JPH1191703A - Packing information decision equipment and packing information deciding method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To improve shipping efficiency by decreasing the number of packing cases. SOLUTION: An article information memory means 5 stores each article with its volume. A candidate data forming means 7 forms at random the candidate data of packing information in a predetermined number based on giver order information. A candidate data converting means 8 converts and processes by a genetic algorithm based on the volume of each article stored in the article information memory means 5 as to each candidate data in the predetermined number so as to obtain packing information with the smaller total number of packing boxes. A candidate decision means 9 outputs a candidate data carrying the smaller total number of packing boxes among candidate data converted by the candidate data converting means 8.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a packaging information determination device, and more particularly to an improvement in delivery efficiency.

[0002]

2. Description of the Related Art Conventionally, a delivery management system using a flow rack as shown in FIG. 14 is known. In brief, the basket starts from the start position 101 and stops at the stop positions A1 and A2, respectively. Items are stored in each flow rack (zone). The worker in each zone causes the basket to take out as many items as possible based on a command given in advance. After passing through the zone 1 and the zone 2, the basket is transferred to a predetermined cardboard box at the packing place. Similarly, the basket started from the start position 102 stops at the stop positions B1 and B2, and the goods are discharged.

[0003] The above-mentioned command is prepared as follows.
The orders given in advance are arranged in order of each customer. Each order is composed of a customer ID, a product number, and a quantity. On the other hand, the volume of each part number is stored in the database. The total volume in the order is obtained based on the product number and the number in each order. Then, the total volume of each order is accumulated up to the allowable volume of one packing case. This is repeated for all orders.

[0004] In this way, items according to a given order can be packed in a box for each customer.

[0005]

However, the above-mentioned retrieval management system has the following problems. First, there is a lot of waste in boxed goods. For example, depending on the order of the order, a large space may be created in one packing case. This requires many packaging cases. Second, in the picking work, the work is concentrated only on a certain worker, the flow as a whole is deteriorated, and the delivery efficiency is reduced.

[0006] It is an object of the present invention to provide a packaging information determination device capable of reducing waste of a packaging case. Further, it is another object of the present invention to provide a packaging information determination device capable of improving the delivery efficiency.

[0007]

According to a first aspect of the present invention, there is provided an apparatus for determining packing information for storing a plurality of types of articles in a plurality of packing boxes. Item information storage means for storing for each article, candidate data generating means for randomly generating a predetermined number of the candidate information of the packing information based on order information including the type of the article and the number thereof, each of the predetermined number of candidates For data, candidate data conversion means for performing a conversion process by a genetic algorithm, so that the total of the packaging boxes is obtained based on the volume of each article stored in the article information storage means, so that less packaging information is obtained. It is characterized by comprising candidate determination means for determining candidate data in which the total of the packing boxes is small among the candidate data converted by the candidate data conversion means.

[0008] In the packing information determination device of the second aspect,
The article information storage means further stores article storage area data in which each of the articles is stored, and the candidate data conversion means, for the predetermined number of candidate data, the total number of the packing boxes is smaller, In addition, the conversion processing by the genetic algorithm is performed so that packing information in which the number of withdrawals in each of the article storage areas is equalized is obtained, and the candidate determination unit determines the candidate data converted by the candidate data conversion unit. And outputting candidate data in which the total number of the packaging boxes is small and the number of drawers in each of the article storage areas is equalized.

In the packing information determining method according to a third aspect,
This is a method of determining packing information for storing a plurality of types of articles in a plurality of packing boxes, wherein the volume of the articles is stored for each article, and the order information including the type and the number of the articles is stored in the order information. On the basis of the predetermined number of the candidate data of the packing information is randomly generated, the total number of the packing box based on the stored volume of each article for each of the predetermined number of candidate data, the packing information is reduced And performing a conversion process by a genetic algorithm so as to obtain candidate data in which the total of the packing boxes is small among the converted candidate data.

A storage medium storing the program according to claim 4, wherein the storage medium stores a program for causing a computer including an input device, a control device, an output device, and a storage device to function as a packaging information determination device. Is a device that causes the computer to function as the following device, and determines packing information for storing a plurality of types of articles in a plurality of packing boxes in a divided manner. 1) article information storage means for storing the volume of the article for each article; 2) randomly generating a predetermined number of the packing information candidate data based on the order information including the type and number of the articles. Candidate data generating means, 3) for each of the predetermined number of candidate data, the total number of the packing boxes based on the volume of each article stored in the article information storage means, So small become packing information can be obtained,
Candidate data conversion means for performing conversion processing by a genetic algorithm, 4) among the candidate data converted by the candidate data conversion means,
Candidate determining means for determining candidate data having a small total of the packing boxes.

[0011]

According to the packing information determination device of the present invention, the packing information determining device determines the candidate data of the packing information based on the order information including the type and the number of the goods. Generate randomly by number. For the predetermined number of each candidate data, the conversion process by the genetic algorithm is performed such that the total number of the packaging boxes is obtained based on the volume of each article stored in the article information storage means, so that the packaging information becomes smaller. Do. Among the converted candidate data, the total of the packing boxes is determined to be small. Therefore, it is possible to easily and easily obtain packing information in which the total number of the packing boxes is small.

[0012] In the packing information determining apparatus of the second aspect,
The candidate data conversion means, for the predetermined number of candidate data, the genetic algorithm, so that the total number of the packing box is smaller, and packing information that equalizes the number of drawers in each of the article storage areas is obtained. Is performed. In addition, the candidate determining means outputs candidate data in which the total number of the packing boxes is small and the number of drawers in each of the article storage areas is equal among the candidate data converted by the candidate data converting means. Therefore, it is possible to easily and quickly obtain packing information in which the total number of packing boxes is small and the number of drawers in each of the article storage areas is equalized.

[0013]

BEST MODE FOR CARRYING OUT THE INVENTION

1. Functional Block Diagram Hereinafter, an embodiment of the present invention will be described with reference to the drawings. FIG. 1 shows a functional block diagram of the packing information determination device 1. The packing information determination device 1 is a device that determines packing information for storing a plurality of types of articles in a plurality of packing boxes in a divided manner. The packing information determination device 1 includes an order information storage unit 3, an article information storage unit 5, a candidate data generation unit 7, a candidate data conversion unit 8, and a candidate determination unit 9.

The order information storage means 3 stores order information including the type and number of articles.

The article information storage means 5 stores the article volume for each article and also stores article storage area data in which each article is stored. Candidate data generation means 7
Generates a predetermined number of candidate data of the packing information at random based on the order information. The candidate data conversion means 8 reduces the total number of the packing boxes based on the volume of each article stored in the article information storage means 5 for each of the predetermined number of candidate data, and draws out each of the article storage areas. A conversion process by a genetic algorithm is performed so that packing information in which the numbers are equalized is obtained. The candidate deciding means 9 outputs candidate data in which the total number of the packing boxes is small and the number of drawers in each of the article storage areas is equal among the candidate data converted by the candidate data converting means 8.

2. Hardware Configuration FIG. 2 shows an example of a hardware configuration in which the packing information determination device 1 is realized using a CPU. Packing information determination device 1
Are the CPU 23, the memory 27, the hard disk 26, and C
RT32, keyboard 28, printer 30, FDD25
And a bus line 29.

The CPU 23 controls each unit via a bus line 29 according to a control program stored in the hard disk 26.

This control program is transmitted via the FDD 25 to the flexible disk 25 storing the program.
a and is installed on the hard disk 26. In addition to the flexible disk, a hard disk may be installed from a computer-readable storage medium in which a program such as a CD-ROM or an IC card is substantially integrated. Furthermore, you may make it download using a communication line.

In this embodiment, the program is installed from the flexible disk to the hard disk 26 so that the computer indirectly executes the program stored in the flexible disk. However, without being limited to this, the program stored in the flexible disk is stored in the FDD25.
Alternatively, it may be executed directly from. Note that, as a program executable by a computer, not only a program that can be directly executed by simply installing it as it is, but also a program that needs to be once converted into another form or the like (for example,
Decompression of data that has been compressed, etc.), and also includes those that can be executed in combination with other module parts.

The memory 27 stores various calculation results and the like. The CRT 32 displays the finally selected candidates and the like.

The hard disk 26 stores article data composed of a picking zone and a volume of each article shown in FIG. Similarly, an order (order) including an order number (order number), a product number, and an order quantity is stored.

The keyboard 28 is input means for inputting various commands and the like. The printer 30 is a hard disk 2
6 temporarily outputs the calculation result and the like.

3. Flowchart Next, the program stored in the hard disk 26 will be described with reference to FIG.

(3-1) Data Structure of Candidate First, the CPU 23 reads out all orders (step ST1 in FIG. 5). The CPU 23 calculates the
An average volume is determined (step ST2). The average volume is
It is determined as follows. First, a total volume is obtained based on all orders. Here, the order No. shown in FIG.
The total volume obtained by the order from 1 to 80 is 14
Assume that it was 4,000 cm ³ . Next, the obtained total volume is set to the predetermined maximum volume of the case (26,000
0 cm ³ ). For example, if the total volume calculated based on all orders is 144,000 cm ³ , and this is divided by 26,000 cm ³ , “5.5...” Is obtained. By rounding up the decimal point, "6"
Is obtained. By dividing the total volume by the obtained “6”, the average volume of one case Vav = 24,000c
m ³ is obtained.

Next, the CPU 23 generates candidates (step ST4). A method of generating candidates will be described with reference to FIG. In this embodiment, since there are 80 orders No. 1 to 80, the data length is set to 80 bytes as shown in FIG. 6A, and O ₁ ,
O ₂ , O ₃ ... O ₈₀ are arranged.

The order is arbitrarily rearranged using random numbers. For _{example, O 1, O 2, O} 3 ··· O 80 is, FIG. 6B
, O ₁₂ , O ₃ , O ₅ , O ₈₀ ... O ₂₆ , O ₃₄
In that order.

An evaluation operation is performed on the candidates generated in this manner. The calculation of the evaluation will be described with reference to FIG.

The CPU 23 first performs initialization (Vx =
0, i = 1) (step ST31 in FIG. 7). Next, the total volume Vi in the i-th order is obtained (step ST3).
3). This is based on the volume of each item shown in FIG.
The sum of the order quantity of the order No is the total volume V
i may be used. For example, the total volume V1 for i = 1 is V1 = 275 * 10 =
2750 cm ³ . As described above, in the present embodiment, an order for one type of part number is handled as one order.

Next, the CPU 23 calculates the cumulative volume Vx. In this case, since the cumulative volume Vx is 0, the cumulative volume Vx becomes the volume V1 obtained in step ST33.

The CPU 23 determines whether the cumulative volume Vx is smaller than the average volume Vav (step ST3).
7). In this case, since it has exceeded, it is determined whether or not the order is the last (step ST39). In this case, since it is not the final, i is incremented (step ST41).
Then, the processes in and after step ST33 are repeated.

If it is determined in step ST37 that the cumulative volume Vx has exceeded the average volume Vav, the CPU 23
Inserts a delimiter mark before the i-th position (step ST51). For example, at i = 10, the cumulative volume Vx becomes 24500 cm ³ and the average volume Vav (2400
If it exceeds 0 cm ³ ), a delimiter mark is inserted before i = 10, that is, between the order of i = 9 and the order of i = 10. Then, the accumulated volume Vx is set to 0 (step ST53).

Next, the CPU 23 determines that i =
Based on the total volume V10 of ten, an accumulated volume Vx is obtained (step ST35), and the processing from step ST37 onward is repeated.

As described above, the CPU 23 sequentially inserts the delimiter marks before the final order (in this case, 80 pieces) of the generated candidate data, exceeding the average volume Vav. If it is determined in step ST39 that the order is the last order, the CPU 23 determines the number of delimiter marks and determines the number of packages (step ST4).
3). For example, if the number of separator marks is 9, the total number of packages is 10.

Next, the CPU 23 determines the number of times of picking based on the given candidate data and the item data shown in FIG. 3 (step ST45). Regarding the number of times of picking, irrespective of the number of orders, taking an article of one part number is counted as one time.

Next, the CPU 23 obtains an evaluation value of the candidate (step ST47). Such an evaluation value may be determined by the degree of dispersion Hv of the cumulative volume of the articles put in each package (case) and the degree of dispersion Hp of the number of times of picking in each zone generated in each case. For example, the degree of dispersion Hv may be obtained by summing the absolute values of the differences between the obtained total volume and the average volume for each case, and integrating this for all cases. The degree of dispersion Hp of the number of times of picking may be obtained by averaging the number of times of picking in each zone for each obtained case, and summing the absolute values of the differences. What is necessary is just to weight each of the two obtained dispersion degrees Hv and Hp, and to calculate an evaluation value. That is, the evaluation value Hs can be obtained by the following equation, Hs = w1 · Hv + w2 · Hp. Thus, an evaluation value can be obtained for each candidate. Note that the weighting also functions as an adjustment when adding evaluation values in different units. Further, the weighting can be changed according to the purpose. In other words, if you want the number of picking to be as stable as possible,
What is necessary is just to make w2 large.

Next, the CPU 23 determines whether a predetermined number of such candidates have been generated (step S5 in FIG. 5).
T8). If a predetermined number of candidates have not been generated, the processing of steps ST4 to ST6 is repeated. In the present embodiment, a predetermined number of 50 candidates are generated.

(3-2) GA Operation When the CPU 23 determines in step ST8 that a predetermined number of candidates have been generated, a GA (Genetic Algorithms)
An operation is performed (step ST10). GA
The operation is to generate a candidate having a high evaluation based on randomly generated candidate combination data. That is, originally,
With respect to the order of order rearrangement, evaluations are obtained for all combinations (brute-force calculation method), and the highest evaluation may be determined. However, in this brute force calculation method, it is necessary to calculate an evaluation for a huge number of candidates,
It is not feasible at the speed of the current general computer. Therefore, in the present embodiment, in order to solve such a time problem, the GA calculation method is used. Although the GA calculation method may not be the best evaluation candidate, it can find a candidate close to this in a relatively short time as described below.

The GA operation will be described with reference to FIG. First, the CPU 23 rearranges the 50 candidates obtained in step ST10 in FIG. 5 in descending order of evaluation (step ST61 in FIG. 8). This state is shown in FIG. 9A. In FIG. 9A, candidates K1 to K5
Rearrangement is performed in the order from 0 to 0 in the order of evaluation values 75 to 270.

Next, the CPU 23 performs a multiplication operation (step ST63 in FIG. 8). The breeding operation is an operation in which a candidate with a high evaluation is duplicated at a predetermined ratio, and a candidate with a low evaluation is deleted by the amount of the duplication. In the present embodiment, the proliferation rate was set to 20%, so that the candidates K1 to K10 were duplicated, and
That is, (K41 to K50) are deleted from the ten candidates. In this way, as shown in FIG. 9B, two candidates having high evaluations are generated.

Next, the CPU 23 performs a mutation operation (step ST65 in FIG. 8). The mutation operation is an operation for performing conversion on an arbitrary bit at a predetermined ratio. In the present embodiment, the mutation rate is 1%. Therefore, 50
The mutation operation was performed on individuals × 80 bytes × 1% (ie, 40 bytes). In addition, which bit to perform the mutation operation depends on
Determined by random numbers. Therefore, a mutation operation may be performed on 40 individuals, and conversely, a mutation operation may be performed on 40 bytes of one individual.

In this case, the mutation operation performs the following permutation type GA operation.

FIG. 1 shows the permutation type GA operation.
Explanation will be made using 0. Here, for ease of explanation, a case where arbitrary rearrangement is performed for five candidates will be described as an example.

First, O ₀ , O ₁ , O ₂ ,
O ₃ and O ₄ are basic types. Each candidate is indicated by how it is arranged with respect to this basic type. For example, as shown in FIG. 10B, O ₀ , O ₂ , O ₄ , O ₃ , O ₁
Will be represented by “12321”. This is because, in this candidate, O ₀ is located first in the basic type, and O ₂ is O _{0 in} the basic type.
Is removed, O ₄ is placed in the basic form when O ₀ and O ₂ are removed, and O ₃ is placed in the third form, and O ₃ is placed in the basic form as O ₀ , O _2. And O ₄ are located secondly, excluding O ₄ , where O ₁ is O ₀ , O ₂ ,
This is because they are arranged first when O ₄ and O ₃ are excluded.

In the mutation operation, when the total number of permutations is N, the random number value that can be generated in the n-th bit is restricted by a value smaller than N−n + 1. Perform For example, O
_When performing the mutation operation on ₂ , the total number is 5, and O ₂ is placed second. Therefore, the random number value that can be generated in O ₂ is 5
A value smaller than -2 + 1, that is, a value of 3 or less.

Under such a constraint, when a mutation operation for changing “2” of O ₂ to “3” is performed, “123” is obtained.
The permutation O ₀ , O ₂ , O ₄ , O ₃ , O ₁ represented by “21” is represented by O ₀ , O ₃ , O ₄ , O ₂ , O ₂ represented by “12221”.
It can be rearranged into a permutation of ₁ .

Thus, the mutation operation is
A random number is generated based on a constraint that a value smaller than N + 1−n used in the permutation type GA is generated.

Next, the CPU 23 performs a crossover operation (step ST67 in FIG. 8). The crossover operation is an operation in which arbitrary bits of two candidates are interchanged at a predetermined ratio. In this embodiment, the crossover rate is 10%.
Therefore, for 50 individuals × 10% = 5 individuals, a replacement operation was performed for an arbitrary bit. It should be noted that which bit the crossover operation is to be performed on and the order of replacement are determined by random numbers, similarly to the mutation operation.

The crossover operation is performed when, for example, the candidate shown in FIG.
This is an operation of exchanging the three bit strings at the end of “1” and “12321”. By this operation, two candidates “11321” and “12111” are generated. By rearranging them by permutation, O ₀ , O ₁ , O ₄ , O ₃ , O ₂ , O ₀ ,
It is represented by O ₂ , O ₁ , O ₃ , and O ₄ . In the crossover operation, since the same bit is replaced for two or more candidates, it is not necessary to consider restrictions such as the mutation operation.

As described above, once the processing of steps ST61 to ST67 is completed, the GA operation of one generation is completed.

After the GA operation is completed, the CPU 23
Performs the evaluation operation again (step ST12 in FIG. 5).
This evaluation calculation is the same as that in step ST6, and thus the description is omitted.

(3-3) Candidate Determination Next, the CPU 23 determines whether or not the GA operation has been completed for a predetermined generation (step ST1 in FIG. 5).
4). In the present embodiment, the predetermined number of generations is set to 100 generations. Unless the GA operation has been completed for a predetermined generation, the processing from step ST10 to step ST12 is repeated.

When the GA operation is completed for a predetermined generation, a candidate having the highest evaluation among them is determined as packing data (step ST18).

Of the candidates thus obtained, the one with the highest evaluation is stored as final packing information. Thereby, packing information as shown in FIG. 12 was obtained. This packing information includes the product number and quantity of the product packed in the case of case No. “1”.
In addition to this, data obtained during the calculation of the evaluation, such as the total volume of each order, the number of times of picking in each zone, and the like are also included. The determined combination data is stored in the hard disk 26 and printed out by the printer 30 (step ST20).

FIG. 13 shows comparison data between the case of the conventional method and the case of using the GA operation according to the present invention. As described above, the number of cases is smaller and the average number of times of picking is smaller than in the case where the conventional rearrangement is not performed.

As described above, in this embodiment, the GA
By performing the operation, a candidate with higher evaluation can be obtained in a short time based on the initial 50 candidate data. This is because, in the GA operation, unlike the brute force calculation method, the calculation is performed only for a predetermined generation, so that even if the number of orders increases, the calculation does not increase by the factorial.

The reason why candidate data having a higher evaluation value is obtained is as follows. If all of the initial candidates randomly generated using random numbers happen to have bad overall evaluations, there is a possibility that candidate combinations with higher overall evaluations can be obtained by mutation and crossover operations. high. The mutation operation and the crossover operation may change candidate combination data having a high overall evaluation into candidate combination data having a low overall evaluation. However, since the multiplication operation leaves candidate combination data with high overall evaluation, such a problem can be dealt with.

By increasing the number of generations, the probability of obtaining candidate combination data close to the candidate combination data having the best overall evaluation, which can be obtained only by the brute force method, increases.

4. Others The present invention can be applied not only to delivery management using an auto flow rack but also to a distribution system that distributes a plurality of articles into a plurality of cases and packs them.

In the present embodiment, the dispensing degree of the total volume and the number of times of picking are used as the retrieval efficiency, but the present invention is not limited to this, and the dispersity of the total volume alone may be used.
Further, a parameter indicating the delivery efficiency, for example, a bucket stop time or the like may be adopted as the degree of dispersion of the total volume.

Further, the degree of dispersion of the weight per box may be taken into consideration. Thereby, when transshipping, the work is easy because the variation between heavy and light depending on the box is reduced. In this case, the weight of each item is stored in advance as data, and step ST in FIG.
Similarly to 2, the average weight may be determined based on all orders.

In this embodiment, since the number of orders is 80, the candidate is set to 80 bytes. However, this may be changed according to the number of orders.

When the type of the article, the number of orders, and the like are very large, the article may be divided into a certain size by preprocessing, and an appropriate candidate may be obtained from the division.

In the above embodiment, the functions shown in FIG. 1 are realized by using the CPU 23 and software. However, some or all of them may be realized by hardware such as a logic circuit.

[Brief description of the drawings]

FIG. 1 is a functional block diagram of an apparatus according to the present invention.

FIG. 2 is a diagram showing a hardware configuration realized using a CPU.

FIG. 3 is a diagram illustrating a data structure of an article number, a volume, and a picking zone.

FIG. 4 is a diagram showing a data structure of an order (order No., product number, order quantity).

FIG. 5 is an overall flowchart.

FIG. 6 is a diagram for explaining a notation method of a permutation type GA.

FIG. 7 is a flowchart for obtaining an evaluation value.

FIG. 8 is a flowchart of a GA operation.

FIG. 9 is a diagram for explaining a crossover operation.

FIG. 10 is a diagram showing decision combination data obtained by performing a 50th generation GA operation.

FIG. 11 is a diagram showing comparison data between a conventional method and a method according to the present invention.

FIG. 12 is a diagram showing a part of finally obtained candidate data.

FIG. 13 shows comparison data with and without rearrangement according to the present invention.

FIG. 14 is a concept of a conventional delivery management system.

[Explanation of symbols]

 3 ... Order information storage means 5 ... Item information storage means 7 ... Candidate data generation means 8 ... Candidate data conversion means 9 ... Candidate determination means

Claims (4)

[Claims] An apparatus for deciding packing information for storing a plurality of types of articles in a plurality of packing boxes separately, wherein the article information storage means stores the volume of the articles for each article; Based on the order information including the quantity,
Candidate data generating means for randomly generating a predetermined number of the candidate data of the packing information; for each of the predetermined number of candidate data, a total of the packing boxes is calculated based on a volume for each article stored in the article information storage means. Candidate data conversion means for performing a conversion process by a genetic algorithm so as to obtain less packing information, and among the candidate data converted by the candidate data conversion means, determine the candidate data in which the total number of the packing boxes is small. A packaging information determination device, comprising: candidate determination means. 2. The packaging information determination device according to claim 1, wherein said article information storage means further stores article storage area data in which each of said articles is stored, For the number of candidate data, a conversion process is performed by the genetic algorithm so as to obtain packing information in which the total number of the packing boxes is smaller and the number of drawers in each of the article storage areas is equalized. Means, among the candidate data converted by the candidate data converting means, outputting candidate data in which the total number of the packing boxes is small and the number of drawers in each of the article storage areas is equalized. Decision device. 3. A method for deciding packing information for storing a plurality of types of articles in a plurality of packing boxes separately, wherein the volume of the articles is stored for each article, and the type and number of the articles. Based on the order information including
A predetermined number of candidate data of the packing information is randomly generated, and for each of the predetermined number of candidate data, packing information in which the total number of the packing boxes is smaller based on the stored volume of each article is obtained. And performing a conversion process using a genetic algorithm to determine candidate data in which the total number of the packaging boxes is small among the converted candidate data. 4. A storage medium storing a program for causing a computer including an input device, a control device, an output device, and a storage device to function as a packaging information determining device, wherein the program causes the computer to function as the following device. An apparatus for deciding packing information for storing a plurality of types of articles in a plurality of packing boxes, wherein the apparatus includes the following 1) to 4); An article information storage means for storing for each item, 2) a candidate data generating means for randomly generating a predetermined number of the candidate information of the packing information based on the order information including the type of the article and the number thereof, 3) the predetermined number of the For each candidate data, based on the volume of each article stored in the article information storage means, so that the total of the packing box, packing information is obtained less,
Candidate data conversion means for performing conversion processing by a genetic algorithm, 4) among the candidate data converted by the candidate data conversion means,
Candidate determination means for determining candidate data in which the total number of the packaging boxes is small, a storage medium storing a program.