A Multi-objective Optimization Model for Intelligent Information Management in Resource-Constrained Road and Transportation Projects

This study proposes a mathematical model to optimize information management in road and transportation contracting projects under resource constraints, utilizing Critical Chain Project Management (CCPM). A multi-objective optimization framework is developed for multi-project scheduling, balancing time, cost, and quality as core performance indicators. The Particle Swarm Optimization (PSO) algorithm is employed to solve the model due to its efficiency in handling complex, nonlinear problems. The model is validated through a case study of three real-world projects encompassing ۲۴ activities with diverse resource constraints. Compared to the traditional Critical Path Method (CPM), the CCPM-based approach reduces total duration from ۶۸ to ۴۷ days, total cost from ۱۰۸,۶۸۰ to ۶۳,۷۱۰ units, and improves the quality index from ۰.۴۱۲ to ۰.۵۷۶۴, achieving a ۲۱-day time saving, ۲۵.۹۷% cost reduction, and ۴۰% quality improvement. Sensitivity analysis reveals that resource availability, particularly resources K۲ and K۳, significantly impacts outcomes, with K۲ limitations increasing duration to ۸۴ days and cost to ۸۸,۱۱۰ units, and K۳ limitations extending duration to ۸۱ days and cost to ۸۶,۹۱۰ units. These findings demonstrate the model’s effectiveness in enhancing decision-making and resource planning in complex construction environments.

Keywords:

Project information management , Critical chain , Resource constraints , Particle Swarm Optimization , Multi-project scheduling