CN115345549B - Vehicle path adjustment method and system combined with loading scheme - Google Patents
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Abstract
The invention provides a vehicle path adjusting method and system combined with a loading scheme, and relates to the technical field of vehicle loading and path optimization. The processing method comprises the steps of establishing sequence information, and establishing a cargo sequence, a vehicle type sequence and a provider node sequence based on cargo information, vehicle type information and provider node information; combining a cargo sequence to be loaded, carriage information corresponding to a vehicle type and preset constraint conditions to generate a path scheme; and (3) optimizing a path scheme, setting a cargo sequence and a vehicle type sequence as chromosome integer codes, obtaining a new cargo sequence and a new vehicle type sequence through genetic operation, generating a new chromosome, and evaluating the advantages and disadvantages of the new chromosome by using a fitness function, so that a Pareto solution is selected by comparing the fitness function values. According to the invention, the optimal solution for the combination optimization of the cargo loading and the vehicle path is found by simulating the natural evolution process, and the optimal combination scheme of the cargo loading and the vehicle path is efficiently solved when the logistics of the automobile parts enter the factory are actually operated.
Description
Technical Field
The invention relates to the technical field of vehicle loading and path optimization, in particular to a vehicle path adjusting method combining a loading scheme.
Background
The problem of cyclic goods taking in the logistics of automobile parts entering factories is always one of the main problems studied in the logistics industry, and mainly relates to the problem of combined optimization of goods loading and vehicle paths.
In actual operation, a provider is accessed by multiple times every day, the requirements can be split and then transported, and vehicles of multiple types execute transportation tasks and define unloading time windows. Meanwhile, considering that a certain number of suppliers are distributed throughout the geographic space, the distribution center (or a logistics company responsible for transportation management) organizes proper driving paths every day according to the production plan of an automobile manufacturer, parts with different numbers, different sizes and different unloading time limits are collected from each supplier, transportation tasks are executed by vehicles of different vehicle types, and the parts are sent to the distribution center for unloading, so that the parts inventory of the distribution center can continuously meet the production plan of the automobile manufacturer. Meanwhile, the purposes of minimum total transportation cost and minimum total transportation time can be achieved under certain constraint, namely vehicle loading constraint, detachable supply requirements of single suppliers, limitation of vehicle types and quantity and the like.
Based on this, assumption conditions are set for both the cargo loading and the vehicle path, and four assumption conditions need to be satisfied for the cargo loading: 1) The carriage and the goods to be loaded are rectangular; 2) The goods placed in must be contained entirely within the compartment; 3) Goods can only be placed with the edges parallel or perpendicular to the edges of the car; 4) The goods are required to rotate only around the height edges and cannot be placed in a toppling manner. Five hypothetical conditions are required to be met for the vehicle path: 1) Each path must start from the distribution center and finally return to the distribution center; 2) Each provider may be accessed more than once by the path; 3) The goods loaded in each path should meet the three-dimensional restrictions of the carriage; 4) Each path can only be serviced by one vehicle; 5) All the goods have paths to pick up and deliver.
Besides meeting the above limiting rules, the resource optimization of links such as vehicle transportation, distribution and the like is realized by taking the minimum total cost of inventory and transportation as the goal in the process of solving the problem of circularly taking the automobile parts into the factory logistics.
Therefore, the vehicle path adjusting method and system combined with the loading scheme are provided, and the optimal combined optimization scheme of the vehicle loading and the vehicle path is obtained by considering various limiting rules in the cargo transportation process from the actual demand of daily operation of transportation enterprises, so that the technical problem to be solved is currently needed.
Disclosure of Invention
The invention aims at: the invention can find the optimal solution of the combination optimization of the cargo loading and the vehicle path through simulating the natural evolution process, thereby efficiently solving the optimal combination scheme of the cargo loading and the vehicle path when the automobile parts enter the factory for physical distribution to be actually operated.
In order to solve the existing technical problems, the invention provides the following technical scheme:
a vehicle path adjustment method incorporating a loading scheme, comprising the steps of:
establishing sequence information: based on the cargo information, the vehicle type information and the provider node information, establishing a cargo sequence I_list, a vehicle type sequence K_list and a provider node sequence S_list, wherein the provider node sequence in the provider node sequence S_list is matched with the arrangement of the position information of the cargo in the cargo sequence I_list;
generating a path scheme: combining a cargo sequence to be loaded, carriage information corresponding to a vehicle type and preset constraint conditions to generate a path scheme, wherein the path scheme comprises loading schemes corresponding to all paths; calculating an adaptability function value of the path scheme based on a preset adaptability function, wherein the adaptability function comprises a total transportation cost R and a total transportation time R of the path scheme;
optimizing a path scheme: and (3) carrying out chromosome integer coding on the cargo sequence I_list and the vehicle type sequence K_list, obtaining a new cargo sequence and a new vehicle type sequence through genetic operation, and evaluating the merits of the new chromosome by utilizing a fitness function after generating the new chromosome, so that a Pareto solution is selected by comparing the fitness function values to determine a Pareto optimal solution set and a corresponding path scheme.
Further, the path scheme comprises a sequence of suppliers on the path, a loading scheme and a transportation time t i Total cost of transportation costR and total time of transportation timeR information.
Further, the step of generating a path plan includes: step S201, initializing il=1 and ik=1, wherein iL represents the iL-th cargo and iK represents the iK-th vehicle type; step S202, if iK is less than or equal to length (K_list), taking out the iK vehicle type from the vehicle type sequence K_list, otherwise, taking out the length (K_list) vehicle type from the vehicle type sequence K_list, and obtainingCarriage volume CV; step S203, the iL-th cargo is taken out from the cargo sequence I_list and added into the cargo sequence L_list to be loaded, when the sum of the cargo volumes in the cargo sequence to be loaded is larger than the carriage volume CV, step S204 is executed, otherwise, step S205 is executed; step S204, taking the carriage information corresponding to the sequence of cargos to be loaded and the vehicle type as parameters, calling a preset simulated annealing algorithm to generate a loading scheme, arranging the corresponding supplier node sequence S_list according to the position sequence of the cargos loaded in the sequence I_list, removing repeated supplier nodes, adding an upper distribution center at the beginning node and the ending node of the supplier node sequence S_list to generate a path, storing the path into the path scheme, and calculating the earliest delivery time window of the cargos loaded in the path and whether each cargos is delayed to alpha or not i Finally, deleting the loaded cargoes from the cargoes sequence to be loaded, and setting ik=ik+1; step S205, when iL is less than or equal to length (i_list), setting il=il+1, and executing step S203; if the sequence L_list of the goods to be loaded is empty, the step S206 is entered, otherwise, the step S204 is returned to; step S206, outputting a path scheme, wherein for any i E R in the path scheme, each R i Information of the route including the supplier sequence, packing scheme, and transportation time t on the route i Whether the loaded goods are late to alpha i The total cost of transportation costR and the total time of transportation timeR for the path scheme.
Further, the alpha i Is 0-1 variable, alpha i =1 indicates that the cargo I, I e I has been loaded on the car, α i =0 indicates not loaded on the vehicle.
Further, the chromosome comprises a cargo fragment of the first half and a vehicle-shaped fragment of the second half, wherein the cargo fragment takes a part packaging container as a unit, a box is taken as a gene, and when cargoes are combined into a tray, the latter tray is taken as a box.
Further, the optimized path scheme includes the steps of: step S301, generating an initial population of goods and vehicle types; step S302, calculating fitness function values of all chromosomes in an initial population to obtain initial Pareto solutions of all the chromosomes and corresponding path schemes, and writing the initial Pareto solutions into a preset Pareto optimal solution set; step S303, carrying out genetic operation on each chromosome in the initial population to generate a goods and vehicle type offspring population; calculating fitness function values of all chromosomes in the offspring population to obtain a new Pareto solution and a path scheme corresponding to the new Pareto solution, and writing a Pareto optimal solution set; step S304, judging whether the preset termination condition is met, and if not, returning to step S302.
Further, after judging whether the preset termination condition is satisfied, step S305 is further included, and if yes, the Pareto optimal solution set and the corresponding path scheme are output.
Further, decoding all Pareto solutions in the Pareto optimal solution set to verify the corresponding path scheme, wherein the decoding comprises the following steps: step S3051, segmenting a vehicle model segment and a cargo segment: when cutting vehicle type fragments, only cutting one vehicle type at a time according to the sequence from beginning to end, and obtaining the carriage volume of the vehicle type; when the goods fragments are segmented, the goods fragments are segmented one by one in sequence from the beginning to the end until the sum of the segmented goods volumes exceeds the carriage volume of the vehicle type, or the goods fragments are segmented completely; step S3052, carrying out loading verification, and verifying the segmented vehicle type and cargoes by using a preset vehicle loading algorithm to generate a loading scheme; judging whether the checked goods are not loaded, if yes, putting the unloaded goods back into the goods fragments, and circularly processing the steps S3051 and S3052 until all the goods are loaded into the corresponding vehicles; in step S3053, a vehicle path is generated, a path plan is generated according to the loaded goods of each vehicle, and the window time of earliest unloading, the total transportation cost and the total transportation time of each path are calculated.
Further, the genetic manipulation includes selection, crossover and mutation manipulations; the genetic operation is to independently perform selection operation, crossover operation and mutation operation on the goods fragments and the vehicle type fragments on the chromosome respectively; wherein, the selecting operation adopts elite retention strategy; the crossover operation adopts an Inver-over method; the mutation operation adopts a single-gene replacement method, and two genes in a chromosome are randomly selected for position exchange.
A vehicle path adjustment system incorporating a loading scheme, comprising:
the information input module is used for acquiring cargo information, vehicle type information, provider node information and preset constraint conditions;
the sequence construction module is used for establishing a cargo sequence I_list, a vehicle type sequence K_list and a provider node sequence S_list based on cargo information, vehicle type information and provider node information, wherein the provider node sequence in the provider node sequence S_list is matched with the arrangement of the position information of the cargo in the cargo sequence I_list;
the scheme generation module is used for generating a path scheme by combining a cargo sequence to be loaded, carriage information corresponding to a vehicle type and preset constraint conditions, wherein the path scheme comprises loading schemes corresponding to all paths; calculating an adaptability function value of the path scheme based on a preset adaptability function, wherein the adaptability function comprises a total transportation cost R and a total transportation time R of the path scheme;
the scheme optimizing module is used for carrying out chromosome integer coding on the cargo sequence I_list and the vehicle type sequence K_list, obtaining a new cargo sequence and a new vehicle type sequence through genetic operation, and evaluating the advantages and disadvantages of the new chromosome by utilizing a fitness function after generating the new chromosome, so that a Pareto solution is selected by comparing the fitness function values to determine a Pareto optimal solution set and a corresponding path scheme.
Based on the advantages and positive effects, the invention has the following advantages: under the condition that the total transportation cost is minimum, the total transportation time is minimum and the constraint conditions of the cargo loading and the vehicle path are considered, the optimal solution of the cargo loading and the vehicle path combination optimization is found through simulating the natural evolution process, so that the optimal combination scheme of the cargo loading and the vehicle path when the automobile parts enter the factory to be operated in actual logistics is solved efficiently.
Further, for the goods fragments and the vehicle type fragments on the chromosome, the selection, crossing and mutation operations are respectively and independently carried out, and the vehicle type distribution and the demand splitting are optimized through genetic operation, so that the time for the result obtained by adopting the method is short, and the accuracy is high.
Drawings
Fig. 1 is a flowchart of a method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of dividing a vehicle path according to an embodiment of the present invention.
Fig. 3 is a flowchart of an optimized path scheme provided in an embodiment of the present invention.
FIG. 4 is a flowchart of genetic operations provided by an embodiment of the present invention.
Fig. 5 is an exemplary diagram of crossover operations in genetic operations provided by an embodiment of the present invention.
Fig. 6 is an exemplary diagram of a mutation operation in genetic manipulation according to an embodiment of the present invention.
Fig. 7 is an exemplary diagram of a codec process according to an embodiment of the present invention.
Fig. 8 is a schematic structural diagram of a system according to an embodiment of the present invention.
Reference numerals illustrate:
the system 100, the information input module 110, the sequence construction module 120, the scheme generation module 130 and the scheme optimization module 140.
Detailed Description
The following describes in further detail a vehicle path adjustment method and system in connection with a loading scheme in accordance with the present invention, with reference to the accompanying drawings and detailed description. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be regarded as being isolated, and they may be combined with each other to achieve a better technical effect. In the drawings of the embodiments described below, like reference numerals appearing in the various drawings represent like features or components and are applicable to the various embodiments. Thus, once an item is defined in one drawing, no further discussion thereof is required in subsequent drawings.
It should be noted that the structures, proportions, sizes, etc. shown in the drawings are merely used in conjunction with the disclosure of the present specification, and are not intended to limit the applicable scope of the present invention, but rather to limit the scope of the present invention. The scope of the preferred embodiments of the present invention includes additional implementations in which functions may be performed out of the order described or discussed, including in a substantially simultaneous manner or in an order that is reverse, depending on the function involved, as would be understood by those of skill in the art to which embodiments of the present invention pertain.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but should be considered part of the specification where appropriate. In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of the exemplary embodiments may have different values.
In the invention, the technical term definitions of the set, parameters, decision variables and the like required by the solving method of the combined optimization problem of the cargo loading and the vehicle path are expressed as follows:
1) Aggregation
I= {1, …, n }: the collection of goods to be loaded comprises n goods to be loaded.
S= {0,1, …, m }: node set, wherein 0 is the distribution center node, 1, …, m is the parts supplier.
K= {1, …, K }: and (5) vehicle model collection.
R= {1, …, R }: and outputting a result path unit set, wherein r result paths are output.
2) Parameter setting
The parameter settings of the mathematical model are shown in the following table:
3) Decision variables
For the combined optimization problem of cargo loading and vehicle path, the decision variables of the mathematical model have v ir 、α rl 、w rk And u ijr The definition corresponding to the decision variable is shown in the following table:
examples
Referring to fig. 1, there is provided a vehicle path adjustment method in combination with a loading scheme, including the steps of:
step S100, establishing sequence information: and establishing a cargo sequence I_list, a vehicle type sequence K_list and a provider node sequence S_list based on the cargo information, the vehicle type information and the provider node information, wherein the provider node sequence in the provider node sequence S_list is matched with the arrangement of the position information of the cargo in the cargo sequence I_list.
Step S200, generating a path scheme: combining a cargo sequence to be loaded, carriage information corresponding to a vehicle type and preset constraint conditions to generate a path scheme, wherein the path scheme comprises loading schemes corresponding to all paths; and calculating the fitness function value of the path scheme based on a preset fitness function, wherein the fitness function comprises the total transportation cost R and the total transportation time R of the path scheme.
Wherein the total cost of transportation is expressed as:
the total time of transportation is expressed as:
in this embodiment, considering the distribution of the vehicle types and the splitting of the corresponding demands in the recycling process of the vehicle parts into the factory logistics, one of the solving targets of the combined optimization problem of the vehicle loading and the vehicle path is set to be the lowest cost of transportation costR, namely
In this formula, the first term is the sum of the cost of each route in transit and the cost of the truck, and the second term is the penalty cost of picking up goods ahead of time.
At the same time, the second solving target is set to be the minimum transportation total time timeR, namelyWherein the total time of transportation is the sum of the time of transportation in transit and the time of loading and unloading at the node.
For the two solving targets, when solving the combined optimization problem of the cargo loading and the vehicle path, constraint conditions for the vehicle loading and the vehicle path, namely, the overall preset conditions in the embodiment, are correspondingly set so as to meet the minimum cost of transportation and the minimum time of transportation under the condition of a plurality of constraint conditions.
The above solution targets require that the vehicle loading constraints be met while the vehicle path constraints are also met.
The vehicle loading constraints are classified into general constraints and special constraints. Wherein the general constraints comprise volume constraints, no embedded constraints among loaded cargoes, and no embedded constraints among loaded cargoes and carriages; the special constraints include full support constraints, single box load bearing constraints, cargo stacking constraints, vehicle load bearing constraints, and center of gravity constraints. Since the contents of the above vehicle loading constraints are all prior art, detailed development is not performed in the present embodiment.
The mathematical expression corresponding to the vehicle path constraint is specifically as follows:
a) A supplier node being accessed by at least one pick-up path, i.e
b) The suppliers have all goods taken paths for transportation, i.e
c) The suppliers all goods need to be delivered to the delivery center for unloading, i.e
d) One pick-up path having only one vehicle service, i.e.
e) A supplier node being accessed by at least one pick-up path, i.e
f) The suppliers all have a pick-up path for accessing, i.e
g) The difference between the access times of two nodes on one path arc being equal to the transit time between the two points, i.e
h) A pick-up path to be serviced by only one vehicle, i.e.
Specifically, the step of generating a path scheme includes:
in step S201, il=1 and ik=1 are initialized, where iL represents the iL-th cargo and iK represents the iK-th vehicle model.
Step S202, if iK is less than or equal to length (K_list), the iK vehicle type is taken out from the vehicle type sequence K_list, otherwise, the length (K_list) vehicle type is taken out from the vehicle type sequence K_list, and the carriage volume CV is obtained.
Step S203, the iL-th cargo is taken out from the cargo sequence I_list, and added into the cargo sequence L_list, when the sum of the cargo volumes in the cargo sequence is larger than the carriage volume CV, step S204 is executed, otherwise, step S205 is executed.
Step S204, taking the carriage information corresponding to the sequence of cargos to be loaded and the vehicle type as parameters, calling a preset simulated annealing algorithm to generate a loading scheme, arranging the corresponding supplier node sequence S_list according to the position sequence of the cargos loaded in the sequence I_list, removing repeated supplier nodes, adding an upper distribution center at the beginning node and the ending node of the supplier node sequence S_list to generate a path, storing the path into the path scheme, and calculating the earliest delivery time window of the cargos loaded in the path and whether each cargos is delayed to alpha or not i And finally deleting the loaded cargoes from the cargoes sequence to be loaded, and setting ik=ik+1.
In the case of generating the loading scheme, it is preferable to generate a corresponding loading scheme by using a simulated annealing algorithm, wherein the simulated annealing algorithm randomly adjusts the cargo loading sequence to generate a new loading sequence through a simulated annealing process, so that different loading sequences correspond to different loading rates, and the optimal loading scheme is selected by comparing the loading rates.
In addition, it should be noted that, in generating the route, a case where each provider allows access a plurality of times per day is considered, in which the same provider may be provided with a plurality of access pickup routes, and a case where the cumulative daily supply amount of each provider may exceed the capacity of one vehicle and the parts may be supplied per number of times of the vehicle, but the transportation of all the supplied parts must be completed on the same day, and the like is considered.
Meanwhile, the situation that suppliers are distributed throughout the geographic space needs to be considered, so that a distribution center (or a logistics company responsible for transportation management) needs to design a proper driving path every day according to the production plan of an automobile manufacturer, so that different numbers of parts with different sizes and different unloading time limits are collected from each supplier, transportation tasks are carried out through vehicles of different vehicle types, and the parts are sent to the distribution center for unloading, so that the parts inventory of the distribution center can continuously meet the production plan of the automobile manufacturer.
By way of example and not limitation, referring to fig. 2, two transport paths are generated corresponding to transport tasks performed by different vehicle models, such as: the route A is that a 12-meter vehicle starts from a distribution center at 6 points on 1 day, firstly runs to a supplier 1 to pick up goods, then runs to a supplier 2 to pick up goods, and finally is sent to the distribution center to be unloaded; and the path B is that an 8-meter vehicle starts from a distribution center at 12 points on 1 day, firstly runs to a supplier 2 to pick up goods, then runs to a supplier 3 to pick up goods, and finally runs to the distribution center to unload goods.
Step S205, when iL is less than or equal to length (i_list), setting il=il+1, and executing step S203; if the sequence l_list of goods to be loaded is empty, the process proceeds to step S206, otherwise, the process returns to step S204.
Step S206, outputting a path scheme, wherein for any i E R in the path scheme, each R i Information of the route including the supplier sequence, packing scheme, and transportation time t on the route i Whether the loaded goods are late to alpha i The total cost of transportation costR and the total time of transportation timeR for the path scheme.
Wherein said alpha i Is 0-1 variable, alpha i =1 indicates that the cargo I, I e I has been loaded on the car, α i =0 indicates not loaded on the vehicle.
Step S300, optimizing a path scheme: and (3) carrying out chromosome integer coding on the cargo sequence I_list and the vehicle type sequence K_list, obtaining a new cargo sequence and a new vehicle type sequence through genetic operation, and evaluating the merits of the new chromosome by utilizing a fitness function after generating the new chromosome, so that a Pareto solution is selected by comparing the fitness function values to determine a Pareto optimal solution set and a corresponding path scheme.
The chromosome comprises a cargo fragment in the first half and a vehicle fragment in the second half.
Wherein, the goods fragments take the parts packaging container as a unit, and a box of goods is taken as a gene. When the goods are combined into the trays, the combined tray is used as a box of goods.
Specifically, referring to fig. 3, the optimized path scheme includes step 300:
in step 300, the initial population, the population size N, the iteration number T, the crossover probability PC, and the mutation probability PM parameters are referred to.
Step S301, generating an initial population of goods and vehicle types.
Step S302, calculating fitness function values of all chromosomes in the initial population, obtaining initial Pareto solutions of all the chromosomes and corresponding path schemes, and writing the initial Pareto solutions into a preset Pareto optimal solution set.
In the solution process for the minimum total cost of transportation and minimum total time of transportation for the target, there are multiple solutions, and each chromosome corresponds to one target solution, namely Pareto solution, which enables the combination of the solutions to obtain the optimal Pareto solution set.
Step S303, carrying out genetic operation on each chromosome in the initial population to generate a goods and vehicle type offspring population; and calculating fitness function values of all chromosomes in the offspring population, obtaining a new Pareto solution and a path scheme corresponding to the new Pareto solution, and writing the new Pareto solution into a Pareto optimal solution set.
Referring to FIG. 4, the genetic manipulation includes selection, crossover and mutation manipulation; the genetic operation is to independently select, cross and mutate the goods segment and the model segment on the chromosome.
Wherein, the selecting operation adopts elite retention strategy; the crossover operation adopts an Inver-over method; the mutation operation adopts a single-gene replacement method, and two genes in a chromosome are randomly selected for position exchange.
By way of example and not limitation, referring to FIG. 5, in a crossover operation, two parent chromosomes are randomly selected: p1 and P2 chromosomes, randomly selecting a gene G1 from the chromosome P1, searching a position G1 'where the gene G1 is located in the chromosome P2, selecting a first gene G2 after the gene G1' is selected, selecting the first gene in the chromosome P2 as the G2 if the gene G1 'is the last gene in the chromosome P2, searching a position G2' of the G2 in the chromosome P1, and carrying out turnover transformation on the G1 and a part between the G1 and the G2', wherein the position of the G2' is kept still. Similarly, chromosome P2 was subjected to crossover treatment. The cross operation adopts an over-over method and has the characteristics of high convergence speed and high precision.
The mutation operation aims at randomly generating a probability value for each pair of chromosomes according to 98 chromosomes obtained through the crossover operation.
If the probability value of the chromosome is smaller than the variation probability initially defined by the algorithm, the chromosome is taken as a parent chromosome, variation operation is carried out to generate a child chromosome, and if the two fitness function values of the child chromosome are better than the two fitness function values of the parent chromosome, the child chromosome is used for replacing the parent chromosome; if the two fitness function values of the offspring chromosomes are worse than the two fitness function values of the parent chromosomes, the parent chromosomes continue to be preserved.
The mutation operation preferably adopts a single-gene replacement method, and the mutation process of the single-gene replacement method is to randomly select two genes in a chromosome for position exchange.
For example, referring to FIG. 6, assuming a parent chromosome P, a gene position G1 is randomly selected from the chromosome P, G2 at a position different from G1 is searched for in the chromosome P, and genes at the G1 and G2 positions in the chromosome P are exchanged.
It is emphasized that in the crossover operation and the mutation operation, the crossover probability value or the mutation probability value between the chromosomes may be the same or may be different.
Step S304, judging whether the preset termination condition is met, and if not, returning to step S302.
In step S305, if yes, a Pareto optimal solution set and a corresponding path scheme are output.
The path scheme comprises a supplier sequence, a loading scheme and a transportation time t on each path i Total cost of transportation costR and total time of transportation timeR information.
Preferably, decoding each Pareto solution in the Pareto optimal solution set to verify a corresponding path solution, where the decoding includes the steps of:
step S3051, segmenting a vehicle model segment and a cargo segment: when cutting vehicle type fragments, only cutting one vehicle type at a time according to the sequence from beginning to end, and obtaining the carriage volume of the vehicle type; when the goods fragments are segmented, the goods fragments are segmented one by one in sequence from the beginning to the end until the sum of the segmented goods volumes exceeds the carriage volume of the vehicle type, or the goods fragments are segmented completely.
Step S3052, carrying out loading verification, and verifying the segmented vehicle type and cargoes by using a preset vehicle loading algorithm to generate a loading scheme; and judging whether the checked goods are not loaded, if so, putting the unloaded goods back into the goods fragments, and circularly processing the steps S3051 and S3052 until all the goods are loaded into the corresponding vehicles.
The vehicle loading algorithm is a method for randomly adjusting a cargo loading sequence to generate a new loading sequence by using a simulated annealing process, enabling different loading sequences to correspond to different loading rates, and selecting an optimal loading scheme by comparing the loading rates so as to obtain the loading scheme.
In step S3053, a vehicle path is generated, a path plan is generated according to the loaded goods of each vehicle, and the window time of earliest unloading, the total transportation cost and the total transportation time of each path are calculated.
By way of example and not limitation, referring to FIG. 7, the chromosomes are organized based on integer codes, and after the codes, cargo+model chromosomes are obtained. In the cargo fragment coding, the number of vehicles responsible for the transportation task, the corresponding vehicle type length and the cargo number are expressed by specific integers, and each cargo box has corresponding loading basic information, wherein the loading basic information comprises supplier information, unloading time window, cargo size, cargo weight, cargo stacking rule and the like.
Preferably, in the cargo fragment code, 9 boxes of cargos are represented by numbers 1 to 9, each box of cargos has corresponding loading base information such as supplier information, unloading time window, cargo size, cargo weight, cargo stacking rule and the like, wherein the 9 th, 6 th and 8 th boxes of cargos belong to supplier S1, 7 th, 5 th, 4 th and 1 st boxes of cargos belong to supplier S2, and the 3 rd and 2 nd boxes of cargos belong to supplier S3; in the vehicle type segment coding, 3 vehicles are preferably indicated by the numbers 11 to 13 and are responsible for transportation tasks, and the vehicle types are 8 meters, 12 meters and 8 meters respectively.
Then, the above 9 boxes of cargo information and 3 vehicle type information were determined to be 096870541320 and 0130120110 by the above decoding method.
Thus, it can be determined that there are 2 corresponding vehicle path schemes. Namely, an 8-meter vehicle is adopted in the path 1, the vehicle starts from a distribution center, runs to a supplier node S1 to take the 9 th, 6 th and 8 th cargoes, then runs to a supplier node S2 to take the 7 th cargoes, and then is sent to the distribution center for unloading operation; the route 2 adopts a 12-meter vehicle type, starts from a distribution center, runs to a supplier node S2 to take the 5 th, 4 th and 1 th cargoes, then runs to a supplier node S3 to take the 3 rd and 2 nd cargoes, and finally is sent to the distribution center for unloading.
Other technical features are referred to the previous embodiments and will not be described here again.
In addition, referring to fig. 8, an embodiment of a vehicle path adjustment system 100 that incorporates a loading scheme is also provided in the present invention, the system 100 including an information input module 110, a sequence construction module 120, a scheme generation module 130, and a scheme optimization module 140.
The information input module 110 is configured to collect cargo information, vehicle type information, provider node information, and preset constraint conditions.
The sequence construction module 120 establishes a cargo sequence i_list, a vehicle type sequence k_list, and a provider node sequence s_list based on cargo information, vehicle type information, and provider node information, wherein a provider node sequence in the provider node sequence s_list matches an arrangement of location information of cargoes in the cargo sequence i_list.
The scheme generating module 130 generates a path scheme by combining a to-be-loaded cargo sequence, carriage information corresponding to a vehicle type and preset constraint conditions, wherein the path scheme comprises loading schemes corresponding to all paths; and calculating the fitness function value of the path scheme based on a preset fitness function, wherein the fitness function comprises the total transportation cost R and the total transportation time R of the path scheme.
The scheme optimizing module 140 is configured to perform integer codes on the cargo sequence i_list and the vehicle type sequence k_list, obtain a new cargo sequence and a new vehicle type sequence through genetic operation, and evaluate the merits of the new chromosome by using a fitness function after generating the new chromosome, thereby selecting a Pareto solution by comparing the fitness function values, so as to determine a Pareto optimal solution set and a corresponding path scheme.
The system 100 may further include a user interface module to collect user input information and output information to the user. Preferably, to enable visualization, the user interface module includes a Graphical User Interface (GUI) for a user to view and analyze the results.
The system may also include other components typically found in computing systems, such as an operating system, a queue manager, a device driver, a database driver, or one or more network protocols, etc., stored in memory and executed by a processor.
Other technical features are referred to the previous embodiments and will not be described here again.
In the above description, the components may be selectively and operatively combined in any number within the scope of the present disclosure. In addition, terms like "comprising," "including," and "having" should be construed by default as inclusive or open-ended, rather than exclusive or closed-ended, unless expressly defined to the contrary. All technical, scientific, or other terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Common terms found in dictionaries should not be too idealized or too unrealistically interpreted in the context of the relevant technical document unless the present disclosure explicitly defines them as such.
Although the exemplary aspects of the present disclosure have been described for illustrative purposes, those skilled in the art will appreciate that the foregoing description is merely illustrative of preferred embodiments of the invention and is not intended to limit the scope of the invention in any way, including additional implementations in which functions may be performed out of the order of presentation or discussion. Any alterations and modifications of the present invention, which are made by those of ordinary skill in the art based on the above disclosure, are intended to be within the scope of the appended claims.
Claims (7)
1. A vehicle path adjustment method in combination with a loading scheme, comprising the steps of:
establishing sequence information: based on the cargo information, the vehicle type information and the provider node information, establishing a cargo sequence I_list, a vehicle type sequence K_list and a provider node sequence S_list, wherein the provider node sequence in the provider node sequence S_list is matched with the arrangement of the position information of the cargo in the cargo sequence I_list;
generating a path scheme: combining a cargo sequence to be loaded, carriage information corresponding to a vehicle type and preset constraint conditions to generate a path scheme, wherein the path scheme comprises loading schemes corresponding to all paths; calculating an adaptability function value of the path scheme based on a preset adaptability function, wherein the adaptability function comprises a total transportation cost R and a total transportation time R of the path scheme; wherein the step of generating the path scheme comprises: step S201, initializing il=1 and ik=1, wherein iL represents the iL-th cargo and iK represents the iK-th vehicle type; step S202, if iK is less than or equal to length (K_list), taking out the iK vehicle type from the vehicle type sequence K_list, otherwise, taking out the length (K_list) vehicle type from the vehicle type sequence K_list, and obtaining the carriage volume CV; in step S203 of the process of the present invention,taking out the iL-th cargo from the cargo sequence I_list, adding the iL-th cargo into the cargo sequence L_list to be loaded, executing the step S204 when the sum of the cargo volumes in the cargo sequence to be loaded is larger than the carriage volume CV, otherwise, executing the step S205; step S204, taking the carriage information corresponding to the sequence of cargos to be loaded and the vehicle type as parameters, calling a preset simulated annealing algorithm to generate a loading scheme, arranging the corresponding supplier node sequence S_list according to the position sequence of the cargos loaded in the sequence I_list, removing repeated supplier nodes, adding an upper distribution center at the beginning node and the ending node of the supplier node sequence S_list to generate a path, storing the path into the path scheme, and calculating the earliest delivery time window of the cargos loaded in the path and whether each cargos is delayed to alpha or not i Finally, deleting the loaded cargoes from the cargoes sequence to be loaded, and setting ik=ik+1; step S205, when iL is less than or equal to length (i_list), setting il=il+1, and executing step S203; if the sequence L_list of the goods to be loaded is empty, the step S206 is entered, otherwise, the step S204 is returned to; step S206, outputting a path scheme, wherein for any i E R in the path scheme, each R i Information of the route including the supplier sequence, packing scheme, and transportation time t on the route i Whether the loaded goods are late to alpha i The total cost of transportation costR and total time of transportation timeR for the path scheme; optimizing a path scheme: carrying out chromosome integer coding on the cargo sequence I_list and the vehicle type sequence K_list, obtaining a new cargo sequence and a new vehicle type sequence through genetic operation, and evaluating the merits of the new chromosome by using a fitness function after generating the new chromosome, so as to select a Pareto solution by comparing the fitness function values to determine a Pareto optimal solution set and a corresponding path scheme; the chromosome comprises a cargo fragment of a first half section and a vehicle-shaped fragment of a second half section, wherein the cargo fragment takes a part packaging container as a unit, a box is taken as a gene, and when cargoes are combined into a tray, the combined tray is taken as a box; the optimized path scheme comprises the following steps: step S301, generating an initial population of goods and vehicle types; step S302, calculating fitness function values of each chromosome in the initial population to obtain initial Pareto solutions of each chromosome and corresponding path schemes thereof, and writing in presetPareto optimal solution set; step S303, carrying out genetic operation on each chromosome in the initial population to generate a goods and vehicle type offspring population; calculating fitness function values of all chromosomes in the offspring population to obtain a new Pareto solution and a path scheme corresponding to the new Pareto solution, and writing a Pareto optimal solution set; step S304, judging whether the preset termination condition is met, and if not, returning to step S302.
2. The method of claim 1, wherein the path plan includes the sequence of suppliers on the path, a loading plan, and a transportation time t i Total cost of transportation costR and total time of transportation timeR information.
3. The method according to claim 1, wherein the α i Is 0-1 variable, alpha i =1 indicates that the cargo I, I e I has been loaded on the car, α i =0 indicates not loaded on the vehicle.
4. The method according to claim 1, further comprising step S305, after determining whether a preset termination condition is satisfied, of outputting a Pareto optimal solution set and a corresponding path scheme when the determination is yes.
5. The method according to any one of claims 1 or 4, wherein all Pareto solutions in the Pareto optimal solution set are decoded to verify the corresponding path scheme, the decoding comprising the steps of:
step S3051, segmenting a vehicle model segment and a cargo segment: when cutting vehicle type fragments, only cutting one vehicle type at a time according to the sequence from beginning to end, and obtaining the carriage volume of the vehicle type; when the goods fragments are segmented, the goods fragments are segmented one by one in sequence from the beginning to the end until the sum of the segmented goods volumes exceeds the carriage volume of the vehicle type, or the goods fragments are segmented completely;
step S3052, carrying out loading verification, and verifying the segmented vehicle type and cargoes by using a preset vehicle loading algorithm to generate a loading scheme; judging whether the checked goods are not loaded, if yes, putting the unloaded goods back into the goods fragments, and circularly processing the steps S3051 and S3052 until all the goods are loaded into the corresponding vehicles;
in step S3053, a vehicle path is generated, a path plan is generated according to the loaded goods of each vehicle, and the window time of earliest unloading, the total transportation cost and the total transportation time of each path are calculated.
6. The method of claim 1, wherein the genetic manipulation comprises selection, crossover and mutation manipulations; the genetic operation is to independently perform selection operation, crossover operation and mutation operation on the goods fragments and the vehicle type fragments on the chromosome respectively; wherein, the selecting operation adopts elite retention strategy; the crossover operation adopts an Inver-over method; the mutation operation adopts a single-gene replacement method, and two genes in a chromosome are randomly selected for position exchange.
7. A vehicle path adjustment system to implement the combined loading scheme of any one of claims 1-6, comprising:
the information input module is used for acquiring cargo information, vehicle type information, provider node information and preset constraint conditions;
the sequence construction module is used for establishing a cargo sequence I_list, a vehicle type sequence K_list and a provider node sequence S_list based on cargo information, vehicle type information and provider node information, wherein the provider node sequence in the provider node sequence S_list is matched with the arrangement of the position information of the cargo in the cargo sequence I_list;
the scheme generation module is used for generating a path scheme by combining a cargo sequence to be loaded, carriage information corresponding to a vehicle type and preset constraint conditions, wherein the path scheme comprises loading schemes corresponding to all paths; calculating an adaptability function value of the path scheme based on a preset adaptability function, wherein the adaptability function comprises a total transportation cost R and a total transportation time R of the path scheme; wherein the path scheme is generatedThe method comprises the following steps: step S201, initializing il=1 and ik=1, wherein iL represents the iL-th cargo and iK represents the iK-th vehicle type; step S202, if iK is less than or equal to length (K_list), taking out the iK vehicle type from the vehicle type sequence K_list, otherwise, taking out the length (K_list) vehicle type from the vehicle type sequence K_list, and obtaining the carriage volume CV; step S203, the iL-th cargo is taken out from the cargo sequence I_list and added into the cargo sequence L_list to be loaded, when the sum of the cargo volumes in the cargo sequence to be loaded is larger than the carriage volume CV, step S204 is executed, otherwise, step S205 is executed; step S204, taking the carriage information corresponding to the sequence of cargos to be loaded and the vehicle type as parameters, calling a preset simulated annealing algorithm to generate a loading scheme, arranging the corresponding supplier node sequence S_list according to the position sequence of the cargos loaded in the sequence I_list, removing repeated supplier nodes, adding an upper distribution center at the beginning node and the ending node of the supplier node sequence S_list to generate a path, storing the path into the path scheme, and calculating the earliest delivery time window of the cargos loaded in the path and whether each cargos is delayed to alpha or not i Finally, deleting the loaded cargoes from the cargoes sequence to be loaded, and setting ik=ik+1; step S205, when iL is less than or equal to length (i_list), setting il=il+1, and executing step S203; if the sequence L_list of the goods to be loaded is empty, the step S206 is entered, otherwise, the step S204 is returned to; step S206, outputting a path scheme, wherein for any i E R in the path scheme, each R i Information of the route including the supplier sequence, packing scheme, and transportation time t on the route i Whether the loaded goods are late to alpha i The total cost of transportation costR and total time of transportation timeR for the path scheme; the scheme optimizing module is used for carrying out chromosome integer coding on the cargo sequence I_list and the vehicle type sequence K_list, obtaining a new cargo sequence and a new vehicle type sequence through genetic operation, and evaluating the advantages and disadvantages of the new chromosome by utilizing a fitness function after generating the new chromosome, so that a Pareto solution is selected by comparing the fitness function values to determine a Pareto optimal solution set and a corresponding path scheme; the chromosome comprises a cargo fragment in the front half section and a vehicle fragment in the rear half section, wherein the cargo fragment takes a part packaging container as a unit and takes a box as a geneWhen the goods are combined into the trays, taking the combined tray as a box; the optimized path scheme comprises the following steps: step S301, generating an initial population of goods and vehicle types; step S302, calculating fitness function values of all chromosomes in an initial population to obtain initial Pareto solutions of all the chromosomes and corresponding path schemes, and writing the initial Pareto solutions into a preset Pareto optimal solution set; step S303, carrying out genetic operation on each chromosome in the initial population to generate a goods and vehicle type offspring population; calculating fitness function values of all chromosomes in the offspring population to obtain a new Pareto solution and a path scheme corresponding to the new Pareto solution, and writing a Pareto optimal solution set; step S304, judging whether the preset termination condition is met, and if not, returning to step S302.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108985677A (en) * | 2018-06-11 | 2018-12-11 | 华东理工大学 | The multiple batches of fresh agricultural products Distribution path optimization method of multi items |
| CN109002680A (en) * | 2018-10-28 | 2018-12-14 | 扬州大学 | A kind of multidisciplinary automatic optimizing design method of axial-flow pump impeller |
| CN109919541A (en) * | 2019-02-27 | 2019-06-21 | 华南理工大学 | A kind of model solution method of multistage positioning inventory routing problem |
| CN110442135A (en) * | 2019-08-06 | 2019-11-12 | 南京赛沃夫海洋科技有限公司 | A kind of unmanned boat paths planning method and system based on improved adaptive GA-IAGA |
| CN112884257A (en) * | 2021-04-29 | 2021-06-01 | 牧星机器人(江苏)有限公司 | Goods taking path optimization method, device and system based on genetic algorithm |
| CN112990528A (en) * | 2020-06-28 | 2021-06-18 | 青岛盈智科技有限公司 | Logistics transportation stowage management method and device |
| WO2021142917A1 (en) * | 2020-01-15 | 2021-07-22 | 深圳大学 | Multi-depot vehicle routing method, apparatus, computer device and storage medium |
| CN113222275A (en) * | 2021-05-26 | 2021-08-06 | 大连海事大学 | Vehicle path optimization method considering space-time distance under time-varying road network |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20220156693A1 (en) * | 2020-11-17 | 2022-05-19 | Exel Inc. d/b/a DHL Supply Chain (USA) | Computerized system and method for developing optimized cargo transportation solutions |
-
2022
- 2022-08-11 CN CN202210962960.0A patent/CN115345549B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108985677A (en) * | 2018-06-11 | 2018-12-11 | 华东理工大学 | The multiple batches of fresh agricultural products Distribution path optimization method of multi items |
| CN109002680A (en) * | 2018-10-28 | 2018-12-14 | 扬州大学 | A kind of multidisciplinary automatic optimizing design method of axial-flow pump impeller |
| CN109919541A (en) * | 2019-02-27 | 2019-06-21 | 华南理工大学 | A kind of model solution method of multistage positioning inventory routing problem |
| CN110442135A (en) * | 2019-08-06 | 2019-11-12 | 南京赛沃夫海洋科技有限公司 | A kind of unmanned boat paths planning method and system based on improved adaptive GA-IAGA |
| WO2021142917A1 (en) * | 2020-01-15 | 2021-07-22 | 深圳大学 | Multi-depot vehicle routing method, apparatus, computer device and storage medium |
| CN112990528A (en) * | 2020-06-28 | 2021-06-18 | 青岛盈智科技有限公司 | Logistics transportation stowage management method and device |
| CN112884257A (en) * | 2021-04-29 | 2021-06-01 | 牧星机器人(江苏)有限公司 | Goods taking path optimization method, device and system based on genetic algorithm |
| CN113222275A (en) * | 2021-05-26 | 2021-08-06 | 大连海事大学 | Vehicle path optimization method considering space-time distance under time-varying road network |
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