CN114493036B - Multi-vehicle type logistics transportation planning method - Google Patents
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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
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:
analyzing the logistics transportation area in the logistics transportation planning element in the step S1, including:
dividing a 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:
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 stdev x Stdev, which represents the standard deviation in longitude of the delivery target and the center of delivery of the logistics y Indicating 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;
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 area h,i Wherein D is h,i The 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:
{(x 1 ,y 1 ),(x 2 ,y 2 ),(x 3 ,y 3 ),…,(x i ,y i ),…,(x m ,y m )}
wherein:
(x i ,y i ) 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 { e 1 ,e 2 ,…,e c The maximum load is { w } 1 ,w 2 ,…,w c Oil consumption per kilometer is { q } 1 ,q 2 ,…,q c In which e c Indicating the number of vehicle types c in the warehouse, w c Representing the maximum load of the vehicle type c in the warehouse, q c Representing 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 r 1 ,r 2 ,…,r c When 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＝Cost 1 +Cost 2
wherein:
r i,j the 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;
s j an actual load of the vehicle representing the vehicle type j;
q j representing 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:
wherein:
s j an actual load of the vehicle representing the vehicle type j;
w j represents the maximum load of the vehicle type j;
the constraints for determining the objective function are as follows:
wherein:
m is the total amount of the goods to be dispensed.
In the step S3, performing optimization solution on the objective function by using a conjugate gradient algorithm, including:
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 U u (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
Parameter factor d u Satisfy the requirement of
Wherein T represents transpose, F u Representing the transportation cost and the average loading rate of the vehicles under the scheme u;
4) calculating a step size coefficient:
determining a step length coefficient alpha of the algorithm according to the two formulas;
6) then the scheme u changes to the scheme u + alphad u And 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 { r 1 ,r 2 ,…,r c When 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＝Cost 1 +Cost 2
wherein: r is i,j The number of vehicles representing the vehicle type j called from the ith warehouse, i is 1,2, 3; q. q.s j Representing 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:
wherein: s j An actual load of the vehicle representing the vehicle type j; w is a j Represents the maximum load of the vehicle type j;
representing the mean loading rate g of the vehicles in the logistics transportation planning scheme * Maximum;
means for minimizing transportation costs in the logistics transportation planning scheme; the constraints for determining the objective function are as follows:
wherein: m is the total amount of the goods to be dispensed. Compared with the traditional scheme, the multi-vehicle type logistics model constructed by the scheme comprehensively considers the number and the loading rate of different vehicle types, and realizes the multi-vehicle type logistics transportation planning with the minimum logistics transportation cost by optimally controlling the number and the loading rate of the vehicle types.
Meanwhile, the scheme provides a method for solving the objective function of the multi-vehicle type logistics transportation model, and the parameter optimization process comprises the following steps: 1) generating U vehicle planning schemes, wherein the vehicle planning schemes comprise all vehicle typesThe number of the required vehicles and the loading capacity of each vehicle type are calculated, and the gradient g of the objective function F in different vehicle planning schemes u is calculated u (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
Parameter factor d u Satisfy the requirement of
Wherein T represents transpose, F u Representing the transportation cost and the average loading rate of the vehicles under the scheme u; 4) calculating a step size coefficient:
determining a step length coefficient alpha of the algorithm according to the two formulas; 6) then the scheme u changes to the scheme u + alphad u And returns to step 3). 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.
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:
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 stdev x Stdev, which represents the standard deviation in longitude of the delivery target and the center of delivery of the logistics y Indicating 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;
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 area h,i Wherein D is h,i The 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:
{(x 1 ,y 1 ),(x 2 ,y 2 ),(x 3 ,y 3 ),…,(x i ,y i ),…,(x m ,y m )}
wherein:
(x i ,y i ) 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 { e 1 ,e 2 ,…,e c The maximum load is { w } 1 ,w 2 ,…,w c Oil consumption per kilometer is { q } 1 ,q 2 ,…,q c In which e is c Indicating the number of vehicle types c in the warehouse, w c Representing the maximum load of the vehicle type c in the warehouse, q c Representing 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 r 1 ,r 2 ,…,r c When 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＝Cost 1 +Cost 2
wherein:
r i,j the 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;
s j an actual load of the vehicle representing the vehicle type j;
q j representing 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:
wherein:
s j an actual load of the vehicle representing the vehicle type j;
w j represents the maximum load of the vehicle type j;
the constraints for determining the objective function are as follows:
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 an objective function F in different vehicle planning schemes U u (ii) a Initializing u-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
Parameter factor d u Satisfy the requirement of
Wherein T represents transpose, F u Representing the transportation cost and the average loading rate of the vehicles under the scheme u;
4) calculating a step size coefficient:
determining a step length coefficient alpha of the algorithm according to the two formulas;
6) then the scheme u changes to the scheme u + alphad u And go back toGo back 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 through 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 (6)
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, wherein the constructing of the multi-vehicle type logistics transportation model comprises the following steps:
let the number of vehicles required for each vehicle type be r 1 ,r 2 ,…,r c When 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＝Cost 1 +Cost 2
wherein:
r i,j the number of vehicles representing the vehicle type j called from the ith warehouse, i is 1,2, 3;
s j an actual load of the vehicle representing the vehicle type j;
q j representing the oil consumption per kilometer of the vehicle type j;
g * representing an average load rate of the vehicle;
D h,i represents the running distance of the vehicle from the ith warehouse to the logistics distribution center, and i is 1,2 and 3;
m represents the division of the cargo logistics transportation area into m sub-areas; n represents the number of delivery targets;
D p the delivery distance of a delivery target in a cargo logistics transportation area is represented;
s2: determining a logistics planning objective function and constraint conditions according to a multi-vehicle type logistics transportation model;
s3: the method for optimizing and solving the logistics planning objective function by using the conjugate gradient algorithm to determine the optimal solution comprises the following steps:
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 U u (ii) a Initializing u as 0;
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 go to 4);
3) taking step size coefficient
Parameter factor d u Satisfy the requirements of
Wherein T represents transpose, F u Representing the transportation cost and the average loading rate of the vehicles under the scheme u;
4) calculating a step size coefficient alpha;
5) then the scheme u changes to the scheme u + alphad u And returning to the step 3);
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:
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 stdev x Stdev, which represents the standard deviation in longitude of the target and the logistics center y Indicating 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;
calculating the running distance D from the warehouse to the logistics distribution center in the warehouse logistics transportation area h,i Wherein D is h,i The 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:
{(x 1 ,y 1 ),(x 2 ,y 2 ),(x 3 ,y 3 ),…,(x i ,y i ),…,(x m ,y m )}
wherein:
(x i ,y i ) 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 { e 1 ,e 2 ,…,e c The maximum load is { w } 1 ,w 2 ,…,w c Oil consumption per kilometer is { q } 1 ,q 2 ,…,q c In which e is c Indicating the number of vehicle types c in the warehouse, w c Representing the maximum load of the vehicle type c in the warehouse, q c Representing the oil consumption per kilometer for model c in the warehouse.
5. 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:
wherein:
s j an actual load of the vehicle representing the vehicle type j;
w j represents the maximum load of the vehicle type j;
the constraints for determining the objective function are as follows:
wherein:
m is the total amount of the goods to be dispensed.
6. The method for planning logistics transportation of multiple vehicle types according to claim 1, wherein the step of S4 using the obtained optimal solution as a 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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| CN112990528A (en) * | 2020-06-28 | 2021-06-18 | 青岛盈智科技有限公司 | Logistics transportation stowage management method and device |
| CN112215473B (en) * | 2020-09-18 | 2023-04-18 | 拉扎斯网络科技（上海）有限公司 | Distribution pressure data obtaining method and device and electronic equipment |
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| CN111324827A (en) * | 2020-02-12 | 2020-06-23 | 上海东普信息科技有限公司 | Method, device, equipment and storage medium for intelligently recommending goods source order information |
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