International Journal of Science, Engineering and TechnologyAuthors: Gawai Santosh Bhaskar, Shashikant B. Dhobale
Abstract: Accurate cost estimation and effective cost control are critical challenges in highway construction tendering and bidding processes. Traditional estimation methods often rely on manual calculations and historical averages, which may lead to inaccuracies due to market fluctuations, negotiation variability, and complex project parameters. This research proposes a hybrid optimization framework integrating Regression Analysis and Genetic Algorithm (GA) to enhance prediction accuracy and optimize Total Contract Cost in highway construction projects. Initially, a regression-based predictive model is developed to estimate total contract cost using key influencing parameters such as material cost, labor cost, equipment cost, and negotiation factors. Subsequently, a Genetic Algorithm is applied to minimize the predicted cost and determine the optimal combination of decision variables under defined constraints. The model successfully generates multiple Pareto optimal solutions, providing flexible trade-off options for strategic decision-making. The MATLAB simulation results demonstrate that the proposed hybrid GA–Regression model effectively identifies the optimized total contract cost and enhances negotiation strategy performance. The findings highlight the practical importance of intelligent optimization techniques in improving bidding competitiveness, minimizing financial risk, and strengthening cost control mechanisms in highway construction projects. This research contributes a data-driven, optimization-based decision support framework suitable for modern tendering practices.
