Discounted Cash Flows Time-Cost Trade-Off Problem Optimization with Uncertainty Cost

Abstract

Optimization problems in time-cost trade-off (TCTO) analysis of construction management have been traditionally solved by two distinctive approaches: heuristic methods and optimization techniques. Although heuristic methods can handle large-size projects, they do not guarantee optimal solutions. Recently, artificial intelligence (AI) techniques such as genetic algorithms (GAs), ant colony optimization (ACO), and particle swarm optimization (PSO) have been introduced to overcome the problems associated with (1) large number of variables and constraints; (2) non linearity of time-cost functions; and (3) multi-objective optimization. Traditional time-cost trade-off (TCTO) analysis of construction management assumes the constant value of activities’ cost along the project time span. In fact, the value of money decreases with time or in other words, disregard time value of money. Therefore, discounted cash flows should be considered when solving TCTO optimization problems. Furthermore, in reality, due to different uncertainty, the actual cost of each option is also not known by the manager in advance as a risk. Hence, the total cost of the project may significantly because of these uncertainty. Unfortunately, traditional TCTO analysis also disregards this factor. This study tries to incorporate time value of money and uncertainty into TCTO analysis. Details of model formulation are illustrated by an example project. The model has the following features: (1) optimum solution is guaranteed; (2) precise discrete activity time-cost relationship is used; (3) time value of money is taken into consideration; and (4) uncertainty in the project also are involved. The results show that inclusion of discounted cash flow results in distinct optimal project duration. Nevertheless, through the Monte Carlo simulation, which is in order to involve uncertainty, this proposed model also lead to distinct optimal duration at the certain percentile level. The proposed model for this study can help the practitioners in considering time value of money and uncertainty cost, in order to make the best time-cost decision and to identify risks involved.

Optimization problems in time-cost trade-off (TCTO) analysis of construction management have been traditionally solved by two distinctive approaches: heuristic methods and optimization techniques. Although heuristic methods can handle large-size projects, they do not guarantee optimal solutions. Recently, artificial intelligence (AI) techniques such as genetic algorithms (GAs), ant colony optimization (ACO), and particle swarm optimization (PSO) have been introduced to overcome the problems associated with (1) large number of variables and constraints; (2) non linearity of time-cost functions; and (3) multi-objective optimization. Traditional time-cost trade-off (TCTO) analysis of construction management assumes the constant value of activities’ cost along the project time span. In fact, the value of money decreases with time or in other words, disregard time value of money. Therefore, discounted cash flows should be considered when solving TCTO optimization problems. Furthermore, in reality, due to different uncertainty, the actual cost of each option is also not known by the manager in advance as a risk. Hence, the total cost of the project may significantly because of these uncertainty. Unfortunately, traditional TCTO analysis also disregards this factor. This study tries to incorporate time value of money and uncertainty into TCTO analysis. Details of model formulation are illustrated by an example project. The model has the following features: (1) optimum solution is guaranteed; (2) precise discrete activity time-cost relationship is used; (3) time value of money is taken into consideration; and (4) uncertainty in the project also are involved. The results show that inclusion of discounted cash flow results in distinct optimal project duration. Nevertheless, through the Monte Carlo simulation, which is in order to involve uncertainty, this proposed model also lead to distinct optimal duration at the certain percentile level. The proposed model for this study can help the practitioners in considering time value of money and uncertainty cost, in order to make the best time-cost decision and to identify risks involved.