Expert-Based Schedule-Risk Modeling in Parcel-Scale Urban Regeneration: An Assumption-Sensitivity Analysis in the Context of Türkiye
Year: 2026
Type: article
Abstract: Parcel-scale urban regeneration combines administrative, legal, stakeholder-related, and construction processes with uncertain durations. This study applies deterministic and probabilistic scheduling to a standardized hypothetical project in the context of Türkiye and examines the sensitivity of schedule-risk outputs to expert aggregation and modeling assumptions. A 25-activity network was parameterized using minimum, most likely, and maximum duration estimates from 50 experts. The baseline combined deterministic scheduling with a 10,000-iteration Monte Carlo simulation (MCS) in Powerproject; independent 100,000-iteration analyses varied aggregation, three-point distributions, activity overlap, and positive within-process dependence. Expert estimates showed substantial concordance (Kendall’s W = 0.756–0.811) despite a median coefficient of variation of 0.417 for most-likely durations. The most-likely-value deterministic duration was 583 days; Powerproject produced P50 = 689 days, whereas the independent beta-PERT reference produced P50 = 652 days. Across assumption-sensitivity specifications, P50 ranged from 632 to 717 days. Deterministic expected-duration references were 649 days for beta-PERT and 713 days for triangular inputs, showing that much of their P50 difference reflected different implied central tendencies. Activity-sensitivity rankings were comparatively stable under aggregation, distribution, and overlap changes but diverged under positive dependence. Reported schedule outputs are conditional on expert inputs, network structure, and modeling assumptions rather than externally validated forecasts.
Source: Buildings
Authors: Oktay Tekin, Serhat Başdoğan
Institutions: Kırklareli University, Yıldız Technical University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7225634431
