JP2021125071A - Layout generation device, layout generation method, and layout generation program - Google Patents

Abstract

To generate an optimum storage layout of an article to be picked up.SOLUTION: A layout generation device includes: an acquisition unit for acquiring a storage layout of each article generated based on coordinates data indicating a position of an operator when each article is picked up; a generation unit for generating binary data for identifying the changed storage layout when the acquired storage layout is changed; a simulation unit for simulating picking operation of the operator in a changed storage layout identified by the generated binary data; and a search unit for searching the binary data in which evaluation results that evaluated simulation results satisfies prescribed conditions using a genetic algorithm.SELECTED DRAWING: Figure 1

Description

The present invention relates to a layout generator, a layout generation method, and a layout generation program.

Conventionally, distribution centers and the like have managed by recording the storage layout of products to be picked (arrangement of shelves (or the position of aisles), types and numbers of products to be distributed to each shelf) on paper media or the like. This is because, in the case of a distribution center or the like, the arrangement of shelves (or the position of the aisle) is various, and the types of products to be distributed to each shelf are frequently changed, which is not suitable for systematization.

Japanese Unexamined Patent Publication No. 2003-109170 Japanese Unexamined Patent Publication No. 2009-163511

On the other hand, the storage layout cannot be managed as digital data only by recording on a paper medium or the like. Therefore, for example, it is not possible to perform improvement work such as analyzing the current storage layout and generating an optimum storage layout.

On one side, it aims to generate an optimal storage layout for picking articles.

According to one aspect, the layout generator
An acquisition unit that acquires the storage layout of each article, which is generated based on coordinate data indicating the position of the worker when each article is picked.
A generator that generates binary data that identifies the changed storage layout when the acquired storage layout is changed,
A simulation unit that simulates worker picking work in the modified storage layout identified by the generated binary data,
It has a search unit for searching binary data in which the evaluation result of evaluating the simulation result satisfies a predetermined condition by using a genetic algorithm.

It is possible to generate an optimal storage layout for picking articles.

It is a figure which shows an example of the system configuration of a layout generation system. It is a figure which shows an example of the hardware composition of the layout generator. It is a figure which shows an example of picking data. It is a figure which shows an example of the worker position data. It is a figure which shows an example of the product position data. It is a figure which shows an example of the basic information. It is a figure which shows the specific example of layout information. It is a figure which shows the example of the binary data which identifies the storage layout of each pattern, and the example of the simulation result in the storage layout of each pattern. It is a figure which shows an example of a simulation result, a scoring result and an evaluation result. It is a figure which shows an example of the process of searching binary data using a genetic algorithm. It is a flowchart which shows the flow of layout generation processing. It is a figure which shows an example of the weighting information of an evaluation standard.

Hereinafter, each embodiment will be described with reference to the accompanying drawings. In the present specification and the drawings, components having substantially the same functional configuration are designated by the same reference numerals, so that duplicate description will be omitted.

[First Embodiment]
<System configuration of layout generation system>
First, the system configuration of the layout generation system will be described. In the first embodiment, the layout generation system is applied to the distribution business in the distribution center, and the storage layout (arrangement of shelves (position of passages) of the products to be picked (an example of goods), the types of products to be distributed to each shelf, Generate digital data to manage numbers, etc.). Further, in the first embodiment, the layout generation system generates an optimum storage layout by simulating the picking work of the operator in the storage layout of each pattern using the generated digital data.

FIG. 1 is a diagram showing an example of a system configuration of a layout generation system. As shown in FIG. 1, the layout generation system 100 includes a distribution center management device 110, a layout management data generation device 120, and a layout generation device 130. In the layout generation system 100, the distribution center management device 110, the layout management data generation device 120, and the layout generation device 130 are communicably connected via the network 160.

The distribution center management device 110 is an example of a WMS (Warehouse Management System), and manages picking data recorded each time a product in the distribution center is picked by an operator. A picking data acquisition program is installed in the distribution center management device 110, and when the program is executed, the distribution center management device 110 functions as a picking data acquisition unit 111.

The picking data acquisition unit 111 communicates with the picking terminal 140 carried by each worker who performs the distribution business in the distribution center, and acquires the picking data. The picking data acquired by the picking data acquisition unit 111 includes identification data for identifying the product picked by each worker from each shelf, and time data (an example of mediation data) indicating the time when each worker picked the product. Is included.

The picking data acquisition unit 111 stores the acquired picking data in the picking data storage unit 112. Further, the picking data acquisition unit 111 transmits the picking data stored in the picking data storage unit 112 to the layout management data generation device 120 in response to the transmission request from the layout management data generation device 120.

The layout management data generation device 120 generates digital data for managing the storage layout of the product. A layout management data generation program is installed in the layout management data generation device 120, and when the program is executed, the layout management data generation device 120 functions as a worker position data acquisition unit 121. .. Further, the layout management data generation device 120 functions as a picking data acquisition unit 122 and a product position data generation unit 123.

The worker position data acquisition unit 121 communicates with the position measurement terminal 150 carried by each worker who performs the distribution business in the distribution center, and acquires the worker position data. The worker position data acquired by the worker position data acquisition unit 121 includes coordinate data indicating the position of each worker in the distribution center and time data indicating the time when the coordinate data was recorded. Further, the worker position data acquisition unit 121 stores the acquired worker position data in the worker position data storage unit 124.

The picking data acquisition unit 122 acquires picking data from the distribution center management device 110 by requesting the distribution center management device 110 to transmit the picking data. The picking data acquisition unit 122 notifies the product position data generation unit 123 of the acquired picking data.

When the picking data is notified from the picking data acquisition unit 122, the product position data generation unit 123 includes the worker position including the coordinate data associated with the time data in the corresponding time range from the worker position data storage unit 124. Read the data.

Further, the product position data generation unit 123 mediates the time data between the identification data for identifying the product included in the notified picking data and the coordinate data indicating the position of the worker included in the read worker position data. Correspond as data. As a result, the product position data generation unit 123 generates product position data (an example of data indicating the storage layout of each product), which is digital data for managing the storage layout of the product. Further, the product position data generation unit 123 stores the layout information including the generated product position data in the layout information storage unit 125.

In the layout management data generation device 120, passage information (details will be described later with reference to FIG. 6) is further generated based on the worker position data, and the layout information storage unit 125 is used as layout information together with the product position data. Store.

The layout generation device 130 simulates the picking work of the worker in the storage layout of each pattern based on the layout information and the like generated by the layout management data generation device 120, and specifies the optimum storage layout. A layout generation program is installed in the layout generation device 130, and when the program is executed, the layout generation device 130 functions as a basic information acquisition unit 131, a generation unit 132, a simulation unit 133, and a search unit 134. do.

The basic information acquisition unit 131 is an example of an acquisition unit, and acquires various information (various information including layout information; hereinafter referred to as basic information) used when simulating a worker's picking work, and is a basic information storage unit. Store in 135.

Based on the basic information, the generation unit 132 generates the storage layout of each changed pattern when the storage layout is changed, and also generates binary data for identifying the storage layout of each changed pattern.

The simulation unit 133 simulates the picking work of the operator in the storage layout of each pattern identified by each binary data generated by the generation unit 132 based on the basic information, and outputs each simulation result.

The search unit 134 evaluates each simulation result output by the simulation unit 133 based on the evaluation criteria included in the basic information, and outputs the evaluation result. Further, the search unit 134 identifies the optimum storage layout by searching for binary data whose evaluation result satisfies a predetermined condition (for example, the maximum) by using, for example, a genetic algorithm.

<Hardware configuration of layout generator>
Next, the hardware configuration of the layout generator 130 will be described. FIG. 2 is a diagram showing an example of the hardware configuration of the layout generator. As shown in FIG. 2, the layout generation device 130 includes a CPU (Central Processing Unit) 201, a ROM (Read Only Memory) 202, and a RAM (Random Access Memory) 203. The CPU 201, ROM 202, and RAM 203 form a so-called computer.

Further, the layout generation device 130 includes an auxiliary storage device 204, a display device 205, an operation device 206, an I / F (Interface) device 207, and a drive device 208. The hardware of the layout generator 130 is connected to each other via the bus 209.

The CPU 201 is an arithmetic device that executes various programs (for example, a layout generation program) installed in the auxiliary storage device 204.

ROM 202 is a non-volatile memory. The ROM 202 functions as a main storage device for storing various programs, data, and the like necessary for the CPU 201 to execute various programs installed in the auxiliary storage device 204. Specifically, the ROM 202 functions as a main storage device for storing boot programs such as BIOS (Basic Input / Output System) and EFI (Extensible Firmware Interface).

The RAM 203 is a volatile memory such as a DRAM (Dynamic Random Access Memory) or a SRAM (Static Random Access Memory). The RAM 203 functions as a main storage device that provides a work area that is expanded when various programs installed in the auxiliary storage device 204 are executed by the CPU 201.

The auxiliary storage device 204 is an auxiliary storage device that stores various programs and various data generated by executing various programs. For example, the basic information storage unit 135 is realized in the auxiliary storage device 204.

The display device 205 is a display device that displays various data generated by executing various programs. The operation device 206 is an input device used by the administrator of the layout generation device 130 when inputting various instructions to the layout generation device 130.

The I / F device 207 is a connection device that is connected to the network 160 and receives layout information, for example, by communicating with the layout management data generation device 120.

The drive device 208 is a device for setting the recording medium 210. The recording medium 210 referred to here includes a medium such as a CD-ROM, a flexible disk, a magneto-optical disk, or the like that optically, electrically, or magnetically records information. Further, the recording medium 210 may include a semiconductor memory or the like for electrically recording information such as a ROM or a flash memory.

The various programs installed in the auxiliary storage device 204 are installed, for example, by setting the distributed recording medium 210 in the drive device 208 and reading the various programs recorded in the recording medium 210 by the drive device 208. Will be done. Alternatively, various programs installed in the auxiliary storage device 204 may be installed by being downloaded from the network 160.

<Specific example of picking data>
Next, a specific example of picking data stored in the picking data storage unit 112 of the distribution center management device 110 will be described. FIG. 3 is a diagram showing an example of picking data. As described above, the picking data is recorded for each picking terminal and is acquired by the picking data acquisition unit 111. Therefore, the picking data storage unit 112 stores picking data for each picking terminal (that is, for each worker).

In FIG. 3, the picking data 301, 302, 303, ... Are picking data acquired from each picking terminal. As shown in FIG. 3, each picking data includes "time data" and "picking product" as information items.

The "time data" stores time data indicating the time when each worker picked the product. In the "picking product", identification data for identifying the product picked by each worker from each shelf is stored. According to the example of FIG. 3, the worker picked each product from each shelf in the order of product 5 → product 7 → product 1 → product 6 → product 3 → product 5 → product 8 → ... There is. Further, according to the example of FIG. 3, it is shown that the times when each product was picked were time 1, time 2, time 3, time 4, time 5, time 6, time 7, ..., Respectively. ing.

<Specific example of worker position data>
Next, a specific example of the worker position data stored in the worker position data storage unit 124 will be described. FIG. 4 is a diagram showing an example of worker position data. As described above, the worker position data is recorded for each position measurement terminal and is acquired by the worker position data acquisition unit 121. Therefore, the worker position data storage unit 124 stores the worker position data for each position measurement terminal (that is, for each worker).

In FIG. 4, the worker position data 401, 402, 403, ... Are the worker position data acquired from each position measurement terminal. As shown in FIG. 4, each worker position data includes "time data" and "position coordinates (x, y)" as information items.

In the "time data", time data indicating the time for each predetermined cycle is stored. In "position coordinates (x, y)", coordinate data indicating the position of the worker in the distribution center at each time is stored. According to the example of FIG. 4, the worker moves in the _{distribution center in the order of (x 51} , y ₅₁ ) → ... → (x ₇₁ , y _{71) → ...} Further, according to the example of FIG. 4, it is shown that the time at the position specified by each coordinate data was time 1, ..., Time 2, ....

<Specific example of product position data>
Next, a specific example of the product position data generated by the product position data generation unit 123 will be described. FIG. 5 is a diagram showing an example of product position data. As described above, the product position data generation unit 123 includes the identification data for identifying the product included in the picking data 301, 302, 303, ... And the worker position data 401, 402, 403, ... Corresponds to the coordinate data indicating the position of the worker. As a result, the product position data generation unit 123 generates the product position data. At this time, the product position data generation unit 123 associates the identification data with the coordinate data via the time data which is an example of the mediation data.

As shown in FIG. 5, the product position data 510 and 520 include "picking product" and "position coordinates (x, y)" or "average position coordinates (X, Y)" as information items. Is done.

The "picking product" stores identification data for identifying the product, which is included in the picking data 301, 302, 303, .... Further, in the "position coordinates (x, y)", coordinate data indicating the position of the worker included in the worker position data 401, 402, 403, ... Is stored. Further, the "average position coordinates (X, Y)" stores the average coordinate data calculated based on the coordinate data indicating the position of the worker included in the worker position data 401, 402, 403, ... Will be done.

The example of the product position data 510 in FIG. 5 shows that the coordinate data of the "product 5" picked at "time 1" in the distribution center was (x ₅₁ , y ₅₁ ). Similarly, the example of the product position data 510 in FIG. 5 shows that the coordinate data in the distribution center of the "product 7" picked at "time 2" was (x ₇₁ , y ₇₁ ).

By generating the product position data 510 indicating the storage layout of each product in this way, according to the product position data generation unit 123, the storage layout of the product (what kind of product is stored in which position). Can be quantitatively managed as digital data.

Further, the example of the product position data 520 in FIG. 5 shows that the average coordinate data of "product 1" in the distribution center was (X ₁ , Y ₁ ). Similarly, examples of the product position data 520 in FIG. 5, the average coordinate data in the distribution center for "Product 2" indicates (X _{2, Y 2)} is a thing.

The "average position coordinates (X, Y)" is the time indicating the time when the same type of product was picked among the coordinate data included in the worker position data 401, 402, 403, ... The average coordinate data of each coordinate data to which the data is associated is stored.

In the example of the product position data 520 of FIG. 5, the picking is performed twice for the "product 1", and the coordinate data indicating the position of the worker at the time of each picking is (x ₁₁ , y ₁₁ ). , (X ₁₂ , y ₁₂ ).

Similarly, in the example of the product position data 520 of FIG. 5, the picking is performed 5 times for the "product 2", and the coordinate data indicating the position of the worker at the time of each picking is (x ₂₁ , ,. It shows that it was y ₂₁ ), ..., (X ₂₅ , y _25).

By generating the product position data 520 indicating the storage layout for each type of product in this way, the product position data generation unit 123 can quantitatively manage the storage layout of the product as digital data.

In the example of the product position data 520 of FIG. 5, when a plurality of coordinate data are associated with one product in the product position data 510, the position of the corresponding one product is determined by calculating the average value. It was decided to specify. However, the method for specifying the position of the corresponding 1 product is not limited to this, and the position of the corresponding 1 product may be specified by performing other statistical processing.

Further, in the example of the product position data 520 of FIG. 5, when the position of one product is specified, if the same type of product is picked at different timings, the time data indicating the respective times is obtained. It has been described that all the associated coordinate data are used. However, it is not necessary to use all the coordinate data when specifying the position of one product, and for example, the coordinate data that becomes a singular point may be excluded.

<Specific examples of basic information>
Next, a specific example of the basic information acquired by the basic information acquisition unit 131 will be described. FIG. 6 is a diagram showing an example of basic information.

As shown in FIG. 6, the basic information acquired by the basic information acquisition unit 131 includes evaluation standard weighting information 610, simulation premise information 620, product attribute information 630, and layout information 640. The details of various information included in the basic information will be described below.

(1) Weighting Information of Evaluation Criteria The weighting information 610 of the evaluation standard is a table that defines the weighting of the evaluation standard when evaluating the simulation result of simulating the picking work of the worker. The evaluation criterion weighting information 610 includes an "evaluation criterion" and a "weighting coefficient" as information items.

The "evaluation criteria" stores the evaluation criteria for evaluating the simulation results. In the present embodiment, the search unit 134 evaluates the simulation result based on the "walking distance", "walking congestion time", and "number of shelving changes".

The "walking distance" refers to the distance that the worker walked during the picking work. Further, the "walking congestion time" refers to the waiting time during picking work by the worker. Further, the "number of times of shelving" refers to the number of times of shelving work by the worker.

The "weighting coefficient" stores the weighting coefficient used when weighting and adding each evaluation standard.

According to the example of the weighting information 610 of the evaluation standard, the search unit 134 simulates by calculating (walking distance) × 0.6 + (walking congestion time) × 0.2 + (number of shelving changes) × 0.2. Evaluate the results.

(2) Simulation prerequisite information The simulation prerequisite information 620 is a table that defines the prerequisite information used when simulating the picking work of the operator. The simulation premise information 620 includes "setting items" and "setting values" as information items.

Items to be set when simulating a worker's picking work are stored in "setting items". In the present embodiment, the simulation unit 133 simulates the picking work of the worker after setting the "walking speed", the "picking time", and the "worker attribute".

The "walking speed" refers to the walking speed during picking work by the operator. Further, the "picking time" refers to the time required for the operator to perform one picking. Further, the "worker attribute" refers to the attribute of the worker who performs the picking work (for example, years of work experience).

The setting value corresponding to the setting item is stored in the "setting value". In the present embodiment, for example, a value calculated based on the actual information of the worker's past picking work is used as the set value.

According to the example of the simulation prerequisite information 620, the simulation unit 133 works using the walking speed = "xx [m / min]", the picking time = "yy [seconds]", and the worker attribute = "type z". Simulate the picking work of a person.

(3) Product attribute information The product attribute information 630 is, for example, a table referred to when the search unit 134 searches for the maximum binary data. The product attribute information 630 includes "product type" and "product attribute" as information items.

The "product type" stores all types of products handled by the distribution center. In "product attribute", attribute information of each type of product is stored.

According to the example of the product attribute information 630, the product type = "product 1 (P1)" is a newly ordered product, and the product type = "product 2 (P2)" is a fixed location. Further, the product type = "product 3 (P3)" indicates that it is a PB (Private Brand) product, and the product type = "product 4 (P4)" indicates that it is a special sale product.

The search unit 134 excludes, for example, a storage layout of a pattern in which the arrangement of products whose product attribute is a fixed location is changed from the simulation target.

(4) Layout information Layout information 640 is a table that defines layout information used when simulating a picking operation of an operator. The layout information 640 includes "product position data" and "aisle information" as information items.

The "product position data" stores the coordinate data in the distribution center of each product. Since the details of the "product position data" have already been explained with reference to FIG. 5, the description thereof will be omitted here.

On the other hand, the "passage information" stores information indicating whether or not the boundary area between the sections is passable when the work area of the worker in the distribution center is divided into a plurality of sections. Specifically, the "passage information" includes "inter-section area" and "passability" as information items. Then, the "inter-section area" stores information indicating the boundary area between sections, and the "passability" stores information indicating whether or not the corresponding boundary area is passable.

According to the example of layout information 640, the inter-section area = "area 1" is passable, indicating that the area is used by the operator as a passage during picking work. On the other hand, the inter-section area = "area 2" is impassable, indicating that the area cannot be used as a passage by the operator during picking work.

When simulating the picking work of the worker, the simulation unit 133 specifies the coordinate data of each product based on the "product position data" and the walking route of the worker based on the "aisle information".

Note that FIG. 7 is a diagram showing a specific example of layout information, and shows a part of the layout information 640 as a plan view (however, in FIG. 7, it is not the average position coordinates of each product, but individual items. Individual products are plotted on the position coordinates of the products).

The example of FIG. 7 shows how a part of the work area 700 of the worker in the distribution center is divided into eight sections (reference numerals 701 to 708).

Further, the example of FIG. 7 shows that the goods are arranged in the six sections shown by the reference numerals 701 to 706 among the eight sections shown by the reference numerals 701 to 708. In the present embodiment, among the sections in which the goods are arranged (reference numerals 701 to 706), the section identifier of the section indicated by reference numeral 701 is "1", and the section identifier of the section indicated by reference numeral 702 is "2". Hereinafter, similarly, the section identifiers of the sections indicated by reference numerals 702 to 706 are referred to as "3" to "6", respectively.

According to the example of FIG. 7, it can be seen that two "P1" (commodity 1) are stored in the section "1". It can also be seen that five "P2" (commodity 2) were stored in section "2". It can also be seen that eight "P3" (commodity 3) were stored in section "3". It can also be seen that six "P4" (commodity 4) were stored in section "4". It can also be seen that two "P5" (commodity 5) were stored in the section "5". Further, it can be seen that three "P6" (commodity 6) were stored in the section "6".

In this way, by showing the storage layout for each section of each product as a plan view, it is possible to manage the storage layout of the products (which section, what type of product, and how many were stored) as digital data. can.

Further, in the case of the example of FIG. 7, since the sections "1", "2", "5", and "6" have the same area, for example,
-"P1" (product 1) is a product of a size that can store at least two in the area of section "1".
-When exchanging the position of "P1" (product 1) with products in other sections, products of similar size (that is, products in which the same number is stored in the section, for example, Efficient exchange with "P5" (Product 5) in section "5",
And so on.

Further, the example of FIG. 7 shows that among the boundary areas between sections, the shaded area is an impassable area, and the unshaded area is passable. Indicates that it is an area.

For example, the boundary region between the section indicated by reference numeral 701 and the section indicated by reference numeral 702 is an impassable area. On the other hand, the boundary region between the section indicated by reference numeral 701 and the section indicated by reference numeral 707 is a passable area.

<Example of binary data and example of simulation result>
Next, an example of binary data generated by the generation unit 132 that identifies the storage layout of each pattern and an example of the simulation result by the simulation unit 133 in the storage layout of each pattern will be described.

FIG. 8 is a diagram showing an example of binary data that identifies the storage layout of each pattern and an example of a simulation result in the storage layout of each pattern.

Of these, reference numeral 810 indicates an example of binary data generated by the generation unit 132 that identifies the storage layout of each pattern. As shown by reference numeral 810, the storage layout of each pattern is defined with a pattern name (in the example of FIG. 8, the pattern names are "Pattern 1", "Pattern 2", "Pattern 3", "". Pattern 4 "... etc. are specified).

Further, as shown by reference numeral 810, the storage layout of each pattern is a combination of six types of products ("P1" (product 1) to "P6" (product 6)) and sections "1" to "6". Shows variations of. In the case of reference numeral 810, the storage layout of each pattern includes a total of 720 patterns (= 6 × 5 × 4 × 3 × 2 × 1) storage layouts. Of these, "Pattern 1" is the current storage layout (FIG. 7), and "Pattern 2" and subsequent "Pattern 2" indicate the storage layout of each pattern after the change when the storage layout is changed.

Further, as shown by reference numeral 810, the generation unit 132 assigns a serial number to each pattern of the current storage layout and the changed storage layout, and converts each serial number into a binary number to generate binary data. do.

The example of FIG. 8 shows that the binary data of the pattern name = "Pattern 1" is "0000000001" and the binary data of the pattern name = "Pattern 2" is "0000000010".

On the other hand, reference numeral 820 indicates an example of the simulation result for the storage layout of the pattern name = "pattern 1". Further, reference numeral 830 indicates an example of the simulation result for the storage layout of the pattern name = "pattern 2".

In reference numerals 820 and 820, the curved arrows indicate walking paths during picking work by the operator. In the pattern name = "Pattern 1" and the pattern name = "Pattern 2", the position of the product ("P5" (Product 5)) arranged in the section "5" and the product arranged in the section "6" ( The position of "P6" (product 6)) has been replaced.

Therefore, when the order of the products to be picked is fixed (in the example of FIG. 8, when the order is fixed in the order of P1 (product 1) → P3 (product 3) → P6 (product 6) → P5 (product 5)), In "Pattern 1" and "Pattern 2", the walking route of the worker is changed.

In this way, by simulating the picking work of the worker who has a different walking route for each pattern, different evaluation results are output for each pattern.

<Example of evaluation result>
Next, a specific example of the simulation result by the simulation unit 133, the scoring result by the search unit 134, and the evaluation result in the storage layout of each pattern will be described. FIG. 9 is a diagram showing an example of a simulation result, a scoring result, and an evaluation result.

As shown in FIG. 9, the simulation result 910 includes "pattern name", "walking distance", "walking congestion time", and "number of shelving changes" as information items.

The pattern name of the storage layout of each pattern is stored in the "pattern name". The "walking distance", "walking congestion time", and "number of shelving changes" store the walking distance, walking congestion time, and number of shelving changes, which are evaluation criteria when evaluating the simulation result.

In the example of FIG. 9, as a result of executing the simulation for the storage layout of the pattern name = "Pattern 1", it was calculated that the walking distance = "D001", the walking congestion time = "J001", and the number of shelving changes = "S001". It is shown that.

Further, as shown in FIG. 9, the scoring result 920 includes "walking distance", "walking congestion time", and "number of shelving changes" as information items.

The "walking distance" stores the result of scoring the walking distance calculated by executing the simulation for the storage layout of each pattern. In the case of "walking distance", for example, 10000 [m] is 0 point, 5000 [m] is 100 point, and 0 to 100 points are assigned in the range of 10000 [m] to 5000 [m].

In the "walking congestion time", the result of scoring the walking congestion time calculated by executing the simulation for the storage layout of each pattern is stored. In the case of "walking congestion time", for example, 5000 [seconds] is set to 0 points, 1000 [seconds] is set to 100 points, and 0 points to 100 points are assigned in the range of 5000 [seconds] to 1000 [seconds].

In the "number of shelving changes", the result of scoring the number of shelving changes calculated by executing the simulation for the storage layout of each pattern is stored. In the case of "number of shelving changes", for example, 6 [shelf] is 0 point, 0 [shelf] is 100 point, and 0 to 100 points are assigned in the range of 6 [shelf] to 0 [shelf].

According to FIG. 9, for example, as a result of executing a simulation for the storage layout of the pattern name = "Pattern 1", the walking distance = "70 points", the walking congestion time = "80 points", and the number of shelving changes = "50 points". "Indicates that it was scored.

Further, as shown in FIG. 9, the evaluation result 930 stores the evaluation result calculated based on the scoring result 920 of the storage layout of each pattern.

As described above, the search unit 134 calculates the evaluation result 930 from the scoring result 920 by weighting and adding based on the weighting information 610 of the evaluation standard. According to the weighting information 610 of the evaluation standard, the weighting coefficients of "walking distance", "walking congestion time", and "number of shelving changes" are "6", "2", and "2", for example. , In the case of the storage layout of pattern name = "Pattern 1"
Evaluation result = 70 points x 0.6 + 80 points x 0.2 + 50 points x 0.2 = 68 points.

<Specific example of processing by the search unit>
Next, a specific example of processing by the search unit 134 will be described. As mentioned above, the storage layout for each pattern is generated based on the combination of the product type and the section in which the product is placed. In the example described with reference to FIG. 8, since there were 6 types of products and 6 sections, the total number of storage layout patterns was 730. However, since the number of product types and sections in an actual distribution center is enormous, the number of storage layout patterns is also enormous. Then, if all of these patterns are simulated in order, the calculation load by the simulation unit 133 is high.

Therefore, the search unit 134 in the present embodiment searches for the binary data that maximizes the evaluation result by using the genetic algorithm (optimizes the combination of the binary data). As a result, according to the search unit 134, the optimum storage layout can be specified in a shorter time.

FIG. 10 is a diagram showing an example of a process of searching for binary data having the maximum evaluation result by using a genetic algorithm. In the example of FIG. 10, for simplification of the explanation, the case where the product types are 6 types and the number of sections is 6 (that is, the total number of storage layout patterns is 720) will be described. do.

As shown in FIG. 10, the search unit 134 prepares the binary data of the initial pattern group and sets it as the binary data of the current generation pattern group (see reference numeral 1001). Subsequently, the search unit 134 acquires the simulation result when the simulation unit 133 simulates each pattern included in the pattern group of the current generation.

Subsequently, the search unit 134 generates binary data of the next-generation pattern group by selective selection from the evaluation result calculated based on the simulation result (see reference numeral 1002). Further, the search unit 134 acquires the simulation result when the simulation unit 133 simulates each pattern included in the generated next-generation pattern group, and calculates the evaluation result.

Subsequently, the search unit 134 performs cross-processing and mutation processing on the binary data of the next-generation pattern group (see reference numerals 1003 and 1004), and performs the processed binary data of the pattern group (see reference numeral 1004). , Set as binary data of the current generation pattern group.

The search unit 134 searches for binary data having the maximum evaluation result by repeating these processes as a cycle of one generation for a plurality of cycles, and ends the process when the final generation is reached.

In this way, the search unit 134 searches for the binary data having the maximum evaluation result by using the genetic algorithm. As a result, according to the search unit 134, the optimum storage layout can be specified in a shorter time.

<Flow of layout generation process>
Next, the flow of the layout generation process by the layout generation device 130 will be described. FIG. 11 is a flowchart showing the flow of the layout generation process.

In step S1101, the basic information acquisition unit 131 acquires basic information (weighting information of evaluation criteria, simulation premise information, product attribute information, layout information).

In step S1102, the generation unit 132 generates binary data that identifies the storage layout of each pattern, and reads out the binary data of the initial pattern group from the generated binary data.

In step S1103, the simulation unit 133 simulates the storage layout of each pattern identified by the read binary data based on the basic information.

In step S1104, the search unit 134 scores the simulation result by the simulation unit 133 and outputs the scoring result.

In step S1105, the search unit 134 evaluates the scoring result and calculates the evaluation result. Further, the search unit 134 holds the calculated evaluation result in association with the binary data.

In step S1106, the search unit 134 determines whether or not the final generation pattern group has been reached. If it is determined in step S1106 that the pattern group of the final generation has not been reached (in the case of No in step S1106), the process proceeds to step S1107.

In step S1107, the search unit 134 reads out the binary data of the next-generation pattern group and returns to step S1103.

On the other hand, if it is determined in step S1106 that the pattern group of the final generation has been reached (Yes in step S1106), the process proceeds to step S1108.

In step S1108, the search unit 134 identifies the storage layout identified by the binary data that maximizes the evaluation result as the optimum storage layout.

In step S1109, the search unit 134 outputs the difference between the current storage layout and the optimum storage layout.

As is clear from the above description, the layout generation device 130 according to the first embodiment is a storage layout of each product generated based on coordinate data indicating the position of the worker when each product is picked. To get. In addition, the layout generation device 130 according to the first embodiment generates binary data that identifies the changed storage layout when the acquired storage layout is changed. Further, the layout generation device 130 according to the first embodiment simulates a picking operation of an operator in the changed storage layout identified by the generated binary data. Further, the layout generator 130 according to the first embodiment searches for binary data that maximizes the evaluation result of evaluating the simulation result by using a genetic algorithm.

As a result, according to the layout generator 130 according to the first embodiment, the storage layout identified by the binary data that maximizes the evaluation result is searched for in a shorter time and specified as the optimum storage layout. Can be done.

Thus, according to the first embodiment, it is possible to generate an optimum storage layout for picking products.

[Second Embodiment]
In the first embodiment described above, the weighting information of the evaluation criteria is fixed and simulated. However, the weighted information of the evaluation criteria may be variable. This is because it is possible to generate an optimum storage layout that meets the needs of the user depending on which evaluation criterion the weighting coefficient is increased. Hereinafter, the second embodiment will be described focusing on the differences from the first embodiment.

FIG. 12 is a diagram showing an example of weighting information of the evaluation criteria. In the example of FIG. 12, as the weighting information of the evaluation criteria, the weighting information 1210 and 1211 of the evaluation criteria are shown as the information other than the weighting information 610 of the evaluation criteria shown in FIG.

Of these, the weighting information 1210 of the evaluation standard shows a case in which the weighting coefficient of the "walking congestion time" is larger than that of the weighting information 610 of the evaluation standard. Further, the weighting information 1211 of the evaluation standard shows a case in which the weighting coefficient of the "number of shelving changes" is larger than that of the weighting information 610 of the evaluation standard.

By adjusting the weighting coefficient in this way, for example, in the case of the weighting information 610 of the evaluation standard, the flow line priority type storage layout was generated, but according to the weighting information 1210 of the evaluation standard, the congestion is alleviated. A storage layout for the type will be generated. Further, according to the weighting information 1211 of the evaluation standard, a shelf-changing labor-saving storage layout is generated.

The search unit 134 may display a plan view (reference numeral 1220-1222) of each storage layout together with each model name (traffic line priority type, congestion mitigation type, shelf change labor saving type).

In this way, by making the weighting information of the evaluation criteria variable and setting the weighting coefficient of the weighting information of the evaluation criteria based on the user's instructions, it is possible to generate the optimum storage layout that meets the needs of the user. Become.

In the above description, the weighting coefficient of the weighting information of the evaluation standard is described as being directly instructed by the user, but the user instructs only the model name, and the basic information acquisition unit 131 responds to each model name. The weighting coefficient of the weighting information of the evaluation standard may be calculated and set.

Further, in the above description, each model name is displayed together with the plan view of the storage layout (reference numerals 1220-1222), but the target to be displayed together with the plan view of the storage layout is not limited to this. The rate of improvement in productivity due to changes in the storage layout, the degree of cost reduction on a monthly basis, and the like may also be displayed.

[Other Embodiments]
In each of the above embodiments, the storage layout of the pattern to be excluded from the simulation target is specified by referring to the product attribute information 630, but the method of specifying the pattern to be excluded from the simulation target is not limited to this. For example, if it is determined that exchanging the storage sections is not efficient from the viewpoint of product size based on the layout information shown in FIG. 7, the storage layout of the corresponding pattern is to be simulated. May be excluded from.

Further, in each of the above embodiments, the case where the layout generation system 100 is applied to the distribution business of the distribution center has been described. However, the application destination of the layout generation system 100 is not limited to the distribution center. For example, it may be applied to any business of picking, such as a parts shelf of an article manufacturing line.

In addition, in the disclosed technology, a form as described in the appendix described below can be considered.
(Appendix 1)
An acquisition unit that acquires the storage layout of each article, which is generated based on coordinate data indicating the position of the worker when each article is picked.
A generator that generates binary data that identifies the changed storage layout when the acquired storage layout is changed,
A simulation unit that simulates worker picking work in the modified storage layout identified by the generated binary data,
A layout generator having a search unit that searches for binary data in which the evaluation result of evaluating the simulation result satisfies a predetermined condition by using a genetic algorithm.
(Appendix 2)
The layout generator according to Appendix 1, wherein the modified storage layout is generated based on a combination of the type of article and the section in which the article is placed.
(Appendix 3)
The layout generation device according to Appendix 1 or 2, wherein the simulation unit simulates a picking operation based on a predetermined walking speed of the operator, picking time by the operator, and attributes of the operator.
(Appendix 4)
The search unit
The layout generator according to Appendix 1, which scores simulation results corresponding to each of a plurality of evaluation criteria calculated by simulation by the simulation unit.
(Appendix 5)
The search unit
The layout generator according to Appendix 4, wherein the evaluation result is calculated by weighting and adding the scoring results of each of the plurality of evaluation criteria using a predetermined weighting coefficient.
(Appendix 6)
The search unit
The layout generator according to Appendix 5, which searches for binary data having the maximum evaluation result.
(Appendix 7)
The layout generation device according to Appendix 5, wherein the weighting coefficient is variable, and the search unit performs weighting and addition using a weighting coefficient set based on a user's instruction.
(Appendix 8)
Acquire the storage layout of each article generated based on the coordinate data indicating the position of the worker when each article is picked.
Generates binary data that identifies the changed storage layout when the acquired storage layout is changed.
Simulate worker picking operations in modified storage layouts identified by the generated binary data
A genetic algorithm is used to search for binary data in which the evaluation result that evaluates the simulation result satisfies a predetermined condition.
A layout generation method in which a computer performs processing.
(Appendix 9)
Acquire the storage layout of each article generated based on the coordinate data indicating the position of the worker when each article is picked.
Generates binary data that identifies the changed storage layout when the acquired storage layout is changed.
Simulate worker picking operations in modified storage layouts identified by the generated binary data
A genetic algorithm is used to search for binary data in which the evaluation result that evaluates the simulation result satisfies a predetermined condition.
A layout generator that lets a computer perform processing.

The present invention is not limited to the configurations shown here, such as combinations with other elements in the configurations and the like described in the above embodiments. These points can be changed without departing from the spirit of the present invention, and can be appropriately determined according to the application form thereof.

100: Layout generation system 110: Distribution center management device 111: Picking data acquisition unit 120: Layout management data generation device 121: Worker position data acquisition unit 122: Picking data acquisition unit 123: Product position data generation unit 130: Layout generation Device 131: Basic information acquisition unit 132: Generation unit 133: Simulation unit 134: Search unit 510, 520: Product position data 610: Evaluation standard weighting information 620: Simulation prerequisite information 630: Product attribute information 640: Layout information 910: Simulation Result 920: Scoring result 930: Evaluation result

Claims (7)

An acquisition unit that acquires the storage layout of each article, which is generated based on coordinate data indicating the position of the worker when each article is picked.
A generator that generates binary data that identifies the changed storage layout when the acquired storage layout is changed,
A simulation unit that simulates worker picking work in the modified storage layout identified by the generated binary data,
A layout generator having a search unit that searches for binary data in which the evaluation result of evaluating the simulation result satisfies a predetermined condition by using a genetic algorithm. The layout generator according to claim 1, wherein the modified storage layout is generated based on a combination of the type of article and the section in which the article is placed. The search unit
The layout generator according to claim 1, wherein a simulation result corresponding to each of a plurality of evaluation criteria calculated by the simulation unit is scored. The search unit
The layout generation device according to claim 3, wherein the evaluation result is calculated by weighting and adding the scoring results of each of the plurality of evaluation criteria using a predetermined weighting coefficient. The layout generation device according to claim 4, wherein the weighting coefficient is variable, and the search unit performs weighting and addition using a weighting coefficient set based on a user's instruction. Acquire the storage layout of each article generated based on the coordinate data indicating the position of the worker when each article is picked.
Generates binary data that identifies the changed storage layout when the acquired storage layout is changed.
Simulate worker picking operations in modified storage layouts identified by the generated binary data
A genetic algorithm is used to search for binary data in which the evaluation result that evaluates the simulation result satisfies a predetermined condition.
A layout generation method in which a computer performs processing. Acquire the storage layout of each article generated based on the coordinate data indicating the position of the worker when each article is picked.
Generates binary data that identifies the changed storage layout when the acquired storage layout is changed.
Simulate worker picking operations in modified storage layouts identified by the generated binary data
A genetic algorithm is used to search for binary data in which the evaluation result that evaluates the simulation result satisfies a predetermined condition.
A layout generator that lets a computer perform processing.

Images