Improving E-Commerce Distribution through Last-Mile Logistics with Multiple Possibilities of Deliveries Based on Time and Location

Abstract

The rapid rise of electronic commerce has entailed an increase in logistic complexity, with last-mile logistics being the most critical element in deliveries. Since users prefer goods to be delivered at home, one of the biggest challenges faced by e-commerce is to reduce the number of incidents that occur in the delivery of goods to the homes of customers. In many cases, these deliveries cannot take place because recipients are not at the agreed delivery point, leading to a decrease in the quality of service and an increase in distribution costs. Furthermore, sometimes the delivery policies are not in tune with the customers’ expectations. This work presents a new perspective of the last-mile logistics in the context of multichannel retail, asking customers to provide several delivery locations (at home, at work, at a familiar home, in a shop, in a locker, etc.) associated with different time windows. In addition, the customer could state their preferences about these locations. This work formulates the problem and develops different approaches to solve it. A benchmark is proposed to analyze the performance and limitations. The results reveal that a distribution policy with several locations can improve the efficiency of electronic commerce by reducing delivery costs. The findings of this study have several implications for distribution companies.

Modelling Consumers’ Preferences for Time-Slot Based Home Delivery of Goods Bought Online: An Empirical Study in Christchurch

“…On the other hand, there are only a few studies [49,50] that investigate consumers' or online shoppers' preferences for receiving OHDs or time-slot-based deliveries. Escudero-Santana et al [49] investigated customers' preferences for receiving deliveries at one of their preferred locations (e.g., at home, at work, at a familiar home, in a shop, or in a locker), each one associated with a different time slot for the delivery day. The results revealed that a distribution policy with several locations can improve the efficiency of electronic commerce by reducing delivery costs.…”

A Review of Literature on Vehicle Routing Problems of Last-Mile Delivery in Urban Areas

“…Customers provide multiple optional delivery points for pickup along with delivery time windows, and then the delivery plan chooses one of them. This delivery mode can reduce the number of visit locations, mitigate costly detours, and ultimately enhance customer satisfaction while simultaneously minimizing the environmental impact and overall distribution costs [126][127][128][129].…”

“…Delivery to an optional point is also named as covering options [130], covering locations [131], different delivery locations [127], and delivery options [132]. In general, as shown in Figure 6, customers specify their preferred delivery points.…”

Logistics Distribution Path Optimization and Application Based on Swarm Intelligence Optimization Algorithm

“…In recent years, with the rise of online shopping, more and more scholars have studied the logistics and distribution path optimisation problem and have achieved good results [5]. For example, experts such as Nilay Noyan conducted a case study of a retailer-logistics service provider partnership around learning between retailers and logistics service providers, in which learning is generated based on the interaction of absorption and desorption, which in turn relies on support between one and two parties, and the results found that learning can improve warehouse operational efficiency Alejandro Escudero-Santana et al constructed a distribution route optimisation model for low-carbon cold chain distribution based on customer satisfaction and combined the constraints to design a distribution model with minimum total cost and maximum satisfaction rate based on cost-benefit considerations, and tested the use of improved The effectiveness and accuracy of the improved ant colony algorithm were tested in an experimental simulation [6]. The research and application of logistics distribution path optimisation based on ant colony genetic algorithm is a very valuable research direction.…”