Product placement planning system and product placement planning method

Abstract

The overall productivity of the logistics warehouse is improved in accordance with fluctuations in the shipment volume of each product over a period of time. [Solution] The product placement planning system of the present invention is characterized by comprising a product placement plan generation unit that generates a product placement plan for a product placement period longer than the unit period by referring to shipment schedule information which stores the planned shipment quantities for each unit period of multiple products handled in a logistics warehouse, and pick method information which stores at least one of the processing efficiency and the maximum number of personnel that can be engaged for each of multiple pick methods implemented in the logistics warehouse, and assigning a pick method from the pick method information to the product based on the fluctuation of the planned shipment quantities for each unit period of the product in the shipment schedule information.

Description

Technical Field

[0001] The present invention relates to a merchandise arrangement planning system and a merchandise arrangement planning method. In particular, the present invention relates to merchandise arrangement planning when a plurality of picking methods with at least different processing efficiencies are mixed.

Background Art

[0002] With the progress of automation technology and the diversification of products, a picking operation that combines a plurality of methods with at least different processing efficiencies has become the mainstream. When the shipment volume of each product in the warehouse fluctuates, it is important to maximize the processing efficiency of each picking method in order to efficiently perform the picking operation. Generally, in a logistics warehouse, the picking method is fixed for each picking area inside. Therefore, for improving the efficiency of the picking operation, the merchandise arrangement for the picking area is important.

[0003] As a technology for realizing an improvement in the efficiency of the picking operation, there is an article arrangement optimization system in Patent Document 1. For each of a plurality of frontage spaces where a plurality of shelves are provided and a plurality of products are arranged, based on the predicted shipment volume as a result of demand prediction for the products arranged in the frontage space during a specified future period, the recommended capacity value of the product is calculated. The system further determines one or more replacement pairs, each of which is a frontage space pair in which product replacement occurs, based on the current capacity value and the recommended capacity value of each of the plurality of frontage spaces.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] In logistics warehouse picking operations, the amount of goods shipped using each picking method, as well as the overall productivity of the logistics warehouse, changes depending on which goods are placed in which picking area. For example, by arranging goods so that more picking operations are performed using the picking method with the highest processing efficiency (details below), it becomes possible to ship a large amount of goods with high efficiency, thereby improving the overall productivity of the logistics warehouse.

[0006] Here, the shipment volume for each product may fluctuate over short periods, such as daily or weekly. In this case, the total shipment volume for each picking method will fluctuate daily. Therefore, if the planned shipment volume exceeds or falls below the capacity limit (details below) of each picking method, overtime work may occur, or the overall productivity of the logistics warehouse may decrease.

[0007] While it is possible to bring the planned shipment volume closer to the capacity limit of each picking method by changing the product arrangement for each unit period, this would require the work of swapping products each unit period, potentially increasing the overall workload of the logistics warehouse and reducing productivity.

[0008] The item placement optimization system described in Patent Document 1 only assumes a picking method in which workers or other personnel pick items from multiple shelves in multiple opening spaces. For example, when picking areas where multiple picking methods are used are mixed, a separate measure was needed to improve overall productivity. Therefore, the present invention aims to improve the overall productivity of a logistics warehouse in response to fluctuations in the shipment volume of each product over a unit period. [Means for solving the problem]

[0009] The product placement planning system of the present invention is characterized by comprising a product placement plan generation unit that generates a product placement plan for a product placement period longer than the unit period by referring to shipment schedule information which stores the planned shipment quantities for each unit period of multiple products handled in a logistics warehouse, and pick method information which stores at least one of the processing efficiency and the maximum number of personnel that can be engaged for each of multiple pick methods implemented in the logistics warehouse, and assigning a pick method from the pick method information to the product based on the fluctuation of the planned shipment quantities for each unit period of the product in the shipment schedule information. Other means will be described within the descriptions of embodiments for carrying out the invention. [Effects of the Invention]

[0010] According to the present invention, the productivity of the entire logistics warehouse can be improved in response to fluctuations in the shipment volume of each product for each unit period. [Brief explanation of the drawing]

[0011] [Figure 1] This is a diagram showing the configuration of a product placement planning system. [Figure 2] This is a diagram showing an example of the configuration of a logistics warehouse. [Figure 3] This diagram illustrates the relationship between fluctuations in shipment volume and capacity limits. [Figure 4] This diagram illustrates the relationship between fluctuations in shipment volume and capacity limits. [Figure 5] This diagram illustrates the distribution of product shipment volumes and product groups. [Figure 6] This diagram illustrates the distribution of product shipment volumes and product groups. [Figure 7] This figure shows an example of product storage information. [Figure 8] This figure shows an example of picking method information. [Figure 9] This figure shows an example of shipping schedule information. [Figure 10] This diagram shows an example of a product layout plan. [Figure 11] This figure shows an example of the output screen. [Figure 12] This is a flowchart of the processing procedure.

Mode for Carrying Out the Invention

[0012] Hereinafter, embodiments of the present invention (referred to as "the present embodiment") will be described in detail with reference to the drawings. The present embodiment is an example in which three different types of picking methods coexist in the same logistics warehouse. However, the present invention is not to be construed as being limited to the content described in the present embodiment. The specific configuration can be changed without departing from the spirit or gist of the present invention. "Goods" in the present embodiment means an article that can be stored in a logistics warehouse regardless of whether it is an object of transaction. Note that "pick" means to select or collect, but here it refers to the operation of taking out goods from storage locations such as warehouses and logistics centers (picking).

[0013] (Configuration of the Goods Placement Planning System) FIG. 1 is a diagram showing the configuration of a goods placement planning system 1. The goods placement planning system 1 is a general computer and includes a central control device 11, input devices 12 such as a camera, microphone, mouse, and keyboard, output devices 13 such as a speaker and display, a main storage device 14, an auxiliary storage device 15, and a communication device 16.

[0014] The input processing unit 21, goods placement plan generation unit 22, and output processing unit 23 in the main storage device 14 are programs. The goods placement plan generation unit 22 includes an optimization calculation model 22b (details will be described later). In the following description, when the subject is described as "○○ unit is", it means that the central control device 11 reads the program from the auxiliary storage device 15 into the main storage device 14 and realizes the functions of the program. The auxiliary storage device 15 stores goods storage information 31, picking method information 32, shipping schedule information 33, and a goods placement plan 34 (details will be described later). The auxiliary storage device 15 may be configured independently as a separate housing. That is, the goods placement planning system 1 performs information processing using the goods storage information 31 and the like, but these information may be stored in a cloud server or the like outside the goods placement planning system 1.

[0015] (Configuration of Logistics Warehouse) FIG. 2 is a diagram showing an example of the configuration of a logistics warehouse 40. The logistics warehouse 40 has a plurality of picking methods in which at least one of at least the processing efficiency and the upper limit of the number of personnel who can be engaged (details will be described later) is different. Corresponding to each picking method, there is a picking area 41 which is a workplace where the picking operation is actually performed. The products picked in each picking area 41 are conveyed to the subsequent process. At this time, the conveyance destination after picking is set for each picking method. The product conveyance operation may be manual conveyance 42 or automatic conveyance using a conveyance robot 43 and a conveyor 44.

[0016] In the logistics warehouse 40, each product is stored in a product box such as a cardboard box or a container, and generally, the product box is stored in the picking area 41 corresponding to each picking method. The typical picking methods in the logistics warehouse 40 are as follows.

[0017] (Picking Method) The first picking method is an "automatic warehouse" in which a product box is automatically stored in the warehouse using an automated mechanical device 45 for storage and retrieval, and the product box is taken out as necessary to pick the product. The second picking method is a "shelf conveyance" in which a product shelf 46 storing product boxes is conveyed to the picking workplace, and a picking worker takes out the necessary products from the product shelf 46. The third picking method is a "cart" in which an operator moves between product shelves storing product boxes with a cart 47 and takes out the necessary products from the product shelves.

[0018] In these picking methods, the picking operation of taking out the product from the product box may be performed manually or automatically using a picking robot or the like. The above three types of picking methods are examples among various picking methods, and the actual picking method is not limited to these. The logistics warehouse 40 performs the product picking operation according to the following procedure.

[0019] (Step 1) The logistics warehouse 40 receives order quantity information for each product, or predicts future shipment quantities based on past performance, to create shipment schedule information 33 (details below), and decides which picking method to use for each product based on the shipment schedule information 33. (Step 2) In each picking method, the logistics warehouse 40 takes out the required quantity of goods from the product boxes and stores them in picking boxes such as containers. (Step 3) The logistics warehouse 40 transports the pick boxes containing the picked goods to the next process.

[0020] (Capacity limit and processing efficiency) The capacity limit for each picking method represents the maximum shipment volume per unit period, calculated from the processing efficiency and the maximum number of personnel that can be employed for that picking method. Processing efficiency (unit: rows / person-hour) is the result of dividing the shipment volume (unit: rows) by the work hours (unit: person-hour). The higher the processing efficiency and the larger the maximum number of personnel that can be employed, the higher the capacity limit becomes. A predetermined period longer than the unit period is called the "product placement period." In this embodiment, the unit period is one day (each day of the week). In this embodiment, the product placement period is one week, which is longer than the unit period (details below). In reality, the product placement period is at least one week and is often an integer multiple of one month. During the product placement period, the placement of products does not change. That is, each product continues to be picked using the same picking method. The length of the product placement period may or may not be fixed.

[0021] In this embodiment, assigning a picking method to a product is synonymous with determining the placement of the product, and is also synonymous with assigning a picking area to the product. This is because the picking method for a product is uniquely determined depending on where the product is placed.

[0022] (Functions of the product placement plan generation unit) The product placement plan generation unit 22 generates a product placement plan 34 in which a picking method is assigned to each product according to fluctuations in the planned shipment volume of each product. Examples of the methods for generating the product placement plan 34 are the following placement method 1 and placement method 2.

[0023] (Placement Method 1) The product placement plan generation unit 22 sorts the products scheduled for shipment in descending order of planned shipment volume during the product placement period, and in descending order of fluctuation in planned shipment volume, and assigns picking methods to the sorted products in descending order of processing efficiency. At this time, the product placement plan generation unit 22 generates a product placement plan 34 so that the planned shipment volume of the products placed in the picking area 41 of each picking method does not exceed the capacity limit.

[0024] (Placement Method 2) The product placement plan generation unit 22 classifies the products scheduled for shipment into one or more product groups. At this time, the product placement plan generation unit 22 determines the product groups such that the total fluctuation in the planned shipment volume of the products belonging to each product group is small during the product placement period. Next, the product placement plan generation unit 22 rearranges each product group in descending order of planned shipment volume during the product placement period, and in descending order of fluctuation in planned shipment volume, and assigns picking methods to the rearranged product groups in descending order of processing efficiency. At this time, the product placement plan generation unit 22 determines the product placement plan 34 such that the planned shipment volume of the product groups placed in the picking area 41 of each picking method does not exceed the capacity limit.

[0025] Generally, the number of product types is far greater than the number of picking methods. Therefore, one picking method can be assigned to many different types of products. Arrangement method 2 is based on this premise. Arrangement method 2 is a characteristic arrangement method of this embodiment.

[0026] The product placement plan generation unit 22 generates a product placement plan 34 using placement method 1 and / or placement method 2. The product placement plan generation unit 22 may use the product placement plan 34 generated by this placement method as an initial solution and generate other initial solutions by swapping products that have been assigned an arbitrary picking method (details below).

[0027] The product placement plan 34 generated by the product placement plan generation unit 22 may consist of one or more product placement plans. Furthermore, it is not necessary for all of the future planned shipment volumes for each product to be known. In this case, the product placement plan 34 can be generated by classifying products into product groups, as in placement method 2, based on information such as fluctuations in planned shipment volumes and shipment trends.

[0028] (Relationship between fluctuations in shipment volume and capacity limits) Figures 3 and 4 illustrate the relationship between fluctuations in shipment volume and capacity limits. Hereafter, unless otherwise specified, the planned shipment volume for the future product placement period will simply be referred to as "shipment volume." The shipment volume of each product fluctuates according to the unit period, such as the day of the week (Monday, Tuesday, ..., Sunday). For example, shipments of office supplies are high on Mondays. Shipments of energy drinks are high on Wednesdays, when work fatigue begins. Shipments of leisure goods are high on Fridays. These are extreme examples that are easy to understand, but it has been empirically observed that for daily necessities (consumables), for example, peaks in shipment volume appear periodically depending on the volume of the sales unit. Furthermore, it has been empirically observed that for office supplies used by financial institutions, etc., peaks in shipment volume appear on settlement dates (5th, 10th, ..., last day of the month).

[0029] First, let's look at Figure 3, which illustrates a conventional example. The product placement plan generation unit 22 determines the placement of each product using the average shipment volume during the product placement period. As a result, products with similar timing of fluctuations (increases and decreases) may be assigned the same picking method. The product placement plan generation unit 22 assigns a picking method with high processing efficiency to products with a large average shipment volume.

[0030] As a result, at specific times, the shipment volume 102 per unit period exceeds or falls significantly below the capacity limit 106. If the shipment volume 102 per unit period exceeds the capacity limit 106, overtime work occurs or shipments are delayed. If the shipment volume 102 per unit period falls significantly below the capacity limit 106, the processing efficiency of the picking method is not fully utilized, and the overall productivity of the logistics warehouse decreases.

[0031] Next, let's look at Figure 4, which illustrates an example of this embodiment. The product placement plan generation unit 22 combines products whose shipment volume 102 fluctuates (increases or decreases) at different timings to form product groups, and assigns the same picking method to each product group so as not to exceed the capacity limit 106 of each picking method. Here again, the product placement plan generation unit 22 assigns picking methods with high processing efficiency to product groups with a large average shipment volume.

[0032] As a result, compared to Figure 3, the fluctuation in shipment volume 102 during product placement periods longer than the unit period becomes smoother, and the shipment volume no longer exceeds the capacity limit 106. This helps to suppress the decline in overall productivity of the logistics warehouse, such as overtime and shipping delays.

[0033] The three types of lines within the ellipse in Figure 4 indicate that products with different peak and bottom shipping volumes generate product groups. More specifically, the line type represents the magnitude of the shipping volume (dotted line > single dotted line > dashed line). The line thickness represents the magnitude of the fluctuation in shipping volume (thick > medium > thin). The product placement plan generation unit 22 assigns each product to either an automated warehouse, shelf transport, or cart. As mentioned above, in this case, the product placement plan generation unit 22 assigns a picking method with higher processing efficiency to a product the larger the shipping volume, and assigns a picking method with higher processing efficiency to a product the smaller the fluctuation in shipping volume.

[0034] The capacity limit of 106 is determined by processing efficiency and the maximum number of personnel that can be employed. Therefore, the higher the processing efficiency or the larger the maximum number of personnel that can be employed, the higher the capacity limit (the amount of goods shipped per unit period for each picking method). On the other hand, the higher the processing efficiency of each picking method, the more personnel are needed to achieve the same amount of goods shipped. From the perspective of overall productivity and labor saving in the logistics warehouse, it is desirable to achieve a amount of goods shipped close to the capacity limit using a picking method with high processing efficiency throughout the product placement period.

[0035] Therefore, the product placement planning system 1 needs to generate a product placement plan 34 that enables a larger shipment volume from the pick area 41, which has a high processing efficiency, and reduce the number of personnel working in the pick area 41, which is well below the capacity limit 106.

[0036] The difference between Figure 3 and Figure 4 lies in the presence or absence of the idea of ​​appropriately combining the variance of each product's shipment volume over the product placement period to reduce the overall variance of the product group. This will be further explained in Figures 5 and 6.

[0037] (Distribution of product shipment volume and product grouping) Figures 5 and 6 illustrate the distribution of product shipment volumes and product groups. First, let's look at Figure 5, which illustrates a conventional example. The product placement plan generation unit 22 manages the daily shipment volume (columns 102a to 102g) and the weekly average of the shipment volume (column 102h) for each product (column 101). "#" is an abbreviation for different numerical values. The product placement plan generation unit 22 corresponds the available picking methods (column 103) to product groups consisting of multiple products, without specifically considering the daily fluctuations in the shipment volume of each product (e.g., a distribution over 7 days). In this case, the product placement plan generation unit 22 assigns a picking method with higher processing efficiency (column 104) to a product group if the group average (column 102j), which is the average of the shipment volumes of the product group, is large.

[0038] In the example in Figure 5, products A, B, and C constitute product group ABC. Similarly, products D, E, and F constitute product group DEF, and products G, H, and J constitute product group GHJ. Of these product groups, product group ABC has the highest group average, and product group CFJ has the lowest. Of the three picking methods, the automated warehouse has the highest processing efficiency, and the cart has the lowest. Therefore, the product placement plan generation unit 22 assigns the automated warehouse to product group ABC, for example.

[0039] The product placement plan generation unit 22 manages processing efficiency (column 104), work hours (column 105), and capacity limit (column 106) in relation to the picking method (column 103). Productivity (column 107) is defined for the entire logistics warehouse and is the result of dividing the daily or weekly shipment volume by the daily or weekly work hours. In conventional examples, productivity decreases as the shipment volume significantly exceeds or falls significantly below the capacity limit at some point in the week.

[0040] Next, let's look at Figure 6, which illustrates an example of this embodiment. The difference between Figure 6 and Figure 5 is that Figure 6 includes variance (column 102k) and group variance (column 102m). Let's assume that product A has a larger shipment volume on Mondays and Wednesdays compared to other days, product D has a larger shipment volume on Tuesdays and Thursdays compared to other days, and product G has a larger shipment volume on Fridays, Saturdays, and Sundays compared to other days. Then, the peaks and bottoms of the shipment volumes of products A, D, and G cancel each other out, and the fluctuation in the shipment volume of product group ADG remains stable throughout the week. In other words, even if the variance of the shipment volumes of products A, D, and G is relatively large, the variance of the shipment volume of product group ADG is relatively small. The "large" in column 102k of Figure 6 should be compared with the "small" in column 102m.

[0041] Of these product groups, product group ADG has the highest group average, while product group CFJ has the lowest. Of the three picking methods, the automated warehouse has the highest processing efficiency, while the cart has the lowest. Therefore, the product placement plan generation unit 22 assigns the automated warehouse to product group ADG, for example. This assignment method is the same as in Figure 5. However, productivity increases as the shipment volume remains consistently below the capacity limit throughout the week.

[0042] (Product storage information) Figure 7 shows an example of product storage information 31. Product storage information 31 stores the required storage quantity (field 112) associated with the product ID (field 111). There is one record (horizontally arranged) for each type of product stored in the logistics warehouse. Note that the information contained in the record is not limited to the information described above. A record does not have to include either the product ID or the required storage quantity, and it may also include other information. This is also true for Figures 8 to 10, which will be described later.

[0043] The Product ID (field 111) is an identifier that uniquely identifies the type of product stored in the logistics warehouse. Even for products of the same type, if it is necessary to classify products based on the arrival date, expiration date, etc., a separate Product ID will be assigned.

[0044] The required storage quantity (column 112) is the physical volume of each product that needs to be stored in the logistics warehouse. The required storage quantity may be expressed as a quantity such as the number of units or cases of the product, or as a volume such as storage volume. "CS" is an abbreviation for "box" or "cases".

[0045] (Information on how to pick) Figure 8 shows an example of picking method information 32. Picking method information 32 stores the picking area ID (field 122), storage capacity limit (field 123), processing efficiency (field 124), and the maximum number of personnel that can be employed (field 125), associated with the picking method ID (field 121). One record exists for each picking method in the logistics warehouse.

[0046] The picking method ID (field 121) is an identifier that uniquely identifies the picking method. Even with the same picking method, if the picking area 41 is divided into multiple sections and the maximum capacity per unit period differs for each divided picking area, a separate picking method ID will be provided. The pick area ID (field 122) is an identifier that uniquely identifies pick area 41. The maximum storage capacity (column 123) is the amount of goods that can be stored in each pick area 41. Storage capacity may be expressed in terms of quantity, such as the number of units or cases of goods, or in terms of volume, such as storage capacity.

[0047] Processing efficiency (column 124) is the amount shipped per person per hour for each picking method, as described above. "Rows / MH" is an abbreviation for "Number of shipping orders / person-hour". The maximum number of personnel that can be employed (column 125) is the maximum number of personnel that can work simultaneously in each pick area 41. Upon examining each record of the picking method information 32, at least one of the processing efficiency and the maximum number of personnel that can be engaged differs from that of the other records.

[0048] If there is information that shows the upper limit of capacity per unit period for each picking method, the picking method information 32 may add that item to each record, while excluding processing efficiency and the upper limit of personnel that can be engaged from each record.

[0049] (Shipping schedule information) Figure 9 shows an example of shipping schedule information 33. The shipping schedule information 33 stores the shipping quantity (field 132) associated with the product ID (field 131). For each type of product scheduled for shipment during the product placement period, there is one record. The product ID (field 131) is the same as the product ID in Figure 7. The shipment volume (column 132) represents the projected future shipment volume of the product for each unit period (day of the week).

[0050] The shipping schedule information 33 does not necessarily need to accurately store the planned future shipping volume for each product. It may store the results of predicting the shipping volume for each unit period based on past performance, or the range of shipping fluctuations expressed as the upper and lower limits of the shipping volume during the product placement period.

[0051] (Product placement plan) Figure 10 shows an example of a product placement plan 34. The product placement plan 34 stores the pick method ID (field 142), pick area ID (field 143), and storage quantity (field 144) associated with the product ID (field 141). There is one record for each type of product. However, if the same product is placed across multiple pick areas, multiple pick area IDs may correspond to one product ID. The product ID (field 141) is the same as the product ID in Figure 7. The pick method ID (field 142) is the same as the pick method ID in Figure 8. The pick area ID (field 143) is the same as the pick area ID in Figure 8. The storage quantity (column 144) is the quantity of goods that should be stored in the pick area.

[0052] The product placement plan generation unit 22 generates a product placement plan 34 that improves the overall productivity of the warehouse, taking into account fluctuations in the shipment volume of each product. For example, the product placement plan generation unit 22 assigns a highly efficient picking method to product groups with a large average shipment volume during the product placement period. Also, the product placement plan generation unit 22 assigns a highly efficient picking method to product groups with small fluctuations (variances) in shipment volume during the product placement period. As a result, the highly efficient picking method consistently utilizes its capacity to the maximum extent throughout the product placement period, thereby improving the overall productivity of the warehouse.

[0053] (Output screen) Figure 11 shows an example of the output screen 51. The product placement plan generation unit 22 generates the output screen 51 based on the product placement plan 34. The output processing unit 23 displays the output screen 51 on the output device 13. The output screen 51 has multiple pages corresponding to tags 52 that describe the picking method. Each page displays storage location information, product ID, product name, and storage quantity in an interconnected manner. The storage location information is detailed information about each storage area (such as the location of the picking area 41).

[0054] (Processing procedure) Figure 12 is a flowchart of the processing procedure. As a prerequisite for starting the processing procedure, the input processing unit 21 is assumed to have acquired product storage information 31, picking method information 32, and shipping schedule information 33 from other devices as needed and stored them in the auxiliary storage device 15. As a result, the product storage information 31, picking method information 32, and shipping schedule information 33 are assumed to be stored in the auxiliary storage device 15 in the state described in Figures 7, 8, and 9.

[0055] In step S201, the product placement plan generation unit 22 of the product placement planning system 1 calculates the average shipment volume of each product. Specifically, the product placement plan generation unit 22 calculates the average shipment volume of each product during the product placement period (1 week) (Figure 6, column 102h) based on the scheduled shipment information 33.

[0056] In step S202, the product placement plan generation unit 22 calculates the variance of the shipment volume of each product. Specifically, the product placement plan generation unit 22 calculates the variance of the shipment volume of each product during the product placement period (Figure 6, column 102k) based on the shipment schedule information 33. Note that "variance" is an example of an indicator that shows the fluctuation in shipment volume during the product placement period. The indicator that shows the fluctuation in shipment volume may be an indicator other than variance (for example, difference = weekly maximum value - weekly minimum value).

[0057] In step S203, the product placement plan generation unit 22 generates N product groups whose variance is less than or equal to a threshold. Specifically, firstly, the product placement plan generation unit 22 generates N product groups by combining multiple types of products. N may be, for example, the number of picking methods (in Figure 6, N=3). Secondly, the product arrangement plan generation unit 22 calculates the variance (Figure 6, column 102m) of the total daily shipment volume of each product belonging to the product group generated in step S203, and compares the calculated variance with a predetermined threshold.

[0058] The product arrangement plan generation unit 22 repeats the "first" and "second" processes of step S203 until it can identify N product groups such that the result of the "second" comparison in step S203 is "variance ≤ threshold".

[0059] For the sake of explanation, here we assume that, as a result of the iterative processing, the product placement plan generation unit 22 has identified product groups ADG, BEH, and CFJ, and that the following is true. "min( , , )" is a function that retrieves the smallest number among the numbers separated by commas. • Variance of product group ADG < Variance of product group BEH < Variance of product group CFJ min(variance of product A, variance of product D, variance of product G) > variance of product group A, D, and G min(variance of product B, variance of product E, variance of product H) > variance of product group BEH min(variance of product C, variance of product F, variance of product J) > variance of product group C, F, and J

[0060] In step S204, the product placement plan generation unit 22 assigns picking methods to N product groups in such a way that the picking method with higher processing efficiency is assigned in order of the largest average shipment volume and the smallest variance of shipment volume. Specifically, firstly, the product placement plan generation unit 22 generates an initial solution (details below). Here, the product placement plan generation unit 22 accepts in advance from the user the degree to which one should be prioritized over the other (priority rule) if the order of "largest average shipment volume" is different from the order of "smallest variance of shipment volume". An example of a priority rule is as follows.

[0061] (Priority Rule 1) The product placement plan generation unit 22 sorts multiple product groups whose average shipment volume falls within the top one-third, in order of decreasing shipment volume variance, and assigns the picking method with the highest processing efficiency to each group, starting from the top of the sorted list. At this time, the product placement plan generation unit 22 sorts the picking methods in order of highest processing efficiency and assigns the picking methods in that order. The product placement plan generation unit 22 sorts multiple product groups whose average shipment volume falls within the median 1 / 3 in order of decreasing shipment volume variance, and then assigns the remaining picking methods from the most efficient picking methods to the first of the sorted groups. The product placement plan generation unit 22 further sorts multiple product groups whose average shipment volume falls in the bottom one-third in order of decreasing shipment volume variance, and then assigns the remaining picking methods from the most efficient picking methods to the first of the sorted groups.

[0062] (Priority Rule 2) The product arrangement generation unit 22 arranges multiple product groups in descending order of average shipment volume, assigning the order p (p=1, 2, 3, ...) and associating the value of p with each product group. The product arrangement generation unit 22 arranges multiple product groups in order of increasing variance in shipment volume, assigning the order q (q=1, 2, 3, ...) to each product group and associates the value of q with it. The product placement generation unit 22 associates the value of r (r = w1 × p + w2 × q) with each product group. Here, w1 and w2 are weights, and it is assumed that "w1 + w2 = 1", "0 ≤ w1 ≤ 1", and "0 ≤ w2 ≤ 1" hold true. The user can arbitrarily set the values ​​of w1 and w2. The product placement plan generation unit 22 assigns a picking method with higher processing efficiency to product groups with smaller associated r values.

[0063] For the sake of explanation, let's assume that the product placement generation unit 22 assigned automated warehouses to product groups ADG, shelf transport to product groups BEH, and carts to product groups CFJ, based on priority rules. The resulting association, such as "ADG: automated warehouse, BEH: shelf transport, CFJ: cart," is called the "initial solution."

[0064] Secondly, the product arrangement generation unit 22 generates M initial solutions by swapping some of the products belonging to a product group across product groups. For example, for “ADG: automated warehouse, BEH: shelf transport, CFJ: cart”, “ADH: automated warehouse, BEJ: shelf transport, CFG: cart” is another initial solution. For the sake of explanation, let's assume that M=4, and that there are initial solutions W1, W2, W3, and W4. Of these, initial solution W1 is the initial solution before the product swap.

[0065] Then, the product layout plan generation unit 22 generates M product layout plans 34 based on the M initial solutions and stores them in the auxiliary storage device 15. As is clear from the above, the initial solutions, the product layout plans 34, and the output screen 51 essentially show the same content. Incidentally, at this stage, the product layout plan generation unit 22 can also store the table information shown in Figure 6 in the auxiliary storage device 15 and display it on the output device 13.

[0066] In step S205, the output processing unit 23 of the product placement planning system 1 displays the output screen 51. Specifically, the output processing unit 23 displays the output screen 51 (Figure 11) on the output device 13. At this stage, the product placement plan generation unit 22 generates the output screen 51 based on the product placement plan 34.

[0067] In step S206, the product placement plan generation unit 22 receives the results of continuing picking with the initial solution for M weeks. Specifically, the product placement plan generation unit 22 applies the initial solution W1 to the logistics warehouse for the first week, the initial solution W2 for the next week, the initial solution W3 for the following week, and the initial solution W4 for the last week. During these four weeks, the product placement plan generation unit 22 receives actual values ​​such as the amount of goods shipped from the devices that manage each pick area 41.

[0068] In step S207, the product placement plan generation unit 22 calculates the productivity of M items. Specifically, the product placement plan generation unit 22 calculates the productivity of the entire logistics warehouse on a weekly basis based on the actual values ​​received in step S206.

[0069] In step S208, the product placement plan generation unit 22 finds the optimal solution that maximizes productivity. Specifically, the product placement plan generation unit 22 defines the initial solution applied in the week with the highest productivity as the "optimal solution".

[0070] In step S209, the product placement plan generation unit 22 initiates picking using the optimal solution from week M+1 onwards. Specifically, the product placement plan generation unit 22 applies the optimal solution to the logistics warehouse from week M+1 onwards. After that, the processing procedure is terminated.

[0071] (Variation example: Optimization calculation model) The above is an example in which the product placement plan generation unit 22 itself performs optimization calculations (such as generating the product placement plan 34). However, the product placement plan generation unit 22 may have an optimization calculation model 22b (Figure 1) as a module within itself. Furthermore, the product placement plan generation unit 22 may have the optimization calculation model 22b handle at least a part of the processing that it would normally perform in the processing procedure (Figure 12).

[0072] For example, the product placement plan generation unit 22 may have the optimization calculation model 22b handle the processing up to generating the initial solution (steps S201 to S205) (Case 1). The product placement plan generation unit 22 may also have the optimization calculation model 22b handle the process from generating an initial solution to finding the optimal solution (steps S206 to S209) after it has generated an initial solution itself (Case 2). Furthermore, the product placement plan generation unit 22 may also have the optimization calculation model 22b handle all processing (steps S201 to S209) (Case 3). In Case 1, Case 2, and Case 3, the optimization calculation model 22b is, for example, a neural network capable of machine learning and deep learning.

[0073] In Case 1, the optimization calculation model 22b takes product storage information 31, picking method information 32, and shipping schedule information 33 as inputs and outputs product placement plan 34 (initial solution). In Case 2, the optimization calculation model 22b takes product placement plan 34 (initial solution) as input and outputs the optimal solution. In Case 3, the optimization calculation model 22b takes product storage information 31, picking method information 32, and shipping schedule information 33 as inputs and outputs the optimal solution.

[0074] In either case, the product placement plan generation unit 22 uses previously used input and output data as supervised learning data to perform machine learning and deep learning on the optimization calculation model 22b at any arbitrary timing, such as when the product composition changes. By utilizing the optimization calculation model 22b in this way, for example, if a new logistics warehouse is constructed to store the same products that were stored in the past, it is possible to utilize past experience.

[0075] (Summary of Optimization Calculation Models) As is clear from the above, the product placement plan generation unit 22 includes an optimization calculation model that outputs the optimal combination of a group of products and a picking method. The product placement plan generation unit 22 generates a product placement plan by assigning the picking method to the product group in order of the largest planned shipment volume during the product placement period and in order of the smallest fluctuation in the planned shipment volume, and inputs the product placement plan as the initial solution into the optimization calculation model.

[0076] Furthermore, the product placement plan generation unit generates product placement plans by assigning the picking methods to the product groups in order of decreasing capacity limit for each picking method.

[0077] (Effects of the embodiment) The product placement planning system 1 generates product placement plans 34 that take into account the capacity limits of multiple picking methods based on fluctuations in the shipment volume of each product. This has the effect of improving the productivity of the entire warehouse. Furthermore, the product placement planning system 1 generates product placement plans 34 so that the workload approaches the capacity limit for each picking method, starting with the picking method with the highest processing efficiency. This makes it possible to process a larger volume of goods with fewer personnel, reducing the total number of workers in the warehouse. In addition, because the number of workers is reduced, the warehouse as a whole can handle a larger volume of goods, and an improvement in throughput can be expected.

[0078] While several embodiments of the present invention have been described, these embodiments are presented as examples only and do not limit the scope of the invention. These novel embodiments can be implemented in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims of the invention and its equivalents. [Explanation of Symbols]

[0079] 1. Product placement planning system 11 Central Control Unit 12 Input devices 13 Output device 14 Main memory 15 Auxiliary storage 16. Communication equipment 21 Input Processing Unit 22 Product placement plan generation department 22b Optimization Calculation Model 23 Output Processing Unit 31 Product storage information 32 Picking Method Information 33 Shipping Schedule Information 34 Product placement plan 40 Logistics warehouse 41 Pick Area 45 Automated machinery 46 Product shelf 47 Cart

Claims

1. Shipping schedule information stores the planned shipping volume for each unit period for multiple products handled in a logistics warehouse, For each of the multiple picking methods implemented in the aforementioned logistics warehouse, the picking method information, which stores at least one of the processing efficiency and the maximum number of personnel that can be employed, is referenced. The system includes a product placement plan generation unit that generates a product placement plan for a product placement period longer than the unit period by assigning a picking method to the product based on the fluctuations in the planned shipment quantity of the product for each unit period in the shipment schedule information, in which case the product placement plan generation unit generates a product placement plan for a product placement period longer than the unit period. A product placement planning system characterized by the following:

2. The device includes a storage device that stores the aforementioned shipping schedule information and the aforementioned picking method information. A product placement planning system according to claim 1, characterized by the following:

3. The aforementioned product placement plan generation unit is: To generate the product placement plan so as not to exceed the capacity limit of the picking method, which is determined based on the processing efficiency and the maximum number of personnel that can be engaged. A product placement planning system according to claim 1, characterized by the following:

4. The aforementioned product placement plan generation unit is: The product group is generated such that the fluctuation in the planned shipment volume of the product group is smaller than the fluctuation in the planned shipment volume of any of the products that make up the product group. The product arrangement plan is generated by assigning the picking method to the product group. A product placement planning system according to claim 1, characterized by the following:

5. The aforementioned product placement plan generation unit is: The system includes an optimization calculation model that outputs the optimal combination of the group of products and the picking method. The product placement plan is generated by assigning the picking method to the product group in order of the largest planned shipment volume during the product placement period for the product group, and in order of the smallest fluctuation in the planned shipment volume. The proposed product placement is input into the optimization calculation model as the initial solution. A product placement planning system according to claim 1, characterized by the following:

6. The aforementioned product placement plan generation unit is: The product arrangement plan is generated by assigning the picking methods to the product group in order of their processing efficiency, A product placement planning system according to claim 5, characterized by the following:

7. The product placement plan generation unit of the product placement planning system is Shipping schedule information stores the planned shipping volume for each unit period for multiple products handled in a logistics warehouse, For each of the multiple picking methods implemented in the aforementioned logistics warehouse, the picking method information, which stores at least one of the processing efficiency and the maximum number of personnel that can be employed, is referenced. Based on the fluctuations in the planned shipment quantity of the product for each unit period in the shipment schedule information, a picking method in the picking method information is assigned to the product, thereby generating a product placement plan for a product placement period longer than the unit period. A product placement planning method characterized by the following.

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