3.1. Problem Description and Assumptions
During the whole process from online ordering to express receipt, each customer has two delivery modes: home delivery or pickup. If the customer chooses the home delivery mode, they need to choose the time window for receiving the express delivery conveniently, and the courier will send the express delivery to the customer according to the receiving address filled by the customer; if the customer chooses the pickup mode, the courier will deliver the express to the express station near the receiving address, and the customer will go to the express station to retrieve it on their own. After the crowdsourcing couriers are assigned an order by the platform, they need to complete the delivery service according to the delivery order and delivery path planned by the platform to the corresponding delivery point. The courier must first pick up the goods at the corresponding distribution center before delivery. The crowdsourcing task assignment result diagram is shown in
Figure 1. Tasks 1, 2, 7, 9, and 10 belong to Distribution Center 1. Tasks 3, 4, 5, 6, 8, 10, and 12 belong to Distribution Center 2. Tasks 6, 11, 13, and 14 belong to Distribution Center 3. The task delivery sequence of courier 1 is {1, 2, 3}, and its delivery route is {Distribution Center 1, Task 2, Task 1, Distribution Center 2, Task 2}. The task delivery sequence of courier 2 is {4, 5, 6}, and its delivery route is {Distribution Center 2, Task 4, Task 5, Task 6}. The task delivery sequence of courier 3 is {7, 8, 9, 10}, and its delivery route is {Distribution Center 1, Distribution Center 2, Task 9, Task 10, Task 8, Task 7}. The task delivery sequence of courier 4 is {11, 12, 13, 14}, and the delivery route is {Distribution Center 3 Task 14, Task 11, Distribution Center 2, Task 12, Task 13}.
This paper analyzes the data from October 2023 to December 2023 on the trading platform of a certain branch of the Shanghai D Express Company. In the traditional RFM model, R represents the time of the most recent transaction, F represents the frequency of transactions during this period, and M represents the amount of transactions during this period. In the model of crowdsourcing delivery in this article, these three indicators are given new meanings.
R represents delivery receipt recency, which is the latest time the customer signed for the express delivery.
F represents the frequency of express delivery receipts, which refers to the total number of times customers receive express delivery during this period.
M represents the amount of express delivery freight, which is the freight paid by the customer for signing for the express delivery during this period. The comparison of the indicators between the traditional RFM model and the express entropy weight RFM model is shown in
Table 1.
The traditional RFM model assumes that the weights of the
R,
F, and
M indicators are the same. However, in reality, the impacts of
R,
F, and
M on crowdsourcing platforms are different. In order to classify customers more accurately, this article evaluates the importance of each indicator in the RFM model. The method suggests the following actions: assign corresponding weights to each indicator of the traditional RFM model using the entropy weight method [
31], establish a new entropy weight RFM model, and classify customers based on this model. This method combines the advantages of entropy weight method and RFM model, and different customer values can be reflected through the different proportional allocations of these three indicators, which can more accurately evaluate customer value and behavior, and provide strong support for business decision-making, thereby improving customer satisfaction and business growth.
In the calculation results based on SPSS26.0 software, express delivery monetary fee has the highest weight, accounting for 65.5% of the proportion, indicating that the customer’s express freight amount has significant importance in this research analysis. Next is express delivery receipt frequency, accounting for 29.099% of the total, indicating that the frequency of customers’ express delivery receipts is more important in this research analysis. However, the weight of express delivery receipt recency is the lowest, only 5.401%, indicating that the importance of this indicator in research analysis is relatively small, as shown in
Table 2.
This study will assign weights to these three indicators based on the entropy weight method and update them with weighted weights. According to the updated data, we use the K-means clustering method to classify customer categories. In Python 3.12.6 software, the elbow method is first used to determine the optimal clustering value
k. As the number of clusters
k increases, the squared error SSE will naturally gradually decrease. The point where SSE begins to slow down is found as the optimal value
k. According to the elbow diagram in
Figure 2, it can be seen that increasing the number of clusters no longer significantly reduces clustering errors after
k = 3. Therefore, in this paper,
k = 3 is taken to cluster customers into three categories.
By using the entropy weight method to determine the weights, it can be concluded that express delivery monetary fee has the greatest impact on the express crowdsourcing platform, bringing more profits to the platform. Express delivery receipt frequency also has a significant impact on the platform, while express delivery receipt recency has a relatively small impact on the platform. Therefore, different proportions of these three indicators can be used to reflect different customer values. By observing
Figure 3,
Figure 4 and
Figure 5, the following conclusions can be drawn: Cluster 1 customers have the highest express delivery monetary fee, moderate express delivery receipt frequency, and relatively low express delivery receipt recency, indicating that Cluster 1 customers are efficient customers; Cluster 2 customers have the highest express delivery receipt frequency, while the express delivery monetary fee and the express delivery receipt recency are moderate, indicating that cluster 2 customers are potential customers; the express delivery receipt frequency and the express delivery monetary fee for cluster 0 customers are relatively small, while the express delivery receipt recency is relatively high, indicating that cluster 0 customers are edge customers. Based on these results, this study categorizes customers into three types: efficient customers, potential customers, and edge customers.
This paper considers the scenario of multiple distribution centers, multiple express stations, and multiple couriers. In actual express delivery scenarios, when a customer places an order, their task location, time window, and other relevant information will be obtained by the crowdsourcing platform and pushed to the corresponding crowdsourcing couriers. A crowdsourcing courier will be assigned different tasks, and they usually use the same transportation tools to start delivery from their respective locations. Due to the nature of crowdsourcing, they do not need to return to the distribution center. In the process of building a model, in order to ensure that the model can be effectively solved, the following reasonable assumptions need to be made based on the actual situation.
- (1)
When the crowdsourcing platform assigns tasks to couriers, the customer category, task location, task time window, delivery method selected by the customer, courier location, courier station location, and delivery center location are all known.
- (2)
Each crowdsourced delivery person starts from their own starting position and completes the crowdsourced delivery task.
- (3)
Each customer point is served by only one delivery person, who can only arrive and depart once, but each crowdsourced delivery person can serve multiple customer points.
- (4)
The delivery speed of each crowdsourced deliveryman is the same and constant, without any traffic congestion.
- (5)
Crowdsourced couriers will not refuse delivery tasks assigned by crowdsourcing platforms.
- (6)
The service time of crowdsourced couriers at the distribution center is the same as the service time of home delivery and pickup delivery methods.
- (7)
If arriving at the customer point earlier or later than the time window, there will be opportunity costs or penalty costs.
- (8)
For the convenience of describing the model, it is assumed that each crowdsourced delivery person will not return to their initial position after completing the delivery.
3.2. Denotational Description
3.2.1. Denotational
In this question, it is assumed that there are multiple distribution centers and courier stations: distribution center , H . Express station node , U . Initial location node of crowdsourcing couriers , C . Express crowdsourcing customer nodes , N . Crowdsourced delivery vehicle serial number , K . The delivery vehicles have the same load capacity, represented by . The weight of each customer’s crowdsourcing task is represented as . The time window for customer n is represented as ; represents the earliest service time requested by customer, n, represents the latest service time requested by customer n. The distance between node n and node m is represented by the Euclidean distance . The unit transportation cost of delivery vehicles is represented by . Vehicle speed is represented by . The time when vehicle k arrives at node m is represented by . The waiting time of vehicle k at customer node n is denoted by . The service time of vehicle k at node m is represented by . The time taken for the delivery vehicle to travel from node n to node m is . The time when vehicle k leaves node m is represented by . is the delivery method chosen by the customer; if home delivery is chosen, then = 0; If pickup is chosen, then = 1.
3.2.2. Variable
: 0–1 variable; if vehicle k travels from node n to node m, then = 1; otherwise, it is 0.
: 0–1 variable; if vehicle k completes the delivery task of customer node n, then = 1; otherwise, it is 0.
All relevant symbols involved in the model are listed in
Table 3.
3.3. Penalty Function of Customer Classification
According to the entropy weight RFM model, three different types of customers are clustered based on the indicators of customer historical express delivery receipt recency R, express delivery receipt frequency F, and express delivery monetary fee M. To maintain high-quality customer resources and the long-term interests of the enterprise, the time penalty function for different categories of customers should be different. Efforts should be made to meet the expected time window of efficient customers to increase platform efficiency; therefore, the penalty value for efficient customers is higher than that for potential and edge customers.
This paper assumes that if the crowdsourced delivery person arrives earlier than the earliest service delivery time requested by the customer, they need to wait until the earliest service delivery time requested by the customer. During this process, opportunity costs will be incurred. This study assumes that the early arrival penalty coefficients for the three types of customers are the same and are represented uniformly by . If the crowdsourced couriers arrive within the customer’s expected time window, there will be no time penalty cost. If the crowdsourced delivery person is later than the latest service time requested by the customer, different time penalty costs will be incurred based on the customer’s different categories.
For crowdsourcing platforms, efficient customers have the highest amount and frequency of express delivery receipts, which is of great significance for the long-term development of the platform and is an indispensable customer resource. Therefore, if a crowdsourced delivery person exceeds the latest service time requested by efficient customers, it will impose a significant penalty on the crowdsourced delivery person to avoid reducing the satisfaction of efficient customers. The penalty function for efficient customers is represented by
. If the crowdsourced couriers exceed the latest service time required by efficient customers, a significant penalty cost will be incurred, represented by
. The efficient customer time penalty function is shown in Equation (1).
Potential customers are an indispensable type of customer for the continued development and growth of crowdsourcing platforms. They have a large amount of express delivery receipts and a high frequency of express delivery receipts, and have the potential to become efficient customers. Therefore, the platform should also try its best to meet the needs of potential customers and stimulate consumption. The time penalty function for potential customers is represented by
. If the crowdsourced couriers exceed the latest service time requested by potential customers, a linear penalty cost of
will increase over time. Potential customers incur the time penalty function as shown in Equation (2).
The amount and frequency of express delivery receipts for edge customers are relatively small, and their contribution to the platform is low. Therefore, the late arrival penalty cost for this type of customer is set to a fixed value. The penalty function for edge customers is represented by
, and for crowdsourced couriers who arrive later than the latest service time requested by edge customers, a fixed value of
is used to represent their late arrival time cost. Edge customers incur a time penalty function as shown in Equation (3).