Multi-Objective Optimization in Pavement Management Systems
Summary
Multi-objective optimisation in pavement management systems employs computational methods to balance competing goals such as minimising agency expenditure, reducing user delay and vehicle operating costs, extending pavement service life and mitigating environmental impacts. Traditional single-goal approaches often fail to capture the complex trade-offs inherent in large road networks under budgetary and technical constraints. By generating a set of Pareto-optimal solutions, decision makers can explore alternative maintenance and rehabilitation strategies that jointly consider pavement condition indices, life-cycle costs and traffic disruption. Recent advances have introduced interactive frameworks that engage stakeholders in refining preferences, dynamic models that account for changing traffic patterns over the asset life-cycle, and integer programming formulations that scale to urban and regional networks. These methods support transparent prioritisation of interventions, enable scenario testing under varying budgetary levels and inform sustainable policies worldwide.
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Multi-Objective Optimization in Pavement Management Systems publication trend
The graph below shows the total number of articles in multi-objective optimization in pavement management systems across all publications each year (not limited to Nature Index journals).
Technical terms
Multi-objective optimisation: Simultaneous optimisation of two or more conflicting objectives to identify trade-off solutions.
Pareto frontier: Set of non-dominated solutions where no objective can be improved without degrading another.
Pavement Management System (PMS): Framework for planning and executing maintenance and rehabilitation of road networks.
Pavement Condition Index (PCI): Quantitative measure of pavement surface quality used to assess maintenance needs.
Life-Cycle Cost Analysis (LCCA): Economic evaluation of pavement interventions over their service life, including construction, maintenance and user costs.
Stochastic User Equilibrium (SUE): Traffic assignment model in which users choose routes based on perceived travel costs, capturing network responses to maintenance actions.
Integer programming: Mathematical optimisation technique using discrete decision variables to select maintenance actions under constraints.
References
- Planning urban pavement maintenance by a new interactive multiobjective optimization approach. European Transport Research Review (2019).
- Multiobjective Optimization for Pavement Network Maintenance and Rehabilitation Programming: A Case Study in Shanghai, China. Mathematical Problems in Engineering (2020).
- Incorporating Dynamic Traffic Distribution into Pavement Maintenance Optimization Model. Sustainability (2019).
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