Calculating Total Delivery Times from Service Time Distribution
This study aims to improve the efficiency of last-mile delivery by quantifying service time and estimating delivery time based on service time distribution. We present a method to increase delivery efficiency. Introduction The size of the electronic commerce (EC) market continues to rise. The global business-to-consumer (BtoC)-EC market USD 1,920 billion in 2016, a 23.7% increase compared with the previous year. For China, the United States, and Japan, the EC conversion rates were 15%, 11%, and 6%, respectively, and they are expected to continue to grow in the future. Furthermore, in the global consumer-to-consumer (CtoC)-EC market, taking marketplace app Mercari as an example, sales in the fiscal year ending June 2016 increased to USD 120 million, a 189% increase from the previous term. With the growth of the EC market, the logistics industry, especially last-mile delivery, is expected to have to cope with an unprecedented high level of demand [1-2]. For example, same-day and next-day delivery services have become the industry standard. In addition, scheduled delivery of packages in a time slot selected by the customer is becoming necessary. Therefore, research on efficient logistics services and system construction is required, particularly on quantifying and estimating delivery time, which are crucial building blocks for constructing efficient logistics and delivery services. To quantify the service time, Ashbrook & Starner [3][4] used the k-means method, Palma et al. [5] and Adams, Phung & Venkatesh [6] used the DBSCAN algorithm, and Kurashima et al. [7] used the mean shift to determine delivery bottlenecks. Furthermore, Nurmi & Bhattacharya [8] proposed a nonparametric Bayesian method. However, because these methods considered only distance information, they did not have a high resolving power when visiting the same area repeatedly at different times and dates. The vehicle routing problem is often discussed for estimating delivery time. For example, Li, Tian & Leung [9], Duyug et al. [10], and Binart et al. [11] investigated the travel time or service time and the probability density function, and they formulated delivery routes considering the specified time as a constraint on the uncertainty of the required time. However, because the probability density function cannot be obtained by analyzing real data, this method is not practical. In this research, we quantify service time and estimate delivery time based on service time distribution. Furthermore, we present a method that may help to construct efficient delivery systems. Delivery Time and Service Time We use actual delivery data on a logistics company collected by an industry-academia cooperative and divide the last-mile delivery data into four stages of wide-area movement between the sales offices and the point of delivery: movement between areas, movement within the area, transfer to customer, and waiting. Figure 1 shows the composition of the time taken for each component of last-mile delivery. Movement between sales offices and areas refers to movement between the sales office and the delivery area. Movement within the area refers to the movement of the delivery truck in the 418 delivery area. Transfer to customer refers to the driver parking, taking the package from the truck, handing the package to the customer, and returning to the truck. Standby refers to the driver waiting in the truck with no delivery until a specified time. The total time in this research is the travel time, transfer-to-customer time, and waiting time. Figure 1. Delivery time inner frame. The transfer-to-customer time is particularly important and its variation is large compared with the travel time and waiting time. Therefore, we quantify and estimate the delivery time first. The flow for the estimation method is shown in Figure 2. Figure 2. Distribution estimation flow of service time and delivery time. Estimation of Service Time Distribution In estimating the service time distribution for each customer, GPS and delivery record input data are used to clarify the probability distribution. GPS data are position information data recorded by portable terminals mounted on the truck and carried by the driver. The delivery record data are records of the deliveries to the customer, consisting of the delivery time and location. The proposed method consists of four steps. Determine dwell points based on the GPS data. We apply a two-step clustering method to extract multiple dwell points in one GPS data set.