CN114493466A - Real-time demand-driven logistics service facility site selection method - Google Patents

Real-time demand-driven logistics service facility site selection method Download PDF

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CN114493466A
CN114493466A CN202210156703.8A CN202210156703A CN114493466A CN 114493466 A CN114493466 A CN 114493466A CN 202210156703 A CN202210156703 A CN 202210156703A CN 114493466 A CN114493466 A CN 114493466A
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饶伟
罗政
吴小龙
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Shenzhen Jialida Supply Chain Management Co ltd
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Abstract

The invention relates to the technical field of facility site selection, and discloses a real-time demand-driven logistics service facility site selection method, which comprises the following steps: analyzing logistics distribution requirements, and constructing a logistics distribution model; determining a logistics distribution center site selection model according to the constructed logistics distribution model; constructing a user satisfaction function and a logistics distribution cost function, and determining a target function and a constraint condition of a logistics distribution center site selection model based on the constructed functions; and performing optimization solution on the constructed objective function by using a particle swarm algorithm to obtain an optimal solution result, namely the logistics distribution center address. The method comprises the steps of constructing various logistics distribution models by analyzing real-time demands of logistics distribution, determining a logistics distribution center site selection model combining user satisfaction and distribution cost according to the constructed logistics distribution models, and performing optimization solution on the models by utilizing a particle swarm algorithm to obtain the addresses of the logistics distribution centers.

Description

Real-time demand-driven logistics service facility site selection method
Technical Field
The invention relates to the technical field of facility site selection, in particular to a real-time demand-driven logistics service facility site selection method.
Background
Logistics as a new industry in China occupies an increasingly important position in national economic development. With the continuous expansion of the logistics scale, the logistics enterprises have higher and higher demands on cargo distribution, sorting and transfer. Due to the fact that site selection of numerous logistics enterprises is unreasonable, logistics cost of the enterprises is high, logistics efficiency is low, and customer experience is poor. Therefore, how to set a reasonable logistics site location has important significance for improving the industry competitiveness of logistics enterprises.
In view of the above, the invention provides a real-time demand driven logistics service facility site selection method, which includes the steps of constructing various logistics distribution models by analyzing real-time demands of logistics distribution, determining a logistics distribution center site selection model combining user satisfaction and distribution cost according to the constructed logistics distribution models, and solving the models by using a heuristic algorithm to obtain a logistics distribution center address.
Disclosure of Invention
The invention provides a real-time demand-driven logistics service facility site selection method, which aims to (1) construct a logistics distribution model based on real-time demand; (2) and determining an objective function of the logistics distribution center site selection model based on the user satisfaction and the logistics distribution cost, and obtaining the address of the logistics distribution center by carrying out optimization solution on the objective function.
The invention provides a real-time demand-driven logistics service facility site selection method, which comprises the following steps:
s1: analyzing logistics distribution requirements, and constructing a logistics distribution model;
s2: determining a logistics distribution center site selection model according to the constructed logistics distribution model;
s3: constructing a user satisfaction function and a logistics distribution cost function, and determining a target function and a constraint condition of a logistics distribution center site selection model based on the constructed functions;
s4: and performing optimization solution on the constructed objective function by using a particle swarm algorithm to obtain an optimal solution result, namely the logistics distribution center address.
As a further improvement of the method of the invention:
in the step S1, constructing a logistics distribution model, including:
constructing a real-time demand driven logistics distribution model, wherein the logistics distribution model comprises the following steps:
1) direct logistics distribution model: the logistics distribution center takes the goods from the supplier and directly delivers the goods to the user;
2) logistics level distribution model: establishing a two-layer logistics distribution network comprising a logistics distribution center and a plurality of regional distribution stations, wherein during distribution, the logistics distribution center directly performs logistics distribution to the regional distribution center, and then the regional distribution center distributes goods to users, and during return of goods, goods firstly enter the regional distribution center and are converged into the logistics distribution center by the regional distribution center;
in one embodiment of the invention, the invention adopts a logistics direct distribution model to distribute some large goods and important goods, and adopts a logistics level distribution model to distribute other goods.
The determining the logistics distribution center site selection model in the step S2 includes:
constructing a logistics distribution center site selection model, wherein the constructed logistics distribution center site selection model is as follows:
the logistics center site selection model of the logistics direct distribution model is as follows:
Figure BDA0003513034510000011
Figure BDA0003513034510000012
wherein:
qidenotes the ith logistics distribution target in the city, qiHas the coordinates of (x)i,yi) I is 1,2, …, and L represents the total number of logistics distribution targets;
(x0,y0) Coordinates representing a logistics distribution center;
u represents a delivery cost for delivery from the logistics center to the target;
the logistics center site selection model of the logistics level distribution model comprises the following steps:
Figure BDA0003513034510000021
wherein:
Dkthe distance of goods from the logistics distribution center to the kth regional distribution center is shown, and K represents the total number of regional distribution centers arranged in the city;
Dknindicating a distance from the k-th regional distribution center to the nth logistics distribution target;
gkrepresenting the delivery volume delivered by the logistics distribution center to the k-th regional distribution center;
gknindicating a delivery amount delivered to the nth logistics delivery target by the kth regional delivery center;
Yk＝{0,1}，Yk0 indicates that the k-th regional distribution center is not selected to perform logistics distribution, and Yk1 represents that the kth area distribution center is selected to execute logistics distribution;
f represents the distribution cost for distribution from the logistics distribution center to the distribution target, wherein the k-th regional distribution center has the coordinate of (x)k,yk) The coordinate of the nth logistics distribution target is (x)n,yn) And N represents the total number of logistics destination.
In one embodiment of the present invention, the distance between the regional distribution center and the logistics distribution target is a euclidean distance.
In the step S3, constructing a user satisfaction function, including:
constructing a user satisfaction function, wherein the constructed user satisfaction function is as follows:
Figure BDA0003513034510000022
wherein:
F(tkn) Constructing a user satisfaction function;
tknindicating distribution from the k-th logistics distribution location to the n-th logistics distribution locationWhen K is equal to 0, the logistics distribution position represents a logistics distribution center, and when K is equal to 1,2, …, K, the logistics distribution position represents a kth regional distribution center;
Rna minimum waiting time period indicating a time when the customer of the nth logistics distribution target is unsatisfied with the distribution service; in one embodiment of the invention, R isnSet to 3 hours.
In the step S3, a logistics distribution cost function is constructed, including:
constructing a logistics distribution cost function, wherein the constructed logistics distribution cost function is as follows:
G＝qidis+sDkgk+sDkngkn
wherein:
s represents the unit transportation cost from logistics and in one embodiment of the invention, the unit transportation cost represents the transportation cost per unit delivery volume per unit distance.
In the step S3, determining an objective function and a constraint condition of the logistic distribution center site selection model based on the constructed function includes:
the objective function of the logistics distribution center site selection model is as follows:
Figure BDA0003513034510000023
wherein:
Figure BDA0003513034510000024
the calculated result is the coordinate of the logistics distribution center, and the coordinate can realize the minimum logistics distribution transportation cost in the logistics direct distribution model;
Figure BDA0003513034510000025
the calculated result is the coordinates of the regional distribution center in the logistics layer distribution model, and the coordinates can realize the logisticsThe logistics distribution transportation cost in the hierarchical distribution model is minimum;
the constraint conditions of the logistics distribution center site selection model are as follows:
Figure BDA0003513034510000031
qi>0
wherein:
Figure BDA0003513034510000032
the method and the system ensure that the dissatisfaction degree of users is minimum when the logistics distribution is carried out.
In the step S4, the particle swarm algorithm is used to perform optimization solution on the objective function, and a solution result of the objective function is the address of the logistics distribution center, including:
carrying out optimization solution on the objective function by utilizing a particle swarm optimization algorithm, wherein the flow of the particle swarm optimization algorithm is as follows:
1) randomly generating 30 particles, wherein the position information of the ith particle is as follows:
Figure BDA0003513034510000033
wherein:
Figure BDA0003513034510000034
coordinates representing the address of the logistics distribution center in the objective function;
Figure BDA0003513034510000035
coordinates representing the address of the Kth regional distribution center in the objective function;
2) initializing the position p of each particleiAnd velocity viAnd the maximum iteration times Max of the particle swarm algorithm;
3) generation of a stream delivery module using position information for each particleModeling, simulating the logistics distribution demand, and calculating the objective function value V of the logistics distribution model generated by each particlei(ii) a In an embodiment of the present invention, the objective function value is a transportation cost value of the logistics distribution model for completing the logistics distribution requirement under the limitation of a constraint condition;
4) update the velocity and position of particle i:
Figure BDA0003513034510000036
Figure BDA0003513034510000037
wherein:
xbest(m) represents the particle position corresponding to the particle with the lowest objective function value in the mth iteration;
w represents the inertial weight when
Figure BDA0003513034510000038
w is 0.72, when
Figure BDA0003513034510000039
w＝0.2；
gaussian(0,σ2) Representing a mean of 0 and a variance of σ2Gaussian of, will2Set to 0.002;
c represents an acceleration factor, the value of which is 0.2;
r is a random number between (0, 1);
until the updating completes the position of each particle;
5) judging whether the iteration times reach the maximum iteration times Max or not, and if the iteration times reach the maximum iteration times, determining the position information corresponding to the particle with the lowest objective function value as the site selection position of the logistics distribution center; and if the maximum iteration times are not reached, returning to the step 3).
Compared with the prior art, the invention provides a real-time demand-driven logistics service facility site selection method, which has the following advantages:
firstly, the scheme provides a method for constructing a logistics center site selection model based on real-time requirements, wherein the constructed logistics distribution model comprises a logistics direct distribution model and a logistics level distribution model, and the logistics direct distribution model refers to the fact that a logistics distribution center takes goods from a supplier to reach the site of a user directly; the logistics level distribution model is characterized in that a two-layer logistics distribution network comprising a logistics distribution center and a plurality of regional distribution stations is established, the logistics distribution center directly performs logistics distribution to the regional distribution center during distribution, the regional distribution center distributes goods to users, and goods firstly enter the regional distribution center and are converged into the logistics distribution center by the regional distribution center during goods returning. The constructed logistics center site selection model is as follows: the logistics center site selection model of the logistics direct distribution model is as follows:
Figure BDA00035130345100000310
Figure BDA00035130345100000311
wherein: q. q.siDenotes the ith logistics distribution target in the city, qiHas the coordinates of (x)i,yi) I is 1,2, …, and L represents the total number of logistics distribution targets; (x)0,y0) Coordinates representing a logistics distribution center; u represents a delivery cost for delivery from the logistics center to the target; the logistics center site selection model of the logistics level distribution model comprises the following steps:
Figure BDA0003513034510000041
wherein: dkThe distance of goods from the logistics distribution center to the kth regional distribution center is shown, and K represents the total number of regional distribution centers arranged in the city; dknIndicating a distance from the k-th regional distribution center to the nth logistics distribution target; gkRepresenting the delivery volume delivered by the logistics distribution center to the k-th regional distribution center; gknIndicating a delivery amount delivered to the nth logistics delivery target by the kth regional delivery center; y isk＝{0,1}，Yk0 indicates that the k-th regional distribution center is not selected to perform logistics distribution, and Yk1 represents that the kth area distribution center is selected to execute logistics distribution; f represents the distribution cost for distribution from the logistics distribution center to the distribution target, wherein the k-th regional distribution center has the coordinate of (x)k,yk) The coordinate of the nth logistics distribution target is (x)n,yn) And N represents the total number of logistics destination. Compared with the traditional scheme, the method adopts the logistics direct distribution model to distribute large goods and important goods, adopts the logistics level distribution model to distribute other goods, respectively constructs the logistics center site selection model for the logistics direct distribution model and the logistics level distribution model, determines the logistics distribution center by utilizing the site selection model based on the logistics direct distribution model, and determines the logistics regional distribution center by utilizing the site selection model based on the logistics level distribution model, so as to realize the site selection of the logistics service facilities.
Meanwhile, the scheme provides a logistics distribution center solving method combining user satisfaction and logistics distribution cost, and an objective function of a logistics distribution center site selection model is as follows:
Figure BDA0003513034510000042
wherein:
Figure BDA0003513034510000043
the calculated result is the coordinate of the logistics distribution center, and the coordinate can realize the minimum logistics distribution transportation cost in the logistics direct distribution model;
Figure BDA0003513034510000044
the result obtained by calculation is a logistics level distribution moduleThe coordinates of the regional distribution center in the model can realize the minimum logistics distribution transportation cost in the logistics level distribution model; the constraint conditions of the logistics distribution center site selection model are as follows:
Figure BDA0003513034510000045
qi>0
wherein:
Figure BDA0003513034510000046
the method and the system ensure that the dissatisfaction degree of users is minimum when the logistics distribution is carried out. According to the scheme, the particle swarm optimization is used for carrying out optimization solution on the objective function combining the user satisfaction and the logistics distribution cost, and the optimization solution result is the site selection position of the logistics distribution center.
Drawings
Fig. 1 is a schematic flowchart of a real-time demand-driven method for locating a logistics service facility according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
S1: and analyzing the logistics distribution demand, and constructing a logistics distribution model.
In the step S1, a logistics distribution model is constructed, including:
constructing a real-time demand driven logistics distribution model, wherein the logistics distribution model comprises the following steps:
1) direct logistics distribution model: the logistics distribution center takes the goods from the supplier and directly delivers the goods to the user;
2) logistics level distribution model: establishing a two-layer logistics distribution network comprising a logistics distribution center and a plurality of regional distribution stations, wherein during distribution, the logistics distribution center directly performs logistics distribution to the regional distribution center, and then the regional distribution center distributes goods to users, and during return of goods, goods firstly enter the regional distribution center and are converged into the logistics distribution center by the regional distribution center;
in one embodiment of the invention, the invention adopts a logistics direct distribution model to distribute some large goods and important goods, and adopts a logistics level distribution model to distribute other goods.
S2: and determining a logistics distribution center site selection model according to the constructed logistics distribution model.
The determining the logistics distribution center site selection model in the step S2 includes:
constructing a logistics distribution center site selection model, wherein the constructed logistics distribution center site selection model is as follows:
the logistics center site selection model of the logistics direct distribution model is as follows:
Figure BDA0003513034510000051
Figure BDA0003513034510000052
wherein:
qidenotes the ith logistics distribution target in the city, qiHas the coordinates of (x)i,yi) I is 1,2, …, and L represents the total number of logistics distribution targets;
(x0,y0) Coordinates representing a logistics distribution center;
u represents a delivery cost for delivery from the logistics center to the target;
the logistics center site selection model of the logistics level distribution model comprises the following steps:
Figure BDA0003513034510000053
wherein:
Dkindicating distribution of goods from logistics distribution centresThe distance from the central station to the kth regional distribution center, wherein K represents the total number of regional distribution centers arranged in the city;
Dknindicating a distance from the k-th regional distribution center to the nth logistics distribution target;
gkrepresenting the delivery volume delivered by the logistics distribution center to the k-th regional distribution center;
gknindicating a delivery amount delivered to the nth logistics delivery target by the kth regional delivery center;
Yk＝{0,1}，Yk0 indicates that the k-th regional distribution center is not selected to perform logistics distribution, and Yk1 represents that the kth area distribution center is selected to execute logistics distribution;
f represents the distribution cost for distribution from the logistics distribution center to the distribution target, wherein the k-th regional distribution center has the coordinate of (x)k,yk) The coordinate of the nth logistics distribution target is (x)n,yn) And N represents the total number of logistics destination.
In one embodiment of the present invention, the distance between the regional distribution center and the logistics distribution target is a euclidean distance.
S3: and constructing a user satisfaction function and a logistics distribution cost function, and determining an objective function and a constraint condition of a logistics distribution center site selection model based on the constructed functions.
In the step S3, constructing a user satisfaction function, including:
constructing a user satisfaction function, wherein the constructed user satisfaction function is as follows:
Figure BDA0003513034510000054
wherein:
F(tkn) Constructing a user satisfaction function;
tknindicating a delivery time period from a k-th logistics delivery location to an n-th logistics delivery target, wherein when k is 0, the logistics delivery location indicates a logistics delivery centerWhen K is 1,2, …, K, the logistics distribution location represents the kth regional distribution center;
Rna minimum waiting time period indicating a time when the customer of the nth logistics distribution target is unsatisfied with the distribution service; in one embodiment of the invention, R isnSet to 3 hours.
In the step S3, a logistics distribution cost function is constructed, including:
constructing a logistics distribution cost function, wherein the constructed logistics distribution cost function is as follows:
G＝qidis+sDkgk+sDkngkn
wherein:
s represents the unit transportation cost from logistics and in one embodiment of the invention, the unit transportation cost represents the transportation cost per unit delivery volume per unit distance.
In the step S3, determining an objective function and a constraint condition of the logistic distribution center site selection model based on the constructed function includes:
the objective function of the logistics distribution center site selection model is as follows:
Figure BDA0003513034510000061
wherein:
Figure BDA0003513034510000062
the calculated result is the coordinate of the logistics distribution center, and the coordinate can realize the minimum logistics distribution transportation cost in the logistics direct distribution model;
Figure BDA0003513034510000063
the calculated result is the coordinates of the regional distribution center in the logistics level distribution model, and the coordinates can realize the minimum logistics distribution transportation cost in the logistics level distribution model;
the constraint conditions of the logistics distribution center site selection model are as follows:
Figure BDA0003513034510000064
qi>0
wherein:
Figure BDA0003513034510000065
the method and the system ensure that the dissatisfaction degree of users is minimum when the logistics distribution is carried out.
S4: and performing optimization solution on the constructed objective function by using a particle swarm algorithm, wherein the obtained optimal solution result is the address of the logistics distribution center.
In the step S4, the particle swarm algorithm is used to perform optimization solution on the objective function, and a solution result of the objective function is the address of the logistics distribution center, including:
carrying out optimization solution on the objective function by utilizing a particle swarm optimization algorithm, wherein the flow of the particle swarm optimization algorithm is as follows:
1) randomly generating 30 particles, wherein the position information of the ith particle is as follows:
Figure BDA0003513034510000066
wherein:
Figure BDA0003513034510000067
coordinates representing the address of the logistics distribution center in the objective function;
Figure BDA0003513034510000068
coordinates representing the address of the Kth regional distribution center in the objective function;
2) initializing the position p of each particleiAnd velocity viAnd the maximum iteration times Max of the particle swarm algorithm;
3) generating a logistics distribution model by using the position information of each particle, simulating a logistics distribution demand, and calculating an objective function value V of the logistics distribution model generated by each particlei(ii) a In an embodiment of the present invention, the objective function value is a transportation cost value of the logistics distribution model for completing the logistics distribution requirement under the limitation of a constraint condition;
4) update the velocity and position of particle i:
Figure BDA0003513034510000069
Figure BDA00035130345100000610
wherein:
xbest(m) represents the particle position corresponding to the particle with the lowest objective function value in the mth iteration;
w represents the inertial weight when
Figure BDA00035130345100000611
w is 0.72, when
Figure BDA00035130345100000612
w＝0.2；
gaussian(0,σ2) Representing a mean of 0 and a variance of σ2Gaussian of, will2Set to 0.002;
c represents an acceleration factor, the value of which is 0.2;
r is a random number between (0, 1);
until the updating completes the position of each particle;
5) judging whether the iteration times reach the maximum iteration times Max or not, and if the iteration times reach the maximum iteration times, determining the position information corresponding to the particle with the lowest objective function value as the site selection position of the logistics distribution center; and if the maximum iteration times are not reached, returning to the step 3).
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes performed by the present invention or directly or indirectly applied to other related technical fields are also included in the scope of the present invention.

Claims (7)

1. A real-time demand driven logistics service facility location method, the method comprising:
s1: analyzing logistics distribution requirements, and constructing a logistics distribution model;
s2: determining a logistics distribution center site selection model according to the constructed logistics distribution model;
s3: constructing a user satisfaction function and a logistics distribution cost function, and determining a target function and a constraint condition of a logistics distribution center site selection model based on the constructed functions;
s4: and performing optimization solution on the constructed objective function by using a particle swarm algorithm to obtain an optimal solution result, namely the logistics distribution center address.
2. The method as claimed in claim 1, wherein the step of S1 for constructing the logistics distribution model comprises:
constructing a real-time demand driven logistics distribution model, wherein the logistics distribution model comprises the following steps:
1) direct logistics distribution model: the logistics distribution center takes the goods from the supplier and directly delivers the goods to the user;
2) logistics level distribution model: the method comprises the steps that a two-layer logistics distribution network comprising a logistics distribution center and a plurality of regional distribution stations is established, logistics distribution is directly carried out on the logistics distribution center to the regional distribution center when distribution is carried out, then the logistics distribution is carried out on the goods to users through the regional distribution center, and when goods are returned, the goods firstly enter the regional distribution center and are converged into the logistics distribution center through the regional distribution center.
3. The method as claimed in claim 1, wherein the step of determining the logistics distribution center location model in S2 comprises:
constructing a logistics distribution center site selection model, wherein the constructed logistics distribution center site selection model is as follows:
the logistics center site selection model of the logistics direct distribution model is as follows:
Figure FDA0003513034500000011
Figure FDA0003513034500000012
wherein:
qidenotes the ith logistics distribution target in the city, qiHas the coordinates of (x)i,yi) I is 1,2, …, and L represents the total number of logistics distribution targets;
(x0,y0) Coordinates representing a logistics distribution center;
u represents a delivery cost for delivery from the logistics center to the target;
the logistics center site selection model of the logistics level distribution model comprises the following steps:
Figure FDA0003513034500000013
wherein:
Dkthe distance of goods from the logistics distribution center to the kth regional distribution center is shown, and K represents the total number of regional distribution centers arranged in the city;
Dknindicating a distance from the k-th regional distribution center to the nth logistics distribution target;
gkrepresenting the delivery volume delivered by the logistics distribution center to the k-th regional distribution center;
gknindicating a delivery amount delivered to the nth logistics delivery target by the kth regional delivery center;
Yk＝{0,1}，Yk0 indicates that the k-th regional distribution center is not selected to perform logistics distribution, and Yk1 represents that the kth area distribution center is selected to execute logistics distribution;
f represents the distribution cost for distribution from the logistics distribution center to the distribution target, wherein the k-th regional distribution center has the coordinate of (x)k,yk) The coordinate of the nth logistics distribution target is (x)n,yn) And N represents the total number of logistics destination.
4. The method as claimed in claim 1, wherein the step of S3 for constructing the user satisfaction function comprises:
constructing a user satisfaction function, wherein the constructed user satisfaction function is as follows:
Figure FDA0003513034500000021
wherein:
F(tkn) Constructing a user satisfaction function;
tknthe distribution time length from the K-th logistics distribution position to the n-th logistics distribution target is shown, when K is 0, the logistics distribution position represents a logistics distribution center, and when K is 1,2, … and K, the logistics distribution position represents the K-th area distribution center;
Rnthe shortest waiting time period at which the customer who indicates the nth logistics distribution destination is unsatisfied with the distribution service.
5. The method as claimed in claim 1, wherein the step of S3 is a step of constructing a logistics distribution cost function, comprising:
constructing a logistics distribution cost function, wherein the constructed logistics distribution cost function is as follows:
G＝qidis+sDkgk+sDkngkn
wherein:
s represents the unit transportation cost from logistics distribution.
6. The real-time demand driven logistics service facility site selection method of claims 4-5, wherein the step of S3 determining the objective function and constraint conditions of the logistics distribution center site selection model based on the constructed function comprises:
the objective function of the logistics distribution center site selection model is as follows:
Figure FDA0003513034500000022
wherein:
Figure FDA0003513034500000023
the calculated result is the coordinate of the logistics distribution center, and the coordinate can realize the minimum logistics distribution transportation cost in the logistics direct distribution model;
Figure FDA0003513034500000024
the calculated result is the coordinates of the regional distribution center in the logistics level distribution model, and the coordinates can realize the minimum logistics distribution transportation cost in the logistics level distribution model;
the constraint conditions of the logistics distribution center site selection model are as follows:
Figure FDA0003513034500000025
wherein:
Figure FDA0003513034500000026
the method and the system ensure that the dissatisfaction degree of users is minimum when the logistics distribution is carried out.
7. The method as claimed in claim 1, wherein the step S4 of optimizing and solving an objective function by using a particle swarm optimization algorithm, where the objective function solution is the address of the logistics distribution center, comprises:
carrying out optimization solution on the objective function by utilizing a particle swarm optimization algorithm, wherein the flow of the particle swarm optimization algorithm is as follows:
1) randomly generating 30 particles, wherein the position information of the ith particle is as follows:
Figure FDA0003513034500000027
wherein:
Figure FDA0003513034500000028
coordinates representing the address of the logistics distribution center in the objective function;
Figure FDA0003513034500000029
coordinates representing the address of the Kth regional distribution center in the objective function;
2) initializing the position p of each particleiAnd velocity viAnd the maximum iteration times Max of the particle swarm algorithm;
3) generating a logistics distribution model by using the position information of each particle, simulating a logistics distribution demand, and calculating an objective function value V of the logistics distribution model generated by each particlei；
4) Update the velocity and position of particle i:
Figure FDA0003513034500000031
Figure FDA0003513034500000032
wherein:
xbest(m) represents the particle position corresponding to the particle with the lowest objective function value in the mth iteration;
w represents the inertial weight when
Figure FDA0003513034500000033
When in use
Figure FDA0003513034500000034
gaussian(0,σ2) Representing a mean of 0 and a variance of σ2Gaussian of, will2Set to 0.002;
c represents an acceleration factor, the value of which is 0.2;
r is a random number between (0, 1);
until the updating completes the position of each particle;
5) judging whether the iteration times reach the maximum iteration times Max or not, and if the iteration times reach the maximum iteration times, determining the position information corresponding to the particle with the lowest objective function value as the site selection position of the logistics distribution center; and if the maximum iteration times are not reached, returning to the step 3).
CN202210156703.8A 2022-02-21 2022-02-21 Real-time demand-driven logistics service facility site selection method Pending CN114493466A (en)

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