Abstract
Time, cost, and quality have been known as the project iron triangles and substantial factors in construction projects. Several studies have been conducted on time-cost-quality trade-off problems so far, however, none of them has considered the time value of money. In this paper, a multi-objective mathematical programming model is developed for time-cost-quality trade-off scheduling problems in construction projects considering the time value of money, since the time value of money, which is decreased during a long period of time, is a very important matter. Three objective functions of time, cost, and quality are taken into consideration. The cost objective function includes holding cost and negative cash flows. In this model, the net present value (NPV) of negative cash flow is calculated considering the costs of non-renewable (consumable) and renewable resources in each time period of executing activities, which can be mentioned as the other contribution of this study. Then, three metaheuristic algorithms including multi-objective grey wolf optimizer (MOGWO), non-dominated sorting genetic algorithm (NSGA-II), and multi-objective particle swarm optimization (MOPSO) are applied, and their performance is evaluated using six metrics introduced in the literature. Finally, a bridge construction project is considered as a real case study. The findings show that considering the time value of money can prevent cost overrun in projects. Additionally, the results indicate that the MOGWO algorithm outperforms the NSGA-II and MOPSO algorithms.
Highlights
Various methods are exploited by different organizations in implementing projects
A multi-objective mathematical programming model was presented for resource constraint project scheduling problem (RCPSP) with the aim of time, cost, and quality trade-off considering the time value of money
The proposed model was implemented on a real case study of a bridge construction project with 88 activities and solved by three different metaheuristic algorithms named NSGA-II, multi-objective particle swarm optimization (MOPSO), and multi-objective grey wolf optimizer (MOGWO)
Summary
Various methods are exploited by different organizations in implementing projects. The optimal tool and technique is usually selected based on the project characteristics as well as the capabilities of the organization [1]. Cost, and quality (TCQ) are three key factors in sustainable construction project scheduling. It is necessary to have an efficient decision support system that could consider all above-said criteria and closer to the real status of the project. Project success is usually measured based on the iron triangle of time, cost, and quality. In other words, it requires systematic methodologies in which quality is considered together with time and cost [3]
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