Modeling Home Grocery Delivery using Electric Vehicles and Transport Network Analysis Results

This paper presents transportation network analysis results based on data from an agent-based simulation study. The research is aimed at establishing whether a fleet of electric vans with different charging options can match the performance of a diesel fleet. The researchers describe a base model imitating the operations of a real-world retailer using agents. They then introduce electric vehicles and charging hubs into their model. After that, they evaluate how the use of electric vehicles, charging power, and charging hubs influence the retailer’s operations. The simulation experiment suggests that, though they are useful, technological interventions alone are not sufficient to match the performance of a diesel fleet. Hence, reorganization of the urban delivery system is required in order to reduce carbon emissions significantly.

The use of Electric LGVs is very important because significant decreases in carbon emissions cannot be achieved by using diesel vehicles. Agent-based simulation is chosen as one of the approaches in this project because of the ill-defined nature of the problem, and the difficulty of isolating one element of the system from the others. For example, the changes in the retailer’s performance will affect customers’ perceptions, and eventually affect their behavior when placing orders to the retailer. In addition, an ill-defined problem can have many correct answers. Nowadays, agent-based simulation combined with transportation network analysis has been recognized as an appropriate approach to model human behaviors and to explore potential interventions to improve a system’s performance.

In this paper, the researchers first propose an agent-based simulation that can imitate the operations of one of the major home grocery retailers in the Manchester area. Iteratively, they introduce EVs and opportunity charging hubs into our modeling. Finally, they evaluate how these technologies influence the target retailer’s operations and propose further modeling steps.

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