CN115759319A - Express delivery network and transfer station site selection method considering job distribution - Google Patents
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Abstract
The invention discloses an express delivery network and transfer station site selection method considering job distribution, which comprises the steps of obtaining relevant information of regional express delivery networks and transfer stations and preprocessing the information; taking distribution characteristics of working population and resident population into consideration, constructing an express delivery network point and transfer station site selection model by taking network point service capability as constraint and the minimum cost of an express delivery network point and a transfer station as a target; solving the model by adopting a simulated annealing algorithm nested genetic algorithm; and outputting the site selection results of the express delivery network points and the transfer station. According to the method, distribution characteristics of working population and resident population are considered when the express distribution points and the transfer stations are used for site selection, the short-distance express service experience of a user can be effectively improved, and the distribution cost can be obviously reduced.
Description
Technical Field
The invention relates to the technical field of geographic spatial data application, in particular to an express delivery network point and transfer station site selection method considering job distribution.
Background
Along with the rapid development of the Internet and the electronic commerce industry, the express industry in China rapidly develops, and plays an increasingly important role in the development of national economy and society. The statistical data of the national post and government bureau shows that the annual average growth rate of the total income and the business volume of the express industry in China exceeds 30 percent in nearly ten years, and the express industry shows a remarkable rising trend. This brings huge development opportunity for express delivery enterprise, has also brought violent market competition. The reasonable express delivery network point and the transfer station site selection not only can improve the express delivery service quality and obtain more customers, but also can reduce the delivery cost, thereby being the core competitiveness of express enterprises in the market. Therefore, it is necessary to develop site selection research of express delivery outlets and transfer stations.
Common site selection methods comprise an analytic hierarchy process, a gravity center method, a mixed integer programming method, 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 distribution points and transfer stations, and also do not consider the layout relationship of the express distribution points and the transfer stations uniformly, so that certain limitations exist. In fact, for express enterprises, the site selection of a network point should take delivery cost and user short-distance express service experience into consideration; the location of the transfer station ensures high-quality distribution service and reduces the distribution cost as much as possible.
Generally, the network points have no special requirements for scale, traffic conditions, etc. except for the requirement of being as long as possible along the street. Therefore, the position selectivity of the lattice point is high, and the lattice point can be randomly selected from the area to be solved. Meanwhile, the express types and the number of the express involved in work and life of people are obviously different, the express in work is generally drawings, files, data and the like, the number is small, the express is generally directly delivered to recipients, the express in life usually belongs to online shopping, the number is large, the recipient is usually not at home due to delivery time, and the express is generally delivered to a post station for storage, so that the overall short-distance express service experience of a user is effectively improved, the delivery cost is reduced, and the difference between the distribution of the working population and the distribution of the living population is considered when site selection is carried out.
Different from the network points, the transfer station has higher requirements on scale, geographic environment, traffic conditions, public facilities and the like, and the positions meeting the requirements in the area are not many, so that the transfer station can be selected from candidate positions for solving. Meanwhile, the transfer station is connected with the transportation hub on one hand to transfer the express into the transfer station, and is connected with the network points on the other hand to transfer the express out, so that the distribution cost is effectively reduced, and the distance between the transfer station and the transportation hub, the distance between the transfer station and the network points and the transfer amount are considered.
Disclosure of Invention
The invention aims to provide an express delivery network and a transfer station site selection method considering job 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: an express delivery network and transfer station site selection method considering job distribution comprises the following steps:
step S1: the method comprises the steps of obtaining relevant information of express distribution points and transfer stations in a region and preprocessing the relevant information, wherein the relevant information comprises the following steps: grid working population, grid resident population, land utilization, current building, candidate transfer station information, traffic hubs, express courier stations and the like.
Step S2: and constructing an express delivery network point and transfer station site selection model.
S21: considering distribution characteristics of working population and living population under the constraint of network service capacityExpress network 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 delivery outlets, a 1 、a 2 The weights R of the working population and the residential population of the plot i 、W i Respectively the number of the working population and the residential population of the plot, Z ij E {0,1}, when Z ij =1, the express service of the ith parcel working population and residential population is provided by the jth network point, otherwise, the express service is 0; d ij Representing the distance from the ith plot to the jth screen point; d ij ' represents the distance from the nearest express post station to the jth network point of the ith plot; b represents the per-person express business volume; l represents a network node operation cost coefficient; q j Representing the service capability of the jth network site, Q 0 Representing the average service capability of the network points; c. C ij Representing the transportation rate from the network point to the user or the post station; y represents the single express post storage cost.
And (3) an objective function of the formula (1) represents the operation cost and the transportation cost of the express delivery network (the transportation cost comprises the express transportation cost of a working population, the express transportation cost of a residential population and the storage cost).
And (3) the constraint condition of the formula (2) represents that express services of all plot working population and plot residential population are provided by only one network point.
And (4) the constraint conditions of the equations (3) and (4) represent the service capability constraint of the network points.
S22: site selection(s) of transit station 2 ) Cost formulaComprises the following steps:
wherein o represents the number of transfer stations, r k E {0,1}, when r k =1 indicates that the k-th candidate transfer station is selected, otherwise, 0,x kj E {0,1}, when x kj And =1 denotes that the transfer station k is delivered by the network point j, otherwise it is 0,q k Represents the traffic of the transfer station k, a 3 、a 4 、a 5 Weight of highway, railway, aviation traffic, p hk 、p rk 、p ak Respectively representing the transportation distance k from the nearest road junction, the nearest railway junction and the nearest aeronautical junction to the transfer station, c k Representing the transportation rate of k, F, from hub to transfer station k Represents the operating cost of the transfer station k, v k Represents the processing cost coefficient of k single piece of the transfer station, q kj Represents traffic volume, p, from transfer station k to mesh point j kj Represents the transport distance, c, from the transfer station k to the network point j kj And (4) representing the transit station k to the network point j transportation rate.
And (5) an objective function representing the transportation cost, the operation cost and the processing cost of the transfer station.
And (6) a constraint condition, which indicates that the traffic of the transfer station is the sum of the traffic of the corresponding network points.
The constraint of equation (7) indicates that each network site has and only one transfer station to distribute.
The constraint of equation (8) indicates that the network site can be distributed only when the candidate transfer station is selected.
The constraint condition of equation (9) indicates that the candidate transfer station must be distributed for the network after being selected.
S23: combining the formula (1) and the formula (5), and constructing an express delivery site and transit station site selection model f(s) by aiming at minimizing the cost of the express delivery site and the transit station 1 ,s 2 )。
And step S3: and (4) solving the site selection models of the express delivery network points and the transfer stations by using a simulated annealing algorithm nested genetic algorithm.
And S31, optimizing site selection of the network points by adopting an outer layer simulated annealing algorithm, and determining the network point traffic.
And S32, optimizing the site selection of the transfer station by adopting an inner-layer genetic algorithm based on the site selection of the network points and the traffic result.
And step S4: and outputting the site selection results of the express delivery network points and the transfer station.
Further, the grid working population and the grid residential population in the step S1 are three months of mesh (continuously more than three months) 50 m × 50 m grid population; and the preprocessing comprises the step of aggregating grid working population and grid residential population to a plot to generate plot working population and plot residential population.
Further, in the step S2, the weights of the plot working population and the plot residential population are determined according to the proportion of the online shopping express delivery to the total express delivery; the weight of the highway, railway and aviation traffic is determined according to the transportation mode proportion of the express enterprises.
Further, in the step S2, the distance from the ith parcel to the jth express delivery site and the distance from the nearest express courier station to the jth site to the ith delivery site are calculated by a Baidu map or a Gaode map riding route planning; the transportation distance from the transfer station k to the network point j is calculated by a Baidu map or Gaode map driving route planning.
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 Setting the current temperature T as an initial temperature;
s312, randomly generating an initial site selection S of an express delivery network point 1 Selection of site s 1 Each network point is located on the building along the street, the network point traffic is determined, and the network point site is selected s 1 Substituting the result of the net point traffic into the inner layer genetic algorithm, and obtaining the site s of the transfer station according to the inner layer genetic algorithm 2 Calculating an initial value f(s) of the objective function 1 ,s 2 ) And sets the optimal solution to f(s) 1 ,s 2 )；
S313, generating a new site selection S of the express delivery network point by disturbance 1′ Calculating new site selection objective function value f(s) 1′ ,s 2′ )；
S314, if f (S) 1′ ,s 2′ )<f(s 1 ,s 2 ) Then the optimal solution is set to f(s) 1′ ,s 2′ ) Otherwise, whether to accept f(s) is determined according to Metropolis criterion 1′ ,s 2′ )；
S315, when the iteration times Q are reached, the next step is carried out, otherwise, the step S313 is repeated;
s316, the current temperature T is less than T e If not, the temperature is reduced, the current temperature is set to be bxT, and the step S313 is returned.
Further, the step S32 specifically includes the following steps:
s321, setting genetic algorithm parameters: population size K, iteration number N, cross probability p c Probability of variation p m ；
S322, initializing population: express delivery 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 selecting addresses of the transfer stations;
s323, using the objective function f (S) 1 ,s 2 ) The reciprocal of the population is a fitness function, and the fitness value of each individual of the population is calculated;
s324, selecting individuals in the population through roulette;
s325, generating next generation population individuals in a crossed manner;
s326, mutating 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, return to step S323.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an express delivery network and transfer station site selection method considering job distribution, which uniformly considers express delivery network and transfer station site selection, and can improve the short-distance express delivery service experience of a user and reduce the distribution cost; meanwhile, the method creatively introduces the working population and the resident population to represent the actual express demand when the express network site and the transfer station site are selected, and overcomes the problems of unreasonable site selection and poor integral user experience caused by the fact that the express service volume is not known in the traditional network site and the transfer station site selection by considering the difference of the pickup modes of the working population and the resident population.
Drawings
Fig. 1 is a schematic flow chart of an express delivery network and a transfer station site selection method considering job distribution according to the present invention;
fig. 2 is a flowchart of an algorithm in the method for locating an express delivery network point and a transfer station in consideration of occupational distribution, provided by the invention;
FIG. 3 is a population distribution graph of (a) kernel density of the working population and (b) kernel density of the residential population in an embodiment of the present invention;
FIG. 4 is a diagram of land use and current building in an embodiment of the present invention;
FIG. 5 is a diagram of candidate transfer station locations and transportation hubs in an embodiment of the present invention;
FIG. 6 is a distribution diagram of courier stations according to an embodiment of the present invention;
fig. 7 is a result diagram of address selection of an express delivery network point and a transfer station in the embodiment of the present invention;
FIG. 8 is a table of parameter settings for a simulated annealing algorithm in an embodiment of the present invention;
FIG. 9 is a table of parameter settings for a genetic algorithm in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In this embodiment, a main urban area (sunshine street, stone mortar street, quaishan street, crouching mountain street, beijing street, and qin building street) of east harbor district in sunshine city is taken as an example to explain the specific implementation of the present invention, and the specific implementation steps for optimizing the address selection of the express delivery network point will be specifically described below with reference to this example:
s1, relevant information of express delivery network points and transfer stations in the area is obtained and preprocessed. Grid working population (50 meters multiplied by 50 meters), grid residential population (50 meters multiplied by 50 meters), land utilization, current buildings, candidate transfer stations, transportation hubs, express courier stations and the like.
Grid job population and grid resident population: three month aperture (continuously over three months) grid working population (50 m x 50 m) and grid residential population (50 m x 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 converted into a CGCS2000 coordinate system, and a grid population database is obtained. In order to reflect the general distribution characteristics of the working population and the resident population in the main urban area of east harbor in sunshine city, a nuclear density map is generated (3 a,3 b).
Land utilization: and extracting the land utilization information (figure 4 a) of the main urban area of the east port area of sunshine city from the third national state survey data of sunshine city, wherein the main land utilization types comprise residential land, commercial facility land, industrial land, logistics storage land, green land, square and the like.
Building at present: the main building types, obtained from the sky map, include residential, commercial, office, warehouse, etc. (fig. 4 b).
Information of candidate transfer stations: the candidate transfer stations are investigated, 15 candidate transfer station positions are collected, the transfer station operation cost is evaluated, and the like, and the candidate transfer stations are stored in an Excel (xls) file format, wherein 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.
A traffic hub: the system comprises a highway hub, a railway hub and an aviation hub. Wherein the highway hubs are 4 highway entrances and exits in the area; the railway hub is a sunshine west station and a sunshine station; the aviation hub is a sunshine mountain river airport (figure 5).
Express delivery post: the number of express courier stations in east harbor district is mainly 98 in total of vegetable and bird courier stations and sunshine city main city district (figure 6).
Preprocessing, generating a plot working population and a plot residential population: and (4) performing an Intersect operation on the grid working population and the grid residential population and land utilization respectively by using ArcGIS software so as to aggregate the grid working population and the residential population to the plot, and further counting the plot working population and the residential population (3 c,3 d).
The nearest express post station in parcel is confirmed: and (4) utilizing ArcGIS software to carry out Near operation on the plot and the express courier station, and matching the plot with the nearest express courier station.
And S2, constructing an express delivery network point and transfer station site selection model.
S21, taking distribution characteristics of working population and residential population into consideration, and selecting addresses of express delivery network points (S) under the constraint of network point service capacity 1 ) The cost formula is:
Q j <3Q 0 (4)
wherein m represents a land massNumber, n represents the number of express outlets, a 1 、a 2 Weights, R, for plot working population and plot residential population, respectively i 、W i Respectively the number of the working population and the residential population of the plot, Z ij E {0,1}, when Z ij =1, the express service of the ith parcel working population and residential population is provided by the jth network point, otherwise, the express service is 0; d ij Representing the distance from the ith plot to the jth screen point; d ij ' represents the distance from the nearest express post station to the jth network point of the ith plot; b represents the per-person express business volume; l represents a network node operation cost coefficient; q j Represents the service capability of the jth network point, Q 0 Representing the average service capability of the network points; c. C ij Representing the transportation rate from the network point to the user or the post station; y represents the single express post storage cost.
Expression (1) represents the operation cost and the transportation cost of the express delivery network (the transportation cost comprises the express transportation cost of a working population, the express transportation cost of a residential population and the storage cost), expression (2) represents that the express delivery service is provided by only one network, and expressions (3) and (4) represent network service capability constraints.
And determining the weight of the plot working population and the weight of the plot residential population according to the proportion of the online shopping express delivery to the total express delivery. In recent years, the proportion of online shopping express to total express business in China exceeds eighty percent, so that a is set 1 ＝20％，a 2 ＝80％。
According to the statistical gazette of development of postal service industry in sunshine market in 2021, the express traffic in sunshine market in 2021 is 14386.57 ten thousand, the market share of the Shunfeng express transportation is 7.61%, and the per-capita Shunfeng express transportation traffic b is 3.68. The average service population of the express delivery points in the sunshine market is 0.80 ten thousand, the average service delivery traffic of the points is 37.18 ten thousand, and based on the smooth and high-speed delivery traffic of the main urban areas in the sunshine market, the number n of the main urban areas is about 29.
According to market research, network to customer transportation rate c ij The speed is 0.5/piece/kilometer, the network operating cost coefficient l is 0.2, and the express post station keeping cost is 0.4 piece/piece.
S22, the transfer station selects the address (S) 2 ) The cost formula is:
wherein o represents the number of transfer stations, r k E {0,1}, when r k =1 denotes that the kth candidate transfer station is selected, otherwise 0,x kj E {0,1}, when x kj =1 denotes that the transfer station k is delivered by the network point j, otherwise 0,q k Represents the traffic of the transfer station k, a 3 、a 4 、a 5 Weight of highway, railway, aviation traffic, p hk 、p rk 、p ak Respectively representing the transportation distances from a nearest road junction, a railway junction and an aviation junction to a transfer station k, c k Representing a transportation rate of k hub to transfer station, F k Represents the operating cost of the transfer station k, v k Represents the processing cost coefficient of k single piece of the transfer station, q kj Represents traffic volume, p, from transfer station k to mesh point j kj Represents the transport distance, c, from the transfer station k to the network point j kj Representing the transit station k to the point j transportation rate.
Equation (5) represents the cost of the transfer station, equation (6) represents that the traffic of the transfer station is the sum of the traffic of the corresponding network points, equation (7) represents that each network point has only one transfer station for the distribution, equation (8) represents that the network point can be distributed only when the candidate transfer station is selected, and equation (9) represents that the network point must be distributed after the candidate transfer station is selected.
Road a 3 Railway a 4 Aviation a 5 And the weight of the business is determined according to the transportation mode proportion of the express enterprises. In recent years, the highway traffic of the smooth and rich express is about 76%, the railway traffic proportion is about 2%, and the aviation traffic proportion is about 22%.
According to market research, transportation rate c from hub to transfer station k 0.08/piece/km, transit station to network point transportation rate c kj The cost coefficient of the single piece of the product is 0.03. And estimating the number o of the transfer stations to be 3 according to the smooth and rich fast transportation traffic.
S23: combining the formula (1) and the formula (5), and constructing an express delivery site and transit station site selection model f(s) by aiming at minimizing the cost of the express delivery site and the transit station 1 ,s 2 )。
And S3, solving the model by adopting a simulated annealing algorithm nested genetic algorithm.
And S31, optimizing site selection of the nodes by adopting an outer layer simulated annealing algorithm, and determining the traffic of the nodes.
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 Setting the current temperature T as an initial temperature;
s312, randomly generating an initial site selection S of the express delivery network 1 Further determining the network node traffic and locating the network node s 1 Substituting the result of the net point traffic into the inner layer genetic algorithm, and obtaining the site s of the transfer station according to the inner layer genetic algorithm 2 Calculating an initial value f(s) of the objective function 1 ,s 2 ) And setting the optimal solution to f(s) 1 ,s 2 )；
The production of the initial site selection of the express delivery network: randomly selecting 29 positions in the area as express distribution sites s 1 . Express delivery network points are located along the street.
And (3) determining the network point traffic: and under the constraint of the network point service capability, distributing the express service to the network point closest to the express service, and counting the network point service volume.
Calculating the riding distance: and calculating the riding distance value between each plot (or the nearest post station to the plot) and the express delivery network point by adopting the API riding route planning service (AMap. Riding) of the high-grade map.
S313, generating a new site selection S of the express delivery network point by disturbance 1′ Calculating new site selection objective function value f(s) 1′ ,s 2′ )；
Generation of new addresses: for each express network point Z in the initial site selection, generating a vector u with the length of random (0, L) and the direction of random (0, 360) ° and replacing Z with Z + u, and newly selecting the site s 1′ Each network point in the building is located 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′ )<f(s 1 ,s 2 ) Then the optimal solution is set to f(s) 1′ ,s 2′ ) Otherwise, whether to accept f(s) is determined according to Metropolis criterion 1′ ,s 2′ )；
S315, when the iteration times Q are reached, the next step is carried out, otherwise, the step S313 is repeated;
s316, the current temperature T is less than T e If not, cooling is carried out, the current temperature is set to be bXT, and the step S313 is returned.
And S32, optimizing the site selection of the transfer station 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, cross probability p c Probability of variation p m 。
S322, initializing a population: express delivery 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 selecting the address of the transfer station.
Encoding population individuals: by adopting natural number coding, a natural number string with the length of e + g is randomly generated, wherein 1-e represent transfer stations, and (e + 1) - (e + g) represent network points, the string is '1 16 20 23 29 6 31 35 37 41 82309', which means that the transfer station 1 is distributed by the network point 16 20 23, the transfer station 6 is distributed by the network point 31 35 37 41, and the transfer station 9 is distributed by other network points.
S323, using the objective function f (S) 1 ,s 2 ) The reciprocal of (a) is a fitness function, and the fitness value of each individual of the population is calculated.
Most recent pivot determination: and respectively calculating the distances between the transfer station and each road junction, each railway junction and the aviation junction by using the Euclidean distances, and matching the transfer station with the nearest road junction, railway junction and aviation junction.
Calculating the transport distance: and calculating the driving distance value from each transfer station to the corresponding express delivery network point and hub by adopting the high-grade map API driving route planning (AMap. Driving).
And S324, selecting individuals in the population through roulette.
And S325, generating next generation population individuals in a crossed manner.
And (3) crossing: since crossover may result in gene loss or duplication, a partial match crossover algorithm (PMX) is used to establish a match between genes in the crossover region and then apply the match to duplicate genes outside the crossover region to eliminate conflicts.
And S326, mutating to obtain a new population.
Mutation: since the code indicates permutation and combination, the mutation is to interchange the 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.
And step S4: and outputting the address selection results of the express delivery network points and the transfer station (figure 6).
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments 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. An express delivery network and transfer station site selection method considering job distribution is characterized by comprising the following steps:
step S1: the method comprises the following steps of obtaining relevant information of express delivery outlets and transfer stations in an area and preprocessing the relevant information, wherein the relevant information comprises: grid working population, grid resident population, land utilization, current building, candidate transfer station information, traffic hubs, express courier stations and the like.
Step S2: and constructing an express delivery network point and transfer station site selection model.
S21: express delivery network site selection(s) under the constraint of network service capacity in consideration of distribution characteristics of working population and residential population 1 ) The cost formula is:
Q j <3Q 0 (4)
wherein m represents the number of plots, n represents the number of express delivery outlets, a 1 、a 2 Weights, R, for plot working population and plot residential population, respectively i 、W i Respectively the number of the working population and the residential population of the plot, Z ij E {0,1}, when Z ij =1, the express service of the ith parcel working population and residential population is provided by the jth network point, otherwise, the express service is 0; d ij Representing the distance from the ith land parcel to the jth screen point; d ij ' represents the distance from the nearest express post station to the jth network point of the ith plot; b represents the per-person express business volume; l represents a network node operation cost coefficient; q j Representing the service capability of the jth network site, Q 0 Representing the average service capability of the network points; c. C ij Representing the transportation rate from the network point to the user or the post station; y represents the single express post storage cost.
And (2) an objective function of the formula (1) represents the operation cost and the transportation cost of the express delivery network.
And (3) the constraint condition of the formula (2) represents that express services of all plot working population and plot residential population are provided by only one network point.
And (4) the constraint conditions of the equations (3) and (4) represent the service capability constraint of the network points.
S22: site selection(s) of transit station 2 ) The cost formula is:
wherein o represents the number of transfer stations, r k E {0,1}, when r k =1 denotes that the kth candidate transfer station is selected, otherwise 0,x kj E {0,1}, when x kj And =1 denotes that the transfer station k is delivered by the network point j, otherwise it is 0,q k Represents the traffic of the transfer station k, a 3 、a 4 、a 5 Weight of highway, railway, aviation traffic, p hk 、p rk 、p ak Respectively representing the transportation distance k from the nearest road junction, the nearest railway junction and the nearest aeronautical junction to the transfer station, c k Representing a transportation rate of k hub to transfer station, F k Represents the operating cost of the transfer station k, v k Represents the processing cost coefficient of k single piece of the transfer station, q kj Represents traffic volume, p, from transfer station k to mesh point j kj Represents the transport distance, c, from the transfer station k to the network point j kj And (4) representing the transit station k to the network point j transportation rate.
And (5) an objective function representing the transportation cost, the operation cost and the processing cost of the transfer station.
And (6) a constraint condition, which indicates that the traffic of the transfer station is the sum of the traffic of the corresponding network points.
The constraint of equation (7) indicates that each network site has and only one transfer station to distribute.
The constraint of equation (8) indicates that the distribution can be performed only when the candidate transfer station is selected.
The constraint condition of equation (9) indicates that the candidate transfer station must be distributed for the network after being selected.
S23: combining the formula (1) and the formula (5), and constructing an express delivery site and transit station site selection model f(s) by aiming at minimizing the cost of the express delivery site and the transit station 1 ,s 2 )。
And step S3: and (4) solving the site selection models of the express delivery network points and the transfer stations by using a simulated annealing algorithm nested genetic algorithm.
And S31, optimizing site selection of the nodes by adopting an outer layer simulated annealing algorithm, and determining the traffic of the nodes.
And S32, optimizing the site selection of the transfer station by adopting an inner-layer genetic algorithm based on the site selection and the traffic result.
And step S4: and outputting the address selection results of the express delivery network points and the transfer station.
2. The express delivery site and transfer station site selection method considering job site distribution as claimed in claim 1, wherein the grid working population and the grid residential population in step S1 are three months (more than three months in succession) 50 m x 50 m grid population; and the preprocessing comprises the step of aggregating grid working population and grid residential population to a plot to generate plot working population and plot residential population.
3. The method for locating the express distribution points and the transfer stations according to claim 1, wherein the weights of the plot working population and the plot residential population in the step S2 are determined according to the ratio of the online shopping express to the total express; the weight of the highway, railway and aviation traffic is determined according to the transportation mode proportion of the express enterprises.
4. The method according to claim 1, wherein the distance from the ith parcel to the jth express delivery website and the distance from the closest courier post to the jth parcel in the step S2 are calculated from a Baidu map or Gaode map riding route planning; the transportation distance from the transfer station k to the network point j is calculated by a Baidu map or a Gade map driving route planning.
5. The method as claimed in claim 1, wherein 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 Setting the current temperature T as an initial temperature;
s312, randomly generating an initial site selection S of the express delivery network 1 Selection of site s 1 Each network point is located on the building along the street, the network point traffic is determined, and the network point site is selected s 1 Substituting the result of the net point traffic into the inner layer genetic algorithm, and obtaining the site s of the transfer station according to the inner layer genetic algorithm 2 Calculating an initial value f(s) of the objective function 1 ,s 2 ) And setting the optimal solution to f(s) 1 ,s 2 )；
S313, generating a new site selection S of the express delivery network point by disturbance 1′ Calculating new site selection objective function value f(s) 1′ ,s 2′ )；
S314, if f (S) 1′ ,s 2′ )<f(s 1 ,s 2 ) Then the optimal solution is set to f(s) 1′ ,s 2′ ) Otherwise, determining whether to accept f(s) according to Metropolis criterion 1′ ,s 2′ )；
S315, when the iteration times Q are reached, the next step is carried out, otherwise, the step S313 is repeated;
s316, the current temperature T is less than T e If not, cooling is carried out, the current temperature is set to be bXT, and the step S313 is returned.
6. The method according to claim 1, wherein the step S32 specifically comprises the following steps:
s321, setting genetic algorithm parameters: population size K, iteration number N, cross probability p c Probability of variation p m ；
S322, initializing a population: express delivery 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 selecting addresses of the transfer stations;
s323, using the objective function f (S) 1 ,s 2 ) The reciprocal of the population is a fitness function, and the fitness value of each individual of the population is calculated;
s324, selecting individuals in the population through roulette;
s325, generating next generation population individuals in a crossed manner;
s326, mutating 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, return to step S323.
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