Abstract

Proper planning is a key factor in all business endeavors. This is especially important in the construction industry as a project is sensitive to countless factors that affect the project's time and cost. This created the need for agile and flexible schedules for close monitoring and dynamic planning for construction projects. The currently prevailing scheduling techniques are CPM and PERT, which professionals in the field widely use. These methods create static schedules that are vulnerable to any changes in the schedule logic, which often happens during construction projects. In addition, these Scheduling techniques do not capture the uncertainties and complex relationships well, which makes them susceptible to cost and schedule overruns. Usually, to overcome this, decision-makers develop a recovery plan that takes time and effort, and even these plans typically take tremendous effort to develop and implement. Instead, if decision-makers had access to a tool that provided them with different scenarios in order to prepare proper risk response techniques that provided dynamic responses to emergencies, this would save a lot of time and resources. In the literature, limited work was found to provide a scheduling method that considers different execution scenarios and the corresponding implications.

Hence, there is a need to develop an innovative method which can increase flexibility and provide adaptation and agility to schedules. The goal of this research is to develop a novel scheduling method based on conditional relationships and stochastic inter-activity associations, which tackles the shortcomings of current deterministic and limited stochastic scheduling techniques. This research utilizes the discrete event simulation on AnyLogic software to model the behavior of activities in stochastic networks to determine the overall project completion. In this model, each activity is simulated by an agent that has certain parameters duration, probability of occurrence, predecessor, and successor. Those agents are then evaluated in the constructed network and this process is repeated for 100 runs. The findings of the model have been compared against the findings of another simulation technique using a case study for historical rehabilitation. While other stochastic models estimated the project duration to be 24.14 days and deterministic models estimated 20.55 days, the model developed in this study estimated a project completion time of 26.4 days, which is the closest to the actual project duration of 35 days.

This research has various potential applications, both strategic and project-specific. Strategically, they can offer top management a decision support tool, supplying sufficient information for making long-term strategic decisions and preparing for different scenarios. At the project level, this tool enables project managers to simulate the complexities of construction projects, which often necessitate a proactive approach. This allows project managers to anticipate different scenarios from the project's outset and develop mitigation plans accordingly.

School

School of Sciences and Engineering

Department

Construction Engineering Department

Degree Name

MS in Construction Engineering

Graduation Date

Fall 1-31-2025

Submission Date

9-11-2024

First Advisor

Ibrahim Abotaleb

Committee Member 1

Khaled Nassar

Committee Member 2

Dina Atef

Committee Member 3

May Haggag

Extent

78p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Not necessary for this item

Download

Share

COinS