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Semiconductor hookup construction (i.e., constructing process tool piping systems) is critical to semiconductor fabrication plant completion. During the conceptual project phase, it is difficult to conduct an accurate cost estimate due to the great amount of uncertain cost items. This study proposes a new model for estimating semiconductor hookup construction project costs. The developed model, called FALCON‐COST, integrates the component ratios method, fuzzy adaptive learning control network (FALCON), fast messy genetic algorithm (fmGA), and three‐point cost estimation method to systematically deal with a cost‐estimating environment involving limited and uncertain data. In addition, the proposed model improves the current FALCON by devising a new algorithm to conduct building block selection and random gene deletion so that fmGA operations can be implemented in FALCON. The results of 54 case studies demonstrate that the proposed model has estimation accuracy of 83.82%, meaning it is approximately 22.74%, 23.08%, and 21.95% more accurate than the conventional average cost method, component ratios method, and modified FALCON‐COST method, respectively. Providing project managers with reliable cost estimates is essential for effectively controlling project costs.

Construction activities are often influenced by factors such as weather, labor, and site conditions. When several activities are influenced by the same factor, their durations may be correlated. If many activities along a path are correlated, the variability of path duration will increase, possibly increasing the uncertainty of completing the project by a target date. This paper presents the simulation-based model NETCOR (NETworks under CORrelated uncertainty) to evaluate schedule networks when activity durations are correlated. Based on qualitative estimates of the sensitivity of each activity to each factor, uncertainty in an activity's duration distribution (grandparent) is distributed to several factor subdistributions (parents). Each subdistribution is broken down further into a family of distributions (children), with each child corresponding to a factor condition. Correlation is captured by sampling from the same-condition child distributions for a given iteration of the simulation. NETCOR integrates the effect due to each factor at the path level. Awareness of the factors to which a path is sensitive can provide management with a better sense of what to control on each path, particularly on large projects.