An eco-friendly Decision Support System for last-mile delivery using e-cargo bikes

Abstract

Real-time information and software support systems are crucial points for performing efficient logistics operations. Recently, most of the logistics companies have been using green-logistics solutions that encourage the use of eco-friendly vehicles, especially cargo bikes. However, for evaluating logistics' business performance, driver's exposure to emissions has often been neglected. Therefore, we proposed a Decision Support System (DSS) that considers, on the one hand, the efficiency of logistics performance and, on the other, the possibility of e-cargo bike drivers to choose the optimal route path considering two options, such as minimum travel time and minimum emission exposure. We applied the proposed DSS in a numerical application that evaluates the customer's assignment to an e-cargo bike according to the hourly traffic flows and emissions. We developed a dynamic algorithm that evaluates the path choice comparison between two route options. The choice of the minimum emission path compared with the shortest travel time path leads to a slight increase in the total travel time. The final path choice, according to the driver's opinion, was obtained using the Fuzzy Inference System (FIS). Moreover, the proposed DSS serves as a general framework for a decision-making process that could be applied to various two-wheels light-duty vehicles for last-mile delivery.