CN115759319B - Express delivery network site and transfer site selection method considering job and location distribution - Google Patents
Express delivery network site and transfer site selection method considering job and location distribution Download PDFInfo
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Abstract
The invention discloses an express delivery network site and transfer station site selection method considering job location distribution, which comprises the steps of obtaining related information of regional express delivery network sites and transfer stations and preprocessing; taking the distribution characteristics of working population and living population into consideration, constructing an address selection model of the express delivery network point and the transfer station by taking the network point service capability as constraint and taking the minimum cost of the express delivery network point and the transfer station as a target; solving the model by adopting a simulated annealing algorithm nested genetic algorithm; and outputting address selection results of the express network and the transfer station. According to the method, the distribution characteristics of the working population and the living population are considered when the express network points and the transit stations are selected, the short-distance express service experience of the user can be effectively improved, and the distribution cost can be obviously reduced.
Description
Technical Field
The invention relates to the technical field of geospatial data application, in particular to an express delivery network site and transfer site selection method considering job and location distribution.
Background
With the rapid development of the Internet and electronic commerce industry, the express industry in China rapidly develops, and plays an increasingly important role in national economy and social development. The statistics of the national post office show that the annual growth rate of the total income and the business volume of the express industry in China exceeds 30% in the last ten years, and the national post office statistics show a remarkable rising trend. The method brings great development opportunities for express enterprises and also brings intense market competition. Reasonable express network site and transfer site selection can not only improve express service quality and obtain more clients, but also reduce distribution cost, so that the core competitiveness of express enterprises in the market is achieved. Therefore, the development of site selection research of express delivery network points and transfer stations is necessary.
Common site selection methods comprise an analytic hierarchy process, a gravity center process, a mixed integer programming process, a simulated annealing algorithm, a genetic algorithm, an immune algorithm and the like, but the existing research results do not consider the influence of population distribution space difference on site selection of express delivery sites and transfer stations, and also do not consider the uniform layout relationship of the express delivery sites and the transfer stations, so that certain limitation exists. In fact, for an express enterprise, site selection should consider both the distribution cost and the user short-distance express service experience; the transfer station address minimizes delivery costs while guaranteeing high quality delivery service.
Generally, the network points have no special requirements for scale, traffic conditions and the like except that the network points are required to be along the street as much as possible. Therefore, the dot position selectivity is high, and the dot position can be randomly selected from the region to be solved. Meanwhile, due to the fact that the express types and the quantity of people in work and life are obviously different, the express in work is generally drawing, files, data and the like, the quantity is small, the express is generally and directly delivered to recipients, the express in life often belongs to online shopping, the quantity is large, and due to the fact that the recipients are often out of home in delivery time, the express is generally delivered to a post for storage, and therefore, in order to effectively improve the overall short-distance express service experience of a user and reduce delivery cost, the difference between the population of work and the population distribution of living needs to be considered in site selection.
Unlike the network points, the transfer station has higher requirements on scale, geographical environment, traffic conditions, public facilities and the like, and the positions meeting the requirements in the area are not more, and can be selected from candidate positions for solving. Meanwhile, as the transfer station is connected with the transportation junction on one hand, the express delivery is transferred in, and is connected with the network point on the other hand, and the express delivery is transferred out, in order to effectively reduce the distribution cost, the site selection of the transfer station should consider the distance between the transfer station and the transportation junction, the network point and the transfer quantity.
Disclosure of Invention
The invention aims to provide an express network site selection method and a transfer site selection method considering the job location distribution, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: a method for selecting addresses of express delivery network points and transfer stations taking account of job and location distribution comprises the following steps:
step S1: related information of express network points and transfer stations in the area is obtained and preprocessed, wherein the related information comprises the following components: grid working population, grid living population, land utilization, current construction, candidate transfer station information, transportation hub and express post;
step S2: constructing an express network site and a transfer site selection model;
s21: taking account of the distribution characteristics of working population and living population, under the constraint of network point service capability, express network point site selection s 1 The cost formula is:
wherein m represents the number of plotsN represents the number of express network points, a 1 、a 2 Weights of land work population and land living population respectively, R i 、W i The number of land work population and land living population are respectively Z ij E {0,1}, when Z ij =1 means that the i-th land parcel working population and the resident population express service is provided by the j-th website, otherwise, 0; d, d ij Representing the distance from the ith land block to the jth net point; d, d ij ′ Representing the distance from the nearest express post to the jth site from the ith land block; b represents the average express business volume; l represents the network point operation cost coefficient; q (Q) j Represents the j-th network point service capability, Q 0 Representing the average service capacity of the network point; c ij Representing the network site to user or post shipping rate; y represents the storage cost of a single express post;
an objective function of formula (1) representing the operation cost and transportation cost of the express delivery site (the transportation cost includes the express delivery transportation cost of the working population, the express delivery transportation cost of the resident population and the custody cost);
the constraint condition of the formula (2) indicates that express delivery services of all land parcel working population and land parcel living population are provided by only one website;
constraint conditions of formulas (3) and (4) represent constraint of network point service capability;
s22: site selection s for transfer station 2 The cost formula is:
wherein o represents the number of transfer stations, r k E {0,1}, when r k =1 means that the kth candidate transfer station is selected, otherwise 0, x kj E {0,1}, when x kj =1 means that transfer station k is delivered by dot j, otherwise 0, q k Representing transfer station k traffic, a 3 、a 4 、a 5 Road, railway, aviation traffic weight, p hk 、p rk 、p ak Respectively representing the transportation distance from the nearest highway junction, railway junction and aviation junction to the transfer station k, c k Representing transport rate from hub to transfer station k, F k Representing the operation cost of the transfer station k, v k Representing the cost coefficient, q, of single piece processing of a transfer station k kj Representing traffic of transfer station k to network point j, p kj Representing the transport distance from the transfer station k to the network point j, c kj Representing the transport rate from transfer station k to website j;
an objective function of formula (5) representing transfer station transportation costs, operation costs, and processing costs;
a constraint condition of a formula (6) indicates that the traffic of the transfer station is the sum of the traffic of the corresponding network points;
a constraint of formula (7) indicating that each site has and only one transfer station is delivering for it;
a constraint of formula (8) indicates that the network point delivery is enabled only when the candidate transfer station is selected;
the constraint condition of the formula (9) indicates that the candidate transfer stations must be distributed for the network points after being selected;
s23: combining the formula (1) and the formula (5), and constructing an address selection model f(s) of the express network point and the transfer station by taking the minimum cost of the express network point and the transfer station as the target 1 ,s 2 )；
Step S3: solving the site selection models of the express network and the transfer station by utilizing a simulated annealing algorithm nested genetic algorithm;
s31, optimizing site selection by adopting an outer layer simulated annealing algorithm, and determining the traffic volume of the network points;
s32, optimizing the transit station address selection by adopting an inner layer genetic algorithm based on the network address selection and the traffic result;
step S4: and outputting address selection results of the express network and the transfer station.
Further, the grid working population and the grid living population in the step S1 are grid population with the caliber of 50 meters by 50 meters in three months; the preprocessing is to aggregate the grid working population and the grid living population to plots, generating the plot working population and the plot living population.
Further, in the step S2, the land parcel working population and the land parcel living population weight are determined according to the proportion of the online shopping express quantity to the total express quantity; road, railway and aviation traffic weights are determined according to the transportation mode proportion of the express enterprises.
Further, in the step S2, the distance from the ith land parcel to the jth express delivery site and the distance from the nearest express post to the jth site from the ith land parcel are calculated by a hundred-degree map or a high-altitude map riding route planning; the transportation distance from the transfer station k to the network point j is calculated by driving route planning of a hundred-degree map or a high-Germany map.
Further, the step S31 specifically includes the following steps:
s311, initializing an outer layer simulated annealing algorithm, and setting parameters: initial temperature T 0 End temperature T e The temperature attenuation coefficient b and the iteration times Q, and setting the current temperature T as an initial temperature;
s312, randomly generating initial address S of express network point 1 Site selection s 1 Each net point should be located in the current building along the street, so as to determine the net point traffic and select the net point site s 1 Substituting the net point traffic result into an inner layer genetic algorithm, and obtaining a transit station site selection s according to the inner layer genetic algorithm 2 Calculating initial value f(s) 1 ,s 2 ) And let the optimal solution θ=f (s 1 ,s 2 )；
S313, generating disturbance to generate new address S of express network point 1′ Calculating a new addressing objective function value f (s 1′ ,s 2′ )；
S314, if f (S) 1′ ,s 2′ )<θ, then the optimal solution θ=f (s 1′ ,s 2′ ) If f(s) 1′ ,s 2′ ) More than or equal to theta, determining whether the optimal solution theta is f(s) according to Metropolis criterion 1′ ,s 2′ )；
S315, if the iteration times Q are reached, entering the next step, otherwise repeating the step S313;
s316, the current temperature T is less than T e And obtaining the optimal solution, otherwise, cooling, setting the current temperature as bxT, and returning to the step S313.
Further, the step S32 specifically includes the following steps:
s321, setting genetic algorithm parameters: population size K, iteration number N, crossover probability p c Probability of variation p m ；
S322, initializing population: express network site selection s obtained by outer layer simulated annealing algorithm 1′ On the basis of the traffic, randomly generating K individuals as an initial group P for site selection of the transfer station;
s323, with an objective function f (S 1 ,s 2 ) The reciprocal of (2) is an fitness function, and each individual fitness value in the population is calculated;
s324, selecting individuals in the population through roulette;
s325, generating next generation population individuals in a crossing way;
s326, mutation to obtain a new population;
s327, when the iteration number K is reached, returning to the current optimal transfer station site selection S 2 Otherwise, the process returns to step S323.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an express network site and transfer station site selection method considering job location distribution, which uniformly considers the site selection of the express network site and the transfer station, thereby improving the short-distance express service experience of users and reducing the distribution cost; meanwhile, the method creatively introduces the actual express demands represented by the working population and the living population when the express network sites and the transfer stations select addresses, considers the difference of the way of taking the parts of the working population and the living population, and solves the problems of unreasonable address selection and poor overall user experience caused by the fact that the express traffic is not known in the traditional network sites and the transfer stations select addresses.
Drawings
FIG. 1 is a schematic flow chart of a method for selecting addresses of express delivery network points and transfer stations taking into account the distribution of jobs and liveness;
FIG. 2 is a flowchart of an algorithm in the method for selecting addresses of express delivery network points and transfer stations taking into account the distribution of jobs and liveness provided by the invention;
FIG. 3 is a graph of population distribution, (a) nuclear density of the working population, (b) nuclear density of the living population, in an embodiment of the invention;
FIG. 4 is a diagram of a land utilization and current construction in an embodiment of the present invention;
FIG. 5 is a diagram of a traffic hub according to an embodiment of the present invention;
FIG. 6 is a diagram of a distribution diagram of courier stations in an embodiment of the present invention;
FIG. 7 is a diagram of the result of selecting addresses of a delivery network and a transfer station in an embodiment of the present invention;
FIG. 8 is a table of parameter settings for a simulated annealing algorithm in an embodiment of the invention;
FIG. 9 is a table of parameter settings for a genetic algorithm in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In this embodiment, the implementation of the present invention is described by taking the forward and reverse shipping of the main urban area of the eastern region of the sun city as an example, and the following specific implementation steps of optimizing the address selection of the express network point of the present invention will be specifically described by combining with the example:
s1, acquiring related information of express network points and transfer stations in the area and preprocessing. Grid work population (50 m x 50 m), grid living population (50 m x 50 m), land utilization, current buildings, candidate transfer stations, transportation hubs, courier stations.
Grid work population and grid living population: three month-caliber grid work population (50 m×50 m) and grid living population (50 m×50 m) were obtained. Because the data coordinate system is a Mars coordinate system, the data coordinate system is firstly converted into a WGS84 coordinate system and then into a CGCS2000 coordinate system, and a grid population database is obtained. To reflect the overall distribution characteristics of the main urban working population and the living population of the eastern district of the sun city, a nuclear density map is generated ((a) and (b) in fig. 3).
Land utilization: the main urban land utilization information of the eastern harbor district of the sun-shine city is extracted from the third national soil investigation data of the sun-shine city (fig. 4 (c)), and the main land utilization types include residential land, commercial facility land, industrial land, logistics storage land, green land, square and the like.
Current state building: obtained from the sky map, the main building types include houses, businesses, office buildings, warehouses, and the like ((d) in fig. 4).
Candidate transfer station information: the candidate transfer stations are investigated, 15 candidate transfer station positions are collected, the transfer station operation cost and the like are evaluated, the candidate transfer stations are stored in an Excel (xls) file format, the file comprises four columns, the first column is a transfer station serial number, the second column and the third column are longitude and latitude respectively, and the fourth column is the operation cost.
Transportation junction: including highway hinges, railway hinges, and aviation hinges. The highway hinges are highway entrances and exits in the area, and the number of the highway hinges is 4; the railway hub is a sunlight western station and a sunlight station; the aviation hub is a solar mountain river airport (figure 5).
Express post: the express post stations in the east harbor district are mainly vegetable bird post stations, and 98 express post stations in the solar city are totally provided (figure 6).
Preprocessing to generate a land work population and a land living population: interrect operations are performed on the grid working population and the grid living population with land utilization respectively by ArcGIS software so as to aggregate the grid working population and the living population to plots, thereby counting the number of plots working population and the number of living population ((a) and (b) in fig. 3).
Determining the nearest express post house of a land block: and (3) performing Near operation on the land parcels and the express post by using ArcGIS software, and matching the land parcels with the nearest express post.
S2, constructing an express network site and a transfer station site selection model.
S21, taking account of the distribution characteristics of working population and living population, and under the constraint of network point service capability, selecting sites S for express network points 1 The cost formula is:
Q j <3Q 0 (4)
wherein m represents the number of plots, n represents the number of express network points, a 1 、a 2 Weights of land work population and land living population respectively, R i 、W i The number of land work population and land living population are respectively Z ij E {0,1}, when Z ij =1 means that the i-th land parcel working population and the resident population express service is provided by the j-th website, otherwise, 0; d, d ij Representing the distance from the ith land block to the jth net point; d, d ij ′ Representing the distance from the nearest express post to the jth site from the ith land block; b represents the average express business volume; l represents the network point operation cost coefficient; q (Q) j Represents the j-th network point service capability, Q 0 Representing the average service capacity of the network point; c ij Representing the network site to user or post shipping rate; y represents the storage cost of the single express post.
Expression (1) represents the operation cost and transportation cost of the express delivery network point (the transportation cost comprises the express delivery transportation cost of the working population, the express delivery transportation cost of the living population and the storage cost), expression (2) represents that the express delivery service is provided by only one network point, and expressions (3) and (4) represent network point service capability constraint.
The land block working population weight and the land block living population weight are determined according to the proportion of the online shopping express quantity to the total express quantity. In recent years, the proportion of the online shopping express quantity in the total express business quantity in China exceeds eight times, so a is set 1 ＝20％，a 2 ＝80％。
According to the development statistical publication of the postal service industry in the sun-shine city of 2021, the number of express traffic in the sun-shine city of 2021 is 14386.57 ten thousand, and the forward and forward shipping market share is 7.61%, so that the number of people per minute forward and forward shipping traffic b is 3.68. The average service population of the express network points in the sunlight city is 0.80 ten thousand people, the average service express service traffic of the network points is 37.18 ten thousand, and the number n of the network points in the main urban area is about 29 based on the express traffic of the express network points in the main urban area of the sunlight city.
According to market research, site-to-user transport rate c ij The network operation cost coefficient l is 0.2, and the courier station keeping cost is 0.4 yuan/piece for 0.5/piece/kilometer.
S22, site selection S of transfer station 2 The cost formula is:
wherein o represents the number of transfer stations, r k E {0,1}, when r k =1 means that the kth candidate transfer station is selected, otherwise 0, x kj E {0,1}, when x kj =1 means that transfer station k is delivered by dot j, otherwise 0, q k Representing transfer station k traffic, a 3 、a 4 、a 5 Road, railway, aviation traffic weight, p hk 、p rk 、p ak Respectively representing the transportation distance from the nearest highway junction, railway junction and aviation junction to the transfer station k, c k Representing transport rate from hub to transfer station k, F k Representing the operation cost of the transfer station k, v k Representing the cost coefficient, q, of single piece processing of a transfer station k kj Representing traffic of transfer station k to network point j, p kj Representing the transport distance from the transfer station k to the network point j, c kj Representing the transit rate from transfer station k to website j.
The formula (5) represents the cost of the transfer station, the formula (6) represents the sum of the traffic of the transfer stations corresponding to each network point, the formula (7) represents that each network point has only one transfer station for the distribution, the formula (8) represents that the distribution of the network point can be performed only when the candidate transfer station is selected, and the formula (9) represents that the distribution of the network point is required after the candidate transfer station is selected.
Highway a 3 Railway a 4 Aviation a 5 And determining the traffic weight according to the transportation mode proportion of the express enterprises. In recent years, the traffic volume of the forward and reverse highway is about 76%, the proportion of railway traffic volume is about 2%, and the proportion of aviation traffic volume is about 22%.
According to market research, the transport rate c from the hub to the transfer station k Transport rate c for transfer station to network point of 0.08/piece/km kj The cost coefficient of single piece processing is 0.03 at 0.12 per piece/km. And estimating the number o of the transfer stations to be 3 according to the forward and full traffic.
S23: combining the formula (1) and the formula (5), and constructing an address selection model f(s) of the express network point and the transfer station by taking the minimum cost of the express network point and the transfer station as the target 1 ,s 2 )。
And S3, solving the model by adopting a simulated annealing algorithm nested genetic algorithm.
And S31, optimizing the site selection by adopting an outer layer simulated annealing algorithm, and determining the traffic volume of the mesh point.
FIG. 2 is a flow chart of a simulated annealing algorithm nested genetic algorithm.
The solving steps are as follows:
s311, initializing an outer layer simulated annealing algorithm, and setting parameters: initial temperature T 0 End temperature T e The temperature attenuation coefficient b and the iteration times Q, and setting the current temperature T as an initial temperature;
s312, randomly generating initial address S of express network point 1 Further determining the traffic of the net point and selecting the net point address s 1 Substituting the net point traffic result into an inner layer genetic algorithm, and obtaining a transit station site selection s according to the inner layer genetic algorithm 2 Calculating initial value f(s) 1 ,s 2 ) And let the optimal solution θ=f (s 1 ,s 2 )；
Generating initial address of express network point: 29 positions are randomly selected in the area to serve as express network site selection s 1 . The express delivery network points should be located in the current buildings along the street.
Mesh point traffic determination: under the constraint of the network point service capability, the express service is distributed to the network point closest to the network point, and the network point traffic is counted.
Riding distance calculation: and calculating the riding distance value of each plot (or the closest post from the plot) and the express delivery network point by adopting the advanced map API riding route planning service (AMap. Riding).
S313, generating disturbance to generate new address S of express network point 1′ Calculating a new addressing objective function value f (s 1′ ,s 2′ )；
Generation of new site selection: for each express net point Z in the initial address, generating a vector u with the length of random (0, L) and the direction of random (0, 360) °, replacing Z with Z+u, and newly selecting the address s 1′ Each net point of the road should be located in the current building along the street. The initial L is set to 10 km and the attenuation coefficient is set to 0.99 as the algorithm iterates.
S314, if f (S) 1′ ,s 2′ )<θ, then the optimal solution θ=f (s 1′ ,s 2′ ) If f(s) 1′ ,s 2′ ) More than or equal to theta, determining whether the optimal solution theta is f(s) according to Metropolis criterion 1′ ,s 2′ )；
S315, if the iteration times Q are reached, entering the next step, otherwise repeating the step S313;
s316, the current temperature T is less than T e And obtaining the optimal solution, otherwise, cooling, setting the current temperature as bxT, and returning to the step S313.
S32, optimizing the transit station site selection by adopting an inner layer genetic algorithm based on the site selection and the traffic result.
The solving steps are as follows:
s321, setting genetic algorithm parameters: population size K, iteration number N, crossover probability p c Probability of variation p m 。
S322, initializing population: express network site selection s obtained by outer layer simulated annealing algorithm 1 And on the basis of the traffic, randomly generating K individuals as the initial population P for site selection of the transfer station.
Population individual codes: and (3) adopting natural number coding to randomly generate a natural number string with the length of e+g, wherein 1-e represents transfer stations, and (e+1) - (e+g) represent network points, and the number string '11620232963133353741439 …' represents that transfer station 1 is distributed by 16202329 network points, transfer station 6 is distributed by 313335374143 network points, and transfer station 9 is distributed by other network points.
S323, with an objective function f (S 1 ,s 2 ) The reciprocal of (2) is the fitness function and each individual fitness value in the population is calculated.
Recent pivot determination: and respectively calculating the distances between the transfer station and each highway junction, each railway junction and each aviation junction by using the Euclidean distance, and matching the transfer station with the nearest highway junction, each railway junction and each aviation junction.
Calculating a transportation distance: and calculating the driving distance value from each transfer station to the corresponding express network and junction by adopting the advanced map API driving route planning (AMap. Driving).
S324, selecting individuals in the population through roulette.
S325, generating next generation population individuals by crossing.
Crossing: since crossover may cause gene loss or duplication, a partial match crossover algorithm (PMX) is used to establish a match relationship between genes at crossover regions, and then the match relationship is applied to duplicate genes outside the crossover regions to eliminate conflicts.
S326, mutation is carried out to obtain a new population.
Variation: since the codes represent permutation combinations, the mutation is to interchange two gene positions.
S327, when the iteration number K is reached, returning to the current optimal transfer station site selection S 2 Otherwise, the process returns to step S323.
Step S4: and outputting the address selection results of the express network and the transfer station (figure 6).
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A method for selecting addresses of express delivery network points and transfer stations by considering job and location distribution is characterized by comprising the following steps:
step S1: related information of express network points and transfer stations in the area is obtained and preprocessed, wherein the related information comprises the following components: grid working population, grid living population, land utilization, current construction, candidate transfer station information, transportation hub and express post;
step S2: constructing an express network site and a transfer site selection model;
s21: taking account of the distribution characteristics of working population and living population, under the constraint of network point service capability, express network point site selection s 1 The cost formula is:
Q j <3Q 0 (4)
wherein m represents the number of plots, n represents the number of express network points, a 1 、a 2 Weights of land work population and land living population respectively, R i 、W i The number of land work population and land living population are respectively Z ij E {0,1}, when Z ij =1 means that the i-th land parcel working population and the resident population express service is provided by the j-th website, otherwise, 0; d, d ij Representing the distance from the ith land block to the jth net point; d, d ij ′ Representing the distance from the nearest express post to the jth site from the ith land block; b represents the average express business volume; l represents the network point operation cost coefficient; q (Q) j Represents the j-th network point service capability, Q 0 Representing the average service capacity of the network point; c ij Representing the network site to user or post shipping rate; y represents the storage cost of a single express post;
the objective function of the formula (1) represents the operation cost and the transportation cost of the express delivery network point;
the constraint condition of the formula (2) indicates that express delivery services of all land parcel working population and land parcel living population are provided by only one website;
constraint conditions of formulas (3) and (4) represent constraint of network point service capability;
s22: site selection s for transfer station 2 The cost formula is:
wherein o represents the number of transfer stations, r k E {0,1}, when r k =1 means that the kth candidate transfer station is selected, otherwise 0, x kj E {0,1}, when x kj =1 means that transfer station k is delivered by dot j, otherwise 0, q k Representing transfer station k traffic, a 3 、a 4 、a 5 Road, railway, aviation traffic weight, p hk 、p rk 、p ak Respectively representing the transportation distance from the nearest highway junction, railway junction and aviation junction to the transfer station k, c k Representing transport rate from hub to transfer station k, F k Representing the operation cost of the transfer station k, v k Representing the cost coefficient, q, of single piece processing of a transfer station k kj Representing traffic of transfer station k to network point j, p kj Representing the transport distance from the transfer station k to the network point j, c kj Representing the transport rate from transfer station k to website j;
an objective function of formula (5) representing transfer station transportation costs, operation costs, and processing costs;
a constraint condition of a formula (6) indicates that the traffic of the transfer station is the sum of the traffic of the corresponding network points;
a constraint of formula (7) indicating that each site has and only one transfer station is delivering for it;
a constraint of formula (8) indicates that the network point delivery is enabled only when the candidate transfer station is selected;
the constraint condition of the formula (9) indicates that the candidate transfer stations must be distributed for the network points after being selected;
s23: combining the formula (1) and the formula (5), and constructing an address selection model f(s) of the express network point and the transfer station by taking the minimum cost of the express network point and the transfer station as the target 1 ,s 2 )；
Step S3: solving the site selection models of the express network and the transfer station by utilizing a simulated annealing algorithm nested genetic algorithm;
s31, optimizing site selection by adopting an outer layer simulated annealing algorithm, and determining the traffic volume of the network points;
s32, optimizing the transit station address selection by adopting an inner layer genetic algorithm based on the network address selection and the traffic result;
step S4: and outputting address selection results of the express network and the transfer station.
2. The method for selecting address of express delivery network and transfer station in consideration of job distribution according to claim 1, wherein the grid working population and grid living population in step S1 are three month-caliber 50 m x 50 m grid population; the preprocessing is to aggregate the grid working population and the grid living population to plots, generating the plot working population and the plot living population.
3. The method for selecting the address of the express delivery network and the transfer station taking account of the occupancy distribution as claimed in claim 1, wherein in the step S2, the land parcel working population and the land parcel living population weight are determined according to the proportion of the online shopping express delivery amount to the total express delivery amount; road, railway and aviation traffic weights are determined according to the transportation mode proportion of the express enterprises.
4. The method for selecting the sites and the transfer stations according to claim 1, wherein the distance from the ith parcel to the jth site and the distance from the nearest express post to the jth site in the step S2 are calculated by a hundred degree map or a high-altitude map riding route planning; the transportation distance from the transfer station k to the network point j is calculated by driving route planning of a hundred-degree map or a high-Germany map.
5. The method for selecting the address of the delivery network and the transfer station taking account of the job and the location distribution as set forth in claim 1, wherein the step S31 specifically includes the steps of:
s311, initializing an outer layer simulated annealing algorithm, and setting parameters: initial temperature T 0 End temperature T e The temperature attenuation coefficient b and the iteration times Q, and setting the current temperature T as an initial temperature;
s312, randomly generating initial address S of express network point 1 Site selection s 1 Each net point should be located in the current building along the street, so as to determine the net point traffic and select the net point site s 1 Substituting the net point traffic result into an inner layer genetic algorithm, and obtaining a transit station site selection s according to the inner layer genetic algorithm 2 Calculating initial value f(s) 1 ,s 2 ) And let the optimal solution θ=f (s 1 ,s 2 )；
S313, generating disturbance to generate new address S of express network point 1′ Calculating a new addressing objective function value f (s 1′ ,s 2′ )；
S314, if f (S) 1′ ,s 2′ )<θ, then the optimal solution θ=f (s 1′ ,s 2′ ) If f(s) 1′ ,s 2′ ) More than or equal to theta, determining whether the optimal solution theta is f(s) according to Metropolis criterion 1′ ,s 2′ )；
S315, if the iteration times Q are reached, entering the next step, otherwise repeating the step S313;
s316, the current temperature T is less than T e And obtaining the optimal solution, otherwise, cooling, setting the current temperature as bxT, and returning to the step S313.
6. The method for selecting the address of the delivery network and the transfer station taking account of the occupancy distribution as set forth in claim 1, wherein said step S32 specifically includes the steps of:
s321, setting genetic algorithm parameters: population size K, iteration number N, crossover probabilityp c Probability of variation p m ；
S322, initializing population: express network site selection s obtained by outer layer simulated annealing algorithm 1 On the basis of the traffic, randomly generating K individuals as an initial group P for site selection of the transfer station;
s323, with an objective function f (S 1 ,s 2 ) The reciprocal of (2) is an fitness function, and each individual fitness value in the population is calculated;
s324, selecting individuals in the population through roulette;
s325, generating next generation population individuals in a crossing way;
s326, mutation to obtain a new population;
s327, when the iteration number K is reached, returning to the current optimal transfer station site selection S 2 Otherwise, the process returns to step S323.
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