Achieving optimal contractor selection: an AI-driven particle swarm optimization method

Abstract

Contractor selection plays a vital role in project management, where factors such as cost, quality, and time must be carefully considered. This study presents an innovative approach to optimize contractor selection using an AI-driven method based on Particle Swarm Optimization (PSO). The objective is to achieve the best possible selection of contractors by considering multiple criteria simultaneously. Real-world data on cost estimates, quality scores, and project times are collected and normalized for fair comparison. The PSO algorithm is utilized to search for the optimal combination of contractors that minimizes cost, maximizes quality, and minimizes project time. The proposed weighted objective function evaluates the performance of each contractor based on the selected criteria. The results demonstrate the effectiveness of the AI-driven PSO method in achieving optimal contractor selection. The findings highlight the potential of using AI techniques for decision-making in project management, enabling project stakeholders to make informed and data-driven contractor selection decisions. This research contributes to the growing body of knowledge on AI applications in project management and provides practical insights for project managers and stakeholders involved in contractor selection processes.