| Abstract |
Repetitive construction projects are those projects which involve multiple similar units and a set of activities that is repeated in these units. They constitute a considerable percentage from all construction projects. Optimization of this type of projects is crucial for the project to succeed and to achieve its objectives. The aim of this research is to develop a dynamic optimization model that can be run in single-objective mode to minimize either project cost, or project duration; or can be run in multi-objective mode to simultaneously minimize the total project cost, overall project duration, and total interruption time altogether. This research was divided into two major phases. First, performing a comprehensive state-of-the-art review for previous repetitive projects optimization, upon which project schedulers and researchers can rely to identify the main features, implications, strengths, and shortcomings of the previous optimization methods. This review has also identified the major parameters and considerations which may affect repetitive project scheduling. A quantitative analysis for the literature is also presented to provide support for future researchers in understanding the major implications and methodologies of the previous optimization models. Second, a novel heuristic multi-objective optimization model has been developed. The model considers several constraints and parameters such as: multiple number of crews for each activity; multiple construction methods for each activity; the learning effect for the crews; soft logic between activities; allowing limited work interruption to any activity; all types of construction costs such as direct cost, indirect cost, delay damages, and early completion incentives; all types of precedence relationships such as FS, SS, FF, and SF; and any number of predecessor and successor activities. The new model has been applied to three case studies. Two case studies were solved to validate the effectiveness and the applicability of the model against the models published in the literature and to explore some other features of the proposed model. The third case study was solved to explore the full features and capabilities of the proposed model and to explore the impact of the considered parameters such as learning effect and interruption allowance on the project duration and cost. The proposed model gave more accurate solutions, and the results proved the efficiency and effectiveness of the developed model in optimizing repetitive construction projects. In addition, the unique features of the model ensure its applicability in optimizing and scheduling real construction repetitive projects. Furthermore, integrating the learning effect, soft logic, and limited work interruption allowance in the optimization process reduced the projects duration and cost significantly, giving more accurate and reliable solutions. On the basis of this work, implications and guidelines for future research are also addressed to enhance repetitive projects optimization and to cover the current unresolved problems. This research is believed to benefit both researchers and practitioners in solving multi-constraint multi-objective repetitive projects optimization problems. |
