An advantage of the TSDSM when planning traffic infrastructure is
its geostatistical analysis, which adds new value to the decision making
process and evaluation of problems connected with urban infrastructure.
Another advantage of the TSDSM is its processing of group decisions
in the process of selecting vehicle routes, since this model takes
into account the evaluation of parameters by a large number of decision
Min.
noise
Min.
CO
2
Min.
NO
x
Min
SO
x
Fuel
consumption
Kara et al. (2007) * * Iterative Decomposition, Heuristic
Maden et al. (2009) * * Parallel insertion algorithm, Heuristic
Kuo (2010) * * * Simulated annealing algorithm
Figliozzi (2010)
Küçükoglu et al. (2013)
Lang et al. (2014)
** Mixed integer linear programming model
Suzuki (2011) * Transformation into a Mixed Integer Linear Program
Bektas and Laporte (2011) * * * Emission and fuel consumption calculation model
Jaber et al. (2012) * * NSGA II algorithm
Lin and Ng (2012) * * Tabu search algorithm with Monte Carlo bounding
techniques
Xiao et al. (2012) ** Simulated annealing algorithm
Erdogan and Miller-Hooks
(2012)
* * Modified Clarke-Wright algorithm, Density-Based
Clustering Algorithm, Heuristic
Adiba et al. (2013) * * Hybrid ant colony system
Gaur et al. (2013) ** Iterative-Optimization-Assignment Algorithm
Kwon et al. (2013) * * * Mixed integer-programming model, Tabu search
makers. This kind of approach becomes increasingly significant when
taking into account the fact that in the future we can expect that transport
companies (especially in countries with developed industry) will
have a growing number of vehicles with reduced exhaust emissions in
their fleets.
An advantage of the TSDSM when planning traffic infrastructure isits geostatistical analysis, which adds new value to the decision makingprocess and evaluation of problems connected with urban infrastructure.Another advantage of the TSDSM is its processing of group decisionsin the process of selecting vehicle routes, since this model takesinto account the evaluation of parameters by a large number of decisionMin.noiseMin.CO2Min.NOxMinSOxFuelconsumptionKara et al. (2007) * * Iterative Decomposition, HeuristicMaden et al. (2009) * * Parallel insertion algorithm, HeuristicKuo (2010) * * * Simulated annealing algorithmFigliozzi (2010)Küçükoglu et al. (2013)Lang et al. (2014)** Mixed integer linear programming modelSuzuki (2011) * Transformation into a Mixed Integer Linear ProgramBektas and Laporte (2011) * * * Emission and fuel consumption calculation modelJaber et al. (2012) * * NSGA II algorithmLin and Ng (2012) * * Tabu search algorithm with Monte Carlo boundingtechniquesXiao et al. (2012) ** Simulated annealing algorithmErdogan and Miller-Hooks(2012)* * Modified Clarke-Wright algorithm, Density-BasedClustering Algorithm, HeuristicAdiba et al. (2013) * * Hybrid ant colony systemGaur et al. (2013) ** Iterative-Optimization-Assignment AlgorithmKwon et al. (2013) * * * Mixed integer-programming model, Tabu searchmakers. This kind of approach becomes increasingly significant whentaking into account the fact that in the future we can expect that transportcompanies (especially in countries with developed industry) willhave a growing number of vehicles with reduced exhaust emissions intheir fleets.
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