Rural area dynamic goods taking and delivering path planning method considering goods taking value and transportation carbon emission

Abstract

The invention relates to a rural area dynamic goods taking and delivering path planning method considering goods taking value and transportation carbon emission, and belongs to the technical field of logistics distribution. The invention provides a dynamic path planning method for solving the problems of mileage increase and carbon emission increase caused by insufficient utilization of express delivery capacity resources in rural areas and non-planed pickup requirements. The method comprises the following steps of: initializing customer sites, distribution vehicles and network information, monitoring the generation condition of unplanned goods taking demands in real time, evaluating goods taking values by adopting an analytic hierarchy process (AHP) and an approximate ideal solution sorting method (TOPSIS), screening high-value demands, establishing a path planning model by taking the lowest carbon emission as a target, and optimizing a path by utilizing an improved genetic algorithm (GA). According to the invention, high-value customer satisfaction is effectively improved, goods taking tasks are reasonably increased, the driving mileage of a distribution vehicle is remarkably reduced, distribution time is shortened, energy consumption and carbon emission are reduced, and reference is provided for improvement of express distribution efficiency and green and low-carbon development in rural areas.

Description

Technical Field

[0001] This invention belongs to the field of logistics and distribution technology, and relates to a method for dynamic pickup and delivery route planning in rural areas that takes into account pickup value and transportation carbon emissions. Background Technology

[0002] In rural areas, the vast and sparsely populated terrain typically means that each administrative village only has one courier station. When courier companies develop next-day pickup and delivery plans, not all stations have pickup and delivery needs. However, when couriers execute their tasks according to the established plan, they often encounter situations where stations that were not originally scheduled for pickup and delivery suddenly generate unplanned pickup requests. To meet these needs, rural courier services generally adopt a next-day pickup model for orders placed on the same day. Under this model, the transportation capacity of courier vehicles is often underutilized. If, in order to utilize existing resources as much as possible, all unplanned pickup requests are responded to indiscriminately, and pickup and delivery routes are updated in real time, it not only easily leads to increased mileage for delivery vehicles and extended delivery times, but also significantly increases energy consumption and carbon emissions from transportation.

[0003] Therefore, establishing a value assessment mechanism for unplanned pickup requests is of great significance. Through scientific evaluation and screening, dynamically inserting only high-value unplanned pickup requests into the original pickup and delivery routes can reasonably increase pickup tasks, improve the satisfaction of high-value customers, and effectively reduce vehicle carbon emissions in the transportation process for express delivery companies, achieving the goal of cost reduction and efficiency improvement. Existing dynamic pickup and delivery route optimization methods, when dealing with ad-hoc unplanned pickup requests, typically include all unplanned requests in the real-time route update scope, without fully considering the pickup value of unplanned requests. While such methods can achieve dynamic adjustment of pickup and delivery routes, they can easily lead to increased delivery vehicle mileage, extended delivery times, and a significant increase in energy consumption and transportation carbon emissions.

[0004] To address the aforementioned issues, this invention proposes a dynamic pickup and delivery route planning method for rural areas that considers pickup value and transportation carbon emissions. It combines the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate the pickup value of unplanned pickup needs, and employs an improved Genetic Algorithm (GA) to optimize route planning. This method aims to efficiently solve the challenges of pickup and delivery route planning in dynamic scenarios in rural areas, providing a reference for improving the efficiency and green, low-carbon level of express delivery in rural areas. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a method for dynamic pickup and delivery route planning in rural areas that takes into account pickup value and transportation carbon emissions.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention addresses the problem of insufficient utilization of express delivery capacity in rural areas. Considering the dispersed nature of local settlements and limited vehicle resources, it proposes a dynamic pickup and delivery route planning method for rural areas, taking into account both the value of goods picked up and carbon emissions from transportation. The technical solution to this problem is as follows:

[0008] A method for dynamic pickup and delivery route planning in rural areas that considers pickup value and transportation carbon emissions includes the following steps:

[0009] 1. Initialize data

[0010] 1.1 Determine customer site information

[0011] Determine the customer site set V within a certain area of ​​a rural region, and the given customer site set V d V, a collection of sites with potential unplanned pickup needs s The relationship between the three is V = V d ∪V s and The customer site set V consists of express delivery service points in each administrative village. If there are m administrative villages within the area, then set V contains m customer sites; given a customer site set V... d It consists of express delivery service points in administrative villages with definite pick-up and delivery needs. If there are n administrative villages with definite needs within the area, then the set V is... d It includes n existing customer sites; and V is a set of sites with potential unplanned pickup needs. s It consists of express delivery service points in administrative villages with potential pickup needs. If there are s administrative villages within the area that have no pickup or delivery needs before the start of the day's pickup and delivery tasks, then the set V is... s Includes s sites with potential unplanned pickup needs.

[0012] 1.2 Obtaining Delivery Vehicle Information

[0013] The distribution center will dispatch one vehicle with a maximum load capacity of Q. max (Unit: t), Maximum volume is V max (m 3 The average delivery speed is V. p (km / h), working time is [t] min ,t max The delivery vehicles perform pick-up and delivery tasks, and the system allows a time range [t] for receiving pick-up orders. f_min ,tf_max ].

[0014] 1.3 Building a Pickup and Delivery Network

[0015] (1) Construct a pickup and delivery network graph G = (N, E), where N is the set of nodes in the network and E is the set of arcs in the network.

[0016] (2) Determine the node set N. Mark the specific locations of customer sites and distribution centers on the map to form the node set N = V∪{0} of the distribution network G, where V = {1,2,3....,m} represents customer sites and 0 represents distribution centers.

[0017] (2) Determine the arc set E. Define the road segment between node i and node j as an arc, then the arc set of the delivery network G is E={(i,j)|i∈N,j∈N,i≠j}.

[0018] (3) Determine the distance d between each location. ij The actual distances d between the distribution center and each customer site, as well as between the customer sites themselves, are obtained using Gaode Maps. ij (Unit: km)

[0019] (4) Determine the formula for calculating carbon emissions: Where P max Q represents the fuel consumption per unit distance when the vehicle is fully loaded (unit: kg). ij P0 represents the vehicle's load (in tons) from node i to node j, and P0 represents the fuel consumption (in kilograms) per unit distance traveled by the vehicle when the vehicle load is 0.

[0020] (5) Determine the weights of the edges in the network graph G. The carbon emissions f(Q) between node i and node j are calculated. ij Using the weights of edges in the pickup and delivery network graph G, construct the pickup and delivery network graph G = (N, E).

[0021] 2. Within the system's permitted time frame for accepting pickup orders, the distribution center monitors the set of sites V with potential unplanned pickup needs in real time. s The status of each station in China.

[0022] 3. Extract set V s Customer sites that generate unplanned pickup requests constitute the optional unplanned pickup request site set V. a .

[0023] 4. Valuation of unplanned pickup requests

[0024] 4.1 Constructing an evaluation index system for the value of goods picked up

[0025] 4.1.1 Determine the primary indicators

[0026] Following the "Regulations on the Administration of the Express Delivery Market" which stipulates that "express delivery companies must optimize customer service," a system for assessing the value of unplanned pickup needs is constructed through systematic analysis.

[0027] Based on the factors influencing the value of unplanned pickup requests, the primary indicators are defined as: Goods Value A1, Convenience Value A2, and Customer Value A3. A1 represents the economic and time-sensitive value of the goods themselves at the point of service. A2 represents factors affecting vehicle accessibility, such as the distance of the point of service from the upstream station, road accessibility, and weather conditions. A3 represents the long-term value that the point of service contributes to the express delivery company's business.

[0028] 4.1.2 Determine secondary indicators based on primary indicators

[0029] (1) Primary index A1 is represented by three secondary indices: single pickup revenue (B1), proportion of high-value goods (B2), and goods timeliness value coefficient (B3). Among them, B1 represents the direct revenue that a single pickup can bring to the express delivery company, calculated using the following formula: (n is the number of goods, w) i Let p be the weight of the i-th item, obtained from weighing at the courier station; i B2 represents the unit price of shipping for this type of goods (sourced from the shipping cost catalog of courier companies); B2 indicates the proportion of high-value goods such as fresh produce and electronic products in a single pickup, calculated using the following formula: (H represents the set of high-value goods, specifically including fresh produce and electronic products); B3 represents the degree of value reduction of goods due to delayed pickup, calculated as: B3 = e ^(-λ·T) (λ is the attenuation rate parameter, set according to the type of goods, and derived from publicly available data from the express delivery company's operations department; T is the length of the pickup time window, derived from the information filled in by the customer when placing the order).

[0030] (2) The primary indicator A2 is represented by four secondary indicators: node distance factor (B4), time window width (B5), road accessibility level (B6), and weather impact discount (B7). Among them, B4 represents the distance between the service demand point and the superior station, calculated using the following formula: (d is the actual distance from the previous node to the service request point, derived from path distance data in the GIS map; r is the average distance of the regional road network, with a regional radius of 3km, derived from the regional road network statistics database of the express delivery company); B5 represents the length of the customer's allowed pickup time window, calculated as: B5 = latest allowed pickup time - earliest allowed pickup time, derived from the information filled in by the customer when placing the order; B6 represents the impact of road conditions on vehicle traffic convenience, calculated as: Sourced from the public road network database of the transportation department; B7 represents the impact of extreme weather conditions on vehicle accessibility, calculated using the following formula: Sourced from the Weather Bureau API.

[0031] (3) Primary indicator A3 is represented by two secondary indicators: historical on-time fulfillment rate (B8) and customer loyalty (B9). B8 represents the number of successful pickup transactions within the pickup time window, calculated using the following formula: (M represents the total number of transactions within the statistical period, derived from the total number of transactions recorded in the courier company's order database; N represents the number of successful transactions within the statistical period, derived from historical data records in the courier company's order database); B9 represents the stability of the cooperation between the courier company and this customer within the statistical period, calculated using the following formula: (T1 represents the current date minus the first cooperation date, derived from the first cooperation date and the current date in the courier company's CRM system; X represents the actual number of cooperations within the statistical period, derived from historical data records in the courier company's order system; Y represents the total number of days in the statistical period).

[0032] 4.2 Calculate the weights of each indicator using the AHP-TOPSIS method

[0033] Since the three primary indicators of economic value, convenience value, and customer value are difficult to quantify directly, this paper selects easily quantifiable secondary indicators from the unplanned pickup demand value assessment indicator system as the core parameters for assessing the value of unplanned pickup demand.

[0034] 4.2.1 Calculate index weights using the AHP method

[0035] (1) Construct the judgment matrix

[0036] The judgment matrix B for secondary indicators B1 to B9 was obtained using the expert consultation method.

[0037]

[0038] in B ij This represents the importance of indicator i relative to indicator j.

[0039] (2) Calculate the weight of each indicator

[0040] Calculate the 9th root of the product of each row of the matrix using the arithmetic square root, and obtain a 9-dimensional vector. The column vector is then standardized, and the weight of the i-th factor is calculated.

[0041] (3) Consistency check

[0042] Let the largest eigenvalue of matrix B be i = 1, 2, ..., 9. Where AW represents the row-wise sum of the standardized weights of matrix B. Calculate the consistency index. Further calculate the consistency ratio The RI value can be obtained from the table. When CR < 0.1, it means that the consistency test of matrix B has passed. Otherwise, B needs to be corrected.

[0043] (4) Determining the weights of primary indicators

[0044] The formula for calculating the weight of primary indicator A1 is: w A1 =w B1 +w B2 +w B3 The formula for calculating the weight of A2 is: w A2 =w B4 +w B5 +w B6 +w B7 The formula for calculating the weight of A3 is: w A3 =w B8 +w B9 .

[0045] 4.2.2 Calculate the pick-up value score using the TOPSIS method

[0046] (1) Construct the initial decision matrix

[0047] Given m unexpected pickup requests and n secondary indicators, based on the optional set V... a The initial decision matrix X is constructed from the data information of each site:

[0048]

[0049] (2) Data standardization processing

[0050] For benefit-related indicators such as B1, B2, B4, and B5, the standardization formula is as follows: For cost-related indicators such as B3 and B6, the standardization formula is as follows: After standardization, matrix X is obtained as matrix Z:

[0051]

[0052] (3) Weighted Standardization Matrix

[0053] The standardized matrix Z is then weighted to obtain the weighted matrix V:

[0054]

[0055] (4) Determine the positive and negative ideal solutions

[0056] Calculate the positive ideal solution for the j-th index:

[0057]

[0058] Calculate the negative ideal solution for the j-th index:

[0059]

[0060] (5) Calculate the distance and pick-up value score

[0061] First, calculate the ideal distance. Then the negative ideal distance was calculated. The optional set V is further calculated based on the positive and negative ideal solutions. a Pickup value score for each station

[0062] 5. Screening for unplanned pickup requests

[0063] Set the pickup value threshold to α. When the selectable set V... a The pickup value C of the planned pickup demand site i in the Central Africa region i ≥α(i∈V a When this condition is met, it is identified as a high-value demand and placed from the optional set V. a The sites that meet the criteria are selected to form a set V of sites with unplanned pickup needs. q .

[0064] 6. Establish a dynamic pickup and delivery route planning model for rural areas that considers pickup value and aims to minimize carbon emissions.

[0065] 6.1 The objective function of the model is:

[0066]

[0067] Where f(Q) ij Let Q be the load of the vehicle from node i to node j. ij The amount of fuel consumed per unit distance traveled by a vehicle, d ij Let x be the transport distance from node i to node j, e be the carbon emission coefficient, and x be the transport distance from node i to node j. ij This indicates whether the vehicle travels from node i to node .

[0068] 6.2 The constraints of the model are:

[0069] (1) Each customer site is visited only once, and must leave from that point after the visit ends.

[0070]

[0071] (2) The vehicle must return to the distribution center after completing the task.

[0072]

[0073] (3) The total number of departures of the vehicle between all customer stations must be equal to the total number of arrivals.

[0074]

[0075] (4) Unplanned pickup requests with a value below a set threshold will not be served.

[0076]

[0077] Where y t This indicates whether an unplanned pickup demand t is served, where α is the pickup value of the unplanned pickup demand t. min The threshold for the value of goods picked up for unplanned pickup needs.

[0078] (5) When the value of an unplanned pickup request exceeds a threshold, there must be a path to the customer's site to provide service.

[0079]

[0080] (6) The weight of the goods carried by the vehicle at the time of departure is equal to the total weight of all goods to be collected by all customers, and cannot exceed the maximum load capacity of the vehicle.

[0081]

[0082] Where d j The weight of the goods to be collected for customer site j.

[0083] (7) The weight of the goods carried by the vehicle after completing the task is equal to the total weight of all goods that customers need to send, and cannot exceed the maximum load capacity of the vehicle.

[0084]

[0085] Where p i The weight of the goods to be shipped from customer site i.

[0086] (8) The load on the vehicle when it leaves a customer station is equal to the load when it arrives at the next customer station.

[0087]

[0088] (9) During the execution of the mission, the weight of the cargo carried by the vehicle shall not exceed the vehicle's maximum load capacity.

[0089]

[0090] (10) During the execution of the mission, the volume of the cargo carried by the vehicle shall not exceed the maximum volume of the vehicle.

[0091]

[0092] Where V ij Let s be the volume of cargo carried by the vehicle from node i to node j. j c is the volume of goods retrieved at customer site j. i The volume of goods delivered to customer site i.

[0093] (11) During the execution of the mission, the number of cargo items carried by the vehicle shall not exceed the maximum number of cargo items that the vehicle can accommodate.

[0094]

[0095] Among them B ij Let b be the number of goods carried by the vehicle from node i to node j. j Let a be the number of items picked up at customer site j. i This represents the number of items delivered to customer site i.

[0096] (12) Only one loop is allowed in the path.

[0097]

[0098] Where μ i Let N be the order in which node i is visited, and N be the number of nodes that need to be served.

[0099] 7. Path Update Algorithm

[0100] To address the dynamic simultaneous pickup and delivery route optimization problem in rural areas, considering both pickup value and carbon emissions, this study designs an improved genetic algorithm for solving the problem. The specific execution steps of this algorithm framework are as follows:

[0101] S1: Determine whether the current time is within the system's allowed order acceptance time range. If it is, proceed to S2; otherwise, continue with the original pickup and delivery route.

[0102] S2: During the process of the vehicle performing fixed customer pick-up and delivery tasks, every 10 minutes, determine set V. s Check if any stations have generated unplanned pickup requests. If a station has generated a pickup request, proceed to S3; otherwise, continue along the original pickup and delivery route.

[0103] S3: Using the AHP-TOPSIS model to analyze set V a For stations that generate unplanned pickup requests, the pickup value is assessed. When an unplanned request occurs at station i (i∈V)... a The value of the goods taken (C) iIf the value is ≥0.6, the point will be included in the daily pickup and delivery route planning; otherwise, the original pickup and delivery route will continue.

[0104] S4: Uses natural number encoding to randomly generate a sequence of length v. d +v s A string of natural numbers. 1 to v d The representative determines the set of customer points, v d +1~v d +v s This represents a set of potential unplanned pickup demands. The path update scheme is obtained by decoding the numerical correspondence.

[0105] S5: Generate an initial population of size N. Randomly generate N populations of length v. d +v s The natural number string is used as the initial population.

[0106] S6: Calculate the fitness of the population using the reciprocal of the objective function as the fitness function.

[0107] S7: Perform the selection-replication operation, that is, select a number of individuals from the original population according to the roulette wheel strategy to form the offspring population. The probability of each individual being selected is:

[0108]

[0109] S8: Select two individuals for crossover. Within the chromosome length, randomly generate two crossover sites P1 and P2, and perform crossover operations on the [P1,P2] regions of the two individuals.

[0110] S9: Introduce mutations into an individual, that is, randomly generate two crossover sites P1 and P2 within the length of the chromosome, and reverse the order of genes in the selected mutation region.

[0111] S10: When the number of iterations reaches the maximum or the optimal solution does not change, output a new delivery route, and the vehicle performs the pickup and delivery tasks according to the new route.

[0112] The beneficial effects of this invention are as follows: Combining the characteristics of dispersed settlements and limited vehicle resources in rural areas, this invention offers higher adaptability and more rational resource allocation compared to traditional dynamic pickup and delivery route optimization methods. This invention constructs a pickup value assessment mechanism for unplanned pickup needs, employing the Analytic Hierarchy Process (AHP) and the Topological Sequencing Method (TOPSIS) to scientifically screen high-value needs. Only eligible unplanned pickup needs are dynamically inserted into the original route, effectively improving the satisfaction of high-value customers while rationally increasing pickup tasks. With the goal of minimizing carbon emissions, this invention optimizes delivery routes through an improved Genetic Algorithm (GA), significantly reducing delivery vehicle mileage, shortening delivery time, and reducing energy consumption and transportation carbon emissions, helping express delivery companies achieve cost reduction and efficiency improvement in rural areas. This invention provides important reference value for dynamic pickup and delivery route optimization in rural areas under the guidance of low-carbon goals, offering a practical solution for improving express delivery efficiency and green, low-carbon levels.

[0113] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0114] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein:

[0115] Figure 1 A dynamic pickup and delivery route planning process for rural areas that takes into account pickup value and transportation carbon emissions;

[0116] Figure 2 The path update algorithm flow. Detailed Implementation

[0117] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed 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 representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0118] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0119] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship 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 orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0120] Figure 1 This is a schematic diagram illustrating the implementation of the present invention. The diagram records the key steps in the implementation of this invention method, which are used to perform dynamic pickup and delivery route planning in rural areas.

[0121] Figure 2 This is a flowchart of the path update algorithm. The diagram illustrates the basic process of this path update algorithm in conjunction with an embodiment.

[0122] Taking a certain courier company's operating area in Tongliang District, Chongqing as an example, a dynamic pickup and delivery route planning method for rural areas that considers pickup value and transportation carbon emissions includes the following steps:

[0123] 1. Initialize data

[0124] 1.1 Determine customer site information

[0125] Determine the customer site set V within a certain area of ​​a rural region, and the given customer site set V d V, a collection of sites with potential unplanned pickup needs s The relationship between the three is V = V d ∪V s and The customer site set V consists of express delivery service points in each administrative village. If there are 28 administrative villages in the area, then set V contains 28 customer sites; a given customer site set V... d It consists of express delivery service points in administrative villages with defined pick-up and delivery needs. If there are 21 administrative villages with defined needs within the area, then the set V is... dIncludes 21 existing customer sites; V is a collection of sites with potential unplanned pickup needs. s It consists of express delivery service points in administrative villages with potential pickup needs. If there are 7 administrative villages within the area that have no pickup or delivery needs before the start of the day's pickup and delivery tasks, then the group V is included. s It includes 7 sites with potential unplanned pickup demand.

[0126] 1.2 Obtaining Delivery Vehicle Information

[0127] The distribution center will dispatch one vehicle with a maximum load capacity of 3 tons and a maximum volume of 2.6 cubic meters. 3 The average delivery speed is V. p Delivery vehicles with a speed of (km / h) and working hours of 9:00-12:00 and 14:00-18:00 are allowed to perform pick-up and delivery tasks. The system allows the acceptance of pick-up orders within the time range of 8:00-17:30.

[0128] 1.3 Constructing a Pickup and Delivery Network Information

[0129] (1) Construct a pickup and delivery network graph G = (N, E), where N is the set of nodes in the network and E is the set of arcs in the network.

[0130] (2) Determine the node set N. Mark the specific locations of customer sites and distribution centers on the map to form the node set N = V∪{0} of the distribution network G, where V = {1,2,3....,m} represents customer sites and 0 represents distribution centers.

[0131] (2) Determine the arc set E. Define the road segment between node i and node j as an arc, then the arc set of the delivery network G is E={(i,j)|i∈N,j∈N,i≠j}.

[0132] (3) Determine the distance d between each location. ij The actual distances d between the distribution center and each customer site, as well as between the customer sites themselves, are obtained using Gaode Maps. ij (Unit: km)

[0133] (4) Determine the formula for calculating carbon emissions: Where P max Q represents the fuel consumption per unit distance when the vehicle is fully loaded (unit: kg). ij P0 represents the vehicle's load (in tons) from node i to node j, and P0 represents the fuel consumption (in kilograms) per unit distance traveled by the vehicle when the vehicle load is 0.

[0134] (5) Determine the weights of the edges in the network graph G. The carbon emissions f(Q) between node i and node j are calculated. ijUsing the weights of edges in the pickup and delivery network graph G, construct the pickup and delivery network graph G = (N, E).

[0135] 2. Within the system's permitted time frame for accepting pickup orders, the distribution center monitors the set of sites V with potential unplanned pickup needs in real time. s The status of each station in China.

[0136] 3. Extract set V s Customer sites that generate unplanned pickup requests constitute the optional unplanned pickup request site set V. a .

[0137] 4. Valuation of unplanned pickup requests

[0138] 4.1 Constructing an evaluation index system for the value of goods picked up

[0139] 4.1.1 Determine the primary indicators

[0140] Following the "Regulations on the Administration of the Express Delivery Market" which stipulates that "express delivery companies must optimize customer service," a system for assessing the value of unplanned pickup needs is constructed through systematic analysis.

[0141] Based on the factors influencing the value of unplanned pickup requests, the primary indicators are defined as: Goods Value A1, Convenience Value A2, and Customer Value A3. A1 represents the economic and time-sensitive value of the goods themselves at the point of service. A2 represents factors affecting vehicle accessibility, such as the distance of the point of service from the upstream station, road accessibility, and weather conditions. A3 represents the long-term value that the point of service contributes to the express delivery company's business.

[0142] 4.1.2 Determine secondary indicators based on primary indicators

[0143] (1) Primary index A1 is represented by three secondary indices: single pickup revenue (B1), proportion of high-value goods (B2), and goods timeliness value coefficient (B3). Among them, B1 represents the direct revenue that a single pickup can bring to the express delivery company, calculated using the following formula: (n is the number of goods, w) i Let p be the weight of the i-th item, obtained from weighing at the courier station; i B2 represents the unit price of shipping for this type of goods (sourced from the shipping cost catalog of courier companies); B2 indicates the proportion of high-value goods such as fresh produce and electronic products in a single pickup, calculated using the following formula: (H represents the set of high-value goods, specifically including fresh produce and electronic products); B3 represents the degree of value reduction of goods due to delayed pickup, calculated as: B3 = e ^(-λ·T)(λ is the attenuation rate parameter, set according to the type of goods, and derived from publicly available data from the express delivery company's operations department; T is the length of the pickup time window, derived from the information filled in by the customer when placing the order).

[0144] (2) The primary indicator A2 is represented by four secondary indicators: node distance factor (B4), time window width (B5), road accessibility level (B6), and weather impact discount (B7). Among them, B4 represents the distance between the service demand point and the superior station, calculated using the following formula: (d is the actual distance from the previous node to the service request point, derived from path distance data in the GIS map; r is the average distance of the regional road network, with a regional radius of 3km, derived from the regional road network statistics database of the express delivery company); B5 represents the length of the customer's allowed pickup time window, calculated as: B5 = latest allowed pickup time - earliest allowed pickup time, derived from the information filled in by the customer when placing the order; B6 represents the impact of road conditions on vehicle traffic convenience, calculated as: Sourced from the public road network database of the transportation department; B7 represents the impact of extreme weather conditions on vehicle accessibility, calculated using the following formula: Sourced from the Weather Bureau API.

[0145] (3) Primary indicator A3 is represented by two secondary indicators: historical on-time fulfillment rate (B8) and customer loyalty (B9). B8 represents the number of successful pickup transactions within the pickup time window, calculated using the following formula: (M represents the total number of transactions within the statistical period, derived from the total number of transactions recorded in the courier company's order database; N represents the number of successful transactions within the statistical period, derived from historical data records in the courier company's order database); B9 represents the stability of the cooperation between the courier company and this customer within the statistical period, calculated using the following formula: (T1 represents the current date minus the first cooperation date, derived from the first cooperation date and the current date in the courier company's CRM system; X represents the actual number of cooperations within the statistical period, derived from historical data records in the courier company's order system; Y represents the total number of days in the statistical period).

[0146] 4.2 Calculate the weights of each indicator using the AHP-TOPSIS method

[0147] Since the three primary indicators of economic value, convenience value, and customer value are difficult to quantify directly, this paper selects easily quantifiable secondary indicators from the unplanned pickup demand value assessment indicator system as the core parameters for assessing the value of unplanned pickup demand.

[0148] 4.2.1 Calculate index weights using the AHP method

[0149] (1) Construct the judgment matrix

[0150] The judgment matrix B for secondary indicators B1 to B9 was obtained using the expert consultation method.

[0151]

[0152] in B ij This represents the importance of indicator i relative to indicator j.

[0153] (2) Calculate the weight of each indicator

[0154] Calculate the 9th root of the product of each row of the matrix using the arithmetic square root, and obtain a 9-dimensional vector. The column vector is then standardized, and the weight of the i-th factor is calculated.

[0155] (3) Consistency check

[0156] Let the largest eigenvalue of matrix B be i = 1, 2, ..., 9. Where AW represents the row-wise sum of the standardized weights of matrix B. Calculate the consistency index. Further calculate the consistency ratio The RI value can be obtained from the table. When CR < 0.1, it means that the consistency test of matrix B has passed. Otherwise, B needs to be corrected.

[0157] (4) Determining the weights of primary indicators

[0158] The formula for calculating the weight of primary indicator A1 is: w A1 =w B1 +w B2 +w B3 The formula for calculating the weight of A2 is: w A2 =w B4 +w B5 +w B6 +w B7 The formula for calculating the weight of A3 is: w A3 =w B8 +w B9 .

[0159] 4.2.2 Calculate the pick-up value score using the TOPSIS method

[0160] (1) Construct the initial decision matrix

[0161] Given m unexpected pickup requests and n secondary indicators, based on the optional set V... a The initial decision matrix X is constructed from the data information of each site:

[0162]

[0163] (2) Data standardization processing

[0164] For benefit-related indicators such as B1, B2, B4, and B5, the standardization formula is as follows: For cost-related indicators such as B3 and B6, the standardization formula is as follows: After standardization, matrix X is obtained as matrix Z:

[0165]

[0166] (3) Weighted Standardization Matrix

[0167] The standardized matrix Z is then weighted to obtain the weighted matrix V:

[0168]

[0169] (4) Determine the positive and negative ideal solutions

[0170] Calculate the positive ideal solution for the j-th index:

[0171]

[0172] Calculate the negative ideal solution for the j-th index:

[0173]

[0174] (5) Calculate the distance and pick-up value score

[0175] First, calculate the ideal distance. Then the negative ideal distance was calculated. The optional set V is further calculated based on the positive and negative ideal solutions. a Pickup value score for each station

[0176] 5. Screening for unplanned pickup requests

[0177] Set the pickup value threshold to α. When the selectable set V... a The pickup value C of the planned pickup demand site i in the Central Africa region i ≥α(i∈V a When this condition is met, it is identified as a high-value demand and placed from the optional set V. a The sites that meet the criteria are selected to form a set V of sites with unplanned pickup needs. q .

[0178] 6. Establish a dynamic pickup and delivery route planning model for rural areas that considers pickup value and aims to minimize carbon emissions.

[0179] 6.1 The objective function of the model is:

[0180] 6.2 The constraints of the model are:

[0181] (1) Each customer site is accessed only once:

[0182] (2) Vehicles must return to the distribution center after completing their mission:

[0183] (3) The total number of departures between all customer stations must equal the total number of arrivals:

[0184] (4) Unplanned pickup requests with a value below a set threshold will not be served: t =0, C t <α min .

[0185] (5) When the value of an unplanned pickup request exceeds a threshold, there must be a path to the customer's site to provide service.

[0186]

[0187] (6) The weight of the goods carried by the vehicle at the time of departure is equal to the total weight of all goods to be collected by all customers, and cannot exceed the maximum load capacity of the vehicle:

[0188]

[0189] (7) The weight of the goods carried by the vehicle after completing the task is equal to the total weight of all goods that customers need to send, and cannot exceed the vehicle's maximum load capacity:

[0190]

[0191] (8) The load of a vehicle when it leaves a customer station is equal to the load when it arrives at the next customer station:

[0192]

[0193] (9) During the execution of the mission, the weight of the cargo carried by the vehicle shall not exceed the vehicle's maximum load capacity:

[0194]

[0195] (10) During the execution of the mission, the volume of cargo carried by the vehicle shall not exceed the maximum volume of the vehicle:

[0196]

[0197] (11) During the execution of the mission, the number of cargo items carried by the vehicle shall not exceed the maximum number of cargo items that the vehicle can accommodate:

[0198]

[0199] (12) Only one loop is allowed in the path: μ i -μ j +Nx ij ≤N-1, j≠0, i≠j.

[0200] 7. Path Update Algorithm

[0201] The model is solved using the following steps:

[0202] S1: Determine whether the current time is within the system's allowed order acceptance time range. If it is, proceed to S2; otherwise, continue with the original pickup and delivery route.

[0203] S2: During the process of the vehicle performing fixed customer pick-up and delivery tasks, every 10 minutes, determine set V. s Check if any stations have generated unplanned pickup requests. If a station has generated a pickup request, proceed to S3; otherwise, continue along the original pickup and delivery route.

[0204] S3: Using the AHP-TOPSIS model to analyze set V a For stations that generate unplanned pickup requests, the pickup value is assessed. When an unplanned request occurs at station i (i∈V)... a The value of the goods taken (C) i If the value is ≥0.6, the point will be included in the daily pickup and delivery route planning; otherwise, the original pickup and delivery route will continue.

[0205] S4: Using natural number encoding, a natural number string of length 22+7=29 is randomly generated. 1~22 represents the set of known customer points, and 23~29 represents the set of potential unplanned pickup needs. The route delivery plan is obtained by decoding according to the correspondence of numbers.

[0206] S5: Generate an initial population of size N by randomly generating 1000 strings of natural numbers of length 29 as the initial population.

[0207] S6: Calculate the fitness of the population using the reciprocal of the objective function as the fitness function.

[0208] S7: Perform a select-copy operation.

[0209] S8: Select two individuals for crossover.

[0210] S9: To mutate an individual.

[0211] S10: When the number of iterations reaches the maximum or the optimal solution does not change, output a new delivery route.

[0212] 8. Solution Results

[0213] 8.1 Calculation of Indicator Weights

[0214] The judgment matrix of the secondary indicators in the goods value assessment index system was obtained by using the expert consultation method, and the weight of each indicator was calculated, as shown in Table 1.

[0215] Table 1 Weights of each indicator

[0216]

[0217] 8.2 Path Planning Results

[0218] Information on unplanned pickup requests generated when delivery vehicles are performing pickup and delivery tasks at designated customer sites is monitored, as shown in Table 2.

[0219] Table 2 Information on Unplanned Pickup Demands

[0220] Serial Number name longitude latitude Time of generation Pickup weight (t) Value of goods 23 FS 29.923 106.065 10:15 1.90 0.6425 24 HS 29.945 106.000 10:15 1.05 0.3706 25 BQ 29.903 106.007 12:01 1.05 0.4918 26 SS 29.880 105.955 13:30 2.25 0.7113 27 WQ 29.870 106.003 13:55 2.10 0.6796 28 TR 29.864 106.086 14:12 1.80 0.7867 29 DS 29.889 106.078 15:37 1.65 0.6607

[0221] Based on the above path update algorithm principle, the updated delivery path after each new pickup request is shown in Table 3.

[0222] Table 3 Optimal Delivery Plan

[0223]

[0224] The above describes the excellent effects of this invention in conjunction with one embodiment. Obviously, this invention is not only applicable to the above embodiment. Without departing from the basic spirit of this invention and without exceeding the content involved in the substantive content of this invention, various changes can be made to implement it in order to adapt to dynamic pickup path optimization under different situations.

[0225] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamic pickup and delivery route planning in rural areas that considers pickup value and transportation carbon emissions, characterized in that: Includes the following steps: Initialize the data: Determine the customer site set, delivery vehicle information, and pickup / delivery network information. The customer site set includes the established customer site set and the set of potential unplanned pickup demand sites. The delivery vehicle information includes maximum load capacity, maximum volume, average delivery speed, and working time. The pickup / delivery network information includes a network graph G = (N, E), where N is the set of nodes, E is the set of arcs, the distance between nodes is obtained through Amap (Gaode Maps), and the edge weight is the carbon emission between nodes. The carbon emission calculation formula is: Among them, e ij Let f be the carbon emissions from node i to node j, f be the fuel consumption per unit distance when the vehicle is fully loaded, f0 be the fuel consumption per unit distance when the vehicle is unloaded, and q be the carbon emissions from node i to node j. ij Let Q be the load of the vehicle from node i to node j, and d be the maximum load capacity of the vehicle. ij Let be the distance from node i to node j; Real-time monitoring: Within the time frame during which the system is allowed to receive pickup orders, the distribution center monitors the status of each site in the set of sites with potential unplanned pickup needs in real time; Extracting unplanned demand: Extracting customer sites that generate unplanned pickup demand from the set of potential unplanned pickup demand sites to form an optional set of unplanned pickup demand sites; Pickup value assessment: The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used to assess the pickup value of the stations in the set of optional unplanned pickup demand stations, construct a pickup value assessment index system, and calculate the pickup value score of each station. Filtering unplanned pickup requests: Set a pickup value threshold θ. When the pickup value of an unplanned pickup request site is greater than or equal to θ, it is included in the set of unplanned pickup request sites. Route planning: Establish a dynamic pickup and delivery route planning model with the objective of minimizing carbon emissions. The objective function is: Where, x ij The variable is a binary variable, representing whether a vehicle travels from node i to node j. The constraints include each customer site being visited once, the vehicle returning to the distribution center, vehicle load, and volume constraints. Route Update: An improved genetic algorithm (GA) is used to solve the route planning model and generate new delivery routes.

2. The method for dynamic pickup and delivery route planning in rural areas considering pickup value and transportation carbon emissions as described in claim 1, characterized in that: The pickup value assessment index system includes primary and secondary indicators. The primary indicators include goods value A1, convenience value A2, and customer value A3. The secondary indicators include single pickup revenue B1, proportion of high-value goods B2, goods timeliness value coefficient B3, node distance factor B4, time window width B5, road accessibility level B6, weather impact discount B7, historical on-time fulfillment rate B8, and customer loyalty B9.

3. The method for dynamic pickup and delivery route planning in rural areas considering pickup value and transportation carbon emissions as described in claim 2, characterized in that: The formula for calculating the single pickup revenue B1 is as follows: Where n is the number of goods, w i Let p be the weight of the i-th item. i Let $\frac{i}{i}$ be the unit price and freight cost for the $i$-th item. The formula for calculating the proportion of high-value goods B2 is as follows: Wherein, H represents the set of high-value goods; The formula for calculating the goods' time-sensitive value coefficient B3 is as follows: B3=e (-α·T) Where α is the decay rate parameter and T is the length of the pickup time window.

4. The method for dynamic pickup and delivery route planning in rural areas considering pickup value and transportation carbon emissions as described in claim 2, characterized in that: The formula for calculating the node distance factor B4 is as follows: Where d is the actual distance from the previous node to the service demand point, and r is the average distance of the regional road network; The formula for calculating the time window width B5 is as follows: B5=t latest -t earliest Among them, t latest To allow the latest pickup time, t earliest To allow the earliest possible pickup time; The formula for calculating the road accessibility level B6 is as follows: The formula for calculating the weather impact discount B7 is as follows:

5. The method for dynamic pickup and delivery route planning in rural areas considering pickup value and transportation carbon emissions according to claim 2, characterized in that: The formula for calculating the historical on-time fulfillment rate B8 is as follows: Where M is the total number of transactions within the statistical period, and N is the number of successful transactions within the statistical period; The formula for calculating customer loyalty B9 is as follows: Where T1 is the current date minus the date of the first collaboration, X is the actual number of collaborations within the statistical period, and Y is the total number of days in the statistical period.

6. The method for dynamic pickup and delivery route planning in rural areas considering pickup value and transportation carbon emissions according to claim 1, characterized in that: The AHP-TOPSIS method includes: constructing a judgment matrix using the AHP method, calculating the weights of secondary indicators and performing consistency checks; constructing an initial decision matrix using the TOPSIS method, performing data standardization processing, calculating a weighted standardization matrix, determining positive and negative ideal solutions, and calculating the pickup value score for each station.

7. The method for dynamic pickup and delivery route planning in rural areas considering pickup value and transportation carbon emissions according to claim 1, characterized in that: The improved genetic algorithm (GA) includes the following steps: Determine whether the current time is within the time range during which the system allows order acceptance; Every 10 minutes, check if any site in the potential unplanned pickup demand site set has generated unplanned pickup demand; Perform AHP-TOPSIS pickup value assessment on sites that generate unplanned pickup requests; An initial population of 1000 is generated using natural number encoding. Population fitness is calculated using the reciprocal of the objective function as the fitness function; Perform selection, crossover, and mutation operations; When the number of iterations reaches the maximum or the optimal solution remains unchanged, output a new delivery route.

8. The method for dynamic pickup and delivery route planning in rural areas considering pickup value and transportation carbon emissions according to claim 1, characterized in that: The constraints of the path planning model include: Each customer site is accessed exactly once: The vehicle returned to the distribution center: The vehicle's load capacity shall not exceed the maximum load capacity: ∑ i∈N q i ·y i ≤Q; where q i For the weight of goods at customer site i, y i Indicates whether site i is being served; Vehicle volume shall not exceed the maximum volume: Where s j V represents the volume of goods at customer site i. ij This represents the maximum volume of the vehicle.

9. The method for dynamic pickup and delivery route planning in rural areas considering pickup value and transportation carbon emissions according to claim 1, characterized in that: The system receives pickup orders between 8:00 and 17:30, and the delivery vehicles operate between 9:00 and 12:00 and between 14:00 and 18:

00.

10. The method for dynamic pickup and delivery route planning in rural areas considering pickup value and transportation carbon emissions according to claim 1, characterized in that: The customer site set consists of express delivery service points in administrative villages. The customer site set includes sites with established pickup and delivery needs, while the potential unplanned pickup demand site set includes sites that have not generated pickup and delivery needs.

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