| dc.description.abstract | The main objectives of construction projects include completing the project on time and within budget. There is always a tradeoff between time and cost. Time loss is costly and time savings can provide benefits to all the parties involved in the project. Time-cost optimization is essential for construction projects. The objective of time-cost optimization is to determine the optimum project duration corresponding to the minimum total cost. This is accomplished through shortening the duration of critical activities in order to reduce the overall project duration. Time-cost optimization techniques were developed to accelerate the project schedule by expediting the construction process and reducing the activities' durations to meet owner's convenience and expectations, or to recover the lost time when the project performs behind schedule or exhibits a negative time variance. Since the 1960's, several methods for time-cost optimization were developed with the aim of minimizing the project cost and duration without paying close attention to the effect of float loss resulting from schedule compression. The float is an important element in the project schedule that can be used by contractors to change the start of noncritical activities for resource management purposes, and by owners to accommodate change orders. Although total float is defined as a time contingency against project delays, the consumption of float can lead to serious increase in project risk and cost. Time-cost optimization techniques result in reducing the available float for noncritical activities and thus reducing the schedule flexibility. The main objective of this research is to develop a new time-cost optimization framework that can provide an optimum cost-time value for a project taking into consideration the effect of float loss. This thesis presents two new frameworks that are developed to solve the time-cost optimization problem taking into account the float loss impact: a stochastic framework and a Non-Linear Integer Programming (NLIP) framework. The stochastic framework uses Monte Carlo Simulation (MCS) to calculate the effect of float loss on risk. This is later translated into an added cost to the optimization problem. The Non-Linear Integer Programming (NLIP) framework uses What's Best solver to find an efficient solution to the optimization problem while incorporating the float loss cost calculated according to the float commodity approach. An application example of the frameworks is presented. The deterministic solution, using classical time-cost tradeoff techniques, shows the optimum duration of 23 days at a minimum cost of $12,490. Using the proposed stochastic framework, the optimum duration is 25 days at a minimum cost of $12,709. The Non-Linear Integer Programming (NLIP) framework shows an optimum duration of 24 days at a minimum cost of $12,659. The results from both proposed frameworks confirmed the research hypothesis, which states that the new optimum solution will be at a higher duration and cost. This is due to the fact that the proposed frameworks incorporate the effect of float loss on project cost and risk. This presents a new tradeoff between time, cost and flexibility (represented in the amount of float). The results obtained using the two frameworks; in comparison with the deterministic time-cost tradeoff, are better in terms of schedule flexibility, activities' criticality index, and probability of finishing the project on time. The probability of completing the project on time is 0.28 and 0.33, using the nonlinear-integer programming framework and the stochastic framework, respectively, as compared to 0.23 in the deterministic scenario. Five examples, selected from literature, are solved via the two proposed frameworks. Overall, the results of the examples used to validate the developed frameworks have justified them as valid, time-saving and reliable methods against float loss oriented risks. The results are significant and allow project managers to exercise new tradeoffs between time, cost and flexibility. This will ultimately improve the chances of achieving project objectives. | en_US |
