The objective of study is to develop a computationally efficient decision-support model to generate actionable maintenance plans under strict budgetary and performance constraints. This study addresses the critical challenge of resource allocation optimization for pavement maintenance in regional road networks, focusing on the inherent conflict between minimizing agency cost and maximizing network quality under stringent budget and safety constraints.
The study develop an optimization framework using multi-objective particle swarm optimization (MOPSO) specifically adapted for discrete maintenance variables. To ensure rigorous adherence to operational constraints, the external penalty function (EPF) method was integrated into the algorithm to strictly manage non-negotiable budget caps and mandated minimum quality levels. The model was applied to a case study of N = 25 intercity road sections in Northwest Iran, utilizing localized unit maintenance costs (denominated in Toman/m2) and an expert-defined quality grading system. Analysis across eight distinct management scenarios – covering weak, medium and high initial quality networks under varying budget levels – was conducted to generate actionable Pareto optimal fronts.
The results demonstrated a clear non-linear relationship: networks with a weak initial quality (Qavg = 0.47) required the maximum budget (up to 32.44 Billion Toman) to achieve a final cumulative quality of 22.37. In contrast, higher-quality networks (Qavg = 0.67) achieved a superior final quality of 24.15 with a substantially lower budget (as low as 24.73 Billion Toman). This quantitatively confirms that a proactive maintenance strategy focused on preserving high-quality assets yields a superior cost-efficiency and return on investment. The study's principal contribution is providing a robust, fast-acting, and constraint-compliant decision-support tool that bridges the research gap between sophisticated MOO (multi-objective optimization) theory and practical, localized pavement management under conditions of financial austerity in developing economies.
The core methodological innovation lies in the explicit application of the EPF method to strictly enforce the non-negotiable budget cap and a mandated minimum quality level (Qmin). This rigorous constraint handling transforms the complex multi-objective problem into an unconstrained form, using a squared penalty for quality violations to prioritize network integrity.
