CN114493036A - Multi-vehicle type logistics transportation planning method - Google Patents

Multi-vehicle type logistics transportation planning method Download PDF

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CN114493036A
CN114493036A CN202210137520.1A CN202210137520A CN114493036A CN 114493036 A CN114493036 A CN 114493036A CN 202210137520 A CN202210137520 A CN 202210137520A CN 114493036 A CN114493036 A CN 114493036A
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张波
寇桂辉
巨少辉
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Shenzhen Jialida Supply Chain Management Co ltd
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Abstract

The invention relates to the technical field of logistics transportation planning, and discloses a multi-vehicle type logistics transportation planning method, which comprises the following steps: analyzing logistics transportation planning elements and constructing a multi-vehicle type logistics transportation model; determining a logistics planning objective function and constraint conditions according to a multi-vehicle type logistics transportation model; carrying out optimization solution on the logistics planning objective function by using a conjugate gradient algorithm to determine an optimal solution; and taking the optimal solution obtained by solving as a logistics transportation strategy of multiple vehicle types. According to the method, a multi-vehicle type logistics transportation model is constructed based on logistics transportation cost, transportation distance and average loading rate of vehicles, an objective function of the model is optimized and solved by using a conjugate gradient algorithm, the optimal solution obtained by solving is used as a logistics transportation planning strategy of the multi-vehicle type, and the logistics transportation planning strategy finally realized guarantees that the logistics transportation cost is minimum, the transportation distance is shortest and the average loading rate of the vehicles is highest.

Description

Multi-vehicle type logistics transportation planning method
Technical Field
The invention relates to the technical field of logistics transportation planning, in particular to a multi-vehicle type logistics transportation planning method.
Background
The vehicle type and vehicle planning problem is a vehicle combination problem of how to select each vehicle type when goods are delivered to each goods demand point from a warehouse point. The current research on the multi-vehicle-type vehicle planning problem is mainly carried out on a single vehicle type, vehicle planning is carried out preferentially on the basis of the loading rate, the vehicle planning on the basis of the loading rate only comprises vehicle loading rate constraints, and the selection of small vehicle types is mostly concentrated during vehicle planning without considering subarea distribution and vehicle transportation distances, so that the whole logistics transportation cost is increased.
In view of the above, the invention provides a multi-vehicle logistics transportation planning method, which includes constructing a multi-vehicle logistics transportation model by analyzing logistics transportation planning elements, performing optimization solution on an objective function of the model by using a conjugate gradient algorithm, and using an optimal solution obtained by the solution as a multi-vehicle logistics transportation strategy to realize multi-vehicle logistics transportation.
Disclosure of Invention
The invention provides a multi-vehicle type logistics transportation planning method, which aims to (1) construct a multi-vehicle type logistics transportation model; (2) and optimizing and solving the objective function of the model, and taking the optimal solution obtained by solving as a logistics transportation strategy of the multiple vehicle types to realize the logistics transportation of the multiple vehicle types.
The invention provides a multi-vehicle type logistics transportation planning method, which comprises the following steps:
s1: analyzing logistics transportation planning elements and constructing a multi-vehicle type logistics transportation model;
s2: determining a logistics planning objective function and constraint conditions according to a multi-vehicle type logistics transportation model;
s3: carrying out optimization solution on the logistics planning objective function by using a conjugate gradient algorithm to determine an optimal solution;
s4: and taking the optimal solution obtained by solving as a logistics transportation strategy of multiple vehicle types.
As a further improvement of the method of the invention:
analyzing the logistics transportation area in the logistics transportation planning element in the step S1, including:
dividing the logistics transportation area into a warehouse logistics transportation area and a goods logistics transportation area, wherein the warehouse logistics transportation area is a transportation area between a logistics distribution center and a warehouse, and the goods logistics transportation area is a transportation area between the logistics distribution center and a distribution target;
in one embodiment of the present invention, each city has 3 warehouses and 1 logistics distribution center, and the goods are transported from the warehouses to the logistics distribution centers and then distributed to the designated goods distribution target points through the logistics distribution centers.
Analyzing the logistics area distribution distance in the logistics transportation planning element in the step S1 includes:
calculating the distribution distance of a distribution target in the cargo logistics transportation area:
Figure BDA0003505566600000011
wherein:
m represents the division of the cargo logistics transportation area into m sub-areas;
cdev represents a standard deviation of a distance between a distribution target and a logistics distribution center;
stdev represents the standard deviation of the distribution target and the logistics distribution center in longitude and latitude, wherein stdevxStdev, which represents the standard deviation in longitude of the delivery target and the center of delivery of the logisticsyIndicating a standard deviation of the distribution target from the logistics distribution center at the latitude;
r represents the area of a cargo logistics transportation area;
Figure BDA0003505566600000012
the mean value of the distances between all distribution targets and the logistics distribution center is represented;
in a specific embodiment of the present invention, the calculation formula of the distance is an euclidean distance calculation formula;
calculating the running distance D from the warehouse to the logistics distribution center in the warehouse logistics transportation areah,iWherein D ish,iThe driving distance of the vehicle from the ith warehouse to the logistics distribution center is represented, and i is 1,2 and 3.
Analyzing the vehicle planning parameters in the logistics transportation planning element in the step S1, including:
determining the number of distribution targets as n and the total quantity of goods to be distributed as M, dividing a goods logistics transportation area into M sub-areas, enabling the number of the distribution targets of each sub-area to be larger than 4, and enabling the coordinate set of the geographic center of each sub-area to be as follows:
{(x1,y1),(x2,y2),(x3,y3),…,(xi,yi),…,(xm,ym)}
wherein:
(xi,yi) Coordinates representing the geographic center of the ith sub-area of the partition;
determining c types of vehicle types in the warehouse, wherein the number of each type of vehicle is { e1,e2,…,ecThe maximum load is { w }1,w2,…,wcOil consumption per kilometer is { q }1,q2,…,qcIn which ecIndicating the number of vehicle types c in the warehouse, wcRepresenting the maximum load of the vehicle type c in the warehouse, qcRepresenting the oil consumption per kilometer for model c in the warehouse.
Constructing a multi-vehicle type logistics transportation model in the step S1, wherein the method comprises the following steps:
let the number of vehicles required for each vehicle type be r1,r2,…,rcWhen the total amount M of goods can be delivered, the total transportation cost is minimum, and the constructed multi-vehicle type logistics transportation model is as follows:
Cost＝Cost1+Cost2
Figure BDA0003505566600000021
Figure BDA0003505566600000022
wherein:
ri,jthe number of vehicles representing the vehicle type j called from the ith warehouse, i is 1,2, 3; in one embodiment of the present invention, the secondThe number of the vehicle types and the number of the vehicles in the 1 warehouse are more than those in the 2 nd warehouse, and the number of the vehicle types and the number of the vehicles in the 2 nd warehouse are more than those in the 3 rd warehouse;
sjan actual load of the vehicle representing the vehicle type j;
qjrepresenting the oil consumption per kilometer of the vehicle type j;
g*representing the average load rate of the vehicle.
The determining of the logistics planning objective function and the constraint conditions in the step S2 includes:
determining a logistics planning objective function in a multi-vehicle type logistics transportation model:
Figure BDA0003505566600000023
wherein:
sjan actual load of the vehicle representing the vehicle type j;
wjrepresents the maximum load of the vehicle type j;
Figure BDA0003505566600000024
representing the mean loading rate g of vehicles in the logistics transportation planning scheme*Maximum;
Figure BDA0003505566600000025
means for minimizing transportation costs in the logistics transportation planning scheme;
the constraints for determining the objective function are as follows:
Figure BDA0003505566600000026
wherein:
m is the total amount of the goods to be dispensed.
In the step S3, the optimization solution of the objective function by using the conjugate gradient algorithm includes:
carrying out parameter optimization on the multi-vehicle logistics transportation model by using a conjugate gradient algorithm, wherein the parameter optimization process comprises the following steps:
1) generating U vehicle planning schemes, wherein the vehicle planning schemes comprise the number of vehicles required by each vehicle type and the loading capacity of each vehicle type, and calculating the gradient g of the objective function F in different vehicle planning schemes Uu(ii) a Initializing u as 0; in a specific embodiment of the invention, the generated vehicle planning schemes all meet the constraint conditions of the objective function, and the larger the value of u is, the more the number of types of the required vehicles is;
2) if g | | |u||<E, stopping parameter optimization, wherein the parameters are the optimal logistics transportation planning scheme, and the e represents an optimization threshold value; otherwise, 4) is turned;
3) step size coefficient is taken
Figure BDA0003505566600000031
Parameter factor duSatisfy the requirement of
Figure BDA0003505566600000032
Wherein T represents transpose, FuRepresenting the transportation cost and the average loading rate of the vehicles under the scheme u;
4) calculating a step size coefficient:
Figure BDA0003505566600000033
determining a step length coefficient alpha of the algorithm according to the two formulas;
6) then the scheme u changes to the scheme u + alphaduAnd returns to step 3).
The step S4, using the obtained optimal solution result as a logistics transportation strategy for multiple vehicle types, includes:
and taking a parameter result obtained by solving by using a conjugate algorithm as an optimal logistics transportation planning scheme, wherein the logistics transportation planning scheme comprises the number of vehicles required by each vehicle type and the cargo loading capacity of each vehicle type, and the logistics transportation planning of multiple vehicle types is realized.
Compared with the prior art, the invention provides a multi-vehicle type logistics transportation planning method, which has the following advantages:
firstly, the scheme provides a multi-vehicle type logistics transportation model, and the number of vehicles required by each vehicle type is set to be { r1,r2,…,rcWhen the total amount of cargo delivery is M, the total transportation cost is the minimum, and the constructed multi-vehicle type logistics transportation model is as follows:
Cost＝Cost1+Cost2
Figure BDA0003505566600000034
Figure BDA0003505566600000035
wherein: r isi,jThe number of vehicles of the vehicle type j is called from the ith warehouse, and i is 1,2 and 3; q. q.sjRepresenting the oil consumption per kilometer of the vehicle type j; g*Representing the average load rate of the vehicle. Determining a logistics planning objective function in the multi-vehicle type logistics transportation model according to the constructed multi-vehicle type logistics transportation model:
Figure BDA0003505566600000036
wherein: s isjAn actual load of the vehicle representing the vehicle type j; w is ajA maximum load amount of the vehicle representing the vehicle type j;
Figure BDA0003505566600000037
representing the mean loading rate g of the vehicles in the logistics transportation planning scheme*Maximum;
Figure BDA0003505566600000038
Figure BDA0003505566600000039
representing making a shipment in a logistics shipment planning schemeThe cost is minimum; the constraints for determining the objective function are as follows:
Figure BDA0003505566600000041
wherein: m is the total amount of the goods to be dispensed. Compared with the traditional scheme, the number and the loading rate of different vehicle types are comprehensively considered by the multi-vehicle type logistics model constructed by the scheme, and the multi-vehicle type logistics transportation planning with the minimum logistics transportation cost is realized by optimally controlling the number and the loading rate of the vehicle types.
Meanwhile, the scheme provides a method for solving a multi-vehicle type logistics transportation model objective function, and the parameter optimization process comprises the following steps: 1) generating U vehicle planning schemes, wherein the vehicle planning schemes comprise the number of vehicles required by each vehicle type and the loading capacity of each vehicle type, and calculating the gradient g of the objective function F in different vehicle planning schemes Uu(ii) a Initializing u as 0; the generated vehicle planning schemes all meet the constraint conditions of the objective function, and the larger the value of u is, the more the number of types of the required vehicles is; 2) if g | | |u||<E, stopping parameter optimization, wherein the parameters are the optimal logistics transportation planning scheme, and the e represents an optimization threshold value; otherwise, 4) is turned; 3) step size coefficient is taken
Figure BDA0003505566600000042
Parameter factor duSatisfy the requirement of
Figure BDA0003505566600000043
Wherein T represents transpose, FuRepresenting the transportation cost and the average loading rate of the vehicles under the scheme u; 4) calculating a step size coefficient:
Figure BDA0003505566600000044
determining a step length coefficient alpha of the algorithm according to the two formulas; 6) then the scheme u changes to the scheme u + alphaduAnd returns to step 3). Taking parameter results obtained by solving by using a conjugate algorithm as optimal logistics transportationAnd the logistics transportation planning scheme comprises the number of vehicles required by each vehicle type and the cargo loading capacity of each vehicle type, and realizes the logistics transportation planning of multiple vehicle types.
Drawings
Fig. 1 is a schematic flow chart of a multi-vehicle logistics transportation planning method 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: analyzing the logistics transportation planning elements and constructing a multi-vehicle type logistics transportation model.
Analyzing the logistics transportation area in the logistics transportation planning element in the step S1, including:
dividing the logistics transportation area into a warehouse logistics transportation area and a goods logistics transportation area, wherein the warehouse logistics transportation area is a transportation area between a logistics distribution center and a warehouse, and the goods logistics transportation area is a transportation area between the logistics distribution center and a distribution target;
in one embodiment of the present invention, each city has 3 warehouses and 1 logistics center, and the goods are transported from the warehouses to the logistics centers and then distributed to the designated goods distribution target points through the logistics centers.
Analyzing the logistics area distribution distance in the logistics transportation planning element in the step S1 includes:
calculating the distribution distance of a distribution target in the cargo logistics transportation area:
Figure BDA0003505566600000045
wherein:
m represents the division of the cargo logistics transportation area into m sub-areas;
cdev represents a standard deviation of a distance between a distribution target and a logistics distribution center;
stdev represents the standard deviation of the distribution target and the logistics distribution center in longitude and latitude, wherein stdevxStdev, which represents the standard deviation in longitude of the delivery target and the center of delivery of the logisticsyIndicating a standard deviation of the distribution target from the logistics distribution center at the latitude;
r represents the area of a cargo logistics transportation area;
Figure BDA0003505566600000046
the mean value of the distances between all distribution targets and the logistics distribution center is represented;
in a specific embodiment of the present invention, the calculation formula of the distance is an euclidean distance calculation formula;
calculating the running distance D from the warehouse to the logistics distribution center in the warehouse logistics transportation areah,iWherein D ish,iThe driving distance of the vehicle from the ith warehouse to the logistics distribution center is represented, and i is 1,2 and 3.
Analyzing the vehicle planning parameters in the logistics transportation planning element in the step S1, including:
determining the number of distribution targets as n and the total quantity of goods to be distributed as M, dividing a goods logistics transportation area into M sub-areas, enabling the number of the distribution targets of each sub-area to be larger than 4, and enabling the coordinate set of the geographic center of each sub-area to be as follows:
{(x1,y1),(x2,y2),(x3,y3),…,(xi,yi),…,(xm,ym)}
wherein:
(xi,yi) Coordinates representing the geographic center of the ith sub-area of the partition;
determining c types of vehicle types in the warehouse, wherein the number of each type of vehicle is { e1,e2,…,ecThe maximum load is { w }1,w2,…,wcOil consumption per kilometer is { q }1,q2,…,qcIn which ecRepresenting a warehouseNumber of middle vehicle type c, wcRepresenting the maximum load of the vehicle type c in the warehouse, qcRepresenting the oil consumption per kilometer for model c in the warehouse.
Constructing a multi-vehicle type logistics transportation model in the step S1, wherein the method comprises the following steps:
let the number of vehicles required for each vehicle type be r1,r2,…,rcWhen the total amount M of goods can be delivered, the total transportation cost is minimum, and the constructed multi-vehicle type logistics transportation model is as follows:
Cost＝Cost1+Cost2
Figure BDA0003505566600000051
Figure BDA0003505566600000052
wherein:
ri,jthe number of vehicles representing the vehicle type j called from the ith warehouse, i is 1,2, 3; in one embodiment of the invention, the number of vehicles and the number of vehicles in the 1 st warehouse are more than those in the 2 nd warehouse, and the number of vehicles in the 2 nd warehouse are more than those in the 3 rd warehouse;
sjan actual load of the vehicle representing the vehicle type j;
qjrepresenting the oil consumption per kilometer of the vehicle type j;
g*representing the average load rate of the vehicle.
S2: and determining a logistics planning objective function and constraint conditions according to the multi-vehicle type logistics transportation model.
The determining of the logistics planning objective function and the constraint conditions in the step S2 includes:
determining a logistics planning objective function in a multi-vehicle type logistics transportation model:
Figure BDA0003505566600000053
wherein:
sjan actual load of the vehicle representing the vehicle type j;
wjrepresents the maximum load of the vehicle type j;
Figure BDA0003505566600000054
representing the mean loading rate g of vehicles in the logistics transportation planning scheme*Maximum;
Figure BDA0003505566600000055
means for minimizing transportation costs in the logistics transportation planning scheme;
the constraints for determining the objective function are as follows:
Figure BDA0003505566600000061
wherein:
m is the total amount of the goods to be dispensed.
S3: and (4) carrying out optimization solution on the logistics planning objective function by using a conjugate gradient algorithm to determine an optimal solution.
Carrying out parameter optimization on the multi-vehicle logistics transportation model by using a conjugate gradient algorithm, wherein the parameter optimization process comprises the following steps:
1) generating U vehicle planning schemes, wherein the vehicle planning schemes comprise the number of vehicles required by each vehicle type and the loading capacity of each vehicle type, and calculating the gradient g of the objective function F in different vehicle planning schemes Uu(ii) a Initializing u as 0; in a specific embodiment of the invention, the generated vehicle planning schemes all meet the constraint conditions of the objective function, and the larger the value of u is, the more the number of types of the required vehicles is;
2) if g | | |u||<E, stopping parameter optimization, wherein the parameters are the optimal logistics transportation planning scheme, and the e represents an optimization threshold value; otherwise, 4) is turned;
3) step size coefficient is taken
Figure BDA0003505566600000062
Parameter factor duSatisfy the requirement of
Figure BDA0003505566600000063
Wherein T represents transpose, FuRepresenting the transportation cost and the average loading rate of the vehicles under the scheme u;
4) calculating a step size coefficient:
Figure BDA0003505566600000064
determining a step length coefficient alpha of the algorithm according to the two formulas;
6) then the scheme u changes to the scheme u + alphaduAnd returns to step 3).
S4: and taking the optimal solution obtained by solving as a logistics transportation strategy of multiple vehicle types.
And taking a parameter result obtained by solving by using a conjugate algorithm as an optimal logistics transportation planning scheme, wherein the logistics transportation planning scheme comprises the number of vehicles required by each vehicle type and the cargo loading capacity of each vehicle type, and the logistics transportation planning of multiple vehicle types is realized.
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 comprises 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 not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (8)

1. A logistics transportation planning method for multiple vehicle types is characterized by comprising the following steps:
s1: analyzing logistics transportation planning elements and constructing a multi-vehicle type logistics transportation model;
s2: determining a logistics planning objective function and constraint conditions according to a multi-vehicle type logistics transportation model;
s3: carrying out optimization solution on the logistics planning objective function by using a conjugate gradient algorithm to determine an optimal solution;
s4: and taking the optimal solution obtained by solving as a logistics transportation strategy of multiple vehicle types.
2. The multi-vehicle type logistics transportation planning method of claim 1, wherein the analyzing the logistics transportation area in the logistics transportation planning element in the step S1 comprises:
the logistics transportation area is divided into a warehouse logistics transportation area and a goods logistics transportation area, wherein the warehouse logistics transportation area is a transportation area between the logistics distribution center and the warehouse, and the goods logistics transportation area is a transportation area between the logistics distribution center and the distribution target.
3. The multi-vehicle type logistics transportation planning method of claim 2, wherein the analyzing the logistics area distribution distance in the logistics transportation planning element in the step S1 comprises:
calculating the distribution distance of a distribution target in the cargo logistics transportation area:
Figure FDA0003505566590000011
wherein:
m represents the division of the cargo logistics transportation area into m sub-areas;
cdev represents a standard deviation of a distance between a distribution target and a logistics distribution center;
stdev represents the standard deviation of the distribution target and the logistics distribution center in longitude and latitude, wherein stdevxStdev, which represents the standard deviation in longitude of the target and the logistics centeryIndicating a standard deviation of the distribution target from the logistics distribution center at the latitude;
r represents the area of a cargo logistics transportation area;
Figure FDA0003505566590000012
the mean value of the distances between all distribution targets and the logistics distribution center is represented;
calculating the running distance D from the warehouse to the logistics distribution center in the warehouse logistics transportation areah，iWherein D ish，iThe driving distance of the vehicle from the ith warehouse to the logistics distribution center is represented, and i is 1,2 and 3.
4. The method for planning logistics transportation of multiple vehicle types according to claim 1, wherein the step of analyzing the vehicle planning parameters in the logistics transportation planning element in S1 comprises:
determining the number of distribution targets as n and the total quantity of goods to be distributed as M, dividing a goods logistics transportation area into M sub-areas, enabling the number of the distribution targets of each sub-area to be larger than 4, and enabling the coordinate set of the geographic center of each sub-area to be as follows:
{(x1，y1)，(x2，y2)，(x3，y3)，...，(xi，yi)，...，(xm，ym)}
wherein:
(xi，yi) Coordinates representing the geographic center of the ith sub-area of the partition;
determining c types of vehicle types in the warehouse, wherein the number of each type of vehicle is { e1，e2，...，ecThe maximum load is { w }1，w2，...，wcOil consumption per kilometer is { q }1，q2，...，qcIn which ecIndicating the number of vehicle types c in the warehouse, wcRepresenting the maximum load of the vehicle type c in the warehouse, qcRepresenting the oil consumption per kilometer for model c in the warehouse.
5. The multi-vehicle type logistics transportation planning method of claim 1, wherein the step of S1 constructing the multi-vehicle type logistics transportation model comprises:
let the number of vehicles required for each vehicle type be r1，r2，...，rcWhen the total amount of cargo delivery is M, the total transportation cost is the minimum, and the constructed multi-vehicle type logistics transportation model is as follows:
Cost＝Cost1+Cost2
Figure FDA0003505566590000013
Figure FDA0003505566590000014
wherein:
ri，jthe number of vehicles representing the vehicle type j called from the ith warehouse, i is 1,2, 3;
sjan actual load of the vehicle representing the vehicle type j;
qjrepresenting the oil consumption per kilometer of the vehicle type j;
g*representing the average load rate of the vehicle.
6. The method for planning logistics transportation of claim 1, wherein the step of determining the logistics planning objective function and the constraint conditions in the step of S2 comprises:
determining a logistics planning objective function in a multi-vehicle type logistics transportation model:
Figure FDA0003505566590000021
wherein:
sjan actual load of the vehicle representing the vehicle type j;
wjrepresents the maximum load of the vehicle type j;
Figure FDA0003505566590000022
representing the mean loading rate g of vehicles in the logistics transportation planning scheme*Maximum;
Figure FDA0003505566590000023
means for minimizing transportation costs in the logistics transportation planning scheme;
the constraints for determining the objective function are as follows:
Figure FDA0003505566590000024
wherein:
m is the total amount of the goods to be dispensed.
7. The multi-vehicle logistics transportation planning method of claim 6, wherein the step of S3 using conjugate gradient algorithm to optimize the objective function comprises:
carrying out parameter optimization on the multi-vehicle logistics transportation model by using a conjugate gradient algorithm, wherein the parameter optimization process comprises the following steps:
1) generating U vehicle planning schemes, wherein the vehicle planning schemes comprise the number of vehicles required by each vehicle type and the loading capacity of each vehicle type, and calculating the gradient g of the objective function F in different vehicle planning schemes Uu(ii) a Initializing u as 0;
2) if g | | |uIf the | <e, stopping optimizing the parameters, wherein the parameters are the optimal logistics transportation planning scheme, and the | <erepresents an optimization threshold; otherwise, 4) is turned;
3) step size coefficient is taken
Figure FDA0003505566590000025
Parameter factor duSatisfy the requirement of
Figure FDA0003505566590000026
Wherein T represents transpose, FuRepresenting the transportation cost and the average loading rate of the vehicles under the scheme u;
4) calculating a step size coefficient:
Figure FDA0003505566590000027
determining a step length coefficient alpha of the algorithm according to the two formulas;
6) then the scheme u changes to the scheme u + alphaduAnd returns to step 3).
8. The method for planning logistics transportation of multiple vehicle types according to claim 7, wherein the step of S4 using the obtained optimal solution as the logistics transportation strategy of multiple vehicle types comprises:
and taking a parameter result obtained by solving by using a conjugate algorithm as an optimal logistics transportation planning scheme, wherein the logistics transportation planning scheme comprises the number of vehicles required by each vehicle type and the cargo loading capacity of each vehicle type.
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