A novel adaptive stochastic model for projects scheduling under uncertainty

Abstract

Project scheduling faces high uncertainty due to fluctuating productivity, weather disruptions, resource shortages, and rework. Existing models such as CPM, PERT, and deterministic optimization lack the capability to dynamically adapt to real-time disturbances. This paper proposes a novel Adaptive Stochastic Resource-Constrained Scheduling Model (ASRCSM) that integrates (i) stochastic task durations, (ii) dynamic resource allocation rules, and (iii) an adaptive re-scheduling mechanism triggered by project state deviations. In addition, this study proposes a novel Adaptive Hybrid Genetic Algorithm (AHGA) which combines Serial Schedule Generation Scheme and local search heuristics to solve the problem. The approach provides a practical, computationally efficient tool for construction planners facing uncertainty.