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.

This paper suggests a novel methodology in collision-free shortest path planning (CFSPP) problems for mobile agents (MAs) using a method that combines a genetic algorithm (GA) and a direction factor toward a target point. In the CFSPP problem, MAs find the shortest path from the starting point to the target point while avoiding certain obstacles. The paper proposes an obstacle-based search methodology that identifies critical collision-free points adjacent to given obstacles. When critical obstacles are found via CFSPP, this study suggests favorable paths in 2-dimensional space found using the obstacle-based GA (OBGA). The OBGA has four advantages. First, it effectively narrows the search spaces compared to free space-based methodologies. It also determines shorter collision-free paths, and it only requires a short amount of time. Finally, convergence occurs more quickly than in previous studies. The proposed method also works properly in larger and more complex environments, indicating that it can be applied to more practical problems.