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3.2

Literature review

The increasing social concern for the environment and a sustainable growth in gen- eral requires the transformation of cities. In this context, the classical VRP may be enriched to include characteristics that allow the reduction of environmental and so- cial impacts in urban zones concerning transport activities. During the last decade, this problem has been complimented by a large number of variants including: the green VRP (green VRP) and the pollution routing problem (PRP). While the former is focused on the environmental impact caused by the fuel or energy consumption of transport, the latter takes into account the pollution and different emissions gen- erated. Thus, both problems analyze the emissions and fuel/energy consumption levels, which depend on traffic congestion, speed, acceleration, type of road, type of vehicle, and load, among other internal and external factors of the operation (Bektaş

and Laporte, 2011; Koc et al.,2014). Rich VRPs encompass special characteristics

from city logistics and smart cities, e.g., the integration of information technologies (IT) in transport operations or the inclusion of dynamism, stochasticity and other attributes related to the urban transport(Caceres-Cruz et al., 2015). The reader interested in a comprehensive review on the MDVRP is referred to Calvet et al.

(2016).

Regarding environmental impacts, the distance and vehicle weight play a cru- cial role in the fuel/energy consumption and carbon emissions. Thereby, Ubeda

et al. (2011) aimed at reducing transport costs and emissions by considering the

distance and some variations in the vehicle maximum capacity. It is concluded that enhancing load factors (which may be achieved by using heterogeneous fleets) is an efficient way to get significant savings and environmental benefits. The authors also discuss negative externalities of transport, such as noise, air pollution, congestion, accident rate, energy consumption, and land use. There are studies tackling the negative impacts from three different perspectives: negative externalities, emissions released, and fuel consumption. Faulin et al. (2011), Liu et al. (2014), and Zhang

et al. (2015) considered environmental indicators for the capacitated vehicle rout-

ing problem (CVRP). They affirm that the load variation defines fuel consumption and emissions caused by transport. Besides, the load variation influences the dis- tribution processes profitability. In this line, Kuo (2010), Demir et al. (2015), and

Xiao and Konak (2015) developed methodologies for the green heterogeneous VRP

(green HVRP). These authors considered traffic congestion, road gradients, speed variations, and distance traveled as variables that influence fuel consumption in ur- ban transport (Jabbarpour et al.,2015). More recently,Niknamfar and Niaki(2016) studied the MDVRP with time windows to optimize the customers-depots allocation and the vehicles selection, with the purpose of minimizing the environmental impact of the transport activity. They proved that a suitable allocation and coordination among stakeholders does not only reduce the negative impacts but also enhances the

40 The sustainable multi-depot vehicle routing problem total profit. Juan et al.(2014b) considered a supply chain with multiple suppliers for minimizing the empty trips and the travel distance in each route. They concluded that it is possible to reduce the CO2 emissions by 23% when the distribution process is carried out in collaboration with multiple suppliers. Wang et al. (2014) proposed the use of environmental criteria in order to reduce total operation costs. These authors developed an algorithm to integrate the economic and environmental goals based on the MDVRP with backhauls. Demir et al. (2015) considered the MDVRP with freight pick-up and delivery to ensure that any customer demand can be met from any depot, thus reducing the operation cost too.

Some studies have focused on the analysis of the environmental impact caused by transport activities in urban zones. However, there is no estimation of the real impact of these activities. About 60% of transport activities take place in urban regions, which concentrate around 80% of the population in some countries (Euro-

pean Commission, 2015). Social impact refers to health problems and other factors

such as quietness, air quality, urban aesthetic, accessibility, and urban safety. The associated social costs are estimated through penalties, taxes, or willingness to pay. According to some studies, about 0.4%, 0.2%, 1.5%, and 2% of the GDP is re- lated to air pollution problems, noise, accidents, and traffic congestion, respectively

(Caceres-Cruz et al.,2015). Therefore, the sustainability concept has started to take

part in the decision-making process. However, there is a lack of structured tools that allow the integration of the three dimensions and provide support to decision-makers

(Chen et al., 2013).

There are only a few works analyzing sustainability criteria in freight transport.

Chibeles-Martins et al.(2016) pose ecological criteria to determine an optimal struc-

ture of distribution networks. They solved a bi-objective problem focused on de- termining the suitable locations, capacities, and attributes in factories, warehouses, and a distribution center. The solution method is based on the simulating anneal- ing metaheuristic framework, and Pareto optimality is considered to get a balance between economic and ecological concerns. In the same sense, Zhang et al. (2016) implement evolutionary algorithms to determine the optimal design of supply chains considering two possible scenarios: in the first one the transport is outsourced, while in the second one the transport is leased. It is a multi-objective problem aimed at minimizing CO2 emissions, fine dust, and costs. The authors implement the non- dominated sorting genetic algorithm-II (NSGA-II) as well as the strength Pareto evolutionary algorithm 2 (SEAP2). Both methods take into account Pareto op- timality through a scalarization method computed by a weighted sum. Similarly,

Kadziński et al. (2017) define a sustainable objective to design an optimal distri-

bution structure considering a supply chain with multi-distribution channels. The considered objectives are two: maximizing customer coverage, and minimizing cost and environmental impacts.