Multi-index evaluation-based intra-city express distribution transfer center site selection optimization method

Abstract

The invention relates to an intra-city express distribution transfer center site selection optimization method based on multi-index evaluation, and belongs to the technical field of urban logistics distribution. The method comprises the steps that rail transit station information is acquired, importance evaluation is carried out on rail transit stations, and alternative transfer centers are screened according to the importance of the stations; determining constraint conditions for establishing a transfer center site selection optimization model; the optimization problem of a transfer center site selection optimization model is established by taking the minimization of the total cost as the target in consideration of the express transportation cost, the express loading and unloading and carrying cost and the transportation fixed cost; and solving an optimization problem by adopting a genetic algorithm, and generating an optimal site selection scheme through encoding, crossover and mutation operations. According to the method, by means of multi-index evaluation of the importance degree of the rail transit stations, alternative transfer centers with more advantages can be accurately screened out, the express transportation cost, the loading, unloading and carrying cost and the transportation fixed cost are comprehensively considered, and optimization solution is carried out with the purpose of minimizing the total cost.

Description

Technical Field

[0001] The present invention belongs to the technical field of urban logistics distribution, and relates to a method for optimizing the location of intra-city express delivery transfer centers based on multi-index evaluation. Background Art

[0002] With the continued acceleration of urbanization, the concentration of urban populations, the increasing frequency of commercial activities, and the explosive growth of e-commerce, the demand for intra-city express delivery has seen a surge. From daily necessities and urgent document delivery to instant food delivery, intra-city express delivery has become deeply integrated into every aspect of urban life, becoming a vital link in maintaining the functioning of the urban economy and the convenience of residents' lives.

[0003] However, traditional ground logistics and delivery models are gradually exposing numerous drawbacks when coping with such a massive demand for express delivery. Urban road traffic congestion is becoming increasingly severe, especially during rush hour and in busy commercial areas. Express delivery vehicles are often stuck in heavy traffic, resulting in significantly longer delivery times and lower efficiency. Furthermore, exhaust emissions from a large number of delivery vehicles exacerbate urban environmental pollution, posing a threat to air quality and public health. Furthermore, rising fuel, vehicle maintenance, and labor costs have led to high operating costs for traditional ground logistics and delivery models, squeezing profit margins for businesses and limiting the further development of intra-city express delivery services.

[0004] Against this backdrop, urban rail transit networks, with their unique advantages, offer new promise for intra-city express delivery. Urban rail transit boasts high transport capacity, with a single subway line carrying tens of thousands of passengers per hour. If its capacity is effectively utilized, it can transport large numbers of express parcels at once, effectively improving delivery efficiency. Furthermore, urban rail transit operates strictly according to a timetable, ensuring exceptional punctuality and being virtually unaffected by surface traffic conditions. This ensures on-time delivery and meets customers' stringent timeliness requirements. More importantly, urban rail transit is electrically powered, emission-free, low-noise, and environmentally friendly, meeting the strategic needs of sustainable urban development.

[0005] Integrating urban rail transit into the intra-city express delivery system is undoubtedly an innovative and forward-looking initiative. However, achieving the efficient operation of this model requires the scientific and rational selection of transfer centers. As a key connection point between urban rail transit and the express delivery network, the rationality of the transfer center's location directly affects the transfer efficiency, delivery costs, and service quality of express packages. Summary of the Invention

[0006] In view of this, the purpose of the present invention is to provide a location optimization method for same-city express delivery transfer centers based on multi-index evaluation, aiming to solve the problems of high cost, low efficiency and heavy environmental burden in the traditional distribution model, and to build an efficient and low-carbon same-city express delivery system by integrating urban rail transit network resources.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A method for optimizing the location of a same-city express delivery transfer center based on multi-index evaluation, the method comprising the following steps:

[0009] Obtain rail transit station information and evaluate the importance of rail transit stations based on the degree DC of the node i , adjacent node importance H-index and betweenness centrality BC i Evaluate the importance of its sites and select alternative transfer centers based on the importance of the sites;

[0010] Determine the constraints for establishing a transshipment center location optimization model;

[0011] Considering the express delivery cost, express loading and unloading and handling cost and transportation fixed cost, the optimization problem of establishing the transshipment center location optimization model with the goal of minimizing the total cost;

[0012] Genetic algorithm is used to solve the optimization problem, and the optimal addressing scheme is generated through encoding, crossover and mutation operations.

[0013] Furthermore, in the rail transit network, the degree DC of the node at rail transit station i is i It refers to the number k of rail transit stations directly connected to rail transit station i, which is expressed as:

[0014] DC i =k

[0015] In the urban rail transit network, if rail transit station i is the starting station, DC i =1, if rail transit station i is an ordinary intermediate station, then DC i =2, if rail transit station i is a transfer station, then DC i Greater than 2; in the urban rail transit network, the DC of rail transit station i i The larger it is, the more important it is.

[0016] Furthermore, in the urban rail transit network, the H-index of rail transit station i means that the degree of h directly connected rail transit stations is greater than or equal to h, which is expressed as:

[0017] H i= max(h), satisfying |{j|d j ≥h, and i and j are directly connected}|≥h

[0018] In the urban rail transit network, the larger the H-index of rail transit station i, the higher the overall importance of other nodes connected to rail transit station i, and the higher the importance of node i.

[0019] Furthermore, in the urban rail transit network, the betweenness centrality BC of rail transit station i is i It refers to the ratio of the number of shortest paths passing through rail transit station i to the number of shortest paths in the network, which is expressed as:

[0020]

[0021] Among them, n st represents the number of shortest paths connecting node s and node t, It represents the number of paths passing through rail transit station i in the shortest path connecting node s and node t. In the urban rail transit network, the greater the betweenness centrality of rail transit station i, the more shortest paths passing through rail transit station i, and the higher the importance of rail transit station i.

[0022] Furthermore, the entropy weight method and TOPSIS comprehensive evaluation method are combined to evaluate the node degree DC i , adjacent node importance H-index and betweenness centrality BC i The comprehensive evaluation process is as follows:

[0023] Suppose there are m nodes, each node contains n indicators, forming an m×n original evaluation matrix A={a ij} k×l , where a ij Indicates the value of the i-th node on the j-th index, and standardizes the n indexes to generate a dimensionless matrix A = {b ij} k×l ,in,

[0024]

[0025] Calculate the information entropy e of the jth indicator j , used to measure the uncertainty of the indicator at all nodes; when the information entropy of the indicator is larger, the indicator is more chaotic; conversely, the indicator is more organized; where b ij The expression for the value of the i-th node on the j-th standardized index is:

[0026]

[0027] Calculate the index weight w of the jth index of the i-th node j :

[0028]

[0029] Among them, g j =1-e j ;g j Represents the information utility value of the jth indicator, which is used to reflect its contribution in the comprehensive evaluation. j The smaller the g j The larger it is, the more discriminative the indicator is and the greater its weight is;

[0030] Construct an evaluation matrix V, where each element v in the evaluation matrix V ij is the index weight w of the jth index of the i-th node j The product of the values ​​corresponding to each indicator, this matrix is ​​used to subsequently calculate the distance between each node and the positive and negative ideal solutions, and then calculate their closeness. It can be expressed as:

[0031] V={v ij}={w j b ij}

[0032] Define positive ideal solution V + ={max i v ij}、Negative ideal solution V - ={min i v ij}, calculate the distance between the i-th node and the positive and negative ideal solutions:

[0033]

[0034] Then calculate the closeness C of the i-th node i :

[0035]

[0036] Among them, C i ∈[0,1], closeness C i The larger it is, the closer node i is to the positive ideal solution, and the higher its comprehensive importance;

[0037] According to the proximity C of all nodes i The nodes are sorted by the value and the first K nodes are selected as candidate transfer centers according to the preset number of locations.

[0038] Furthermore, the constraints for establishing the transshipment center location optimization model include the following assumptions:

[0039] Assumption 1: Only the transportation through rail transit stations is considered, and the transportation directly through the terminal ground vehicle dispatch system is not considered;

[0040] Assumption 2: A transit center can serve multiple demand points, and each demand point can only be served by one transit center;

[0041] Assumption 3: Every alternative rail transit station is accessible to every demand point;

[0042] Assumption 4: The distance from each alternative rail transit station to the demand point is a straight-line distance;

[0043] Assumption 5: The pickup and delivery service times are the same for all customers, and price fluctuations caused by not completing the service within the customer's specified time window are not considered;

[0044] Assumption 6: Customer demand is calculated based on weight, regardless of product type.

[0045] Assumption 7: The number of packages at all demand points is estimated based on city population data;

[0046] Assumption 8: The parcel handling capacity of each candidate rail transit station can meet the demand points of its service;

[0047] Assumption 9: All sorted items and parcels are correctly transported to the corresponding rail trains every time;

[0048] Assumption 10: Assume that the vehicle travels at a constant speed during the delivery process;

[0049] Assumption 11: The cost of transfer is not considered.

[0050] Furthermore, the express transportation cost C1 refers to the transportation cost between the demand point and the transshipment center, which is expressed as:

[0051] C1=∑c1a i d ij x ij ,i∈N,k∈N,j∈M

[0052] Among them, the express transportation cost C1 is proportional to the demand volume at the demand point and the transportation distance from the demand point to the transfer center; c1 represents the highway transportation cost per unit express per unit distance; a i represents the demand quantity of demand point i; d ij represents the distance from demand point i to rail transit station j; x ij is a 0-1 variable, x ij = 0 means that the item package of demand point i is not served by rail transit station j, x ij=1 means that the item package of demand point i is served by rail transit station j; N represents the set of demand points, and M represents the set of rail transit stations;

[0053] The express loading and unloading and handling cost C2 refers to the labor costs of the express delivery workers at rail transit stations, which is expressed as:

[0054] C2=∑c2a i ,i∈N

[0055] Among them, the express loading, unloading and handling cost C2 at the rail transit station is proportional to the demand volume at the demand point; c2 represents the unit cost of entering and exiting the rail transit station;

[0056] The fixed transportation cost C3 includes land lease fees or land transfer fees, water charges and electricity charges, which can be expressed as:

[0057] C3=∑c3y j ,j∈M

[0058] Among them, the fixed transportation cost C3 is proportional to the number of transfer centers built; c3 represents the fixed operating cost of each rail transfer center built; y j is a 0-1 variable, y j =0 means that rail transit station j is not selected as the express transfer center; j =1 means that rail transit station j is selected as the express transfer center;

[0059] Therefore, the objective function of minimizing the total cost is expressed as:

[0060] Min C=min(∑c1a i d ij x ij +∑c2a i +∑c3y j )

[0061] st.

[0062] C1:x ij ≤y j ,i∈N,j∈M

[0063] C2:

[0064] C3:∑y j =p,j∈M

[0065] Among them, constraint C1 means that the unselected rail transit stations cannot provide services to the demand points, constraint C2 means that a demand point is only served by one transfer center; constraint C3 means that there are p transfer centers in total.

[0066] Furthermore, a genetic algorithm is used to solve the model, which includes:

[0067] Coding: natural number coding is used to encode candidate rail transit stations and demand points; the set of candidate rail transit stations is [1, 2, 3, …, m], and the set of demand points is [1, 2, 3, …, n];

[0068] Initialize the population: The chromosomes in the population contain four parts: the first number represents the number of transfer centers, the second part represents the numbers of a randomly generated group of transfer centers, the third part represents the number of demand points served by each transfer center, and the fourth part represents the numbers of demand points served by each transfer center;

[0069] Definition of fitness function: The objective function is the minimum total cost of the intra-city express delivery system based on the urban rail transit network. The inverse of the objective function of the location optimization model for the transfer center is used as the fitness function. The fitness function is as follows:

[0070]

[0071] Genetic operation: Selection operation, which selects the best individual and copies it directly to the next population; the operation includes crossover operation and mutation operation. Crossover operation adopts single-point crossover method, and mutation operation allows a small number of individuals to undergo single-point mutation.

[0072] Determine the termination condition: When the iteration reaches a certain number of times, the iteration will automatically stop and the solution with the highest fitness will be selected;

[0073] Decoding: Converting codes into actual entities.

[0074] The beneficial effects of the present invention are:

[0075] This method integrates urban rail transit network resources, rationally screens potential transfer centers, and constructs a site selection optimization model. This model comprehensively considers express delivery costs, loading and unloading, handling costs, and fixed transportation costs, optimizing the solution with the goal of minimizing total costs. This process effectively avoids the cost waste caused by poor site selection in traditional delivery models, significantly reduces the overall cost of intra-city express delivery, and improves the economic benefits of enterprises.

[0076] This invention leverages a multi-metric evaluation of the importance of rail transit stations to accurately select the most advantageous alternative transfer centers, making the flow of express delivery smoother and more efficient. Furthermore, the application of a genetic algorithm can rapidly generate the optimal site selection plan, reducing the time and error associated with manual decision-making, accelerating site selection, and ultimately improving the overall efficiency of intra-city express delivery.

[0077] In summary, the present invention achieves optimization of cost, efficiency, environmental protection and other aspects of same-city express delivery through a scientific and reasonable site selection optimization method, providing strong technical support for the development of the same-city express delivery industry.

[0078] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0080] Figure 1 This is a flow chart of a method for optimizing the location of intra-city express delivery and transfer centers based on an urban rail transit network according to an embodiment of the present invention;

[0081] Figure 2 A distribution map of the top 75 rail transit stations by comprehensive importance according to an embodiment of the present invention;

[0082] Figure 3 A flow chart of a genetic algorithm for solving a transshipment center location optimization model according to an embodiment of the present invention;

[0083] Figure 4 : is a spatial distribution diagram of p=5 demand points and transfer centers in an embodiment of the present invention;

[0084] Figure 5 4 is a spatial distribution diagram of p=10 demand points and transfer centers in an embodiment of the present invention. DETAILED DESCRIPTION

[0085] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0086] Among them, the accompanying drawings are only for illustrative purposes and represent only schematic diagrams rather than actual pictures, and should not be understood as limiting the present invention. In order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the dimensions of actual products. For those skilled in the art, it is understandable that some well-known structures and their descriptions may be omitted in the accompanying drawings.

[0087] The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar parts; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "back", etc. indicating directions or positional relationships, they are based on the directions or positional relationships shown in the drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operate in a specific direction. Therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes and cannot be understood as limiting the present invention. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0088] See also Figures 1 to 5 , which is a location optimization method for intra-city express delivery transfer centers based on multi-index evaluation.

[0089] Example

[0090] This embodiment introduces a method for optimizing the location of intra-city express delivery and transfer centers based on multi-index evaluation, and explains the method in detail through specific examples. Figure 1 As shown, the method comprises at least the following steps:

[0091] S1. Obtain rail transit station information and evaluate the importance of rail transit stations. The importance of stations is evaluated based on the node degree, adjacent node importance, and betweenness centrality. Alternative transfer centers are selected based on the station importance.

[0092] S2. Determine the constraints for establishing the optimal model for transshipment center location selection;

[0093] S3. Considering the express delivery cost, express loading and unloading and handling cost and transportation fixed cost, an optimization problem is established to establish a transshipment center location optimization model with the goal of minimizing the total cost;

[0094] S4. Use genetic algorithm to solve the optimization problem and generate the optimal addressing solution through encoding, crossover and mutation operations.

[0095] In step S1, the evaluation indicators are selected, and three indicators are selected: node degree (number of directly connected stations), H-index (importance of adjacent nodes), and betweenness centrality (network path dependence) to quantify the hub value of the station in the rail transit network.

[0096] In a network, the degree DC of node i i Refers to the edges directly connected to node i from other nodes. In the rail transit network, the degree of rail transit station i refers to the number k of rail transit stations directly connected to rail transit station i. The expression is:

[0097] DC i =k

[0098] In the urban rail transit network, if rail transit station i is the starting station, DC i =1, if rail transit station i is an ordinary intermediate station, then DC i =2, if the rail transit station is a transfer station, then DC i Greater than 2. In a network, the greater the degree of node i, the more important node i is in the network. In an urban rail transit network, the DC of rail transit station i is i The larger it is, the more routes pass through rail transit station i, the stronger the connectivity of rail transit station i is, the more convenient the transfer is, and its importance is higher.

[0099] Measuring the importance of a node by its degree is too one-sided. It should also be combined with the degree of the nodes directly connected to it. To this end, the H-index indicator is introduced. The H-index is a mixed quantitative method for evaluating the value of scientific research articles. In the rail transit network, the H-index of rail transit station i means that the degree of the h directly connected rail transit stations of rail transit station i is greater than or equal to h, specifically expressed as:

[0100] H i = max(h), satisfying |{j|d j ≥h, and i and j are directly connected}|≥h.

[0101] In the rail transit network, the larger the H-index of rail transit station i, the higher the overall importance of other nodes connected to rail transit station i, and the higher the importance of node i.

[0102] The degree and H-index of a node both measure the importance of a node from the perspective of the number of nodes it connects to. However, there is no indicator that measures the importance of a node from the perspective of network paths. To address this, the betweenness centrality of a node is introduced. In an urban rail transit network, the betweenness centrality BC of a rail transit station i is i It refers to the ratio of the number of shortest paths passing through rail transit station i to the number of shortest paths in the network. The expression is:

[0103]

[0104] Among them, n strepresents the number of shortest paths connecting node s and node t, It represents the number of shortest paths connecting nodes s and t that pass through node i. In an urban rail transit network, a greater betweenness centrality of a rail transit station i indicates that there are more shortest paths passing through the station, which can reduce the number of transfers and improve delivery efficiency. Therefore, the more important the rail transit station i is, the greater its importance.

[0105] Subsequently, the three indicators of the node are comprehensively evaluated by combining the entropy weight method and the TOPSIS comprehensive evaluation method. There are m nodes, each node contains n indicators, and an m×n original evaluation matrix A={a ij} k×l , where a ij Indicates the value of the i-th node on the j-th index. Standardize the n indicators to generate a dimensionless matrix A = {b ij} k×l ,in,

[0106]

[0107] Calculate the information entropy e of the jth indicator j , is used to measure the uncertainty of the indicator at all nodes; when the information entropy of the indicator is larger, the indicator is more chaotic; conversely, the indicator is more organized. ij The expression for the value of the i-th node on the j-th standardized index is:

[0108]

[0109] Calculate the index weight w of the jth index of the i-th node j :

[0110]

[0111] Among them, g j =1-e j ;g j Represents the information utility value of the jth indicator, which is used to reflect its contribution in the comprehensive evaluation. j The smaller the g j The larger it is, the more discriminatory the indicator is and the greater its weight is.

[0112] Construct an evaluation matrix V, where each element v in the evaluation matrix V ij is the index weight w of the jth index of the i-th node j The product of the values ​​corresponding to each indicator, this matrix is ​​used to subsequently calculate the distance between each node and the positive and negative ideal solutions, and then calculate their closeness. It can be expressed as:

[0113] V={vij}={w j b ij}

[0114] Define positive ideal solution V + ={max i v ij}、Negative ideal solution V - ={min i v ij}, calculate the distance between the i-th node and the positive and negative ideal solutions:

[0115]

[0116] Then calculate the proximity (closeness) C of the i-th node i :

[0117]

[0118] Among them, C i ∈[0,1], closeness C i The larger the value is, the closer node i is to the positive ideal solution, and the higher its comprehensive importance is.

[0119] According to the proximity C of all nodes i The nodes are sorted by the value, and the first K nodes are selected as the candidate transfer centers according to the preset number of locations (for example, the first K). Alternatively, a proximity threshold C can be set. i ≥τ (such as τ=0.5) to determine important nodes.

[0120] In this embodiment, taking the rail transit stations in Chongqing as an example, the degree, H-index and betweenness centrality of the rail transit stations in Chongqing are calculated by the driving method. Table 1 shows the degree distribution table of the rail transit stations in Chongqing:

[0121] Table 1

[0122]

[0123] Data analysis reveals the H-index distribution of various rail transit stations in Chongqing. There are 23 rail transit stations with an H-index of 1, 231 with an H-index of 2, and two with an H-index of 3. The two stations with an H-index of 3 are Chongqing North Railway Station North Square and Liyuchi. Both Chongqing North Railway Station North Square and Liyuchi are connected to three transfer stations, all with a high degree of 3 or greater. This indicates that the other stations connected to Chongqing North Railway Station North Square and Liyuchi rail transit stations offer a high level of transfer convenience.

[0124] Through data analysis, the distribution of betweenness centrality of each rail transit station in Chongqing can be obtained, and the comprehensive importance of each rail transit station can be calculated using the constructed comprehensive evaluation model. After the comprehensive importance calculation is completed, the proximity C of each rail transit node is calculated according to the TOPSIS method. i . According to the importance from high to low, the top 75 nodes are selected as alternative transfer centers. In the actual results, the proximity value of the 75th node is 0.0053, so this study sets 0.0053 as the minimum threshold for comprehensive importance screening. According to the comprehensive importance from high to low, they are Ranjiaba, Xietaizi, Dalongshan, Shiqiaopu, etc. These 75 rail transit stations are used as alternative transfer centers, such as Figure 3 shown.

[0125] In step S2 of this embodiment, the following constraints are determined:

[0126] Assumption 1: Only the transportation through rail transit stations is considered, and the transportation directly through the terminal ground vehicle dispatch system is not considered.

[0127] Assumption 2: A transfer center can serve multiple demand points, and each demand point can only be served by one transfer center.

[0128] Assumption 3: Every alternative rail transit station is accessible to every demand point.

[0129] Assumption 4: The distance from each alternative rail transit station to the demand point is a straight-line distance.

[0130] Assumption 5: The pickup and delivery service times are the same for all customers, and any price changes due to service not being completed within the time window specified by the customer are not considered.

[0131] Assumption 6: Customer demand is calculated based on weight, regardless of product type.

[0132] Assumption 7: The number of packages at all demand points is estimated based on city population data.

[0133] Assumption 8: The parcel handling capacity of each alternative rail transit station can meet the demand points of its service.

[0134] Assumption 9: Sorted items and parcels can be accurately transported to the corresponding rail train every time.

[0135] Assumption 10: Assume that the vehicle travels at a constant speed during the delivery process, without considering the impact of traffic restrictions and speed limits.

[0136] Assumption 11: The cost of transfer is not considered.

[0137] In step S3 of this embodiment, based on the above constraints, a transfer center location optimization model is established with the goal of minimizing the total cost. The total cost includes the express transportation cost, the loading, unloading and handling costs of express at rail transit stations, and the fixed transportation cost. Among them, the express transportation cost refers to the transportation cost between the demand point and the transfer center. The loading, unloading and handling costs of express at rail transit stations refer to the labor costs of loaders and movers. The fixed transportation costs mainly include land lease fees / land transfer fees, water charges and electricity charges. According to the above description, the express transportation cost and the loading, unloading and handling costs of express at rail transit stations are all related to the transportation volume, and the fixed transportation cost is related to the number of transfer centers built. Including:

[0138] Determine the express delivery cost C1:

[0139] C1=∑c1a i d ij x ij ,i∈N,k∈N,j∈M

[0140] Among them, the express transportation cost C1 is proportional to the demand volume at the demand point and the transportation distance from the demand point to the transfer center; c1 represents the highway transportation cost per unit express per unit distance; a i represents the demand quantity of demand point i; d ij represents the distance from demand point i to rail transit station j; x ij is a 0-1 variable, x ij = 0 means that the item package of demand point i is not served by rail transit station j, x ij =1 means that the item package of demand point i is served by rail transit station j; N represents the set of demand points, and M represents the set of rail transit stations.

[0141] Determine the express loading, unloading and handling costs C2:

[0142] C2=∑c2a i ,i∈N

[0143] Among them, the express loading, unloading and handling cost C2 at the rail transit station is proportional to the demand volume at the demand point; c2 represents the unit cost of entering and exiting the rail transit station;

[0144] Determine the fixed cost of transportation C3:

[0145] C3=∑c3y j ,j∈M

[0146] Among them, the fixed transportation cost C3 is proportional to the number of transfer centers built; c3 represents the fixed operating cost of each rail transfer center built; y j is a 0-1 variable, y j=0 means that rail transit station j is not selected as the express transfer center; j =1 means that rail transit station j is selected as the express transfer center;

[0147] Therefore, the objective function of minimizing the total cost is expressed as:

[0148] Min C=min(∑c1a i d ij x ij +∑c2a i +∑c3y j )

[0149] st.

[0150] C1:x ij ≤y j ,i∈N,j∈M

[0151] C2:

[0152] C3:∑y j =p,j∈M

[0153] Among them, constraint C1 means that the unselected rail transit stations cannot provide services to the demand points, constraint C2 means that a demand point is only served by one transfer center; constraint C3 means that there are p transfer centers in total.

[0154] In this embodiment, the longitude and latitude coordinates of the 75 candidate transfer centers selected by consulting the data are shown in Table 2:

[0155] Table 2

[0156]

[0157] According to data released by the Chongqing Postal Administration, Chongqing's intra-city express delivery volume reached 267 million pieces in 2022. According to data released by the Chongqing Municipal Bureau of Statistics, Chongqing's permanent population in 2022 was 32.1334 million. Combining intra-city express delivery data with permanent population information, we further calculated that the average daily intra-city express delivery volume per capita in Chongqing was 0.0227 pieces. Furthermore, multiplying this average daily intra-city express delivery volume by the permanent population of the street or town yields the total daily demand for intra-city express delivery in that street or town. Based on analogous estimates based on road-rail intermodal transport parameters, subway fares, and truck fuel consumption, the cost of truck trunk line transportation per unit distance is 0.02 yuan / (piece per kilometer), and the cost of loading, unloading, and handling per unit at rail transit stations is 0.2 yuan / piece. Given that the service area is primarily within Chongqing's main urban area, the fixed transportation cost for each transfer center is 60,000 yuan / year.

[0158] In step S4 of this embodiment, a genetic algorithm is used to solve the model, and the optimal addressing scheme is generated through operations such as encoding, crossover, and mutation. The flow chart is as follows: Figure 3 As shown, the specific steps include:

[0159] Coding: This embodiment uses natural number coding to encode candidate rail transit stations and demand points. The set of candidate rail transit stations is [1, 2, 3, ..., m], and the set of demand points is [1, 2, 3, ..., n].

[0160] Initializing the population: The chromosomes in the population consist of four parts: the first number represents the number of transfer centers, the second number represents the numbers of a randomly generated set of transfer centers, the third number represents the number of demand points served by each transfer center, and the fourth number represents the number of demand points served by each transfer center. For example, if there are 20 demand points, the chromosomes are [4,1,6,3,4,2,4,8,6,1,2,4,5,7,10,11,14,12,19,3,20,16,6,13,18,9,8,15,17]. The first number 4 indicates that there are four transfer centers, the second number [1,6,3,4] indicates that the rail transit stations selected as transfer centers are rail stations 1, 6, 3, and 4, respectively. The third number [2,4,8,6] indicates that rail station 1 serves two demand points, station 6 serves four demand points, station 3 serves eight demand points, and station 4 serves six demand points. The fourth part of the numbers [1,2,4,5,7,10,11,14,12,19,3,20,16,6,13,18,9,8,15,17] means that demand points 1 and 2 are served by station 1, demand points 4, 5, 7 and 10 are served by station 6, demand points 11, 14, 12, 19, 3, 20, 16 and 6 are served by station 3, and demand points 13, 18, 9, 8, 15 and 17 are served by station 4.

[0161] Defining the fitness function: The objective function of this embodiment is to minimize the total cost of the intra-city express delivery system based on the urban rail transit network. Therefore, the inverse of the objective function of the location optimization model for the transfer center is used as the fitness function. The fitness function is shown below.

[0162]

[0163] Genetic Operations: Selection: Select the best individual and copy it directly to the next population. Crossover: This embodiment uses a single-point crossover. Mutation: This embodiment allows a small number of individuals to undergo single-point mutation. Mutation probabilities are 0.05 and 0.1.

[0164] Determine the termination condition: When the iteration reaches a certain number of times, the iteration will stop automatically and the solution with the largest fitness will be selected.

[0165] Decoding: Converting codes into actual entities.

[0166] Specific parameters: the population size is set to 50; the maximum number of iterations is set to 1000; when the number of iterations is less than 25, the mutation probability is 0.1, and when the number of iterations is greater than 25, the mutation probability is 0.05.

[0167] In this embodiment, according to the above method, when the number of selected transfer centers p=5, the transfer centers are Honghu East Road, Wulidian, Dongbu Park, Taipingchong, and Niujiaotuo. Most of these transfer centers are transfer stations or adjacent to transfer stations, with a high degree of convenience and easy transfer. Among them, the Honghu East Road station undertakes the distribution tasks of 28 demand points, the Wulidian station undertakes the distribution tasks of 88 demand points, and the Dongbu Park station undertakes the distribution tasks of 32 demand points. The Taipingchong station undertakes the distribution tasks of 11 demand points, and the Niujiaotuo station undertakes the distribution tasks of 17 demand points. The total cost corresponding to the selection of these 5 transfer centers is 11541750.282493 yuan. The transfer center results are shown in Table 3. The spatial distribution of demand points and transfer centers is shown in Figure 4 shown.

[0168] Table 3

[0169]

[0170] When p=10, the transfer centers are Niujiaotuo, Baiju Temple, Yuelai, Gaoyikou, Zoo, Sikm, Chongqing North Station South Square, Sanya Bay, Huayan Center Station, and Jinjian Road. Most of these transfer centers are transfer stations or adjacent to transfer stations, with a high degree of convenience and easy transfer. Among them, Niujiaotuo Station undertakes the distribution tasks of 125 demand points, Baiju Temple Station undertakes the distribution tasks of 18 demand points, and Yuelai Station undertakes the distribution tasks of 23 demand points. Gaoyikou Station undertakes the distribution tasks of 2 demand points, and Zoo Station undertakes the distribution tasks of 3 demand points. The transfer center results are shown in Table 4, and the spatial distribution of demand points and transfer centers is shown in Table 4. Figure 5 shown.

[0171] Table 4

[0172]

[0173] The top 75 rail transit stations with the highest comprehensive importance were selected from 256 rail transit stations. Genetic algorithm was then used to find the rail transit station with the lowest total cost for the same-city express delivery system based on the urban rail transit network as the transfer center, and the results were analyzed.

[0174] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for optimizing the location of intra-city express delivery and transfer centers based on multi-index evaluation, characterized by: The method comprises the following steps: Obtain rail transit station information and evaluate the importance of rail transit stations based on the degree DC of the node i , adjacent node importance H-index and betweenness centrality BC i Evaluate the importance of its sites and select alternative transfer centers based on the importance of the sites; Determine the constraints for establishing a transshipment center location optimization model; Considering the express delivery cost, express loading and unloading and handling cost and transportation fixed cost, the optimization problem of establishing the transshipment center location optimization model with the goal of minimizing the total cost; Genetic algorithm is used to solve the optimization problem, and the optimal addressing scheme is generated through encoding, crossover and mutation operations.

2. The method for optimizing the location of intra-city express delivery and transfer centers based on multi-index evaluation according to claim 1, characterized in that: In the rail transit network, the degree DC of the node at rail transit station i is i It refers to the number k of rail transit stations directly connected to rail transit station i, which is expressed as: DC i =k In the urban rail transit network, if rail transit station i is the starting station, DC i =1, if rail transit station i is an ordinary intermediate station, then DC i =2, if rail transit station i is a transfer station, then DC i Greater than 2; in the urban rail transit network, the DC of rail transit station i i The larger it is, the more important it is.

3. The method for optimizing the location of intra-city express delivery and transfer centers based on multi-index evaluation according to claim 1, characterized in that: In the urban rail transit network, the H-index of rail transit station i means that the degree of h directly connected rail transit stations is greater than or equal to h, which is expressed as: H i = max(h), satisfying |{j|d j ≥h, and i and j are directly connected}|≥h In the urban rail transit network, the larger the H-index of rail transit station i, the higher the overall importance of other nodes connected to rail transit station i, and the higher the importance of node i.

4. The method for optimizing the location of intra-city express delivery and transfer centers based on multi-index evaluation according to claim 1, characterized in that: In the urban rail transit network, the betweenness centrality BC of rail transit station i is i It refers to the ratio of the number of shortest paths passing through rail transit station i to the number of shortest paths in the network, which is expressed as: Among them, n st represents the number of shortest paths connecting node s and node t, It represents the number of paths passing through rail transit station i in the shortest path connecting node s and node t. In the urban rail transit network, the greater the betweenness centrality of rail transit station i, the more shortest paths passing through rail transit station i, and the higher the importance of rail transit station i.

5. The method for optimizing the location of intra-city express delivery and transfer centers based on multi-index evaluation according to claim 1, characterized in that: Combining entropy weight method and TOPSIS comprehensive evaluation method to evaluate the degree DC of nodes i , adjacent node importance H-index and betweenness centrality BC i The comprehensive evaluation process is as follows: Suppose there are m nodes, each node contains n indicators, forming an m×n original evaluation matrix A={a ij } k×l , where a ij Indicates the value of the i-th node on the j-th index, and standardizes the n indexes to generate a dimensionless matrix A = {b ij } k×l ,in, Calculate the information entropy e of the jth indicator j , used to measure the uncertainty of the indicator at all nodes; when the information entropy of the indicator is larger, the indicator is more chaotic; conversely, the indicator is more organized; where b ij The expression for the value of the i-th node on the j-th standardized index is: Calculate the index weight w of the jth index of the i-th node j : Among them, g j =1-e j ;g j represents the information utility value of the jth indicator, which is used to reflect its contribution in the comprehensive evaluation; e j The smaller the g j The larger it is, the more discriminative the indicator is and the greater its weight is; Construct an evaluation matrix V, where each element v in the evaluation matrix V ij is the index weight w of the jth index of the i-th node j The product of the values ​​corresponding to each indicator, this matrix is ​​used to subsequently calculate the distance between each node and the positive and negative ideal solutions, and then calculate their closeness, expressed as: V={v ij }={w j b ij } Define positive ideal solution V + ={max i v ij }、Negative ideal solution V - ={min i v ij }, calculate the distance between the i-th node and the positive and negative ideal solutions: Then calculate the closeness C of the i-th node i : Among them, C i ∈[0,1], closeness C i The larger it is, the closer node i is to the positive ideal solution, and the higher its comprehensive importance; According to the proximity C of all nodes i The nodes are sorted by the value and the first K nodes are selected as candidate transfer centers according to the preset number of locations.

6. The method for optimizing the location of intra-city express delivery and transfer centers based on multi-index evaluation according to claim 1, characterized in that: The constraints identified for establishing the transshipment center location optimization model include the following assumptions: Assumption 1: Only the transportation through rail transit stations is considered, and the transportation directly through the terminal ground vehicle dispatch system is not considered; Assumption 2: A transit center can serve multiple demand points, and each demand point can only be served by one transit center; Assumption 3: Every alternative rail transit station is accessible to every demand point; Assumption 4: The distance from each alternative rail transit station to the demand point is a straight-line distance; Assumption 5: The pickup and delivery service times are the same for all customers, and price fluctuations caused by not completing the service within the customer's specified time window are not considered; Assumption 6: Customer demand is calculated based on weight, regardless of product type. Assumption 7: The number of packages at all demand points is estimated based on city population data; Assumption 8: The parcel handling capacity of each candidate rail transit station can meet the demand points of its service; Assumption 9: All sorted items and parcels are correctly transported to the corresponding rail trains every time; Assumption 10: Assume that the vehicle travels at a constant speed during the delivery process; Assumption 11: The cost of transfer is not considered.

7. The method for optimizing the location of intra-city express delivery and transfer centers based on multi-index evaluation according to claim 1, characterized in that: The express transportation cost C1 refers to the transportation cost between the demand point and the transshipment center, which is expressed as: C1=∑c1a i d ij x ij ,i∈N,k∈N,j∈M Among them, the express transportation cost C1 is proportional to the demand volume at the demand point and the transportation distance from the demand point to the transfer center; c1 represents the highway transportation cost per unit express per unit distance; a i represents the demand quantity of demand point i; d ij represents the distance from demand point i to rail transit station j; x ij is a 0-1 variable, x ij = 0 means that the item package of demand point i is not served by rail transit station j, x ij =1 means that the item package of demand point i is served by rail transit station j; N represents the set of demand points, and M represents the set of rail transit stations; The express loading and unloading and handling cost C2 refers to the labor costs of the express delivery workers at rail transit stations, which is expressed as: C2=∑c2a i ,i∈N Among them, the express loading, unloading and handling cost C2 at the rail transit station is proportional to the demand volume at the demand point; c2 represents the unit cost of entering and exiting the rail transit station; The fixed transportation cost C3 includes land lease fees or land transfer fees, water charges and electricity charges, which can be expressed as: C3=∑c3y j ,j∈M Among them, the fixed transportation cost C3 is proportional to the number of transfer centers built; c3 represents the fixed operating cost of each rail transfer center built; y j is a 0-1 variable, y j =0 means that rail transit station j is not selected as the express transfer center; j =1 means that rail transit station j is selected as the express transfer center; Therefore, the objective function of minimizing the total cost is expressed as: st. C1:x ij ≤y j ,i∈N,j∈M C3:∑y j =p,j∈M Among them, constraint C1 means that the unselected rail transit stations cannot provide services to the demand points, constraint C2 means that a demand point is only served by one transfer center; constraint C3 means that there are p transfer centers in total.

8. The method for optimizing the location of intra-city express delivery and transfer centers based on multi-index evaluation according to claim 7, characterized in that: The genetic algorithm is used to solve the model, which includes: Coding: natural number coding is used to encode candidate rail transit stations and demand points; the set of candidate rail transit stations is [1, 2, 3, …, m], and the set of demand points is [1, 2, 3, …, n]; Initialize the population: The chromosomes in the population contain four parts: the first number represents the number of transfer centers, the second part represents the numbers of a randomly generated group of transfer centers, the third part represents the number of demand points served by each transfer center, and the fourth part represents the numbers of demand points served by each transfer center; Definition of fitness function: The objective function is the minimum total cost of the intra-city express delivery system based on the urban rail transit network. The inverse of the objective function of the location optimization model for the transfer center is used as the fitness function. The fitness function is as follows: Genetic operation: Selection operation, which selects the best individual and copies it directly to the next population; the operation includes crossover operation and mutation operation. Crossover operation adopts single-point crossover method, and mutation operation allows a small number of individuals to undergo single-point mutation. Determine the termination condition: When the iteration reaches a certain number of times, the iteration will automatically stop and the solution with the highest fitness will be selected; Decoding: Converting codes into actual entities.

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