A Two-Stage Stochastic Model for Maintenance and Rehabilitation Planning of Pavements

Table 1

Comparison of applied methodologies in investigated studies.

Study	Optimization Method			Level of Study		Formulation	Model Type	Condition Indicator
	MuOb	SiOb	Others	Net	Pro
Fwa et al. [15]	√			√		GA	Det	PSI
Fwa et al. [16]	√			√		GA	Robust	PCI
Fwa et al. [17]	√			√		GA	Robust	PCI
Chen and Flintsch. [14]			√		√	LCCA	Fuzzy	PSI, PCI
Wu et al. [5]	√			√		GP & AHP	Det	Condition State
Wu and Flintsch. [22]	√			√		MDP	Prob	Condition State
Moazami et al. [13]			√	√		AHP	Fuzzy	PCI
Mathew and Isaac. [10]	√			√		GA	Det	PCI
Meneses and Ferreira. [9]	√			√		GA	Det	PSI
Saha, and Ksaibati. [11]		√		√		LCCA	Det	PSI
Yepes et al. [21]		√		√		GRASP	Det	PCI
Swei et al. [23]		√		√		MINLP	Det & Prob	PCR
Current study	√			√		MILP	Det & Prob	q (qf, qt, qs, qr), qo

MuOb: multiobjective; SiOb: single objective; Net: network; Pro: project; Det: deterministic; Prob: probabilistic; PCI: Pavement Condition Index; PSI: present serviceability index; PCR: pavement condition rate; GA: Genetic Algorithm; LCCA: life cycle cost analysis; AHP: analytic hierarchy process; MDP: multidimensional problem; GRASP: greedy randomized adaptive search procedure; MINLP: Mix integer nonlinear programming; MILP: mix integer linear programming.