An integrated multi-criteria decision analysis and optimization modeling approach to spatially operational road repair decisions

In this study, we developed a generic cost-effective approach for spatially explicit decision support involving the allocation of road repair treatments. The approach begins with an assessment of the existing road condition to identify the extent of environmental impacts and to determine road repair regimes in a subjective manner using group-decision making efforts. An integer programming model is then formulated by combining expert opinions with operational costs to guide repair schedules required for each road segment at the operational planning level. To demonstrate model performance, we applied it to a 400 km(2) landscape consisting of 289 km of paved roads in the mountainous region of the Hyrcanian forests in Iran. We assessed sensitivity of the inputs, such as weight verification, budgetary limitations, and rehabilitation weights. The results of the subjective analysis show that 76% of the roads analyzed in these forests must be prioritized to receive treatments as intended for logistical purposes. Incorporating the extent of environmental dimensions into operational costs allows us to generate an optimal tradeoff curve by selecting an appropriate treatment for segments of a road network. The approach demonstrated here can be used to design detailed alternative solutions for addressing spatially-informed road decisions under various terrain conditions.

Automated summary

This study presents a generic and cost-effective approach for spatially explicit decision support regarding the allocation of road repair treatments. By formulating an integer programming model that combines expert opinions with operational costs, repair schedules for each road segment are determined. Sensitivity analysis reveals that 76% of roads in the Hyrcanian forests need prioritized treatment. Incorporating environmental dimensions into operational costs enables the generation of an optimal tradeoff curve for road network segments.