CN112070444A - Box type recommendation method and device and computer storage medium - Google Patents
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Abstract
The invention discloses a box type recommendation method, which comprises the following steps: acquiring an order list of which any article in the order meets the loading condition of the largest box type single box in the candidate box types; for each order in the order list, performing the steps of: acquiring the sizes of all articles in the order; predicting a minimum box shape capable of loading all the articles according to the sizes of all the articles; adopting a three-dimensional boxing algorithm to try to load all articles according to the minimum box type; when the minimum box type can be loaded with all the articles, the minimum box type is recommended; otherwise, the box number is increased to carry out loading attempt until the loading attempt for obtaining the recommended box type or all box numbers fails. The invention also discloses a device and a computer readable storage medium, which solve the problem of multi-box boxing in the prior art.
Description
Technical Field
The invention relates to the field of article boxing, in particular to a box type recommendation method, a device and a computer storage medium.
Background
With the rapid development of the logistics industry and the warehousing industry, more and more methods are involved for packing articles.
In the e-commerce warehousing operation process, the warehouse receives orders issued by clients, sorting of the orders and the articles is completed, the articles need to be boxed, and due to the fact that the difference of the composition, the length, the width, the height, the volume and other factors of the articles in each order is large, various box types can be used. The traditional article boxing method cannot well solve the problem of multi-box type boxing, so that the problem of multi-box type boxing still exists in the prior art.
Disclosure of Invention
The invention mainly aims to provide a box type recommendation method, a box type recommendation device and a computer storage medium, and aims to solve the problem that multi-box type boxing exists in the prior art.
In order to achieve the above object, the present invention provides a box type recommendation method, including:
acquiring an order list of which any article in the order meets the loading condition of the largest box type single box in the candidate box types;
for each order in the order list, performing the following steps:
acquiring the sizes of all articles in the order;
predicting a minimum box shape capable of loading all the articles according to the sizes of all the articles;
adopting a three-dimensional boxing algorithm to perform loading attempt on all the articles according to the minimum box type;
recommending the minimum box type when the minimum box type can be loaded with all the items; otherwise, the box number is increased to carry out loading attempt until the loading attempt for obtaining the recommended box type or all box numbers fails.
In one embodiment, the obtaining the order list in which any item in the order satisfies the loading condition of the largest box type single box in the candidate box types comprises:
acquiring a list of orders to be processed, and executing the following steps for each order in the list of orders to be processed:
traversing the articles in the order and judging whether the articles meet the loading condition of the largest box type single box type in the candidate box types;
and if the special-shaped articles which do not meet the loading conditions of the maximum box type single box exist, removing the special-shaped articles from the order to obtain an order list of all articles which meet the loading conditions of the maximum box type single box.
In one embodiment, when the loading attempts of all box numbers fail, the following steps are repeatedly performed until the loading of all the items is completed:
loading a part of all the articles to the maximum extent by adopting a maximum box shape;
generating a new virtual order for the rest of all the articles;
and adding the virtual order into the order list to wait for processing.
In one embodiment, the method further comprises:
calculating whether there is a smaller bin capable of holding a portion of the total number of items;
when there is a smaller box capable of loading a portion of the total items, the smaller box is used to load a portion of the total items.
In one embodiment, the step of attempting to load all the items according to the minimum box type by using a three-dimensional box packing algorithm includes:
loading all the articles in different modes based on a preset space division mode to generate an initial boxing scheme population;
calculating the filling rate of each packing scheme as an adaptive function;
and traversing the initial boxing scheme population, and outputting any boxing scheme meeting the loading requirement when judging that the boxing scheme capable of loading all the articles exists according to the adaptive function.
In an embodiment, before the step of loading all the articles in different manners based on the preset space division manner, the method further includes:
stacking all the articles according to 6 placing directions respectively, and calculating the length, the width and the height of all the stacked articles in each placing direction;
and generating a simple block list by stacking all the articles according to each direction to form simple blocks, and arranging the simple blocks according to the volume sequence.
In one embodiment, the method further comprises:
when the boxing scheme capable of loading all the articles does not exist, generating a new solution through crossing and mutation operations, and adding the new solution into the initial boxing scheme population to form a new population;
calculating the filling rate of each boxing scheme in the new population as an adaptive function, performing iterative optimization according to the adaptive function, and setting the maximum iteration times;
and when a new boxing scheme capable of loading all the articles appears in the new population, terminating the iteration and outputting the new boxing scheme.
In one embodiment, the method further comprises:
when the maximum iteration times are reached, terminating the iteration, and calculating the filling rate of each boxing scheme in the last generation as an adaptive function;
and selecting and outputting the packing scheme corresponding to the maximum adaptive function value.
In one embodiment, the packing scheme includes a loaded items list, loaded boxes, loaded items location information, and an unloaded items list.
In order to achieve the above object, the present invention further provides an apparatus, which includes a memory, a processor, and a box-type recommender stored in the memory and operable on the processor, wherein the box-type recommender, when executed by the processor, implements the steps of the box-type recommender method as described above.
In order to achieve the above object, the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a box-type program, and the box-type program, when executed by a processor, implements the steps of the box-type recommendation method as described above.
According to the box type recommendation method and the computer storage medium, the minimum box type is estimated according to the sizes of all articles of each order, loading attempt is carried out according to the estimated minimum box type by adopting a three-dimensional box loading algorithm based on a genetic algorithm, and when the estimated minimum box type can load all articles, the estimated minimum box type is recommended to be used for loading all articles; and when the estimated minimum box type cannot load all the articles through the three-dimensional box loading algorithm, performing loading attempt by increasing the box number, namely increasing the size of the box type through the three-dimensional box loading algorithm until a recommended box type is obtained or the loading attempt fails. Various box types are utilized to load articles, so that the problem of multi-box type boxing in the prior art is solved.
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FIG. 1 is a schematic diagram of an apparatus according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a first embodiment of the box recommendation method of the present invention;
FIG. 3 shows 6 different placement directions for the article;
FIG. 4 illustrates one of 6 spatial partitioning schemes;
FIG. 5 illustrates one of 6 spatial division schemes;
FIG. 6 is a flowchart illustrating a second embodiment of the box recommendation method of the present invention;
FIG. 7 is a flowchart illustrating a third embodiment of the box recommendation method of the present invention;
FIG. 8 is a flowchart illustrating a fourth embodiment of the box recommendation method of the present invention;
FIG. 9 is a flowchart illustrating a fifth embodiment of the box recommendation method according to the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The main solution of the embodiment of the invention is as follows: acquiring an order list of which any article in the order meets the loading condition of the largest box type single box in the candidate box types; for each order in the order list, performing the steps of: acquiring the sizes of all articles in the order; predicting a minimum box shape capable of loading all the articles according to the sizes of all the articles; adopting a three-dimensional boxing algorithm to try to load all articles according to the minimum box type; when the minimum box type can be loaded with all the articles, the minimum box type is recommended; otherwise, the box number is increased to carry out loading attempt until the loading attempt for obtaining the recommended box type or all box numbers fails. Because the minimum box type is estimated according to the sizes of all the articles of each order, a three-dimensional box loading algorithm is adopted to perform loading attempt according to the estimated minimum box type based on a genetic algorithm, and when the estimated minimum box type can load all the articles, the estimated minimum box type is recommended to be used for loading all the articles; and when the estimated minimum box type cannot load all the articles through the three-dimensional box loading algorithm, performing loading attempt by increasing the box number, namely increasing the size of the box type through the three-dimensional box loading algorithm until a recommended box type is obtained or the loading attempt fails. Various box types are utilized to load articles, so that the problem of multi-box type boxing in the prior art is solved.
As an implementation manner, fig. 1 may be shown, where fig. 1 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
It will be appreciated that memory 1200 in embodiments of the invention may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile Memory may be a Read Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of illustration and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double Data Rate Synchronous Dynamic random access memory (ddr Data Rate SDRAM, ddr SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 1200 of the systems and methods described in connection with the embodiments of the invention is intended to comprise, without being limited to, these and any other suitable types of memory.
For a software implementation, the techniques described in this disclosure may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described in this disclosure. The software codes may be stored in a memory and executed by a processor. The memory may be implemented within the processor or external to the processor.
Based on the structure, the embodiment of the box type recommendation method is provided.
Referring to fig. 2, fig. 2 is a first embodiment of the box-type recommendation method of the present invention, which includes:
step S110, an order list is obtained, wherein any article in the order meets the loading condition of the largest box type single box in the candidate box types.
In this embodiment, the box type recommendation method is mainly applied to an environment of loading articles in multiple box types, for example, in the e-commerce warehousing operation process, a warehouse receives an order issued by a client, finishes sorting of the ordered articles, and needs to perform box loading processing on the articles, because the difference between the composition of the articles under each order and the length, width, height, volume and other factors is large, multiple box types are used, and the size of each box type is different. The items are all treated approximately as regular cubes. Candidate boxes refer to a variety of boxes of different sizes, for example, there are A, B, C, D four boxes now, and size a < B < C < D. The maximum box type single box loading condition means that the length, the width and the height of any article in an order are not greater than the length, the width and the height corresponding to the maximum box type, for example, the length, the width and the height of any article are not greater than the length, the width and the height corresponding to a D box type. The order list refers to an order list composed of orders satisfying the maximum box type single box loading condition.
For each order in the order list, performing the following steps:
step S120, obtaining the sizes of all the items in the order.
In this embodiment, the sizes of all the items in the order, that is, the lengths, widths and heights of all the items are obtained, for example, if the order contains 10 items, the lengths, widths and heights of the 10 items are obtained.
And S130, estimating a minimum box type capable of loading all the articles according to the sizes of all the articles.
In this embodiment, a minimum box shape capable of loading all the articles is estimated from the acquired sizes of all the articles, that is, the length and width corresponding to all the articles, among the candidate box shapes, and for example, for the acquired length and width of 10 articles, a minimum box shape capable of loading the 10 articles is estimated as a B box shape.
And step S140, adopting a three-dimensional boxing algorithm to try to load all the articles according to the minimum box type.
In this embodiment, the three-dimensional binning algorithm is an optimized binning algorithm based on a genetic algorithm, and in real life, the binning problem can be divided into one-dimensional binning, two-dimensional binning and three-dimensional binning according to dimensions to be considered for binning. One-dimensional binning typically only considers constraints in one dimension, such as volume, weight, etc.; two-dimensional boxing needs to consider constraints in a two-dimensional plane, such as the problem of blanking of the two-dimensional plane; three-dimensional boxing requires consideration of the length, width and height constraints of boxes and articles.
Genetic Algorithm (GA), which was originally proposed by John holland in the united states in the 70's 20 th century, was designed according to the rules of organism evolution in nature. The method is a calculation model of the biological evolution process for simulating natural selection and genetic mechanism of Darwinian biological evolution theory, and is a method for searching an optimal solution by simulating the natural evolution process. The algorithm converts the solving process of the problem into the processes of crossover, variation and the like of chromosome genes in the similar biological evolution by a mathematical mode and by utilizing computer simulation operation. The method has a prominent characteristic in solving a complex combined genetic algorithm, and can solve some very abstract problems. The structure object can be directly operated without accurate mathematical model representation, so that the problem is not considered whether derivation or continuity can be obtained or not; specific internal operation can be hidden, namely, good hidden parallelism is achieved; the optimal solution can be searched from the global scope; it is probabilistic, but can be self-adaptive, self-guided, and does not require additional custom rules. In particular, genetic algorithms have their own unique advantages over other traditional optimization algorithms.
A complete genetic algorithm has the following key parts: 1. generation (generation), each race in nature is to be propagated, the generation of a new generation may bring new excellent individuals to the race, or bring new species competitive advantage, weak individuals will be eliminated naturally, strong individuals have greater chance to propagate offspring, in the algorithm, iteration is to screen feasible solutions, according to the principle of survival and elimination of the best of the fittest, the whole population is evolved to a good direction; 2. population (population), wherein the biological evolution is carried out in the form of population, such a population is called population, and a set formed by a plurality of feasible solutions in the algorithm is a population; 3. individuals (individual), the individual factors that make up a population, are individuals, and an individual is a viable solution in the algorithm.
The step S140 may include the steps of:
s141: and respectively stacking all the articles according to 6 placing directions, and calculating the length, the width and the height of all the stacked articles in each placing direction.
In this embodiment, since the articles are approximately regarded as a regular cube, each article has 6 different placement directions, all the articles in the order are first stacked according to the 6 placement directions, and the length, width and height of all the articles stacked in each placement direction are calculated. Referring to fig. 3, fig. 3 shows 6 different placing directions corresponding to the articles, where (l, w, h) shows the length, width and height corresponding to the articles, and then the corresponding 6 different placing directions are: (l, w, h), (w, l, h), (h, w, l), (w, h, l), (h, l, w), (l, h, w).
S142: and generating a simple block list by stacking all the articles according to each direction to form simple blocks, and arranging the simple blocks according to the volume sequence.
In this embodiment, all the articles are stacked in 6 different placement directions to form simple blocks, the simple blocks are generated into a simple block list, for the case that all the articles are stacked in different placement directions, the formed simple block volumes may be different, and then the simple blocks are arranged in an ascending order or a descending order according to the volume.
S143: and loading all the articles in different modes based on a preset space division mode to generate an initial boxing scheme population.
An article is loaded into the box, and the article divides the space of the box into an upper space, a front space and a right space, and referring to fig. 4 and 5, fig. 4 and 5 show two space division methods, and the rest of the division methods are not illustrated. Wherein (L, W, H) represents the length, width and height corresponding to the article, and (L, W, H) represents the length, width and height corresponding to a certain box type.
There may be 6 different space divisions when the next item is loaded, and all items are loaded based on the 6 different space divisions. Since there are many choices for each article loading, considering all packing schemes in the initial stage will certainly require a lot of time and bring a lot of computation cost, an initial packing scheme population is randomly generated according to 6 placing directions and 6 different space division ways, the population size is fixed, for example, for 10 articles in an order, the population size may be preferably 20, that is, the initial packing scheme population contains 20 packing schemes.
And then, encoding the packing scheme of the initial packing scheme population by adopting a two-dimensional encoding mode, for example, adopting a two-dimensional encoding [ < k, m >, ] where k represents the kth simple block and m represents the corresponding space division mode.
S144: the fill rate for each binning scheme is calculated as an adaptation function.
In this embodiment, a decoding operation is performed, a current simple block list availList is calculated, n ═ availList.
Wherein S represents the volume sum of all the articles and V represents the volume of the box.
The adaptive function refers to the fitness of individuals in the genetic algorithm, the larger the adaptive function value is, the higher probability is selected into the next generation, and the smaller the adaptive function value is, the higher probability is eliminated, so that the performance of the genetic algorithm such as excellence and decline is realized.
Here, the simple blocks are preferably arranged in descending order, and if k > n, where k is mod (k, n), and k > n indicates that the volume of the k simple block is larger than that of the n simple block, the remainder operation is performed on the simple blocks, for example, k is 3, n is 2, and the remainder operation is 1, which indicates that the probability of taking the first simple block is high, and the filling rate is high.
S145: and traversing the initial boxing scheme population, and outputting any boxing scheme meeting the loading requirement when judging that the boxing scheme capable of loading all the articles exists according to the adaptive function.
In this embodiment, the initial boxing scheme population is traversed, and when it is determined that a boxing scheme capable of loading all the articles exists according to the fitness function, that is, the estimated minimum box type can load all the articles, the boxing scheme corresponding to the minimum box type is recommended.
Step S140 may further include the steps of:
step S146: when the boxing scheme capable of loading all the articles does not exist, generating a new solution through crossing and mutation operations, and adding the new solution into the initial boxing scheme population to form a new population;
in this embodiment, for example, the probability of intersection is preferably 0.7, the probability of mutation is preferably 0.05, and the intersection may be performed by exchanging the order of loading two articles and exchanging the space division method of the two articles; the variation is to rotate the articles according to 6 placing directions and to change the articles according to 6 space division modes. And adding the new solution into the population of the initial boxing scheme to form a new population.
Step S147: and calculating the filling rate of each boxing scheme in the new population as an adaptive function, performing iterative optimization according to the adaptive function, and setting the maximum iteration times.
In this embodiment, the new population is two-dimensionally encoded and decoded, the filling rate of each packing scheme in the new population is calculated as an adaptive function, and iterative optimization is performed according to the adaptive function, where the iterative optimization means that a larger probability is selected to enter the next generation if the adaptive function value is larger, and a larger probability is eliminated if the adaptive function value is smaller. The maximum number of iterations is then set according to the actual situation of the loaded item.
Step S148: and when a new boxing scheme capable of loading all the articles appears in the new population, terminating the iteration and outputting the new boxing scheme.
In this embodiment, when a new boxing scheme capable of loading all the articles occurs during the iterative optimization, the iterative operation is terminated, and the corresponding boxing scheme is output.
Step S150, recommending the minimum box type when the minimum box type can load all the articles; otherwise, the box number is increased to carry out loading attempt until the loading attempt for obtaining the recommended box type or all box numbers fails.
In this embodiment, when the estimated minimum box type can be used to load all the articles, a box loading scheme corresponding to the minimum box type is recommended, and the box loading scheme includes a loading box type, a list of loaded articles, a filling rate, article loading position information, and the like.
When the predicted box type can not load all the articles, increasing the box number and adopting a three-dimensional box loading algorithm to perform loading attempt until a certain type of box type can load all the articles or the maximum box type can not load all the articles, wherein the maximum box type can not load all the articles, which indicates that the single type of box type loading attempt fails. For example, if the predicted minimum box type B cannot be loaded with all the articles, selecting C, D box types to perform loading attempt by adopting a three-dimensional boxing algorithm, and if all the articles can be loaded, recommending corresponding loading box types; if none of the maximum boxes D load successfully, the load attempt fails.
In the technical scheme provided by the embodiment, the minimum box type is estimated according to the sizes of all articles of each order, the loading attempt is carried out according to the estimated minimum box type by adopting a three-dimensional box loading algorithm based on a genetic algorithm, and when the estimated minimum box type can load all articles, the estimated minimum box type is recommended to be used for loading all articles; and when the estimated minimum box type cannot load all the articles through the three-dimensional box loading algorithm, performing loading attempt by increasing the box number, namely increasing the size of the box type through the three-dimensional box loading algorithm until a recommended box type is obtained or the loading attempt fails. Various box types are utilized to load articles, so that the problem of multi-box type boxing in the prior art is solved.
Referring to fig. 6, fig. 6 is a second embodiment of the box-type recommendation method of the present invention, which includes:
acquiring a list of orders to be processed, and executing the following steps for each order in the list of orders to be processed:
compared with the first embodiment, the second embodiment includes step S210 and step S220, and other steps are the same as those of the first embodiment and are not repeated.
Step S210, traversing the articles in the order and judging whether the articles meet the loading condition of the largest box type single box type in the candidate box types.
In this embodiment, the articles in the order are traversed, and according to the size of the article, that is, the length, width and height corresponding to the article, the length, width and height of the largest box type among the candidate box types are compared, and the largest box type single box loading condition refers to that the length, width and height of any article is not greater than the length, width and height of the largest box type, so as to determine whether the article meets the largest box type single box loading condition.
And S220, if the special-shaped articles which do not meet the maximum box-type single-box loading condition exist, removing the special-shaped articles from the order to obtain an order list of all articles which meet the maximum box-type single-box loading condition.
In the present embodiment, if there is a special-shaped article that does not satisfy the maximum box-type single-box loading condition, that is, if there is a case where the length and width of the article is larger than the maximum box-type length and width, the article is determined as a special-shaped article, and the article is removed from the order and directly packaged and output. And if the residual articles in the order meet the maximum box type loading condition, all the orders meet the maximum box type single box type loading condition, and an order list is obtained.
In step S230, an order list is obtained, where any item in the order satisfies the loading condition of the largest box type single box among the candidate box types.
For each order in the order list, performing the following steps:
step S240, obtaining the sizes of all the items in the order.
And S250, estimating a minimum box type capable of loading all the articles according to the sizes of all the articles.
Step S260, adopting a three-dimensional boxing algorithm to try to load all the articles according to the minimum box type;
step S270, recommending the minimum box type when the minimum box type can load all the articles; otherwise, the box number is increased to carry out loading attempt until the loading attempt for obtaining the recommended box type or all box numbers fails.
In the technical scheme provided by this embodiment, traversing the articles in the order form is performed for each order form in the to-be-processed order form list, determining whether the articles meet the maximum box type single box loading condition in the candidate box types, removing the articles that do not meet the requirements as special-shaped articles, and directly packaging and outputting the articles to obtain that the articles in all the order forms meet the maximum box type single box loading condition.
Referring to fig. 7, fig. 7 is a third embodiment of the box-type recommendation method of the present invention, which includes:
step S310, an order list is obtained, wherein any article in the order meets the loading condition of the largest box type single box in the candidate box types.
For each order in the order list, performing the following steps:
step S320, obtaining the sizes of all the items in the order.
And S330, estimating the minimum box type capable of loading all the articles according to the sizes of all the articles.
Step S340, adopting a three-dimensional boxing algorithm to try to load all the articles according to the minimum box type;
step S350, recommending the minimum box type when the minimum box type can load all the articles; otherwise, the box number is increased to carry out loading attempt until the loading attempt for obtaining the recommended box type or all box numbers fails.
Compared with the first embodiment, the third embodiment includes step S360, step S370, and step S380, and other steps are the same as those of the first embodiment and are not repeated.
When the loading attempts of all box numbers fail, repeatedly executing the following steps until all the articles are loaded completely:
and step S360, adopting the maximum box type to load a part of all the articles to the maximum extent.
In the embodiment, a part of all the articles is loaded to the maximum extent by adopting the maximum box type, namely, the loading attempt is carried out by a three-dimensional box loading algorithm to adapt to the box loading scheme corresponding to the maximum value of the function. For example, the number of items in the order is 10, and 6 items are loaded to the maximum extent by using the maximum box type D box type.
Step S370, generating a new virtual order for the remaining items in all the items.
In this embodiment, a new virtual order is generated for the remaining items of all the items. For example, if the number of items in the order is 10, and 6 items are loaded to the maximum extent using the maximum box type D, a new virtual order is created for the remaining 4 items.
Step S380, adding the virtual order into the order list for waiting, that is, waiting for a loading attempt by a three-dimensional packing algorithm.
In this embodiment, the virtual order is added to the order list for processing, for example, the virtual orders of the remaining 4 items are added to the order list for processing.
In the technical scheme provided by the embodiment, when the maximum box type cannot load all the articles, the maximum box type is adopted to load the articles to the maximum extent, then the rest articles are generated into a new virtual order to be added into an order list to wait for the loading attempt of the three-dimensional packing algorithm, and the operation is circulated until all the articles are completely loaded. The loaded box type is the most suitable box type, and the loading cost is saved.
Referring to fig. 8, fig. 8 is a fourth embodiment of the box-type recommendation method according to the present invention, based on the third embodiment, in step S360, a part of all the items is loaded to the maximum extent using the maximum box-type. Then also comprises the following steps:
step S410, calculating whether a smaller box type can load a part of all the articles;
in this embodiment, it is determined by calculation whether there are some of the smaller boxes that hold all the items, for example, 10 items in the order, 6 items in the order are held to the maximum extent using the maximum box type D, and A, B, C boxes are calculated to hold the 6 items.
And step S420, when a smaller box type can be used for loading a part of all the articles, adopting the smaller box type to load the part of all the articles.
In this embodiment, when there is a smaller box capable of loading a part of all the articles, the smaller box loading is adopted, for example, C box is adopted to load 6 articles of D box loading, and C box is adopted to load the 6 articles.
In the technical scheme provided by the embodiment, whether a smaller box type can be used for loading a part of the maximum box type loaded all the articles is calculated, and when the smaller box type can be used for loading a part of the maximum box type loaded all the articles, the smaller box type loading is adopted, so that the loading cost is saved.
Referring to fig. 9, fig. 9 is a fifth embodiment of the box-type recommendation method of the present invention, including:
and step S510, when the boxing scheme capable of loading all the articles does not exist, generating a new solution through crossing and mutation operations, and adding the new solution into the initial boxing scheme population to form a new population.
Step S520, calculating the filling rate of each boxing proposal in the new population as an adaptive function, carrying out iterative optimization according to the adaptive function, and setting the maximum iterative times.
And step S530, terminating iteration when the maximum iteration times are reached, and calculating the filling rate of each boxing scheme in the last generation as an adaptive function.
In this embodiment, the iteration is terminated when the maximum number of iterations is reached, for example, for the case of 10 items in the order, the maximum number of iterations may preferably be 100. Because the filling rate is used as the adaptive function in the iterative process, the higher probability that the adaptive function value is larger is selected to enter the next generation, the higher probability that the adaptive function value is smaller is eliminated, the population size is preferably 20, and the boxing scheme in each generation is 20. The fill rate of each binning scheme in the last generation is calculated as an adaptation function.
And step S540, selecting and outputting the packing scheme corresponding to the maximum adaptive function value.
In this embodiment, according to the magnitude of the calculated adaptive function index, if the value is larger, the corresponding filling rate is larger, and the packing scheme corresponding to the largest adaptive function value is selected to be output, where the packing scheme includes a loaded item list, loaded boxes, loaded item position information, and unloaded item list.
In the technical scheme provided by the embodiment, a large number of iterations can be performed according to the placing direction of the articles, the placing sequence of the articles and the space division mode of the articles, but after a certain number of iterations is reached, more articles cannot be loaded, and excessive iterations can increase a large amount of calculation cost and time, so a maximum number of iterations can be specifically set according to the number of the articles, the iterations are terminated when the maximum number of iterations is reached, the filling rate of each packing scheme of the last generation is calculated as an adaptive function, the larger the value is, the larger the filling rate is, the better the loading effect is, the packing scheme corresponding to the maximum adaptive function value is output, an optimal loading scheme is obtained, the calculation and time cost is reduced, and the loading cost is saved.
The invention also provides a device which comprises a memory, a processor and a box-type recommendation method program stored in the memory and capable of running on the processor, wherein the box-type recommendation method program realizes the steps of the box-type recommendation method when being executed by the processor.
The invention also provides a computer-readable storage medium, which is characterized in that the computer-readable storage medium stores a box-type recommendation program, and the box-type recommendation program realizes the steps of the box-type recommendation method when being executed by a processor.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A box recommendation method, comprising:
acquiring an order list of which any article in the order meets the loading condition of the largest box type single box in the candidate box types;
for each order in the order list, performing the following steps:
acquiring the sizes of all articles in the order;
predicting a minimum box shape capable of loading all the articles according to the sizes of all the articles;
adopting a three-dimensional boxing algorithm to perform loading attempt on all the articles according to the minimum box type;
recommending the minimum box type when the minimum box type can be loaded with all the items; otherwise, the box number is increased to carry out loading attempt until the loading attempt for obtaining the recommended box type or all box numbers fails.
2. The box type recommendation method of claim 1, wherein obtaining an order list in which any item in the order satisfies a maximum box type single box loading condition in the candidate box type comprises:
acquiring a list of orders to be processed, and executing the following steps for each order in the list of orders to be processed:
traversing the articles in the order and judging whether the articles meet the loading condition of the largest box type single box type in the candidate box types;
and if the special-shaped articles which do not meet the loading conditions of the maximum box type single box exist, removing the special-shaped articles from the order to obtain an order list of all articles which meet the loading conditions of the maximum box type single box.
3. The box type recommendation method according to claim 1, wherein when the loading attempts of all box numbers fail, the following steps are repeatedly performed until the loading of all the items is completed:
loading a part of all the articles to the maximum extent by adopting a maximum box shape;
generating a new virtual order for the rest of all the articles;
and adding the virtual order into the order list to wait for processing.
4. The box-type recommendation method according to claim 3, further comprising:
calculating whether there is a smaller bin capable of holding a portion of the total number of items;
when there is a smaller box capable of loading a portion of the total items, the smaller box is used to load a portion of the total items.
5. The box-type recommendation method of claim 1, wherein said step of attempting to load all of said items in said minimum box-type using a three-dimensional packing algorithm comprises:
loading all the articles in different modes based on a preset space division mode to generate an initial boxing scheme population;
calculating the filling rate of each packing scheme as an adaptive function;
and traversing the initial boxing scheme population, and outputting any boxing scheme meeting the loading requirement when judging that the boxing scheme capable of loading all the articles exists according to the adaptive function.
6. The box-type recommendation method according to claim 5, wherein said step of loading all the items differently based on the predetermined spatial division further comprises:
stacking all the articles according to 6 placing directions respectively, and calculating the length, the width and the height of all the stacked articles in each placing direction;
and generating a simple block list by stacking all the articles according to each direction to form simple blocks, and arranging the simple blocks according to the volume sequence.
7. The box-type recommendation method according to claim 5, further comprising:
when the boxing scheme capable of loading all the articles does not exist, generating a new solution through crossing and mutation operations, and adding the new solution into the initial boxing scheme population to form a new population;
calculating the filling rate of each boxing scheme in the new population as an adaptive function, performing iterative optimization according to the adaptive function, and setting the maximum iteration times;
and when a new boxing scheme capable of loading all the articles appears in the new population, terminating the iteration and outputting the new boxing scheme.
8. The box-type recommendation method according to claim 7, further comprising:
when the maximum iteration times are reached, terminating the iteration, and calculating the filling rate of each boxing scheme in the last generation as an adaptive function;
and selecting and outputting the packing scheme corresponding to the maximum adaptive function value.
9. An apparatus comprising a memory, a processor, and a box-type recommender stored in the memory and operable on the processor, the box-type recommender when executed by the processor implementing the steps of the box-type recommender method according to any of claims 1 to 8.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores a box-type recommender program, which when executed by a processor implements the steps of the box-type recommender method according to any of claims 1 to 8.
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| CN112874927A (en) * | 2021-02-03 | 2021-06-01 | 四川物联亿达科技有限公司 | Box type recommendation method for logistics packaging boxes |
| CN113095603A (en) * | 2021-06-08 | 2021-07-09 | 易纳购科技(北京)有限公司 | Box type prediction method and device, computer equipment and storage medium |
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