Balancing time and cost in the construction of public investment projects using multi-objective optimization algorithms

Abstract

In Vietnam today, public investment plays a crucial role in driving growth and will remain the primary driver for achieving double-digit growth targets in the 2026-2030 period. For construction projects using public investment capital, balancing construction time and investment costs is becoming a critical issue in project management, rather than focusing solely on cost as in the past. These two objectives have a complex trade-off relationship, requiring effective optimization methods from the planning stage. This study proposes a new approach based on the multi-objective symbiotic search optimization (MOSOS) algorithm to simultaneously optimize time and cost in public investment projects. The model is constructed to determine the optimal Pareto solution set, clearly reflecting the trade-off relationship between the two objectives. The proposed method is applied to a real construction project and compared with common multi-objective evolution algorithms, including swarm multi-objective and non-dominant genetic sorting algorithms. Experimental results show that MOSOS is capable of efficiently searching and generating high-quality Pareto solution sets, contributing to improved time-cost optimization in public investment project management.