Automated Allocation of Designers to Construction Projects: A Multi-Phase Multi-Skill Approach

Abstract

The design phase of construction projects significantly influences subsequent stages, including construction, operation, and maintenance. While previous studies have examined design project’s performance individually, real-world practice often involves managing multiple concurrent projects. Such multi-project environment introduces challenges like coordinating overlapping timelines, aligning designers' varied skill levels to diverse task requirements, and managing dependencies and scope changes across project’s phases. While resource management has been extensively studied in construction, the specific complexities of resource allocation during the design phase, particularly within multi-project, multi-phase, and multi-grade environments, remain insufficiently addressed in current research. This thesis addresses this gap by developing an optimization-based scheduling tool specifically designed for multi-project, multi-phase, multi-grade resource management. The tool integrates key factors such as grade-specific skills, phase sequencing, and multi project coordination. Using Design Science Research (DSR) as the methodology, the tool was iteratively improved through continuous stakeholder’s feedback. Through this feedback, novel features were also added to increase the practical feasibility of the tool, including grade cascading, workload smoothing, and inter-phase client review delays. The scheduling tool is implemented in Python using Mixed-Integer Linear Programming (MILP) model to automate designer allocation, with the objective of minimizing overall project cost. The tool was validated through a series of controlled experiments and a real-world application within a leading Lebanese engineering firm. The results demonstrate the model's practical applicability, its potential to reduce manual planning efforts, and its capacity to improve both cost efficiency and scheduling accuracy. By providing an adaptable, user-oriented decision-support system, this research contributes to bridging the gap between theoretical resource optimization and the practical demands of design project management. Moreover, the tool enables managers to perform scenario-based analyses, supporting more informed and proactive decision-making in dynamic, multi project design environments.