CN114897359A - Finished automobile transport capacity scheduling method under multidimensional data - Google Patents

Finished automobile transport capacity scheduling method under multidimensional data Download PDF

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CN114897359A
CN114897359A CN202210514594.2A CN202210514594A CN114897359A CN 114897359 A CN114897359 A CN 114897359A CN 202210514594 A CN202210514594 A CN 202210514594A CN 114897359 A CN114897359 A CN 114897359A
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王勇
鄢伟安
侯雨彤
徐倩
秦灼垚
董苗苗
周启东
阮康丽
王莉艳
刘晨宇
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Abstract

The invention discloses a finished automobile transport capacity scheduling method under multidimensional data, which comprises the following steps: establishing a vehicle loading and path combined optimization model, which comprises respectively establishing a loading and loading cost model and a path planning cost model; an order prediction step of predicting an order amount in a future period of time; and substituting the prediction result and the known parameters of the order prediction step into the vehicle loading and path combined optimization model to solve, and obtaining the distribution network point where each vehicle passes and the path where each vehicle passes. According to the method for scheduling the transport capacity of the whole vehicle under the multidimensional data, the intelligent scheduling platform of the transport capacity of the whole vehicle under the multidimensional data is established, the order prediction model and the combined optimization model of vehicle loading and path are utilized to realize the function of vehicle pre-loading, the delivery timeliness is improved, unreasonable transportation activities are reduced, the logistics transportation efficiency is improved, and the logistics transportation cost is saved.

Description

Finished automobile transport capacity scheduling method under multidimensional data
Technical Field
The invention belongs to the technical field of intelligent logistics scheduling, and particularly relates to a finished automobile transportation capacity scheduling method under multidimensional data.
Background
With the development of information technology, the logistics industry enters the development stages of intellectualization, intelligence numeralization and intellectualization nowadays, and the development of the industry is developed towards the direction of connection upgrading, data upgrading, mode upgrading, experience upgrading, intelligence upgrading, supply chain upgrading and continuous innovation of an operation mode.
The logistics industry is increasingly competitive with the continuous increase of the personalized demands of customers, the emergence of factors such as hard time windows for transportation and the like. The method puts higher requirements on various aspects of business resource integration, process rationalization, logistics networking, informatization, internationalization and the like of a company.
The current vehicle dispatching system mainly has the following problems:
(1) the predicted order cannot be assembled efficiently. If the estimated order quantity is manually estimated at 14 points every day, the vehicle is estimated according to manual experience and the order can be added when the vehicle is estimated to 19 points, at the moment, misjudgment is easily caused on the vehicle and the vehicle quantity, so that the efficiency of the whole vehicle scheduling process is low, the time cost is overhigh, the estimated carrying quantity is greatly different from the actual carrying quantity, and the vehicle loading and vehicle allocation cannot be accurately realized.
(2) The boxing time is low, and the loading rate is not high. The loading rate is not high and the human resource consumption is large due to excessive dependence on the experience of personnel. The change range of the packing time consumption is large, the supervision of the operation flow is low, and the management optimization is not timely. Management is visualized as far as possible, but most of the current loading operations judged manually cannot realize the management, so that the management is uncertain too much.
(3) The route selection is judged by manual experience, and unreasonable transportation is easy to cause. In addition to increasing time cost and man-hour, improper transportation also affects customer satisfaction and service evaluation index scoring, and measures against sudden situations are incomplete, which easily causes cargo retention and delayed delivery.
(4) Vehicle management cannot be visualized throughout. The real-time correction of goods live condition, driving route and driver non-standardized operation in the vehicle can not be monitored by a dispatcher during vehicle running, meanwhile, the estimated arrival time in the vehicle distribution process can not be estimated, the mismatching of information or the non-timely updating leads to complicated and slow vehicle dispatching steps, and the dispatcher can not flexibly dispatch the vehicle.
Disclosure of Invention
The invention provides a method for scheduling the transport capacity of a whole vehicle under multidimensional data, which aims at solving the technical problems that the estimated order can not be efficiently assembled and the unreasonable transport is easily caused by path selection in the current vehicle scheduling system in the logistics field in the prior art.
In order to realize the purpose of the invention, the invention is realized by adopting the following technical scheme:
a method for scheduling the transport capacity of a finished automobile under multidimensional data comprises the following steps:
the method comprises the steps of establishing a vehicle loading and path combined optimization model, wherein the vehicle loading and path combined optimization model comprises the steps of respectively establishing a loading cost model and a path planning cost model, the loading cost model is in negative correlation with a vehicle load utilization rate and a vehicle volume utilization rate, the vehicle load utilization rate refers to the ratio of the total weight of cargos loaded by a delivery vehicle to the vehicle load weight, and the vehicle volume utilization rate refers to the ratio of the total volume of cargos loaded by the delivery vehicle to the vehicle volume;
the route planning cost model is positively correlated with vehicle fixed cost, transportation point location cost and fuel consumption cost, the transportation point location cost refers to the cost required by vehicle maintenance and service, and the fuel consumption cost refers to the fuel cost consumed in the process of truck transportation;
an order forecasting step, forecasting the order quantity in a future period of time, wherein the order quantity at least comprises the volume of goods, the weight of the goods, the goods picking time and a goods receiving address, and determining each delivery network point according to the goods receiving address;
and substituting the prediction result and the known parameters of the order prediction step into the vehicle loading and path combined optimization model to solve, and obtaining the distribution network point where each vehicle passes and the path where each vehicle passes.
In some embodiments of the present invention, the vehicle volume utilization rate is denoted as A 1 ，A 1 The expression of (a) is:
Figure BDA0003639044260000031
the vehicle volume utilization rate is recorded as A 2 ，A 2 The expression of (a) is:
Figure BDA0003639044260000032
fixed cost of the vehicle is denoted B 1 ，B 1 The expression of (c) is:
Figure BDA0003639044260000033
the cost of the transportation point is recorded as B 2 ，B 2 The expression of (c) is:
Figure BDA0003639044260000034
fuel consumption cost is recorded as B 3 ，B 3 The expression of (a) is:
Figure BDA0003639044260000035
where K is the vehicle number, K is 1,2, …, K is the total number of available vehicles, N is the total number of distribution points, a k ih Comprises the following steps: if the goods of h order number of distribution network point i are loaded by vehicle k, a k ih 1, otherwise a k ih ＝0，y ik Comprises the following steps: if the goods of the h order number of the distribution network point i are finished by the vehicle k, y ik 1, otherwise y ik ＝0，g ih The weight of the goods of h order number representing the distribution site i,
Figure BDA0003639044260000036
comprises the following steps: if the vehicle k arrives directly from the distribution network i to the distribution network j, the vehicle k is delivered to the distribution network j
Figure BDA0003639044260000037
Otherwise
Figure BDA0003639044260000038
Figure BDA0003639044260000039
Comprises the following steps: if the vehicle k arrives directly from the distribution center at the distribution network j, the vehicle k is delivered to the distribution network j
Figure BDA00036390442600000310
Otherwise
Figure BDA00036390442600000311
G k Representing the load of the vehicle k, v ih Volume of goods, V, representing h-sheet number of distribution site i k Which represents the effective volume of the vehicle k,
Figure BDA00036390442600000312
which represents the starting price of the vehicle k,
Figure BDA00036390442600000313
representing the point cost of the vehicle k, omega represents the unit cost of fuel,
Figure BDA00036390442600000314
represents the specific fuel consumption when the vehicle k is empty,
Figure BDA00036390442600000315
represents the unit fuel consumption when the vehicle k is fully loaded,
Figure BDA00036390442600000316
representing the real load ratio of the vehicle k from point i to point j, d ij Represents the distance between the delivery site i and the delivery site j, d 0j Representing the distance between the distribution point j and the center of the stream.
In some embodiments of the present invention, in the loading cost model, the converting the vehicle load utilization rate and the vehicle volume utilization rate into a cost function further includes:
W s ＝Z s ×G k
V s ＝Q s ×V k
W S indicating the cost, V, of the vehicle when fully loaded, in terms of load s Representing the cost, Z, of the vehicle when fully loaded, calculated by volume s Indicating unit freight rate, Q, of heavy goods s Indicating a unit freight rate of the blister;
the vehicle loading and path combined optimization model comprises the following steps:
minZ 1 ＝α·W s ·(1-A 1 )+β·V s ·(1-A 2 )+B 1 +B 2 +B 3
Figure BDA0003639044260000041
in some embodiments of the present invention, the constraint function of the vehicle loading and path joint optimization model is:
Figure BDA0003639044260000042
Figure BDA0003639044260000043
Figure BDA0003639044260000044
Figure BDA0003639044260000045
Figure BDA0003639044260000046
Figure BDA0003639044260000047
Figure BDA0003639044260000048
Figure BDA0003639044260000049
Figure BDA00036390442600000410
a j ≤t j ≤b j j＝1,...,N
Figure BDA0003639044260000051
Figure BDA0003639044260000052
Figure BDA0003639044260000053
or 1i, j ═ 0, 1.., N;
Figure BDA0003639044260000054
y ik 1, N, or 1i 1;
Figure BDA0003639044260000055
wherein the content of the first and second substances,
Figure BDA0003639044260000056
indicating the time, t, for the vehicle k to leave the logistics centre and go to the distribution point i ij Representing the travel time of the vehicle from delivery site i to delivery site j,
Figure BDA0003639044260000057
represents the average running speed of the vehicle, a 0 Representing the earliest service time provided by the logistics center to the vehicle, b 0 Representing the latest service time provided by the logistics centre to the vehicle, a j Represents the earliest service time of the distribution network point j, b j Represents the latest service time, t, of the distribution network point j j Indicating the time, s, at which the delivery network j begins to receive vehicle service i Indicating the time, η, for the distribution network i to complete the service 1 Showing the unloading efficiency of the distribution network.
In some embodiments of the present invention, solving the vehicle loading and path joint optimization model by using a genetic algorithm comprises:
the chromosome construction and population initialization steps take a demand point as a gene, adopt integer coding, and use a character string with the coding length of n to represent the chromosome, and the format is as follows:
T＝{t 1 ,t 2 ……t N }；
wherein T belongs to M {1,2 … … K };
the fitness function is:
Figure BDA0003639044260000058
F i representing the fitness value, Z, corresponding to chromosome i i Representing an objective function corresponding to chromosome i;
designing a genetic operator by adopting a roulette selection method, wherein the genetic operator design step comprises the following steps of:
calculating fitness value F of all individuals in the population i ；
Calculating a sum of fitness values ∑ F i ；
Calculating the probability of chromosome i being selected to be inherited to the next population
Figure BDA0003639044260000059
And selecting chromosome individuals entering the next generation by adopting a random rule.
Some embodiments of the present invention further comprise an AR intelligent binning step, comprising:
extracting packaging models of vehicles and all goods as display elements of the AR visual page to form an element library;
acquiring a three-dimensional model of goods in the predicted order, matching the three-dimensional model with a product model in an element library, and displaying and outputting the product model by replacing the three-dimensional model with a product model with basic information in the element library after successful matching;
and finding corresponding goods according to the product model for boxing.
In some embodiments of the present invention, the order prediction step uses a BP neural network prediction model for prediction.
In some embodiments of the invention, the GM (1, N) model of the grey prediction theory predicts in the order prediction step.
Compared with the prior art, the invention has the advantages and positive effects that:
according to the method for scheduling the transport capacity of the whole vehicle under the multidimensional data, the intelligent scheduling platform of the transport capacity of the whole vehicle under the multidimensional data is established, the order prediction model and the combined optimization model of vehicle loading and path are utilized to realize the function of vehicle pre-loading, the delivery timeliness is improved, unreasonable transportation activities are reduced, the logistics transportation efficiency is improved, and the logistics transportation cost is saved. Starting from actual problems, the system arranges and dispatches the transport vehicles in a lump, reduces manual intervention, reduces cost, creates an unmanned and intelligent vehicle intelligent dispatching system, and solves the problem of mutual coordination between the transport vehicles and actual distribution points for enterprises.
Other features and advantages of the present invention will become more apparent from the following detailed description of the invention when taken in conjunction with the accompanying drawings.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic diagram of an embodiment of a vehicle capacity scheduling method under multidimensional data according to the present invention;
FIG. 2 is a schematic diagram illustrating a cross operation performed in an embodiment of a method for scheduling the transportation capacity of a vehicle under multidimensional data according to the present invention;
fig. 3 is a schematic diagram of variation performed in an embodiment of a method for scheduling vehicle transportation capacity under multidimensional data according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that in the description of the present invention, the terms of direction or positional relationship indicated by the terms "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, which are merely for convenience of description, and do not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood according to specific situations by those of ordinary skill in the art.
The development and progress of the internet of things technology promote the intellectualization of logistics management, the effective combination of the internet of things technology and the logistics management greatly improves the quality of intelligent logistics management service. Aiming at the problems, the dynamic data such as the position, the speed, the parking state and the like of the vehicle are acquired by combining the existing internet of things technology, and real and timely data support is provided for reasonable scheduling, so that the occurrence of wrong command and inefficient scheduling is avoided.
The wireless sensor network nodes need to use corresponding wireless network protocols for data mutual communication, the wireless network transmission technologies are various, such as a wireless local area network technology, a bluetooth technology, an ultra wide band technology, a mobile communication technology, a wired carrier network technology and the like, the transmission modes are difficult to adapt to the requirements of low cost, low consumption, high fault tolerance and the like of the wireless sensors, and the Zigbee protocol adapts to the requirements of the wireless sensors.
The information acquisition in the logistics tracking mainly needs to acquire the information of the transport vehicles, the most common mode for acquiring the information of the transport vehicles at present is a GPS (global positioning system), and the information and a network can be combined to regulate and control the logistics tracking by utilizing the GPS. The received GPS data is processed to identify the basic information of the vehicle mainly by the GPS identification code of the logistics and distribution vehicle. The GIS map often reflects the delivery information of the vehicles used in the logistics tracking, such as the delivery time of the delivery location, the next node, and the like. The GIS can feed back the round-trip vehicle information to the Internet in a point-by-point manner. The current state of a delivery vehicle, the distance from a target place, even the time for receiving materials specifically and the like are positioned by using a GPS. And a logistics site and a logistics business point are also displayed on the GIS internet map. In this way it displays the correct logistics information to each user. When the information is collected, a vehicle track query management module is required to be processed, and the vehicle track query management module can be used for providing tracks and routes of vehicle information on a map.
Example one
The embodiment provides a method for scheduling the transport capacity of a finished automobile under multidimensional data, which comprises the following steps:
the method comprises the steps of establishing a vehicle loading and path combined optimization model, wherein the vehicle loading and path combined optimization model comprises the steps of respectively establishing a loading cost model and a path planning cost model, the loading cost model is in negative correlation with a vehicle load utilization rate and a vehicle volume utilization rate, the vehicle load utilization rate refers to the ratio of the total weight of cargos loaded by a delivery vehicle to the vehicle load, and the vehicle volume utilization rate refers to the ratio of the total volume of cargos loaded by the delivery vehicle to the vehicle volume;
the route planning cost model is positively correlated with vehicle fixed cost, transportation point location cost and fuel consumption cost, wherein the transportation point location cost refers to the cost required by vehicle maintenance and service, and the fuel consumption cost refers to the fuel cost consumed in the process of truck transportation;
an order forecasting step, forecasting the order quantity in a future period of time, wherein the order quantity at least comprises the volume of goods, the weight of the goods, the goods picking time and a goods receiving address, and determining each delivery network point according to the goods receiving address; the order quantity also comprises the demand quantity of the network points, the distribution time length, the goods picking efficiency and the goods picking time.
And substituting the prediction result and the known parameters of the order prediction step into the vehicle loading and path combined optimization model to solve, and obtaining the distribution network point where each vehicle passes and the path where each vehicle passes.
According to the method for scheduling the transport capacity of the whole vehicle under the multidimensional data, the intelligent scheduling platform of the transport capacity of the whole vehicle under the multidimensional data is established, the order prediction model and the combined optimization model of vehicle loading and path are utilized to realize the vehicle pre-loading function, the delivery timeliness is improved, unreasonable transportation activities are reduced, the logistics transportation efficiency is improved, and the logistics transportation cost is saved. Starting from actual problems, the system arranges and dispatches the transport vehicles in a lump, reduces manual intervention, reduces cost, creates an unmanned and intelligent vehicle intelligent dispatching system, and solves the problem of mutual coordination between the transport vehicles and actual distribution points for enterprises.
The vehicle loading and path combined optimization model is used for delivering goods to a plurality of network points by a plurality of vehicles of different vehicle types in a distribution center, the position and the demand of each network point are certain, the service time window is known, the types of the vehicles are different, the rated load capacity and the rated volume of each vehicle are known, the reasonable goods loading is required to be determined, a proper distribution route is selected, the utilization rate of the vehicles is maximum under the condition that constraint conditions are met, the running distance of the vehicles is shortest, and the purpose of lowest cost is finally achieved.
In some embodiments of the invention, the vehicle volume utilization rate is marked as A 1 ，A 1 The expression of (a) is:
Figure BDA0003639044260000091
the vehicle volumetric efficiency is denoted as A 2 ，A 2 The expression of (a) is:
Figure BDA0003639044260000092
generally, the fixed cost of a vehicle is a known constant, and is only related to driver wages, vehicle depreciation costs, and the like. Fixed cost of the vehicle is denoted B 1 ，B 1 The expression of (a) is:
Figure BDA0003639044260000101
the cost of the transportation point generally refers to the cost required by vehicle maintenance and repair, and the cost of the transportation point is recorded as B 2 ，B 2 The expression of (a) is:
Figure BDA0003639044260000102
the fuel consumption cost refers to the fuel cost consumed in the transportation process of the truck, the fuel cost is related to the vehicle running path and the real-time load of the vehicle, and the vehicle loading rate is taken into consideration. The fuel consumption cost is recorded as B 3 ，B 3 The expression of (a) is:
Figure BDA0003639044260000103
where K is the vehicle number, K is 1,2, …, K is the total number of available vehicles, N is the total number of distribution points, a k ih Comprises the following steps: if the goods of h order number of distribution network point i are loaded by vehicle k, a k ih 1, otherwise a k ih ＝0，y ik Comprises the following steps: if the goods of the h order number of the distribution network point i are finished by the vehicle k, y ik 1, otherwise y ik ＝0，g ih The weight of the goods of h order number representing the distribution site i,
Figure BDA0003639044260000104
comprises the following steps: if the vehicle k arrives directly from the distribution network i to the distribution network j, the vehicle k is delivered to the distribution network j
Figure BDA0003639044260000105
Otherwise
Figure BDA0003639044260000106
Figure BDA0003639044260000107
Comprises the following steps: if the vehicle k arrives directly from the distribution center at the distribution network j, the vehicle k is delivered to the distribution network j
Figure BDA0003639044260000108
Otherwise
Figure BDA0003639044260000109
G k Representing the load of the vehicle k, v ih Volume of goods, V, representing h-sheet number of distribution site i k Which represents the effective volume of the vehicle k,
Figure BDA00036390442600001010
which represents the starting price of the vehicle k,
Figure BDA00036390442600001011
point cost representing the vehicle k, ω representing unit cost of fuel,
Figure BDA00036390442600001012
represents the specific fuel consumption when the vehicle k is empty,
Figure BDA00036390442600001013
represents the unit fuel consumption when the vehicle k is fully loaded,
Figure BDA00036390442600001014
representing the real load ratio of the vehicle k from point i to point j, d ij Represents the distance between the delivery site i and the delivery site j, d 0j Representing the distance between the distribution point j and the center of the stream.
In the above model, A 1 ，A 2 The vehicle load and the volumetric efficiency are shown and the maximum values are found. B is 1 、B 2 、B 3 Showing the vehicle transport processThe cost generated in (1) is minimized. And dimensions between the two are different, so that the method can more intuitively reflect the advantages and disadvantages of loading and distribution of the vehicle and the distribution scheme of the vehicle path, consider the cost, convert the maximum utilization rate of the load and the volume of the vehicle into the minimum cost of the unused opportunity of the load and the volume of the vehicle, convert the minimum cost into a cost function, and add all the targets, thereby converting the multi-target solving problem into a single-target model solving problem with the minimum total cost.
r h Is the volume to mass ratio of the goods of the h order number of the distribution network point i, and r is the ratio of the total volume to the total weight of all the goods to be loaded, i.e. the distribution network point
Figure BDA0003639044260000111
R k Is the ratio of the volume to the mass of the vehicle type k, and R is the ratio of the total volume of all vehicles to the total mass of all vehicles, i.e.
Figure BDA0003639044260000112
If R is larger than R, the mass of the goods to be loaded is small and the volume is large, the goods attribute is blister goods, and the volume is used for pricing; otherwise, the product is heavy, and the price is calculated by weight.
When the distribution center distributes goods for the network points, the more the loading capacity is, the lower the cost required by the vehicle arranged by the distribution center is, in other words, for the distribution center, the less the unused space of the vehicle is, and the less the opportunity cost occupied by the vehicle is. The opportunity cost is calculated in terms of the freight rate charged by the vehicle loading one ton or one cubic meter of cargo.
In some embodiments of the present invention, the loading the cost model further comprises converting the vehicle load utilization rate and the vehicle volume utilization rate into a cost function, including:
W s ＝Z s ×G k
V s ＝Q s ×V k
W S indicating the cost, V, of the vehicle when fully loaded, in terms of load s Representing the cost, Z, of the vehicle when fully loaded, calculated by volume s Indicating unit freight rate, Q, of heavy goods s Indicating a unit freight rate of the blister;
the vehicle loading and path combined optimization model comprises the following steps:
minZ 1 ＝α·W s ·(1-A 1 )+β·V s ·(1-A 2 )+B 1 +B 2 +B 3 。
W S and V s The opportunity cost of the unused space occupation of the vehicle is minimum, when the targets are combined, the targets (4-24) and (4-25) are different pricing modes for the same batch of goods, and only one mode is selected for pricing according to the difference of goods attributes when the distribution cost is finally calculated, so that parameters alpha and beta are defined, wherein alpha + beta is equal to 1, and the parameters alpha and beta are defined as follows:
Figure BDA0003639044260000113
in some embodiments of the present invention, the constraint function of the vehicle loading and path joint optimization model is:
Figure BDA0003639044260000121
all of the goods representing each site may be loaded on different vehicles.
Figure BDA0003639044260000122
Figure BDA0003639044260000123
Figure BDA0003639044260000124
The two constraints respectively represent the constraint relation between two variables.
Figure BDA0003639044260000125
Figure BDA0003639044260000126
The two constraints respectively indicate that the delivery of goods to the delivery point for each vehicle does not exceed the maximum capacity and the maximum payload of the vehicle.
Figure BDA0003639044260000127
Each vehicle is shown to start from the distribution center and return to the distribution center after the distribution is finished.
Figure BDA0003639044260000128
Representing time window constraints for distribution centers and mesh points.
Figure BDA0003639044260000129
Indicating the time at which the vehicle reaches delivery point j.
a j ≤t j ≤b j j 1.. N, which represents the time window constraints for the distribution centers and the mesh points.
Figure BDA00036390442600001210
Indicating the time for the vehicle to be unloaded at the point of delivery.
Figure BDA00036390442600001211
Representing the time from when the vehicle travels from i to j.
Figure BDA00036390442600001212
Or 1i, j ═ 0, 1.., N;
Figure BDA00036390442600001213
y ik 0 or 1i 1, N;
Figure BDA00036390442600001214
the above two equations represent the corresponding 0-1 variable constraints.
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00036390442600001215
indicating the time, t, for the vehicle k to leave the logistics centre and go to the distribution point i ij Representing the travel time of the vehicle from delivery site i to delivery site j,
Figure BDA0003639044260000131
represents the average running speed of the vehicle, a 0 Representing the earliest service time provided by the logistics center to the vehicle, b 0 Representing the latest service time provided by the logistics centre to the vehicle, a j Represents the earliest service time of the distribution network point j, b j Represents the latest service time, t, of the distribution network point j j Indicating the time, s, at which the distribution network j begins to receive vehicle service i Indicating the time, η, for the distribution network i to complete the service 1 Showing the unloading efficiency of the distribution network.
In some embodiments of the present invention, solving the vehicle loading and path joint optimization model by using a genetic algorithm comprises:
the chromosome construction and population initialization steps take a demand point as a gene, adopt integer coding, and use a character string with the coding length of n to represent the chromosome, and the format is as follows:
T＝{t 1 ,t 2 ……t N }；
wherein T is equal to M {1,2 … … K }.
If there are a total of 20 delivery points and 5 vehicles of different models, for example, T ═ {0123045067890} indicates that the delivery routes of 3 delivery vehicles are 0 → 1 → 2 → 3, 0 → 4 → 5, 0 → 6 → 7 → 8 → 9, respectively.
The problem that the total distribution cost is minimum is solved by the model of the scheme, the fitness function is not negative, the larger the fitness function is, the better the fitness function is, and therefore the objective function needs to be converted when the fitness function is determined. We now convert the fitness function as follows:
Figure BDA0003639044260000132
F i representing the fitness value, Z, corresponding to chromosome i i Representing the objective function for chromosome i.
Designing a genetic operator by adopting a roulette selection method, wherein the genetic operator design step comprises the following steps of:
calculating fitness value F of all individuals in the population i ；
Calculating a sum of fitness values ∑ F i ；
Calculating the probability of chromosome i being selected to be inherited to the next population
Figure BDA0003639044260000133
And selecting chromosome individuals entering the next generation by adopting a random rule.
Firstly, calculating the fitness value F of all individuals in the group i Then, the sum of the fitness values ∑ F is calculated i And calculating the probability of the chromosome i being selected to be inherited to the next population
Figure BDA0003639044260000141
And finally, selecting chromosome individuals entering the next generation according to a certain random rule.
The crossing operation is performed by adopting a two-point crossing mode, and as shown in fig. 2, the crossing strategy adopts dynamic crossing.
Since the influence of charging is taken into consideration in decoding, a method of changing the order of delivery of the clients is considered in mutation, thereby increasing the diversity of the group. Therefore, as shown in fig. 3, the scheme adopts a two-point reciprocal variation mode. The simultaneous variation strategy adopts dynamic variation, let p m1 0.1 and p m2 ＝0.05,。
Constraint condition processing, constraint condition processing in an algorithm, solving a fuel vehicle stowage path optimization model, and dividing a code R according to load constraint and time window constraint, wherein the specific method comprises the following steps:
(1)i＝1；
(2) starting the ith route L i ＝[0]0 is a distribution center;
(3) attempt to add the first point in the code R to L i If L is added i If the rear vehicle load and the time window are both satisfied, then go to (4), otherwise i ═ i +1, go to (2);
(4) deleting the 1 st bit code of the R, if the R is empty, turning to (5), otherwise, turning to (3);
(5) and outputting each sub-path.
The augmented reality technology is a technology for calculating the position and angle of a camera image in real time and adding corresponding images, videos and 3D models, and virtual information is applied to the real world, so that the sensory experience beyond reality is achieved. The principle of the AR technology is that a camera captures real scenes, real-time position information is obtained, then a system background generates a virtual model according to the real-time position information, and finally a synthesized graph of the two is displayed on a display to be watched by a user.
Some embodiments of the present invention further comprise an AR intelligent binning step, comprising:
extracting packaging models of vehicles and all goods as display elements of the AR visual page to form an element library;
acquiring a three-dimensional model of goods in the predicted order, matching the three-dimensional model with a product model in an element library, and displaying and outputting the product model by replacing the three-dimensional model with a product model with basic information in the element library after successful matching;
and finding corresponding goods according to the product model for boxing.
And (3) projecting a picture by adopting AR glasses, deploying the three-dimensional boxing simulation diagram to the AR glasses, and taking the carriage as a projection plane. When the camera scans the carriage, the left rear point (base point) of the carriage is collated with the three-dimensional boxing simulation image (0,0,0), and then a picture is projected. The projection is used for correcting the picture according to the size of the actual vehicle model, the projection can be used for enabling the three-dimensional boxing simulation effect to be projected in blocks according to setting, a worker can finish the loading work rapidly according to the projection picture prompt, and the consistency of the actual loading effect and the simulation effect is improved.
Under the assistance of AR technology, the realistic fitting is realized, the visualization of the three-dimensional boxing simulation effect is greatly enhanced, the simulation effect is not limited on a two-dimensional plane any more, the utilization rate of the simulation effect is improved, and the boxing efficiency of staff is improved.
By introducing the AR technology, the three-dimensional boxing effect is displayed to workers in a more three-dimensional and real mode, boxing operation is conducted according to projection image prompting, the three-dimensional boxing utilization rate is greatly improved, and boxing time is shortened.
In some embodiments of the present invention, the order prediction step uses a BP neural network prediction model for prediction.
Through analyzing historical operation data, order demands in a short period of time in the future can be accurately predicted, enterprise operation activities such as warehousing, delivery and vehicle scheduling are planned according to prediction results, warehouse inventory pressure can be reduced, effective organization and overall utilization of vehicle resources, human resources and the like are achieved, three-dimensional boxing and path optimization are combined, delivery speed is improved, time cost and logistics cost are reduced, and cost is reduced and efficiency is improved.
The construction process of the BP neural network comprises the following steps:
(1) network initialization
Firstly, a random weight is given to the connection between each layer of neural network, the weight is within (-1, 1), and then an error delta, an accuracy value sigma and a maximum learning time M are preset for the training result.
(2) Randomly selecting input and output samples
Inputting samples
Figure BDA0003639044260000151
And expected output samples
Figure BDA0003639044260000152
Is provided to the network.
(3) Input b with hidden layer j Calculating the output c of each unit of the hidden layer through an intermediate function j 。
Figure BDA0003639044260000161
c j ＝f(b j ),j＝1,2,…,p (3-2)
The connection weight of the input layer and the hidden layer is recorded as w ij The output threshold of each unit of the hidden layer is marked as theta j 。
(4) Calculating inputs g of units of output layer i And an output h i
Figure BDA0003639044260000162
h t ＝f(g t ),t＝1,2,…,q(3-4)
The connection weight of the hidden layer and the middle layer is recorded as v jt The output threshold of each cell of the output layer is recorded as y t 。
(5) An error function of
Figure BDA0003639044260000163
The partial derivatives of the output layer error function neurons are calculated.
(6) The output error is reversely transmitted into the input layer, each layer corrects the weight according to the error signal obtained by each layer, the adjustment of the error value of each layer is carried out circularly, the training is not finished until the error reaches the precision set before the neural network training or the learning frequency is more than the set maximum frequency, otherwise, the above work is continuously repeated.
In some embodiments of the invention, the GM (1, N) model of the grey prediction theory predicts in the order prediction step.
The Grey prediction theory (GM) is an analysis system that mainly aims at performing correlation analysis, Model or algorithm construction, prediction and decision-making on problems in the presence of incomplete information. The main principle of the GM (1, N) model is that differential fitting is carried out on original data by establishing a differential equation, and importantly, after the original data are processed by an accumulation method, the obtained data are merged into a prediction model, and then the prediction result is reduced.
The scheme further comprises a vehicle visual early warning step, real-time monitoring of a distribution link and early warning management under a truck fault state are achieved, the speed of the vehicle is monitored through the wireless sensor, the state of the vehicle is displayed on a large screen in real time, and if the situation that the vehicle cannot reach a network point in time is detected, information of the relevant vehicle is displayed by the system and a manager is warned with bright colors. If the driver cannot arrive at the website on time in an emergency situation on the road, the background feeds back information to the website and the driver in time so that the two parties can make adjustment according to the information.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims (8)

1. A method for scheduling the transport capacity of a finished automobile under multidimensional data is characterized by comprising the following steps:
the method comprises the steps of establishing a vehicle loading and path combined optimization model, wherein the vehicle loading and path combined optimization model comprises the steps of respectively establishing a loading cost model and a path planning cost model, the loading cost model is in negative correlation with a vehicle load utilization rate and a vehicle volume utilization rate, the vehicle load utilization rate refers to the ratio of the total weight of cargos loaded by a delivery vehicle to the vehicle load weight, and the vehicle volume utilization rate refers to the ratio of the total volume of cargos loaded by the delivery vehicle to the vehicle volume;
the route planning cost model is positively correlated with vehicle fixed cost, transportation point location cost and fuel consumption cost, the transportation point location cost refers to the cost required by vehicle maintenance and service, and the fuel consumption cost refers to the fuel cost consumed in the process of truck transportation;
an order forecasting step, forecasting the order quantity in a future period of time, wherein the order quantity at least comprises the volume of goods, the weight of the goods, the goods picking time and a goods receiving address, and determining each delivery network point according to the goods receiving address;
and substituting the prediction result and the known parameters of the order prediction step into the vehicle loading and path combined optimization model to solve, and obtaining the distribution network point where each vehicle passes and the path where each vehicle passes.
2. The method for scheduling the transportation capability of the whole vehicle under the multidimensional data according to claim 1,
the vehicle volume utilization rate is recorded as A 1 ，A 1 The expression of (a) is:
Figure FDA0003639044250000011
the vehicle volume utilization rate is recorded as A 2 ，A 2 The expression of (a) is:
Figure FDA0003639044250000012
fixed cost of the vehicle is denoted B 1 ，B 1 The expression of (a) is:
Figure FDA0003639044250000021
the cost of the transportation point is recorded as B 2 ，B 2 The expression of (a) is:
Figure FDA0003639044250000022
the fuel consumption cost is recorded as B 3 ，B 3 The expression of (a) is:
Figure FDA0003639044250000023
wherein K is a vehicle number, K is 1,2, K is the total number of available vehicles, N is the total number of distribution points, a k ih Comprises the following steps: if the goods of h order number of distribution network point i are loaded by vehicle k, a k ih 1, otherwise a k ih ＝0，y ik Comprises the following steps: if the goods of the h order number of the distribution network point i are finished by the vehicle k, y ik 1, otherwise y ik ＝0，g ih The weight of the goods of h order number representing the distribution site i,
Figure FDA0003639044250000024
comprises the following steps: if the vehicle k arrives directly from the distribution network i to the distribution network j, the vehicle k is delivered to the distribution network j
Figure FDA0003639044250000025
Otherwise
Figure FDA0003639044250000026
Figure FDA0003639044250000027
Comprises the following steps: if the vehicle k arrives directly from the distribution center at the distribution network j, the vehicle k is delivered to the distribution network j
Figure FDA0003639044250000028
Otherwise
Figure FDA0003639044250000029
G k Representing the load of the vehicle k, v ih Volume of goods, V, representing h-sheet number of distribution site i k Which represents the effective volume of the vehicle k,
Figure FDA00036390442500000210
which represents the starting price of the vehicle k,
Figure FDA00036390442500000211
indicating the point cost of the vehicle k, omega fuelThe unit cost of (a) is,
Figure FDA00036390442500000212
represents the specific fuel consumption when the vehicle k is empty,
Figure FDA00036390442500000213
represents the unit fuel consumption when the vehicle k is fully loaded,
Figure FDA00036390442500000214
representing the real load ratio of the vehicle k from point i to point j, d ij Represents the distance between the delivery site i and the delivery site j, d 0j Representing the distance between the distribution point j and the center of the stream.
3. The vehicle capacity scheduling method under the multidimensional data as recited in claim 2, wherein the loading and stowage cost model further comprises a step of converting a vehicle load utilization rate and a vehicle volume utilization rate into a cost function, which comprises:
W s ＝Z s ×G k
V s ＝Q s ×V k
W S indicating the cost, V, of the vehicle when fully loaded, in terms of load s Representing the cost, Z, of the vehicle when fully loaded, calculated by volume s Indicating unit freight rate, Q, of heavy goods s Indicating a unit freight rate of the blister;
the vehicle loading and path combined optimization model comprises the following steps:
minZ 1 ＝α·W s ·(1-A 1 )+β·V s ·(1-A 2 )+B 1 +B 2 +B 3
Figure FDA0003639044250000031
4. the method for scheduling the capacity of the whole vehicle under the multidimensional data according to claim 3, wherein the constraint function of the vehicle loading and path joint optimization model is as follows:
Figure FDA0003639044250000032
Figure FDA0003639044250000033
Figure FDA0003639044250000034
Figure FDA0003639044250000035
Figure FDA0003639044250000036
Figure FDA0003639044250000037
Figure FDA0003639044250000038
Figure FDA0003639044250000039
Figure FDA00036390442500000310
a j ≤t j ≤b j j＝1,...,N
Figure FDA00036390442500000311
Figure FDA00036390442500000312
Figure FDA00036390442500000313
or 1
Figure FDA00036390442500000314
y ik 0 or 1
Figure FDA00036390442500000315
Wherein the content of the first and second substances,
Figure FDA00036390442500000316
indicating the time, t, for the vehicle k to leave the logistics centre and go to the distribution point i ij Representing the travel time of the vehicle from delivery site i to delivery site j,
Figure FDA0003639044250000041
represents the average running speed of the vehicle, a 0 Representing the earliest service time provided by the logistics center to the vehicle, b 0 Representing the latest service time provided by the logistics centre to the vehicle, a j Represents the earliest service time of the distribution network point j, b j Represents the latest service time, t, of the distribution network point j j Indicating the time, s, at which the distribution network j begins to receive vehicle service i Indicating the time, η, for the distribution network i to complete the service 1 Showing the unloading efficiency of the distribution network.
5. The method for scheduling the transportation capacity of the whole vehicle under the multidimensional data as recited in claim 3 or 4, wherein the step of solving the vehicle loading and path joint optimization model by adopting a genetic algorithm comprises the following steps:
the chromosome construction and population initialization steps take a demand point as a gene, adopt integer coding, and use a character string with the coding length of n to represent the chromosome, and the format is as follows:
T＝{t 1 ,t 2 ……t N }；
wherein T belongs to M {1,2 … … K };
the fitness function is:
Figure FDA0003639044250000042
F i representing the fitness value, Z, corresponding to chromosome i i Representing an objective function corresponding to chromosome i;
designing a genetic operator by adopting a roulette selection method, wherein the genetic operator design step comprises the following steps of:
calculating fitness value F of all individuals in the population i ；
Calculating a sum of fitness values ∑ F i ；
Calculating the probability of chromosome i being selected to be inherited to the next population
Figure FDA0003639044250000043
And selecting chromosome individuals entering the next generation by adopting a random rule.
6. The method for dispatching the capacity of the whole vehicle under the multidimensional data according to any one of claims 1 to 5, further comprising an AR intelligent packing step comprising:
extracting packaging models of vehicles and all goods as display elements of the AR visual page to form an element library;
acquiring a three-dimensional model of goods in the predicted order, matching the three-dimensional model with a product model in an element library, and displaying and outputting the product model by replacing the three-dimensional model with a product model with basic information in the element library after successful matching;
and finding corresponding goods according to the product model for boxing.
7. The method for scheduling the transportation capacity of the whole vehicle under the multidimensional data according to any one of the claims 1 to 5,
and in the order prediction step, a BP neural network prediction model is adopted for prediction.
8. The method for scheduling the transportation capacity of the whole vehicle under the multidimensional data according to any one of the claims 1 to 5,
and (3) forecasting a GM (1, N) model of a grey forecasting theory in the order forecasting step.
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Publication number Priority date Publication date Assignee Title
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117455100A (en) * 2023-12-26 2024-01-26 长春市优客云仓科技有限公司 Intelligent warehouse logistics scheduling method based on global optimization
CN117455100B (en) * 2023-12-26 2024-03-15 长春市优客云仓科技有限公司 Intelligent warehouse logistics scheduling method based on global optimization

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