Paper Title
Enhancing Flexibility In Construction Schedules With Adaptive, Scenario-Driven Modeling

Abstract
Effective planning is crucial in all business ventures, particularly in the construction industry, where numerous factors influence project timelines and costs. This has led to the need for agile and adaptable scheduling methods to enable continuous monitoring and dynamic planning. Currently, the most widely used scheduling techniques in the field are the Critical Path Method (CPM) and the Program Evaluation and Review Technique (PERT). However, these methods generate rigid schedules that are highly susceptible to disruptions due to changes in schedule logic, which frequently occur in construction projects. A more efficient alternative would be a tool that presents multiple scenarios, allowing project teams to implement proactive risk response strategies. Existing research offers limited scheduling approaches that consider various execution scenarios and their potential impacts. This study attempts to develop ascheduling tool that integrates conditional relationships and stochastic activity interactions, addressing the limitations of traditional deterministic and stochastic models. Using discrete event simulation, this research models activities within stochastic networks to estimate overall project completion times. Each activity in the model is represented as an agent with defined parameters, including duration, probability of occurrence, and dependency relationships. The model's findings were compared to other simulation techniques using a historical rehabilitation case study. While existing stochastic models estimated a project duration of 24.14 days and deterministic models predicted 20.55 days, the proposed model estimated a completion time of 26.4 days—closer to the actual project duration of 35 days.This research has significant strategic and project-specific applications. From a strategic perspective, it provides management with a decision-support tool, offering valuable insights for long-term planning and risk management. At the project level, it enables managers to simulate the complexities of construction projects, allowing them to anticipate potential scenarios and implement mitigation strategies. Keywords - Construction Planning, Stochastic Planning, Scenario Analysis