Several studies have implemented various pavement condition
prioritization techniques (which are summarized in Table 1), and inspected the efficiency
of time-cost optimization using genetic algorithms (GAs), fuzzy logical
control, integer programming, and ant colony algorithms 11-14. GAs and Particle-Swarm
Optimization (PSO) are excellent at pavement management analyses as well as managing
M&R activities 15,16.

Ouma et al. 17 explores a multi-attribute strategy to
determine pavement maintenance prioritization. The study contrasts the use of a
fuzzy analytical hierarchy process (AHP) with fuzzy Technique for Order
Preference by Ideal Situation (TOPSIS). A case study was conducted in Kenya and
it was observed that both fuzzy AHP and fuzzy TOSIS produced very similar
maintenance prioritization ranking. However, fuzzy TOPSIS results were more
accurate as fuzzy AHP as fuzzy AHP had a tendency to overestimate the ranking.

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Gao et al. 18 discussed the use of a parametric method for
pavement condition improvement and budget utilization simultaneously. The
method is tested on a real-world case study using data from the Dallas
District’s Pavement Management Information System. The results proved that the
parametric method can more efficiently solve the bi-objective pavement
maintenance and rehabilitation scheduling problem than other methods like the
weighting method. The parametric can produce the complete set of Pareto-optimal
solutions at a more efficient computing time per solution.

Babashamsi et al. 19 incorporates fuzzy analytic hierarchy
process (AHP) with the VIKOR method (a multi-criteria decision-making method
based on the ideal point technique) to determine the pavement prioritization
maintenance for various real-world alternatives. Numerous pavement network
indices like the pavement condition index (PCI), traffic congestion, pavement
width, improvement and maintenance costs, and the time required to operate were
considered. Fuzzy AHP was utilized to ascertain the weights of these indices
whereas the VIKOR model helped prioritize the ranking of the alternatives’.

Chang 20 successfully applied particle swarm optimization
(PSO) method to prioritize 135
pavement sections using eight pavement condition
parameters, which are standard deviation (SD) for smoothness, rutting, deflections, cracking, pothole, bleeding, patching, and

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