect the three project factors of overall cost, final quality of the project, and risk of implementation. This paper proposes a multipurpose cumulative complex linear programming to minimize "project overhead," "increase projects total risk" and "increase overall project quality" due to “time constraints." In other words, the proposed study is fully implemented among the four goals mentioned to shorten the project duration. Computational experiments have also been used to evaluate the performance of the proposed model. The main objective of this paper is to optimize the integration of the four factors of the survival pyramid (time, cost, quality, and risk) in industrial projects simultaneously under uncertainty. An innovative solution based on the multi-objective genetic algorithm (NSGA-II) is presented. This model is then used to solve a problem in another study and its results, strengths, and weaknesses compared to the previous model are evaluated. The results show the performance of the proposed model in all four factors is better than the previous models.
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