Fuzzy clustering opposition multi-objective forensic-based investigation to tradeoff construction project time cost under limited resource

Abstract

Balancing the critical factors of project time and cost is essential for maximizing the overall benefits of construction projects. In construction scheduling, challenges often arise due to the varying start times of activities based on precedence relationships and resource availability. Moreover, the cost and duration of activities can fluctuate based on resource allocation. This paper presents a novel framework named Fuzzy Clustering Opposition Multi-Objective Forensic-Based Investigation (FOMOFBI) for solving the construction project time cost tradeoff under limited resource. The proposal algorithm utilizes opposition-based learning at initialization step and during optimization process to enhance exploration capabilities. Integration of fuzzy c-means clustering into the FBI framework aids in accelerating convergence by leveraging population information. A real-world construction case study illustrates ability of FOMOFBI to generate non-dominated solutions, assisting project managers in selecting suitable plans to balance project time and cost within resource limitations, a task typically challenging and time-intensive. In all evaluations, FOMOFBI consistently outperformed other multi-objective evolutionary algorithms, offering top-tier solutions. This evidence strongly suggests that FOMOFBI is well-suited for addressing intricate optimization challenges in real-world contexts.