CN109190816B - GIS-based commodity distribution center site selection method - Google Patents

GIS-based commodity distribution center site selection method Download PDF

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CN109190816B
CN109190816B CN201810986281.0A CN201810986281A CN109190816B CN 109190816 B CN109190816 B CN 109190816B CN 201810986281 A CN201810986281 A CN 201810986281A CN 109190816 B CN109190816 B CN 109190816B
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张贵军
袁丰桥
姚飞
孙沪增
周晓根
秦子豪
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Zhejiang University of Technology ZJUT
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Abstract

A commodity distribution center site selection method based on GIS combines GIS technology, and based on the distribution density of distribution points in a certain area of a certain city, the actual distribution of urban road network and the actual gradient of the location of a preselected distribution center, a proper commodity distribution center position is obtained based on GIS technology; the factors influencing the location of the distribution center are many and complex, such as terrain conditions, logistics expense traffic, peripheral conditions and the like, the factors are mutually influenced and closely related, and according to requirements, the method overcomes the defect that the existing location selection mode of the distribution center only considers the influence of a single factor or sets the route from the distribution center to a demand point to be a straight line and does not accord with the objective actual condition.

Description

GIS-based commodity distribution center site selection method
Technical Field
The invention relates to a geographic information data processing technology, the field of computer application, geography, an Internet of things technology, network analysis and management science and engineering, in particular to a GIS-based commodity distribution center site selection method.
Background
With the rapid development of society, the technology is continuously improved. The demand of people on commodities is increasingly strengthened, the market potential of distribution centers in China is also increasingly large, and the commodity distribution centers play an important role in the process of rapid economic development. With the increasing demand of people on commodities, the number of distribution centers is increasing continuously, the required commodities are provided for people continuously, and the commodity distribution centers play an important role in promoting the development of society and meeting the living needs of people.
The demand of distribution centers is increasing due to the continuous expansion of commodity distribution markets, the site selection of the position of the distribution center becomes an important subject of long-term development of distribution companies, and whether the site selection of the position of the commodity distribution center is scientific and reasonable directly influences the distribution efficiency and the profitability of the distribution companies. If the location of the distribution center is not reasonable, the resources are wasted, or the distribution efficiency is low, on one hand, the income of the distribution company is affected, and on the other hand, the customers are lost. If the distribution center can scientifically and reasonably select the site, the distribution efficiency can be greatly improved, the maximum utilization of the distribution center resources is realized, the profit of the distribution company is increased, the distribution company is helped to expand the market, and the development of the distribution company is helped. The factors influencing the site selection of the distribution center are many and complex, such as terrain conditions, logistics cost, traffic conditions, surrounding conditions and the like, and the factors are mutually influenced and closely related. Therefore, the influence factors need to be reasonably processed and analyzed according to requirements. In order to maximize benefits, distribution companies often place distribution centers in areas with developed traffic, low price areas and flat terrain, and determine suitable locations by comprehensively analyzing a plurality of influencing factors.
Disclosure of Invention
In order to overcome the mode that the existing distribution center site selection mode only considers the influence of a single factor or sets the route from the distribution center to a demand point to be a straight line and is not in accordance with the objective actual condition, the invention considers the multiple factors influencing the distribution center and combines the actual geographic condition of a site selection area, and provides a GIS-based commodity distribution center site selection method which has high site selection efficiency and accords with the actual condition, combines the GIS technology, and is based on the distribution condition of the distribution point density of an urban area and the actual distribution condition of a road network.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a GIS-based distribution center site selection method comprises the following steps:
1) importing road network data of a certain area of a certain city into ArcGIS Pro software to obtain a road network distribution map;
2) importing hydrological data and topographic data of a certain area of a certain city into ArcGIS software to obtain an area gradient map;
3) extracting distribution point data of a certain area of a certain city, introducing the address of the distribution point into ArcGIS Pro software, and converting the address into a geographical coordinate on a map so as to obtain a distribution point density distribution map;
4) generating m high-density area pre-selection points of the distribution points, and taking the pre-selection points as primary screening points of the commodity distribution center, wherein the process is as follows:
4.1 Total daily amount of orders in a certain area and a certain street of a certain city is PaThe radiation area of the street is SaAnd calculating the density of the street distribution points:
Figure BDA0001779784520000021
wherein KaThe distribution point density of the a-th street is set as a ∈ {1, 2.,. eta }, wherein eta is the total number of the regional streets;
4.2, calculating the average density of the distribution points of the area:
Figure BDA0001779784520000022
wherein
Figure BDA0001779784520000023
Is the average density of the distribution points of the area, KaThe distribution point density of the a-th street is set as a ∈ {1, 2.,. eta }, wherein eta is the total number of the regional streets;
4.3, repeating the step 4.1 and the step 4.2 to calculate the distribution point density of the area when
Figure BDA0001779784520000024
Defining the area as a distribution point high-density area, and obtaining m distribution point high-density areas by setting a K value as a set standard density value, and correspondingly generating m pre-selection points as primary screening points of the commodity distribution center site selection;
5) importing the addresses of the pre-selected points, the distribution point addresses and the road network data of the m commodity distribution centers into ArcGIS software, and obtaining the coordinate positions of the pre-selected points and the distribution points of each commodity distribution center under the support of the GIS software;
6) and calculating the distance from the pre-selected point of the ith commodity distribution center to the jth distribution point according to the pre-selected point of the commodity distribution center and the coordinates of the distribution points, wherein the process is as follows:
6.1, if no geographical blocking point exists between the pre-selection point and the distribution point of the commodity distribution center, expressing the distance function as:
Figure BDA0001779784520000031
wherein L isijFor the function of the distance from the pre-selected point of the ith commodity distribution center to the jth distribution point, (x)i,yi) (x) coordinate position of preselected point for ith commodity distribution centerj,yj) The j is the coordinate position of the jth distribution point, i belongs to {1,2,. multidot.m }, and m is the number of preselected points of the commodity distribution center;
6.2, if c geographical blocking points exist between the pre-selection point and the distribution point of the commodity distribution center, the distance function is the minimum segment of the distance function in the plurality of geographical blocking points, and the distance function with the c geographical blocking points is represented as:
Figure BDA0001779784520000032
wherein L isijFor the function of the distance from the pre-selected point of the ith commodity distribution center to the jth distribution point, (x)i,yi) (x) coordinate position of preselected point for ith commodity distribution centerj,yj) Is the coordinate position of the jth dispensing point, (p)t,dt) For the position of the tth geographical blocking point, t belongs to {1, 2., c }, c represents the number of the geographical blocking points, i belongs to {1, 2., m }, and m is the number of the pre-selected points of the commodity distribution center;
7) the total shipping cost from the pre-selected point to the distribution point of the commodity distribution center is expressed as:
Figure BDA0001779784520000033
wherein Di(x, y) represents the total transportation cost from the i-th goods distribution center pre-selected point to the j-th distribution point, eiRepresents the cost per unit weight and per unit distance of the shipment from the i-th commodity distribution center pre-selected point to the j-th distribution point, wiIndicating the total quantity of commodities to be shipped at one time from the i-th commodity distribution center pre-selection point to the j-th distribution point, Lij(x, y) represents a shortest distance function from the ith commodity distribution center pre-selected point to the jth distribution point, i belongs to {1, 2., m }, and m is the number of the commodity distribution center pre-selected points;
8) importing hydrological data and topographic data of an area where the pre-selection point of the commodity distribution center is located into ArcGIS Pro software to generate gradient grid data of the area, and obtaining a gradient value H of the location of the pre-selection point of the commodity distribution center by using a Zonal statismics as Table tool of ArcGISiI belongs to {1,2,. multidot.m }, m is the number of preselected points of the commodity distribution center, and the gradient value HiSmaller indicates that the terrain is flatter;
9) calculating the probability that the delivery point in the J area visits the commodity delivery center in the I area according to the road network distribution of the preselected point area of the commodity delivery center, the number of the delivery points and the popularity of the commodity delivery center:
Figure BDA0001779784520000041
wherein P isIJF is a probability that the delivery point in the J area visits the commodity delivery center in the I areaIIndicating the degree of awareness, T, of the commodity distribution centerIJThe time spent from the distribution point of the J area to the commodity distribution center of the I area is represented, I belongs to {1, 2., m }, J belongs to {1, 2., m }, and m is the number of the preselected points of the commodity distribution center;
calculating the area of the commodity distribution center according to the probability that the distribution point visits the commodity distribution center:
Figure BDA0001779784520000042
wherein SiThe area of the ith commodity distribution center,
Figure BDA0001779784520000043
for a standard area of a goods distribution center, PIJThe probability that the distribution point in the J area visits the commodity distribution center in the I area is represented, I belongs to {1, 2.., m }, and m is the number of the preselected points of the commodity distribution center;
10) determining a distribution center site selection model according to four main influence factors of total transportation cost, average slope, customer service rate and distribution center area, wherein the model expression is as follows:
Wi＝r1Si+r2Hi+r3Pi+r4Di
W＝minWi
wherein WiTo influence the weighting value of the factor of the pre-selected point of the ith goods distribution center, DiRepresenting the total transportation cost, P, of the i-th commodity distribution center pre-selected pointiExpressing the customer factor, H, of the i-th commodity distribution center pre-selected pointiRepresents a gradient value S of the location of the pre-selected point of the ith commodity distribution centeriThe area of the ith commodity distribution center is represented, i belongs to {1, 2., m }, and m is the number of preselected points of the commodity distribution center;
the four influence factors in the model are sorted according to importance, the four influence factors are assigned according to numbers from large to small, the number is 4, 3, 2 and 1, the larger the number is, the larger the importance is, the larger the effect is, and finally, the importance value is normalized to obtain the weight value of each influence factor, wherein the normalization processing formula is as follows:
Figure BDA0001779784520000044
wherein r isz，ezRespectively representing a weight value of the z-th influence factor and a value representing importance, wherein z belongs to {1, 2.., 4 };
11) and calculating the minimum W value according to the site selection model of the distribution center, wherein the pre-selection point of the commodity distribution center represented by the value is the final site selection position of the commodity distribution center.
Further, in the step 7), the smaller the total transportation cost is, the better the total transportation cost is, the closer the distance from the position of the pre-selected point of the commodity distribution center to the distribution point is, and in the step 7), the smaller the gradient value is, the better the gradient value is, the flatter the terrain of the position of the commodity distribution center is.
The invention has the following beneficial effects: the invention provides a reasonable site selection method for a distribution center by combining a GIS technology and based on distribution point density distribution conditions, actual distribution of a road network and actual distribution conditions of gradients.
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Fig. 1 is a flow chart of a method for locating a commodity distribution center based on a GIS.
Fig. 2 is a distribution point density distribution diagram generated by importing the area distribution point data into ArcGIS Pro.
Fig. 3 shows that the regional road network data is imported into ArcGIS Pro to generate a road network map.
Fig. 4 is regional hydrological data, and terrain data is imported into ArcGIS to generate a regional gradient map.
Fig. 5 is a diagram of the effect of the final location of the distribution center within the area.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
Referring to fig. 1 to 5, a method for locating a commodity distribution center based on a GIS includes the following steps:
1) importing road network data of a certain region of a certain city into ArcGIS Pro software to obtain a road network distribution diagram as shown in FIG. 3;
2) importing hydrological data and topographic data of a certain area of a certain city into ArcGIS software to obtain an area gradient diagram as shown in FIG. 4;
3) importing the distribution point data of a certain area of a certain city into ArcGIS Pro software to generate a distribution point density distribution map as shown in figure 1;
4) generating m high-density area pre-selection points of the distribution points, and taking the pre-selection points as primary screening points of the commodity distribution center, wherein the process is as follows:
4.1 Total daily amount of orders in a certain area and a certain street of a certain city is PaThe radiation area of the street is SaCalculating the density of the street distribution points:
Figure BDA0001779784520000051
wherein KaThe distribution point density of the a-th street is set as a ∈ {1, 2.,. eta }, wherein eta is the total number of the regional streets;
4.2, calculating the average density of the distribution points of the area:
Figure BDA0001779784520000061
wherein
Figure BDA0001779784520000062
Is the average density of the distribution points of the area, KaThe distribution point density of the a-th street is set as a ∈ {1, 2.,. eta }, wherein eta is the total number of the regional streets;
4.3 repeating step 3.1 and step 3.2 to calculate the distribution point density in the area when
Figure BDA0001779784520000063
When the number of the generated high-density areas does not accord with the expected number, the size of the K value can be properly adjusted. Obtaining m distribution point high-density areas, and correspondingly generating m pre-selection points as primary screening points of distribution center site selection;
5) importing the addresses of the pre-selected points, the distribution point addresses and the road network data of the m commodity distribution centers into ArcGIS software, and obtaining the coordinate positions of the pre-selected points and the distribution points of each commodity distribution center under the support of the GIS software;
6) and calculating the distance from the pre-selected point of the ith commodity distribution center to the jth distribution point according to the pre-selected point of the commodity distribution center and the coordinates of the distribution points, wherein the process is as follows:
6.1, if there is no geographic blockage between the pre-selected point of the commodity distribution center and the distribution point, the distance function is expressed as:
Figure BDA0001779784520000064
wherein L isij(x) a function of the distance from the i-th commodity distribution center pre-selected point to the j-th distribution pointi,yi) (x) coordinate position of preselected point for ith commodity distribution centerj,yj) The j is the coordinate position of the jth distribution point, i belongs to {1,2,. and m }, and m is the number of budget points of the commodity distribution center;
6.2, if c geographical interruptions exist between the pre-selection point and the distribution point of the commodity distribution center, the distance function is the minimum segment of the distance function in the geographical interruptions, and the distance function with the c geographical interruptions is represented as:
Figure BDA0001779784520000065
wherein L isij(x) a function of the distance from the i-th commodity distribution center pre-selected point to the j-th distribution pointi,yi) (x) coordinate position of preselected point for ith commodity distribution centerj,yj) For the coordinate position of the jth delivery point, i belongs to {1, 2., m }, and m is the number of budget points of the commodity delivery center, (p)t,dt) The t belongs to {1,2,. c }, and c represents the number of the geographic breaking points;
7) the total shipping cost from the pre-selected point to the distribution point of the commodity distribution center is expressed as:
Figure BDA0001779784520000071
wherein Di(x, y) represents the pre-selected point from the ith goods distribution center to the jth distributionTotal transportation cost of points, eiRepresents the cost per unit weight and per unit distance of the shipment from the i-th commodity distribution center pre-selected point to the j-th distribution point, wiIndicating the total quantity of commodities transported from the pre-selected point of the ith commodity distribution center to the jth distribution point at one time, Lij(x, y) represents a function of the shortest distance from the pre-selected point of the ith commodity distribution center to the jth distribution point, i belongs to {1, 2., m }, and m is the number of the pre-selected points of the commodity distribution center;
8) importing hydrological data and topographic data of an area where the pre-selection point of the commodity distribution center is located into ArcGIS Pro software to generate gradient grid data of the area, and obtaining a gradient value H of the location of the pre-selection point of the commodity distribution center by using a Zonal statistics as Table tool of ArcGISiI belongs to {1,2,. multidot.m }, m is the number of preselected points of the commodity distribution center, and the gradient value HiSmaller indicates that the terrain is flatter;
9) calculating the probability that the delivery point in the J area visits the commodity delivery center in the I area according to the road network distribution of the preselected point area of the commodity delivery center, the quantity of the delivery points and the popularity of the commodity delivery center:
Figure BDA0001779784520000072
wherein P isIJF is a probability that the delivery point in the J area visits the commodity delivery center in the I areaIIndicating the degree of awareness, T, of the commodity distribution centerIJThe time spent from the distribution point of the J area to the commodity distribution center of the I area is represented, I belongs to {1, 2., m }, J belongs to {1, 2., m }, and m is the number of the preselected points of the commodity distribution center;
calculating the area of the commodity distribution center according to the probability that the distribution point visits the commodity distribution center:
Figure BDA0001779784520000073
wherein SiThe area of the ith commodity distribution center,
Figure BDA0001779784520000074
for a standard area of a goods distribution center, PIJThe probability that the distribution point in the J area visits the commodity distribution center in the I area is represented, I belongs to {1, 2.., m }, and m is the number of the preselected points of the commodity distribution center;
10) determining a commodity distribution center site selection model according to four main influence factors of total transportation cost, gradient, customer service rate and commodity distribution center area, wherein the model expression is as follows:
Wi＝r1Si+r2Hi+r3Pi+r4Di
W＝minWi
wherein WiTo influence the weighting value of the factor of the pre-selected point of the ith goods distribution center, DiRepresents the total transportation cost, P, of the ith goods distribution centeriDenotes a customer factor H of the ith commodity distribution centeriRepresents a gradient value S of the location of the ith commodity distribution centeriThe area of the ith commodity distribution center is represented, i belongs to {1, 2., m }, and m is the number of preselected points of the commodity distribution center;
the four influence factors in the model are sorted according to importance, the four influence factors are assigned according to numbers from large to small, the number is 4, 3, 2 and 1, the larger the number is, the larger the importance is, the larger the effect is, and finally, the importance value is normalized to obtain the weight value of each influence factor, wherein the normalization processing formula is as follows:
Figure BDA0001779784520000081
wherein r isz，ezRespectively representing a weight value of the z-th influence factor and a value representing importance, wherein z belongs to {1, 2.., 4 };
11) fig. 5 shows the minimum W value calculated from the site selection model of the commodity distribution center, and the product distribution center pre-selection point represented by the minimum W value, which is the final site selection position of the commodity distribution center.
Further, in the step 7), the smaller the total transportation cost is, the better the total transportation cost is, the closer the distance from the position of the pre-selected point of the commodity distribution center to the distribution point is, and in the step 8), the smaller the gradient value is, the better the gradient value is, the flatter the terrain of the position of the commodity distribution center is.
Taking the new goat mountain area in Xinyang city as an example, a GIS-based commodity distribution center site selection method comprises the following steps:
1) importing the road network data of the new goat mountain area in Xinyang city into ArcGIS Pro software to obtain a road network distribution map;
2) importing hydrological data and geographic data of a new area of the sheep mountain in Xinyang city into ArcGIS Pro software to obtain an area slope map;
3) importing the data of the distribution points of the new goat mountain area in Xinyang city into ArcGIS Pro software to generate a distribution point density distribution map;
4) and generating m-10 distribution point high-density area pre-selection points as primary screening points of the commodity distribution center site selection, wherein the process is as follows:
4.1 the total amount of orders in a certain street of the Yangyang City, every day is paThe street has a 5 ten thousand square meters radiating area, the street demand point density is calculated as 2000:
Figure BDA0001779784520000091
wherein KaThe demand point density of the a-th street is as follows, a is equal to {1, 2.,. eta }, and eta is 20, which is the total number of the streets in the area;
4.2, calculating the average density of the distribution points of the area:
Figure BDA0001779784520000092
wherein
Figure BDA0001779784520000093
The average density a ∈ {1, 2., η } for the delivery point for the zone, η ═ 20, η for the zoneTotal number of domain streets;
4.3 repeating step 4.1 and step 4.2 to calculate the distribution point density in the area when
Figure BDA0001779784520000094
If the number of the generated high-density regions does not meet the expected number, the value of K can be adjusted appropriately. Obtaining m distribution point high-density areas, and correspondingly generating m pre-selection points as primary screening points of the commodity distribution center site selection;
5) importing the addresses of the preselected points of 10 commodity distribution centers, the distribution point addresses and the road network data into ArcGIS Pro software, and obtaining the coordinate positions of the preselected points and the distribution points of each commodity distribution center under the support of GIS software;
6) and calculating the distance from the pre-selected point of the ith commodity distribution center to the jth distribution point according to the pre-selected point of the commodity distribution center and the coordinates of the distribution points, wherein the process is as follows:
6.1, if there is no geographic blockage between the pre-selected point of the commodity distribution center and the distribution point, the distance function is expressed as:
Figure BDA0001779784520000095
wherein L isij(x) a function of the distance from the i-th commodity distribution center pre-selected point to the j-th distribution pointi,yi) (x) coordinate position of preselected point for ith commodity distribution centerj,yj) The j is the coordinate position of the jth distribution point, i belongs to {1,2,. m }, m is 10, and m is the number of preselected points of the commodity distribution center;
6.3, if c geographical interruptions exist between the pre-selection point and the distribution point of the commodity distribution center, the distance function is the minimum segment of the distance function in the geographical interruptions, and the distance function with the c geographical interruptions is represented as:
Figure BDA0001779784520000101
wherein L isij(x) a function of the distance from the i-th commodity distribution center pre-selected point to the j-th distribution pointi,yi) (x) coordinate position of preselected point for ith commodity distribution centerj,yj) I belongs to {1,2,. m }, m is 10, and m is the number of preselected points of the commodity distribution center, (p) is the coordinate position of the jth distribution pointt,dt) For the position of the tth geographic blocking point, t belongs to {1,2,. 4 };
7) the total shipping cost from the pre-selected point to the distribution point of the commodity distribution center is expressed as:
Figure BDA0001779784520000102
wherein Di(x, y) represents the total transportation cost from the i-th goods distribution center pre-selected point to the j-th distribution point, eiRepresents the cost per unit weight and per unit distance of the shipment from the i-th goods distribution center pre-selected point to the j-th distribution point, wiIndicating the total quantity of commodities transported once from the i-th commodity distribution center pre-selected point to the j-th distribution point, Lij(x, y) represents a shortest distance function from the ith commodity distribution center preselected point to the jth distribution point, i belongs to {1,2,. multidot.m }, m is 10, and m is the number of the commodity distribution center preselected points;
8) importing hydrological data and topographic data of the area where the pre-selection point of the commodity distribution center is located into ArcGISPO software to generate a gradient value H of the location of the pre-selection point of the commodity distribution centeriI belongs to {1,2,. multidot.m }, wherein m is 10, and m is the number of preselected points of the commodity distribution center;
9) calculating the probability that the delivery point in the J-th area visits the commodity delivery center in the I-th area according to the road network distribution of the preselected point area of the commodity delivery center, the quantity of the delivery points and the popularity of the commodity delivery center:
Figure BDA0001779784520000103
wherein P isIJF is a probability that the delivery point in the J area visits the commodity delivery center in the I areaIIndicating the degree of awareness, T, of the commodity distribution centerIJThe time spent from the distribution point of the J area to the commodity distribution center of the I area is represented, I belongs to {1, 2., m }, J belongs to {1, 2., m }, m is 10, and m is the number of the preselected points of the commodity distribution center;
calculating the area of the commodity distribution center according to the probability that the distribution point visits the distribution center:
Figure BDA0001779784520000104
wherein SiThe area of the ith commodity distribution center,
Figure BDA0001779784520000111
for a standard area of a goods distribution center, PIJA probability that the distribution point in the J area visits the commodity distribution center in the I area;
10) determining a commodity distribution center site selection model according to four main influence factors of total transportation cost, a slope value, a customer service rate and a distribution center area, wherein the model expression is as follows:
Wi＝r1Si+r2Hi+r3Pi+r4Di
W＝minWi
wherein WiTo influence the weighting value of the factor of the pre-selected point of the ith goods distribution center, DiRepresents the total transportation cost, P, of the ith goods distribution centeriDenotes a customer factor H of the ith commodity distribution centeriIndicating the gradient of the location of the ith goods distribution center, SiThe area of the ith commodity distribution center is represented, i belongs to {1, 2., m }, m is 10, and m is the number of preselected points of the commodity distribution center;
the four influence factors in the model are sorted according to importance, the four influence factors are assigned according to numbers from large to small, the number is 4, 3, 2 and 1, the larger the number is, the larger the importance is, the larger the effect is, and finally, the importance value is normalized to obtain the weight value of each influence factor, wherein the normalization processing formula is as follows:
Figure BDA0001779784520000112
wherein r isz，ezRespectively representing a weight value of the z-th influence factor and a value representing importance, wherein z belongs to {1, 2.., 4 };
11) and calculating a minimum W value according to the site selection model of the commodity distribution center, wherein the pre-selection point of the commodity distribution center represented by the value is the final site selection position of the commodity distribution center.
While the foregoing has described the preferred embodiments of the present invention, it will be apparent that the invention is not limited to the embodiments described, but can be practiced with modification without departing from the essential spirit of the invention and without departing from the spirit of the invention.

Claims (1)

1. A GIS-based commodity distribution center site selection method is characterized by comprising the following steps: the method comprises the following steps:
1) importing road network data of a certain region of a certain city into ArcGis Pro software to obtain a road network distribution map;
2) importing hydrological data and topographic data of a certain area of a certain city into ArcGIS software to obtain an area gradient map;
3) extracting distribution point data of a certain area of a certain city, introducing the address of the distribution point into ArcGis Pro software, and converting the address into a geographical coordinate on a map so as to obtain a distribution point density distribution map;
4) generating m distribution point high-density area pre-selection points, and taking the pre-selection points as primary screening points of the commodity distribution center, wherein the process is as follows:
4.1 Total daily amount of orders in a certain area and a certain street of a certain city is PaThe radiation area of the street is SaAnd calculating the density of the street distribution points:
Figure FDA0002972057030000011
wherein KaThe distribution point density of the a-th street is set as a ∈ {1, 2.,. eta }, wherein eta is the total number of the regional streets;
4.2, calculating the average density of the distribution points of the area:
Figure FDA0002972057030000012
wherein
Figure FDA0002972057030000013
The average density a ∈ {1, 2.,. eta }, K } for the delivery points for the regionaThe distribution point density of the a-th street is shown, and eta is the total number of the area streets;
4.3 repeating step 4.1 and step 4.2 to calculate the distribution point density in the area when
Figure FDA0002972057030000014
Defining the area as a distribution point high-density area, wherein the K value is a set standard density value; obtaining m distribution point high-density areas, and correspondingly generating m pre-selection points as primary screening points of the commodity distribution center site selection;
5) importing the addresses of the pre-selected points, the distribution point addresses and the road network data of the m commodity distribution centers into ArcGIS Pro software, and obtaining the coordinate positions of the pre-selected points and the distribution points of each commodity distribution center under the support of GIS software;
6) and calculating the distance from the pre-selected point of the ith commodity distribution center to the jth distribution point according to the pre-selected point of the commodity distribution center and the coordinates of the distribution points, wherein the process is as follows:
6.1, if there is no geographic blockage between the pre-selected point of the commodity distribution center and the distribution point, the distance function is expressed as:
Figure FDA0002972057030000021
wherein L isijA function of the distance from the pre-selected point to the delivery point for the center of the distribution of the goods, (x)i,yi) (x) coordinate position of preselected point for ith commodity distribution centerj,yj) The j is the coordinate position of the jth distribution point, i belongs to {1,2,. multidot.m }, and m is the number of preselected points of the commodity distribution center;
6.2, if c geographical interruptions exist between the pre-selection point and the distribution point of the commodity distribution center, the distance function is the minimum segment of the distance function in the plurality of geographical interruption points, and the distance function when the c geographical interruption points exist is represented as:
Figure FDA0002972057030000022
wherein L isijFor the function of the distance from the pre-selected point of the ith commodity distribution center to the jth distribution point, (x)i,yi) (x) coordinate position of preselected point for ith commodity distribution centerj,yj) Is the coordinate position of the jth dispensing point, (p)t,dt) The position of the tth geographic breaking point belongs to {1, 2., c }, c is the number of breaking points, i belongs to {1, 2., m }, and m is the number of preselected points of the commodity distribution center;
7) the total shipping cost from the pre-selected point to the distribution point of the commodity distribution center is expressed as:
Figure FDA0002972057030000023
wherein Di(x, y) represents the total transportation cost from the i-th goods distribution center pre-selected point to the j-th distribution point, eiRepresents the cost per unit weight and per unit distance of the shipment from the i-th commodity distribution center pre-selected point to the j-th distribution point, wiIndicating the total quantity of commodities to be shipped at one time from the i-th commodity distribution center pre-selection point to the j-th distribution point, Lij(x, y) represents the distribution of the ith commodity from the commodity distribution centerThe shortest distance function from the pre-selected point to the jth delivery point, i belongs to {1,2,. multidot.m }, and m is the number of the pre-selected points of the commodity delivery center;
8) importing hydrological data and topographic data of an area where the pre-selection point of the commodity distribution center is located into ArcGIS Pro software to generate gradient grid data of the area, and obtaining a gradient value H of the location of the pre-selection point of the commodity distribution center by using a Zonal statismics as Table tool of ArcGISiI belongs to {1,2,. multidot.m }, m is the number of preselected points of the commodity distribution center, and the gradient value HiSmaller indicates flatter topography;
9) calculating the probability that the delivery point in the J-th area visits the commodity delivery center in the I-th area according to the road network distribution of the preselected point area of the commodity delivery center, the quantity of the delivery points and the popularity of the commodity delivery center:
Figure FDA0002972057030000031
wherein P isIJF is a probability that the delivery point in the J area visits the commodity delivery center in the I areaIIndicating the degree of awareness, T, of the commodity distribution centerIJThe time spent from the distribution point of the J area to the commodity distribution center of the I area is represented, I belongs to {1, 2., m }, J belongs to {1, 2., m }, and m is the number of the preselected points of the commodity distribution center;
calculating the area of the distribution center according to the probability that the distribution point visits the commodity distribution center:
Figure FDA0002972057030000032
wherein SiThe area of the ith commodity distribution center,
Figure FDA0002972057030000033
for a standard area of a goods distribution center, PIJThe probability that the distribution point in the J area visits the commodity distribution center in the I area is represented, I belongs to {1, 2.., m }, and m is the number of the preselected points of the commodity distribution center;
10) determining a commodity distribution center site selection model according to four main influence factors of total transportation cost, gradient, customer service rate and distribution center area, wherein the model expression is as follows:
Wi＝r1Si+r2Hi+r3Pi+r4Di
W＝minWi
wherein WiTo influence the weighting value of the factor of the pre-selected point of the ith goods distribution center, DiRepresents the total transportation cost, P, of the ith goods distribution centeriDenotes a customer factor H of the ith commodity distribution centeriRepresents a gradient value S of the location of the ith commodity distribution centeriThe area of the ith commodity distribution center is represented, i belongs to {1, 2., m }, and m is the number of preselected points of the commodity distribution center;
the four influence factors in the model are sorted according to importance, the four influence factors are assigned according to numbers from large to small, the number is 4, 3, 2 and 1, the larger the number is, the larger the importance is, the larger the effect is, and finally, the importance value is normalized to obtain the weight value of each influence factor, wherein the normalization processing formula is as follows:
Figure FDA0002972057030000034
wherein r isz，ezRespectively representing a weight value of the z-th influence factor and a value representing importance, wherein z belongs to {1, 2.., 4 };
11) and calculating a minimum W value according to the site selection model of the commodity distribution center, wherein the pre-selection point of the distribution center represented by the value is the final site selection position of the distribution center.
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