CN111882200A - Vehicle and goods matching method considering vehicle path and three-dimensional boxing - Google Patents

Vehicle and goods matching method considering vehicle path and three-dimensional boxing Download PDF

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CN111882200A
CN111882200A CN202010719420.0A CN202010719420A CN111882200A CN 111882200 A CN111882200 A CN 111882200A CN 202010719420 A CN202010719420 A CN 202010719420A CN 111882200 A CN111882200 A CN 111882200A
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赵姣
张佳蕊
王宁
王茵
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Abstract

The invention discloses a vehicle and goods matching method considering a vehicle path and three-dimensional boxing, and belongs to the field of vehicle and goods matching. According to the vehicle and goods matching method considering the vehicle path and the three-dimensional boxing, the small-scale problem solving speed is stabilized at about 10s, the large-scale problem is stabilized at about 1200s, and the error rate is stabilized within 1% in the aspect of operation efficiency; the optimization targets are various, and the operation cost, no-load cost, center-of-gravity offset cost and punishment cost of matching different vehicles by the same cargo owner are considered, so that the operation cost of transportation and placement is reduced, the no-load rate of the vehicles is reduced, and the transportation safety and loading efficiency of the vehicles are ensured; in addition, the vehicle and goods matching method simultaneously considers the path problem of the vehicle and the three-dimensional loading problem of goods, and not only can the vehicle and goods matching result be obtained through the method, but also the optimal position and loading sequence of the vehicle access path and the goods loaded in the carriage can be obtained.

Description

Vehicle and goods matching method considering vehicle path and three-dimensional boxing
Technical Field
The invention belongs to the field of vehicle and goods matching, and particularly relates to a vehicle and goods matching method considering a vehicle path and three-dimensional boxing.
Background
In recent years, the total amount of automobile freight has been reduced, but it still accounts for a large proportion. However, the problems of high empty rate, disordered loading of cargos, unreasonable loading and the like still exist in the transportation of cargos by automobiles. These problems result in high freight cost of automobiles, cause huge freight loss and seriously restrict the further development of the automobile freight industry. Therefore, in the vehicle-cargo matching phase, it is necessary to consider the Container Loading Problem (CLP) and the Vehicle Routing Problem (VRP). Both of these problems are challenging problems, considered to be the classical NP-hard problem. Combining CLP and VRP creates a vehicle path problem (3L-CVRP) with three-dimensional loading constraints.
The problem is not much studied domestically or abroad. Tarrantilis et al, in 2009, combined with TS and GLS strategies, propose a mixed meta-heuristic method GTS to solve the 3L-CVRP problem, but the considered containers are the same in size, without considering the priority problem caused by batch delivery, simplifying loading constraints; escorbar et al proposed a mixed meta-heuristic method in 2015, called as GTS-GRASP 3L-CVRP, using a Granulatabu search method (GTS) to solve corresponding Vehicle Routing Problems (VRP) and container loading problems under a given route, in the aspect of problem solution, mostly adopting segmentation matching to cut the vehicle routing problems and the target of the three-dimensional packing problem, and the overall benefit of the optimization result is poor; in the aspect of algorithm performance, the GTS algorithm combines the ant colony algorithm with the tabu search algorithm to improve the search capability of the solution, but the ant colony algorithm is more complex and has low operation efficiency.
Disclosure of Invention
The invention aims to overcome the defects that the existing vehicle and goods matching problem solving method is low in operation efficiency, single in optimization target and incapable of realizing integral optimization of a vehicle path problem, a three-dimensional loading problem and a vehicle and goods matching problem, and provides a vehicle and goods matching method considering a vehicle path and three-dimensional boxing.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a vehicle and goods matching method considering a vehicle path and three-dimensional boxing comprises the following steps:
1) generating vehicle and goods matching results according to the number of vehicles, the number of goods owners and the number of goods of the goods owners and initializing; wherein, part of the generated vehicle and goods matching results keeps randomness, and the integer part of the real number corresponding to the same goods in the rest part of the vehicle and goods matching results is corrected to be the same value;
the vehicle and goods matching result is obtained by random real number combined coding, the coding of the vehicle and goods matching result is a real number with two decimal numbers, wherein the integer part of the real number represents the vehicle serial number matched with goods, and the numerical value of the decimal part is the sequence of goods put into the vehicle and the sequence of the owner of the goods visited by the vehicle;
2) setting the maximum iteration times and the current iteration algebra to be 1, initializing the historical optimal matching result and the fitness value thereof, setting the maximum iteration speed and the current iteration speed to be 0, initializing the learning factor and the inertia weight range, and starting iteration;
3) judging whether the current iteration algebra reaches the maximum iteration times;
if the maximum iteration times are reached, jumping to the step 8);
otherwise, jumping to the step 4);
4) updating the vehicle and goods matching result;
5) calculating the fitness value of the updated vehicle and goods matching result;
the fitness function is: the performance index function of the operation cost of the vehicle, the performance index function of the no-load cost of the vehicle, the performance index function of the gravity center offset cost of the vehicle and the performance index function of the penalty cost;
the operating cost of the vehicle comprises a cargo driving cost;
the vehicle no-load cost comprises the weight reduction cost of the mass no-load rate and the weight reduction cost of the space no-load rate;
the center of gravity shift cost of the vehicle comprises the cost of weighted reduction of the distance between the center of gravity of the loaded vehicle and the center of gravity of the unloaded vehicle;
the penalty cost of the vehicle comprises the weighted cost of loading different vehicles by the same cargo owner;
6) comparing the fitness values of the vehicle and cargo matching results, setting the lowest fitness value of all the current vehicle and cargo matching results as the current optimal matching result, updating each historical optimal matching result, and updating the evolution direction and speed of the matching result according to the historical optimal vehicle and cargo matching result, the learning factor and inertia weight range initialized in the step 1) and the current iteration algebra;
7) judging whether the result with the minimum fitness value in the current vehicle-cargo matching result meets a privilege condition or not;
if yes, setting the current particle as a historical optimal matching result, updating a taboo table, and jumping to the step 8);
otherwise, taking the suboptimal matching result as a historical optimal matching result to jump out local optimality, updating a taboo table and jumping to the step 3);
the taboo table records the vehicle and goods matching result which is repeatedly iterated for a plurality of times to form the historical optimal matching result, and stores the taboo algebra carried out on the result, so that all vehicle and goods matching results are prevented from being repeatedly used as the historical optimal matching result and continuously approaching to the result to fall into local optimal matching;
if the historical optimal matching results after a plurality of continuous iterations cannot exceed the taboo matching results or the forbidden algebra reaches the preset maximum value, releasing the results to have an opportunity to become the historical optimal matching results;
8) and outputting a global approximately optimal vehicle and goods matching result.
Further, the real number coding is carried out on the vehicle and goods matching result in the step 1), and the specific operation is as follows:
and randomly generating real numbers with two decimal numbers, wherein the number of the real numbers is equal to the total number of the cargos, and the value range of the real numbers is 1 to (the number of the vehicles is + 1).
Further, the specific operations of decoding in step 1) are:
the integer part of the code is a vehicle serial number matched with the goods, and a matching result of the vehicle, the goods owner and the goods is obtained;
sorting the code values corresponding to the cargos matched with the vehicles from large to small to obtain the loading sequence of the cargos matched with the vehicles;
dividing the code into a plurality of sections according to the number of the goods held by each goods owner, wherein each section corresponds to all goods of one goods owner, and sequencing the maximum values of the corresponding code values in the goods of the goods owners matched with the vehicles from large to small to obtain the order of the vehicle visiting the goods owners, namely the vehicle driving path.
Further, the step 4) of updating the vehicle-cargo matching result specifically includes:
xid(t+1)＝xid(t)+vid(t+1) (1)
wherein x is a real numerical value corresponding to the goods in the vehicle and goods matching result, t is the iteration times of the previous generation, and v is the evolution speed of the vehicle and goods matching result.
Further, in step 5), a fitness function for calculating the updated fitness value of the vehicle-cargo matching result is as follows:
Figure BDA0002599418920000041
n is the number of goods owners, M is the number of vehicles, pp is the total number of goods, mm is the mass of goods, X is the matching result of the vehicles and the goods, E is the cost of driving a unit distance every time a unit mass of the vehicles is added, F is the cost of driving a unit distance when the vehicles are in no-load, S is the driving distance of the vehicles, I is the result of whether the vehicles are used, M is the maximum load capacity of the vehicles, V is the volume of a carriage, V is the volume of the goods, T is the converted cost of the no-load rate of the vehicles, Y is the matching result of the goods owners of the vehicles, H is the converted cost of the gravity center offset distance of the vehicles, and R is the punish;
the vehicle travel distance S in equation (2) is obtained by solving equation (3):
Figure BDA0002599418920000051
wherein Z is the matching result of the vehicle and the successively arriving owner, D is the distance between the owner and the owner, and between the owner and the owner destination, i represents the vehicle, j represents the owner, and k represents the goods.
Further, the fitness function includes a performance index function a of the operating cost of the vehicle:
Figure BDA0002599418920000052
in the formula, i represents a vehicle, j represents a cargo owner, k represents cargo, n represents the quantity of the cargo owner, m represents the quantity of the vehicle, pp represents the total quantity of the cargo, mm represents the mass of the cargo, X represents a matching result of the vehicle and the cargo, E represents the cost of the vehicle for driving a unit distance when the vehicle is added with a unit mass, F represents the cost of the vehicle for driving a unit distance in an idle state, S represents the driving distance of the vehicle, Z represents the matching result of the vehicle and the cargo owner which arrive successively, D represents the distance between the cargo owner and the cargo owner, and the distance between the cargo owner and the cargo owner destination, and A represents the operation cost of the vehicle;
the fitness function comprises a performance index function B of the unloaded cost of the vehicle:
Figure BDA0002599418920000053
in the formula, I represents a vehicle, k represents goods, M represents the number of the vehicles, pp represents the total number of the goods, mm represents the mass of the goods, X represents the matching result of the vehicles and the goods, I represents the using result of the vehicles, M represents the maximum load capacity of the vehicles, V represents the volume of a compartment, V represents the volume of the goods, T represents the conversion cost of the no-load rate of the vehicles, and B represents the no-load cost of the vehicles;
the fitness function comprises a performance index function C of punishment cost of loading different vehicles on the same cargo:
Figure BDA0002599418920000061
in the formula, i represents a vehicle, j represents a cargo owner, n represents the cargo owner number, m represents the vehicle number, Y represents the vehicle cargo owner matching result, R represents the punishment coefficient of different vehicles loaded by the same cargo owner, and C represents the punishment cost of different vehicles loaded by the same cargo owner;
the fitness function includes the center of gravity offset potential safety hazard cost D of the vehicle:
Figure BDA0002599418920000062
in the formula, i represents a vehicle, k represents goods, m represents the number of vehicles, pp represents the total number of the goods, mm represents the quality of the goods, X represents the matching result of the vehicles and the goods, (X, y, z) represents the barycentric coordinates of the goods, (xc, yc, zc) represents the barycentric coordinates of a carriage, H represents the folding cost of the barycentric offset distance of the vehicle, and D represents the potential safety hazard cost of the barycentric offset of the vehicle.
Further, the speed of updating the vehicle-cargo matching result evolution in the step 5) is specifically as follows:
vid(t+1)＝ωvid(t)+c1r1[Pid(t)-xid(t)]+c2r2[PGd(t)-xid(t)](4)
Figure BDA0002599418920000063
wherein: iteriFor the current iteration number, iter is the maximum iteration number, c1、c2As a learning factor, ω is an inertial weight, r1、r2The random number is 0-1, Pid (t) is the optimal matching result of each history, and PGd (t) is the optimal matching result of the history of the particle swarm.
Further, the operation of updating the tabu table in the step 6) is as follows:
and setting the corresponding vehicle and goods matching position of the optimal particles in the taboo table as the taboo length, and subtracting 1 from the value of the rest positions of the taboo table.
Compared with the prior art, the invention has the following beneficial effects:
according to the vehicle and goods matching method considering the vehicle path and the three-dimensional boxing, the small-scale problem solving speed is stabilized at about 10s, the large-scale problem is stabilized at about 1200s, and the error rate is stabilized within 1% in the aspect of operation efficiency; the optimization targets are various, and the operation cost, no-load cost, center-of-gravity offset cost and punishment cost of matching different vehicles by the same cargo owner are considered, so that the operation cost of transportation and placement is reduced, the no-load rate of the vehicles is reduced, and the transportation safety and loading efficiency of the vehicles are ensured; in addition, the vehicle and goods matching method simultaneously considers the path problem of the vehicle and the three-dimensional loading problem of goods, and not only can the vehicle and goods matching result be obtained through the method, but also the optimal position and loading sequence of the vehicle access path and the goods loaded in the carriage can be obtained.
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FIG. 1 is a data processing flow chart of a vehicle-cargo matching core module according to the present invention;
FIG. 2 illustrates the encoding and decoding method of the matching result of the vehicle and goods according to the present invention;
FIG. 3 is a schematic diagram of a three-dimensional loading process of cargo according to the present invention;
fig. 4 is a diagram showing the results of matching the vehicle and the cargo in the embodiment, fig. 4(a) shows the vehicle access owner sequence of the vehicle 2 and the results of matching the vehicle and the cargo, fig. 4(b) -fig. 4(e) show the cargo owners 11, 24, 3, 16 corresponding to the colors shown in fig. 4(f) -fig. 4(y), respectively, and fig. 4(f) -fig. 4(y) show the results of three-dimensional loading of the cargo in the vehicles 1-20, respectively.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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 only a part of the embodiments of the present invention, and not all of the embodiments. 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 the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in further detail below with reference to the accompanying drawings:
examples
Referring to fig. 1, the data processing flow chart of the vehicle and goods matching core module of the invention comprises the following implementation steps:
step 1, inputting a vehicle maximum load capacity, a carriage size and a vehicle operation cost information form by a decision maker interactive interface, referring to table 1, and demonstrating the stability, the high efficiency and the universality of the method by solving large, medium and small scale embodiments, taking a large scale embodiment as an example, vehicle information comprises unit distance no-load running cost, running cost of each unit distance increasing unit goods, vehicle number, vehicle maximum load capacity and carriage size, wherein the running cost needs to be determined according to a vehicle type, the running cost is assumed to be 1, and other information is random numbers in a certain range to verify the universality of the method;
TABLE 1 vehicle information form
Figure BDA0002599418920000081
The method comprises the steps of obtaining a cargo quantity of a cargo owner, a cargo distance matrix of the cargo owner and a destination of the cargo, a cargo weight and a cargo size information list, and referring to table 2, taking a large-scale embodiment as an example, the cargo owner information comprises the cargo quantity, the cargo size, the cargo weight and distance matrixes between the cargo owner and between the cargo owner and the cargo destination, wherein each cargo size and weight are random numbers in a certain range, the distance matrix is a 69 x 69 matrix, the matrix element value is a random number of 5-40, and the diagonal element value is 0;
TABLE 2 owner information form
Figure BDA0002599418920000091
A conversion and penalty cost information form is obtained, see table 3, taking a large-scale embodiment as an example, the decision of the conversion cost is decided by a decision maker, and 50000, 50 and 1000 selected in the table represent the vehicle operation cost in the total cost: no-load cost: center of gravity offset cost: penalty cost 12: 2: 1: 1, the vehicle operation cost is more important, the empty load rate and the gravity center offset distance are properly reduced, the same cargo owner is allowed to match different vehicles, the reduced penalty cost is influenced by the embodiment with different scales and the coefficients with different importance degrees given by decision makers, the operation cost needs to be pre-calculated after the embodiment scale and the importance degree coefficient are determined, the specific penalty cost is determined, and the pre-calculation result of the operation cost of the large-scale embodiment is 300000.
TABLE 3 reduced and penalized costs
Figure BDA0002599418920000092
Step 2, a vehicle and goods matching core module in the cloud server calls a hybrid algorithm to process input data; skipping to step 2.1;
step 2.1, initializing vehicle and goods matching results according to the number of vehicles, the number of goods owners and the number of goods owners; the vehicle and goods matching result adopts random real number coding, wherein the number of the coded bits is the total number of goods, and the range of the random real number value is 1 to (the number of vehicles + 1); the decoding method comprises the following steps: the integral part of the code is a vehicle serial number matched with the goods, the code values corresponding to the goods matched with the vehicles are sorted from large to small into a goods loading sequence, the code is divided into a plurality of sections according to the number of the goods held by each goods owner, each section corresponds to all goods of one goods owner, the maximum value of the corresponding code values in the goods of the goods owners matched with the vehicles is sorted from large to small into a vehicle access goods owner sequence, and the steps are repeated for 100 times to generate initial 100 vehicle and goods matching results.
Referring to fig. 2, fig. 2 shows the encoding and decoding method of the vehicle and cargo matching result according to the present invention; different from the traditional two-section optimization, the result generated by the three-dimensional loading result directly influences the vehicle and goods matching result, and the overall benefit is improved.
After the vehicle and goods matching result is initialized, initializing the evolution speed (randomly generating 0-2 real numbers), the iteration times and a tabu table of the vehicle and goods matching result; the taboo list stores historical matching times of the vehicle and the goods, prevents the same goods from being matched with the vehicle for multiple times and falls into local optimum, the taboo list transversely refers to the serial number of the goods and longitudinally refers to the serial number of the vehicle, the corresponding matrix elements store taboo length, and the taboo length refers to the number of times of taboo iteration;
after the initialization is finished, iteration is started, and the step 2.2 is skipped;
step 2.2, judging whether the maximum iteration times is reached, if so, skipping to the step 2.11; otherwise, jumping to step 2.3;
step 2.3, updating the matching result of the vehicles and the cargos to obtain a new matching result of the vehicles and the cargos and a cargo owner and a loading sequence of the cargos and the cargo owner; skipping to step 2.4;
step 2.4, calculating the fitness value of the updated 100 vehicle and cargo matching results, wherein the fitness value comprises four parts: the method comprises the following steps of (1) operating cost of a vehicle, vehicle empty load rate conversion cost, vehicle gravity center offset distance conversion cost and punishment cost;
step 2.4.1, calculating the vehicle operation cost, including the vehicle no-load running cost and the vehicle loading running cost;
step 2.4.2, calculating the vehicle no-load rate, including the mass no-load rate and the space no-load rate, and adding the cost and the vehicle operation cost by multiplying and converting the vehicle no-load rate and the space no-load rate into the cost;
step 2.4.3, calculating the gravity center offset distance of the vehicle, namely the distance between the gravity center of the loaded vehicle and the gravity center of the unloaded vehicle, carrying out three-dimensional loading on the current cargo sequence by utilizing a heuristic strategy, wherein the cargo sequence is obtained by decoding, the first matched cargo is placed into a carriage to generate a carriage residual space set, the second matched cargo is placed, judging whether the cargo is allowed to be placed into the residual space, if the cargo cannot be placed into the residual space set, setting the fitness value of the particle as a maximum value, otherwise, the third matched cargo is placed, and the like until all the cargos matched with the vehicle are placed into the carriage, wherein the specific loading step is shown in figure 3, the constraint conditions mainly comprise cargo loading volume and cargo weight not more than the rated volume and loading weight of the carriage, the suspended parts of the vertically contacted cargo are not more than one fifth of the total contact area, the weight is not less than the pressure, the, if so, calculating the gravity center offset distance after the coordinates of all goods are determined, otherwise, setting the gravity center offset distance as a maximum value, and finally, adding the cost, the vehicle operation cost and the no-load rate reduced cost through multiplying and converting the cost by a coefficient;
step 2.4.4, calculating punishment cost, namely calculating the sum of the number of vehicles matched with a cargo owner, and adding the cost, the vehicle operation cost, the no-load rate conversion cost and the gravity center offset distance conversion cost through multiplying and converting the sum by a coefficient;
skipping to step 2.5;
step 2.5, updating the historical optimum of each vehicle and goods matching result, namely the result with the minimum fitness value in the matching results of each vehicle and goods generation at the same position; skipping to step 2.6;
step 2.6, updating the evolutionary speed of the vehicle and goods matching results, and enabling the vehicle and goods matching results of the next generation to be closer to the optimal results in all the vehicle and goods matching results and the historical optimality of each vehicle and goods matching result on the premise of ensuring the advantages of the previous generation; skipping to step 2.7;
step 2.7, judging whether the taboo tables corresponding to the results of the current optimal matching results of all the vehicles and the cargos are cleared or not, namely whether the updated optimal matching results of the vehicles and the cargos are the same as the previous generation or not; if yes, setting the optimal result as a historical optimal vehicle and goods matching result, resetting the corresponding vehicle and goods matching position of the optimal result in the taboo table as the taboo length, and subtracting one from the value of the rest positions of the taboo table; otherwise, the suboptimal result is used as a historical optimal vehicle and goods matching result to jump out the local optimal, and the step 2.8 is skipped;
step 2.8, turning to step 2.2;
step 2.9, outputting the optimal particles and the fitness values corresponding to the particles, comparing the calculation result and the running time of the small-scale, medium-scale and large-scale embodiments obtained by the method with the accurate solution result error obtained by solving CPLEX in a table 4, wherein the errors of the small-scale and medium-scale embodiments are stabilized at about 1 percent, the running time is stabilized within 60s, the running time of the large-scale embodiments is stabilized at about 1200s, and the large-scale problem that CPLEX cannot be solved can be solved; the processed vehicle cargo matching result, vehicle owner matching result, vehicle path and three-dimensional cargo loading position are shown in fig. 4, fig. 4(a) shows the vehicle 2 as an example, the vehicle accessing owner sequence and the vehicle cargo matching result, fig. 4(b) -fig. 4(e) respectively show the colors of the cargos 11, 24, 3 and 16 corresponding to fig. 4(f) -fig. 4(y), and fig. 4(a) -fig. 4(y) respectively show the results of the three-dimensional loading of the matched cargos of the vehicle 1-the vehicle 20.
TABLE 4 comparison of the results of the vehicle-cargo matching method with the results of the CPLEX solution
Figure BDA0002599418920000121
Figure BDA0002599418920000131
Preferably, the fitness function in step 2.4 and step 2.5 not only considers the common operating cost problem of such problems, but also considers the gravity center shift condition caused by uneven distribution of the cargo load, and the gravity center shift causes the cargo to fall off if being light, and causes the vehicle to roll over if being heavy, thereby affecting the transportation safety.
In conclusion, the intelligent matching system for the vehicle path and the three-dimensional boxed vehicle and goods is considered, so that the operation cost is reduced, and the transportation safety is ensured.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (8)

1. A vehicle and goods matching method considering a vehicle path and three-dimensional boxing is characterized by comprising the following steps:
1) generating vehicle and goods matching results according to the number of vehicles, the number of goods owners and the number of goods of the goods owners and initializing; randomly generating a plurality of vehicle and cargo matching results, wherein one part of the vehicle and cargo matching results keeps randomness, and the integer parts of real numbers corresponding to the same cargo in the remaining vehicle and cargo matching results are corrected to be the same values;
the vehicle and goods matching result is obtained by combining and coding real numbers with two decimal numbers randomly, wherein the integer part of the real numbers represents the serial number of the goods matched vehicle, and the numerical values of the decimal parts are sorted from large to small into the goods sequence of the vehicle and the goods owner sequence of the vehicle access;
2) setting the maximum iteration times and the current iteration algebra to be 1, initializing the historical optimal matching result and the fitness value thereof, setting the maximum iteration speed and the current iteration speed to be 0, initializing the learning factor and the inertia weight range, and starting iteration;
3) judging whether the current iteration algebra reaches the maximum iteration times;
if the maximum iteration times are reached, jumping to the step 8);
otherwise, jumping to the step 4);
4) updating the vehicle and goods matching result;
5) calculating the fitness value of the updated vehicle and goods matching result;
the fitness function is: a performance index function of vehicle operation cost, a performance index function of vehicle no-load cost, a performance index function of vehicle gravity center offset cost and a performance index function of penalty cost;
the operating cost of the vehicle comprises a vehicle freight driving cost;
the vehicle no-load cost comprises the weight reduction cost of the mass no-load rate and the weight reduction cost of the space no-load rate;
the vehicle center of gravity offset cost comprises a weighted reduction cost of the distance between the center of gravity of the loaded vehicle and the center of gravity of the unloaded vehicle;
the vehicle penalty cost comprises the weighted cost of loading different vehicles by the same cargo owner;
6) comparing the fitness values of all the vehicle and cargo matching results, setting the lowest fitness value in all the current vehicle and cargo matching results as the current optimal matching result, updating each historical optimal matching result, and updating the evolution direction and speed of the matching result according to the historical optimal vehicle and cargo matching result, the learning factor and inertia weight range initialized in the step 1) and the current iteration algebra;
7) judging whether the result with the minimum fitness value in the current vehicle-cargo matching result meets a privilege condition or not;
if yes, setting the current particle as a historical optimal matching result, updating a taboo table, and jumping to the step 8);
otherwise, taking the suboptimal matching result as a historical optimal matching result to jump out local optimality, updating a taboo table and jumping to the step 3);
the taboo table records the vehicle and goods matching result which is repeatedly iterated for a plurality of times to become the historical optimal matching result, and stores the taboo algebra of the result, so as to prevent all the vehicle and goods matching results from being repeatedly used as the historical optimal matching result and continuously approaching to the result to fall into the local optimal matching; if the historical optimal matching results after a plurality of continuous iterations cannot exceed the taboo matching results or the forbidden algebra reaches the preset maximum value, releasing the results to have an opportunity to become the historical optimal matching results;
8) and outputting a global approximately optimal vehicle and goods matching result.
2. The vehicle-cargo matching method considering the vehicle path and the three-dimensional container as claimed in claim 1, wherein the vehicle-cargo matching result of step 1) is real number encoded, and is specifically operated as follows:
and randomly generating real numbers with two decimal numbers, wherein the number of the real numbers is equal to the total number of the cargos, and the value range of the real numbers is 1 to (the number of the vehicles is + 1).
3. The vehicle-cargo matching method considering vehicle path and three-dimensional boxing according to claim 2, wherein the specific operations of decoding in step 1) are as follows:
the integer part of the code is a vehicle serial number matched with the goods, and a matching result of the vehicle, the goods owner and the goods is obtained;
sorting the code values corresponding to the cargos matched with the vehicles from large to small to obtain the loading sequence of the cargos matched with the vehicles;
dividing the code into a plurality of sections according to the number of the goods held by each goods owner, wherein each section corresponds to all goods of one goods owner, and sequencing the maximum values of the corresponding code values in the goods of the goods owners matched with the vehicles from large to small to obtain the order of the vehicle visiting the goods owners, namely the vehicle driving path.
4. The vehicle and cargo matching method considering the vehicle path and the three-dimensional boxing as claimed in claim 1, wherein the step 4) of updating the vehicle and cargo matching result specifically comprises the following steps:
xid(t+1)＝xid(t)+vid(t+1) (1)
wherein x is a real numerical value corresponding to the goods in the vehicle and goods matching result, t is the iteration times of the previous generation, and v is the evolution speed of the vehicle and goods matching result.
5. The method as claimed in claim 1, wherein in step 5), the fitness function for calculating the fitness value of the updated vehicle-cargo matching result is as follows:
Figure FDA0002599418910000031
n is the number of goods owners, M is the number of vehicles, pp is the total number of goods, mm is the mass of goods, X is the matching result of the vehicles and the goods, E is the cost of driving a unit distance every time a unit mass of the vehicles is added, F is the cost of driving a unit distance when the vehicles are in no-load, S is the driving distance of the vehicles, I is the result of whether the vehicles are used, M is the maximum load capacity of the vehicles, V is the volume of a carriage, V is the volume of the goods, T is the converted cost of the no-load rate of the vehicles, Y is the matching result of the goods owners of the vehicles, H is the converted cost of the gravity center offset distance of the vehicles, and R is the punish;
the vehicle travel distance S in equation (2) is obtained by solving equation (3):
Figure FDA0002599418910000041
wherein Z is the matching result of the vehicle and the successively arriving owner, D is the distance between the owner and the owner, and between the owner and the owner destination, i represents the vehicle, j represents the owner, and k represents the goods.
6. The vehicle-cargo matching method considering vehicle path and three-dimensional boxing according to claim 5, wherein the fitness function comprises a performance index function A of the operation cost of the vehicle:
Figure FDA0002599418910000042
in the formula, i represents a vehicle, j represents a cargo owner, k represents cargo, n represents the quantity of the cargo owner, m represents the quantity of the vehicle, pp represents the total quantity of the cargo, mm represents the mass of the cargo, X represents a matching result of the vehicle and the cargo, E represents the cost of the vehicle for driving a unit distance when the vehicle is added with a unit mass, F represents the cost of the vehicle for driving a unit distance in an idle state, S represents the driving distance of the vehicle, Z represents the matching result of the vehicle and the cargo owner which arrive successively, D represents the distance between the cargo owner and the cargo owner, and the distance between the cargo owner and the cargo owner destination, and A represents the operation cost of the vehicle;
the fitness function comprises a performance index function B of the unloaded cost of the vehicle:
Figure FDA0002599418910000043
in the formula, I represents a vehicle, k represents goods, M represents the number of the vehicles, pp represents the total number of the goods, mm represents the mass of the goods, X represents the matching result of the vehicles and the goods, I represents the using result of the vehicles, M represents the maximum load capacity of the vehicles, V represents the volume of a compartment, V represents the volume of the goods, T represents the conversion cost of the no-load rate of the vehicles, and B represents the no-load cost of the vehicles;
the fitness function comprises a performance index function C of punishment cost of loading different vehicles on the same cargo:
Figure FDA0002599418910000051
in the formula, i represents a vehicle, j represents a cargo owner, n represents the cargo owner number, m represents the vehicle number, Y represents the vehicle cargo owner matching result, R represents the punishment coefficient of different vehicles loaded by the same cargo owner, and C represents the punishment cost of different vehicles loaded by the same cargo owner;
the fitness function includes the center of gravity offset potential safety hazard cost D of the vehicle:
Figure FDA0002599418910000052
in the formula, i represents a vehicle, k represents goods, m represents the number of vehicles, pp represents the total number of the goods, mm represents the quality of the goods, X represents the matching result of the vehicles and the goods, (X, y, z) represents the barycentric coordinates of the goods, (xc, yc, zc) represents the barycentric coordinates of a carriage, H represents the folding cost of the barycentric offset distance of the vehicle, and D represents the potential safety hazard cost of the barycentric offset of the vehicle.
7. The vehicle and cargo matching method considering the vehicle path and the three-dimensional boxing as claimed in claim 5, wherein the step 6) of updating the evolution speed of the vehicle and cargo matching result is specifically as follows:
vid(t+1)＝ωvid(t)+c1r1[Pid(t)-xid(t)]+c2r2[PGd(t)-xid(t)](4)
Figure FDA0002599418910000053
wherein: iteriFor the current iteration number, iter is the maximum iteration number, c1、c2As a learning factor, ω is an inertial weight, r1、r2The random number is 0-1, Pid (t) is the optimal matching result of each history, and PGd (t) is the optimal matching result of the history of the particle swarm.
8. The vehicle-cargo matching method considering vehicle path and three-dimensional boxing as claimed in claim 1, wherein the operation of updating the tabu table in step 7) is:
and setting the corresponding vehicle and goods matching position of the optimal particles in the taboo table as the taboo length, and subtracting 1 from the value of the rest positions of the taboo table.
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