Various factors must be considered when running a courier service in an urban area, because the infrastructure of a city differs from those in suburban or countryside areas. Of note, population density is higher, and vehicles encounter greater restrictions. Moreover, air pollution from fossil fuel combustion is more severe. As tailpipe emissions are becoming costly to both corporations and the environment, researchers are increasingly exploring more appealing transportation options. Electric bicycles have become an important mode of transportation in some countries in the past decade. Electric bicycles and automobiles have their respective merits and demerits when used to provide courier services. E-bikes in particular can ply their trade in densely packed areas that are off-limits to cars and trucks. This paper focuses on (1) developing a truck–bike mixture model to reduce operating costs for an existing truck-only service by replacing some of the trucks with bicycles, and (2) exploring the resulting effects in terms of reducing overall carbon emissions. Data from one of the major courier companies in South Korea were utilized. The problem was tackled as a heterogeneous fleet vehicle routing problem using simulated annealing because the actual size of the problem cannot be solved directly with a mathematical approach. The most effective fleet mix was found for the company’s case. Effects on operating costs and reduced emissions were analyzed for 15 different scenarios with varying demands and off-limits areas. Computational results revealed that the new model is viable from economic and sustainability standpoints. They indicated that costs decrease to varying degrees in all scenarios, and that carbon emissions also decrease by around 10% regardless of the selected scenario.

In Southeast Asian cities, it is common for logistic companies to operate a heterogeneous fleet of delivery vehicles with motorcycles being the preferred vehicle to handle the final phase of delivery. In such scenarios, heterogeneous fleet vehicle routing problem (HFVRP) is generally applied to plan an optimal delivery. However, in many downtown cores of large and rapidly developing Southeast Asian cities, HFVRP is neither viable nor reliable because of road usage restrictions. The purpose of this article is to develop and test a different approach that accurately takes these restrictions into account and provides viable and more sustainable results. Restrictions in this paper refer to situations of urban areas in Vietnam where (i) certain vehicle types are prohibited in specified areas or where narrow alleyways limit the utilization of vehicles that exceed the road capacity and (ii) certain roads are exclusive to certain vehicle types. In networks, limited access and exclusive lanes are represented as links, or arcs, exclusive to one or another. Taking these limitations into consideration, we have developed a unique model, which we have termed Vehicle Routing Problem with Exclusive Links (VRP-EL). The model was validated and tested for its performance on scenarios with varying ratios of exclusive links. Scenarios up to 500 customers were tested on a meta-heuristic algorithm, simulated annealing. VRP-EL produces realistic outcomes. Limiting certain links to be selected according to vehicle types increases overall travel distance. However, this increase outweighs the cost of re-planning and rerouting had they not been constrained initially. The reduction in traveling distance leads to fossil fuel reduction for the overall system. The estimation of reduced carbon emissions through applying the proposed model is presented. Considering the severe traffic congestion and carbon emissions caused by motorcycles in Vietnam, the proposed model leads to a sustainable road environment.

Despite their importance, relatively little attention has been paid to vehicle routing problems with asymmetric costs (ACVRPs), or their benchmark instances. Taking advantage of recent advances in map application programming interfaces (APIs) and shared spatial data, this paper proposes new realistic sets of ACVRP benchmark instances. The spatial data of urban distribution centers, postal hubs, large shopping malls, residential complexes, restaurant businesses and convenience stores are used. To create distance and time matrices, the T map API, one of the most frequently used real time path analysis and distance measurement tools in Korea, is used. This paper also analyzes some important issues prevailing in urban transportation environments. These include the challenges of accounting for the frequency and distance in which air travel differs from reality when measuring closeness, the differences in distance and time for outgoing and return trips, and the rough conversion ratios from air distance to road distance and to road time. This paper contributes to the research community by providing more realistic ACVRP benchmark instances that reflect urban transportation environments. In addition, the cost matrix analyses provide insights into the behaviors of urban road networks.

The vehicle routing problem (VRP) attempts to find optimal (minimum length) routes for a set of vehicles visiting a set of locations. Solving a VRP calls for a cost matrix between locations. The size of the matrix grows quadratically with an increasing number of locations, restricting large-sized VRPs from being solved in a reasonable amount of time. The time needed to obtain a cost matrix is expensive when routing engines are used, which solve shortest path problems in the back end. In fact, details on the shortest path are redundant; only distance or time values are necessary for VRPs. In this study, an artificial neural network (ANN) that receives two geo-coordinates as input and provides estimated cost (distance and time) as output is trained. The trained ANN model was able to estimate with a mean absolute percentage error of 7.68%, surpassing the quality of 13.2% with a simple regression model on Euclidean distance. The possibility of using a trained model in VRPs is examined with different implementation scenarios. The experimental results with VRPs confirm that using ANN estimation instead of Euclidean distance produces a better solution, which is verified to be statistically significant. The results also suggest that an ANN can be a better choice than routing engines when the trade-off between response time and solution quality is considered.