JP5434204B2 - Product transfer work amount prediction apparatus, product transfer work amount prediction method, and computer program - Google Patents

Description

  The present invention relates to a product transfer work amount prediction apparatus, a product transfer work amount prediction method, and a computer program. In particular, a product before being received in a warehouse is used for an entry operation into the warehouse and a shipment operation from the warehouse. The products that have already been received are suitable for use in predicting the work amount of the transfer device used for the shipment work from the warehouse.

  When a large variety of products stored in a warehouse are shipped (shipped), it is important to formulate a shipping plan in order to ship those products efficiently before the due date. When a shipping plan for carrying out a shipping operation using a transport device is made, it is necessary to appropriately predict the work amount of the transport device. This is because it is difficult to create an appropriate shipment plan if the work amount of the transfer device is inaccurate.

Therefore, conventionally, each model of the product warehousing work to the warehouse by the transport equipment, the work in the warehouse by the transport equipment, and the product shipment work by the transport equipment is constructed using the logistics simulator, and the logistics simulation is used. It is generally performed to predict the work amount of the transport device.
Further, in Patent Document 1, the amount of picking work in the warehouse is predicted using the relationship between the location of the product placed in the warehouse and the standard time required for picking from that location to the next process. Yes.

JP-A-11-157617

By the way, there are many restrictions on the shipping work by the transfer device in the warehouse. For example, when a crane is used as a transfer device, the crane operating range may be restricted by the presence of other cranes, or the location in the warehouse where the product can be placed may be limited depending on the size of the product. . In addition, when there are a plurality of transfer devices that can execute a certain shipment operation, it is necessary to determine which of the plurality of transfer devices is used for the shipment operation. In addition, when a plurality of products are stacked and placed in a warehouse (to build up), in order to take out the product under the mountain, it is necessary to move the product on the mountain to another place.
However, there has been a problem that a great deal of labor is required in order to construct a physical distribution simulator that takes into account the restrictions relating to the shipping work by the transport device as described above.

  Moreover, in the technique described in Patent Document 1, it is necessary to determine where in the warehouse each product to be shipped is placed in order to predict the amount of picking work required for shipment. Therefore, when predicting the work volume of transport equipment over a long period of time (for example, by day of the month), it is necessary to determine the placement in the warehouse of products that have not yet entered the warehouse. It's hard to decide about your product. Therefore, the technique described in Patent Document 1 has a problem that it is difficult to predict the work amount of the transport device over a long period of time. Further, in Patent Document 1, when there are a plurality of transport devices capable of performing the picking work, it is necessary to determine which transport device is assigned the picking work, and to predict the work amount based on the determination. Patent Document 1 does not have a specific description about such prediction.

  The present invention has been made in view of the problems as described above, and the product before entering the warehouse is used for entry into the warehouse and shipment from the warehouse, and the product that has been received in the warehouse. An object of the present invention is to make it possible to more easily and accurately predict the work amount of the transfer device used for shipment from the warehouse.

The product transfer work amount prediction device of the present invention is used for entering a warehouse before shipping into the warehouse and shipping from the warehouse, and for products already received in the warehouse, shipping from the warehouse. An apparatus for predicting the work load of a plurality of transfer devices used for the purpose of the present invention, an order attribute acquisition means for acquiring an order attribute which is information relating to the order of the plurality of products, and the order attribute A shipping group generating means for generating a shipping group by grouping for each product having a plurality of predetermined order attributes including order attributes that affect an output position of the product in the warehouse, and the product acquires use a number conveying devices work occurrence probability per unit product of respective conveying devices used to ship by the ship group handling equipment work occurrence probability obtaining Stage and a by product shipment schedule acquisition means for acquiring the product-specific delivery schedule is shipments in the plurality of product-specific future predetermined period, based on the product-specific delivery schedule, another predetermined said delivery group Based on the shipment group-specific shipment plan calculation means for calculating the shipment amount in the period, the transfer device work occurrence probability in each transfer device by the shipment group, and the shipment amount in the predetermined period by the shipment group, Conveying device work amount calculating means for calculating the amount of work in each predetermined period of each conveying device, and display means for displaying information on the amount of work in each predetermined period of each conveying device on a display device. It is characterized by.

The product transfer work amount prediction method of the present invention is used for receiving products into the warehouse and shipping from the warehouse for products before entering the warehouse, and shipping products from the warehouse for products already received in the warehouse. A method for predicting the work load of a product, which is executed by a computer to predict the work load of a plurality of transfer devices used for the purpose, and obtaining an order attribute that is information relating to an order of a plurality of the products A shipping group that generates a shipping group by grouping for each product having a plurality of predetermined order attributes including the order attribute that influences a delivery position of the product in the warehouse among the order attributes generating step and said ship guru handling equipment work occurrence probability of the number of times of use per unit product of each conveying devices that are used to ship the product A conveying device working probability acquiring by flops, and by product shipment schedule acquisition step of acquiring the product-specific delivery schedule is shipments in the plurality of product-specific future predetermined period, the product-specific delivery schedule Based on the shipping group, the shipping plan calculation step for calculating the shipping amount in the predetermined period for each shipping group, the transfer device work occurrence probability in each transfer device for each shipping group, and the predetermined for each shipping group A transfer device work amount calculating step for calculating a work amount of each of the transfer devices in the predetermined period based on a shipment amount of the transfer device, and a display device for displaying information on the work amount of the transfer devices in the predetermined period And a display step of displaying on the screen.

The computer program according to the present invention is used for receiving products from the warehouse and shipping from the warehouse for products before entering the warehouse, and for shipping products from the warehouse for products already received in the warehouse. An order attribute obtaining step for obtaining an order attribute that is information relating to an order of a plurality of the products, wherein the order attribute is a computer program for causing a computer to predict a work amount of a plurality of transported devices, and the order attribute A shipping group generation step of generating a shipping group by grouping for each product having a plurality of predetermined order attributes including order attributes that affect the delivery position of the product in the warehouse; the use is the number conveying equipment work occurrence probability per unit product of respective conveying devices used to ship the shipping A conveying device working probability acquiring the loop by a by product shipment schedule acquisition step of acquiring the product-specific delivery schedule is shipments in the plurality of product-specific future predetermined period, the product-specific delivery schedule Based on the shipping group, the shipping plan calculation step for calculating the shipping amount in the predetermined period for each shipping group, the transfer device work occurrence probability in each transfer device for each shipping group, and the predetermined for each shipping group A transfer device work amount calculating step for calculating a work amount of each of the transfer devices in the predetermined period based on a shipment amount of the transfer device, and a display device for displaying information on the work amount of the transfer devices in the predetermined period And a display step of displaying on the computer.

  According to the present invention, the work amount of each transport device in a predetermined period is calculated based on the transport device work occurrence probability in each transport device by shipping group and the shipment amount in a predetermined period by shipping group. I made it. Therefore, it is possible to obtain the transport equipment work occurrence probability and the shipping amount for each product transported in the same manner from the order attribute. Therefore, the work amount of the transfer device can be predicted as accurately as possible without constructing a physical distribution simulator. In addition, since information regarding the actual arrangement of products in the warehouse is not necessary, the work amount of the transfer device can be predicted over a long period of time. As described above, according to the present invention, the product before entering the warehouse is used for entry into the warehouse and shipment from the warehouse, and the product already received in the warehouse is shipped from the warehouse. Therefore, it is possible to easily and accurately predict the work amount of the transfer device used for the purpose.

It is a figure which shows an example of a structure of the warehouse where the conveying apparatus is arrange | positioned. It is a figure which illustrates notionally an example of goods warehousing operation | work and shipping operation | work. It is a figure which shows an example of the hardware constitutions of a product conveyance work amount prediction apparatus. It is a figure which shows an example of a functional structure of a product conveyance work amount prediction apparatus. It is a figure which shows an example of an order attribute. It is a figure which shows an example of conveyance performance data. It is a figure which illustrates notionally an example of the method of classifying the order (product) contained in an order attribute into a shipping group. It is a figure which shows an example of the conveyance performance data of each conveyance apparatus for every shipment group. It is a figure which shows an example of the conveyance apparatus work occurrence probability for every shipment group. It is a figure which shows an example of the shipment plan amount classified by product. It is a figure which shows an example of the shipping plan amount for every predetermined period in each shipping group. It is a figure which shows an example of the work amount for every predetermined period in each conveyance apparatus. It is a figure which shows the example of a display of the prediction result of the work amount for every predetermined period in each conveyance apparatus. It is a flowchart explaining an example of the operation | movement process of a product conveyance work amount prediction apparatus. It is a flowchart explaining an example of the shipment group discrimination | determination process in step S1403 of FIG.

[First Embodiment]
Hereinafter, a first embodiment of the present invention will be described with reference to the drawings.
<Structure of warehouse>
FIG. 1 is a diagram illustrating an example of a configuration of a warehouse in which transfer devices are arranged. In the present embodiment, a case will be described as an example in which the amount of work of the transfer device is predicted when the transfer device arranged in a warehouse as shown in FIG. 1 transfers a steel plate.
In FIG. 1, the product warehouse is composed of two warehouses 10a and 10b. The warehouse 10a is composed of two buildings (buildings 1 and 2), and each building is divided into four areas represented by addresses 1 to 4. On the other hand, the warehouse 10b is composed of one building (building 3) divided into two areas represented by addresses 1 and 2.
In the warehouses 10 a and 10 b, a track 20 is laid, and freight cars 40 a and 40 b move on the track 20. In addition, although the freight cars 40a and 40b are different freight cars, the range which can be traveled and a conveyance capability are the same. In the warehouse 10a, a cart 50 for transferring products from one building to the other building is arranged.

The product manufactured at the factory is received at address 1 of the ridge 1 of the warehouse 10a. The received product is transported by the crane 30a of the warehouse 10a and is arranged at a predetermined location (for example, the second address of the ridge 1 of the warehouse 10a). At that time, as shown in FIG. 2A, the product 70 is arranged on the top of the product pile 71 where a plurality of products are stacked. Of course, when no product has been arranged yet, there is no product pile 71, so the product 70 conveyed by the crane 30 is arranged at the bottom of the arrangement area. In addition, when the product pile 71 reaches a certain height, another product pile is created.
When the product 70 transported by the crane 30a is arranged at a place other than the ridge 1 of the warehouse 10a, the product 70 is transported using at least one of the freight cars 40a and 40b and the carriage 50. If the crane 30c or the ridge 3 is used, either the crane 30d or 30e is used to place the product 70 at a predetermined location.

At the time of shipment (shipment), as a general rule, as shown in FIG. 2B, the product 70 at the top of the product pile 71 is taken out using the crane 30 (however, below the product pile 71). May take out the product in the direction). When sea transport is designated as the transport mode, the taken out product 70 is transported to the shipping quay by the freight car 40. The product 70 is then shipped and transported to the destination. On the other hand, when land transport is designated as the transport mode, the product 70 is loaded on the truck 60 at the first address of the warehouse 10b and is transported to the relay point. Thereafter, the product 70 is transported to the destination.
As described above, in the present embodiment, the conveyance devices are the cranes 30a to 30e, the freight cars 40a and 40b, and the carriage 50. Moreover, in this embodiment, the 1st address of the ridge 1 of the warehouse 10a is the storage position of the product 70. In addition, the ridge 1 and the ridge 2 of the warehouse 10a and the ridge 3 of the warehouse 10b are the delivery positions of the products 70.

<Hardware configuration of product transfer work amount prediction device>
FIG. 3 is a diagram illustrating an example of a hardware configuration of the product conveyance work amount prediction apparatus 100.
As shown in FIG. 3, the product transfer work amount prediction device 100 includes a CPU (Central Processing Unit) 101, a ROM (Read Only Memory) 102, a RAM (Random Access Memory) 103, and a PD (Pointing Device) 104. HD (Hard Disk) 105, display device 106, speaker 107, communication I / F (Interface) 108, and system bus 109.

The CPU 101 controls the operation of the product transport work amount prediction apparatus 100 in an integrated manner, and controls each component (102 to 108) of the product transport work amount prediction apparatus 100 via the system bus 109.
The ROM 102 stores a basic input / output system (BIOS) and an operating system program (OS) that are control programs for the CPU 101, programs necessary for the CPU 101 to execute processing according to flowcharts described later, and the like.

The RAM 103 functions as a main memory, work area, and the like for the CPU 101. When executing the processing, the CPU 101 loads various computer programs and the like from the ROM 102 and necessary information from the HD 105 into the RAM 103, and executes various processes by executing the computer programs and the information. .
The PD 104 includes, for example, a mouse, a keyboard, and the like, and constitutes an operation input unit for an operator to input an operation to the product transport work amount prediction apparatus 100 as necessary.
The HD 105 constitutes storage means for storing various information, data, files, and the like.
The display device 106 constitutes display means for displaying various information and images based on the control of the CPU 101.
The speaker 107 constitutes an audio output unit that outputs audio related to various types of information based on the control of the CPU 101.

The communication I / F 108 communicates various information and the like with an external device via a network based on the control of the CPU 101.
A system bus 109 is a bus for connecting the CPU 101, ROM 102, RAM 103, PD 104, HD 105, display device 106, speaker 107, and communication I / F 108 so that they can communicate with each other.

<Functional configuration of product transfer work amount prediction device>
FIG. 4 is a diagram illustrating an example of a functional configuration of the product conveyance work amount prediction apparatus 100.
As shown in FIG. 4, the product transportation work amount prediction apparatus 100 includes, as its functions, an order attribute acquisition unit 401, an order attribute storage unit 402, a transportation result data acquisition unit 403, a transportation result data storage unit 404, and a shipping group definition logic. Acquisition unit 405, shipment group definition logic storage unit 406, transfer device work amount prediction model creation unit 407, transfer device work amount prediction model storage unit 408, shipment plan acquisition unit 409, shipment plan data storage unit 410, transfer device work amount prediction A unit 411, a transfer device work amount display unit 412, and a shipment plan correction unit 413.

<< Order attribute >>
The order attribute acquisition unit 401 acquires the attribute of the order (product 70) based on the operation input of the PD 104 by the operator or communication with the external device via the network, and stores it in the order attribute storage unit 402. In the following description, an attribute of an order (product 70) is referred to as an order attribute.
FIG. 5 is a diagram illustrating an example of the order attributes 500 and 501. Specifically, FIG. 5A shows an example of an order attribute 500 of an order that has already been manufactured and shipped, and FIG. 5B shows an order to be shipped to be transported by a transport device that is a target of work amount prediction. An example of the order attribute 501 is shown. In FIG. 5A and FIG. 5B, for the convenience of explanation, the contents of the order attributes are the same except for the product number, but these are usually different.
FIG. 5 shows an example in which the order attribute 500 for 12 orders (product 70) is acquired. In the example illustrated in FIG. 5, the order attributes 500 and 501 include information on product number, size, weight, transportation form, destination, exporting country, and customer presence.

Here, the product number is a number for identifying an order (product 70) and is unique for each product. The size is the size of the product 70. Here, the size of the product 70 is indicated by the thickness, the width, and the length. The weight is the weight of the product 70.
The transport form is information indicating in what form the product 70 is transported, and here, either sea transport or land transport is the transport form.
The destination is information indicating the shipping destination of the product 70. In the present embodiment, the destination of the product 70 refers to a place where the product 70 is first lowered after leaving the warehouses 10a and 10b. When the mode of transport is sea transportation, a port with a shipping quay is the destination. On the other hand, when the transport mode is land transport, the destination of the truck 60 is a relay point or the like. However, the destination is not necessarily limited to this as long as it is information related to the shipping destination of the product 70. For example, information for identifying a ship on which the product 70 is loaded may be the destination.

The exporting country is information indicating the country where the product 70 is finally transported. When the product 70 is not exported, “domestic” is designated as the export country information as shown in FIG. As the information of “exporting country”, information indicating “not applicable” may be given instead of the information of “domestic”.
The customer presence is information indicating whether or not the customer is present in the confirmation work of the product 70 before shipping the product 70 from the warehouses 10a and 10b.
The order attribute acquisition unit 401 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the PD 104 (or the communication I / F 108). The order attribute storage unit 402 is realized by using, for example, the HD 105.
In the present embodiment, for example, an order attribute acquisition unit is realized by the processing of the order attribute acquisition unit 401 as described above.

<< Conveyance data >>
The transport result data acquisition unit 403 acquires transport result data from the transport device management apparatus 420 that manages the operation of the transport devices (the cranes 30a to 30e, the freight cars 40a and 40b, and the carriage 50) via the network. Store in the result data storage unit 404. In the present embodiment, the conveyance result data is data indicating how many times each conveyance device has been used from when the product 70 is received into the warehouse 10 until it is shipped. Further, the transport result data acquisition unit 403 acquires transport result data for all products 70 of the order attribute 500 (order attribute of orders already manufactured and shipped) acquired by the order attribute acquisition unit 401.

FIG. 6 is a diagram illustrating an example of the conveyance result data 600. Note that the names of the conveying devices shown in the table of FIG. 6 correspond to the names described after the reference numerals in FIG.
In FIG. It is shown that the crane 30a (crane 1) is used twice and the freight car 40a (freight car 1) is used once during the period from when the first product 70 is received into the warehouse 10 until it is shipped. As described above, since the product 70 is received from address 1 of the ridge 1 of the warehouse 10a, the crane 30a (crane 1) is always used.
The conveyance result data acquisition unit 403 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the communication I / F 108. Further, the transport result data storage unit 404 is realized by using the HD 105, for example.
Here, the case where the conveyance result data 600 is acquired from the conveyance device management apparatus 420 is described as an example, but it is not always necessary to do so. For example, the product transfer work amount prediction device 100 may have the function of the transfer device management device 420. Moreover, you may make it acquire the conveyance performance data 600 based on the operation input of PD104 by an operator.
In the present embodiment, for example, a transfer record acquisition unit is realized by the process of the transfer record data acquisition unit 403 as described above.

<<< shipping group definition logic >>>
The shipment group definition logic acquisition unit 405 acquires the shipment group definition logic based on the operation input of the PD 104 by the operator or communication with the external device via the network, and stores it in the shipment group definition logic storage unit 406.
In this embodiment, it is assumed that the shipment group definition logic is as follows. First, among the orders (products 70) of the order attribute 500, the orders (products 70) having the same “transportation form” and “destination” are grouped so as to form one shipping group. Next, for all orders (product 70), a shipping group having the same “transportation mode” and an overseas country having the same “exporting country” content is extracted. Then, among the extracted shipping groups, shipping groups having the same “transportation mode” and “exporting country” are grouped together and regrouped. In addition, for a shipment group including orders (product 70) having different contents of “customer witness”, the shipment group includes orders (product 70) having the same content of “customer witness” as one shipment group. And regroup. A specific example of the shipping group definition logic will be described later with reference to FIG.
The shipment group definition logic acquisition unit 405 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the PD 104 (or the communication I / F 108). Further, the shipment group definition logic storage unit 406 is realized by using, for example, the HD 105.

<< Conveyance equipment workload prediction model >>
(Delivery of shipping group)
The transport device work amount prediction model creation unit 407 displays each order (product 70) of the order attribute 500 stored in the order attribute storage unit 402 in accordance with the shipment group definition logic stored in the shipment group definition logic storage unit 406. Sort into shipping groups.
FIG. 7 is a diagram conceptually illustrating an example of a method for classifying each order (product 70) of the order attribute 500 into a shipping group.
First, the transport device work amount prediction model creation unit 407 refers to the order attribute 500, and the “transportation mode” and “destination” which are order attributes that affect the product delivery position in the warehouses 10a and 10b are the same. A certain order (product 70) is combined into one. For example, as shown in FIG. 1 (product 70) and product no. The “transportation form” of the order 2 (product 70) is “sea transport”, and the “destination” is both “port A”. Orders that have the same transport mode and destination tend to have similar usage conditions of transport equipment for shipping. For example, the product No. 1 is also the product No. The product of No. 2 is also shipped by using the crane 30a (crane 1) twice and using either the freight car 40a or the freight car 40b only once. From such a viewpoint, the transport device work amount prediction model creation unit 407 combines these orders (products 70) into one. In this way, a shipping group as shown in the second column of FIG. 7 is obtained.

  Next, the transport equipment work amount prediction model creation unit 407 is an overseas country having the same “transportation mode” and the same “exporting country” for the shipping group obtained as described above. Multiple shipping groups are extracted and combined into one. For example, product no. In the third order (product 70), the “transportation mode” is “maritime”, and the “exporting country” is “country B”. Product No. The “transportation mode” of order 4 (product 70) is “maritime”, and the “exporting country” is “country B”. Accordingly, the transfer device work amount prediction model creation unit 407 sets these as one shipment group.

  Products exported to the same overseas country are often shipped together from the warehouse 10 even if their destinations are different, and the location within the warehouse 10 is likely to be close. Are likely to be transported in the same way. Therefore, for products exported to the same overseas country, even a single shipping group does not significantly affect the prediction accuracy of the work amount of the transfer equipment described later. Therefore, as described above, in this embodiment, after grouping for each order (product 70) having the same “transportation form” and “destination”, the “transportation form” and “exporting country” are the same. A plurality of shipping groups are combined into one shipping group. Here, the “transportation mode” is also taken into consideration in order to prevent as much as possible that orders (product 70) having greatly different delivery positions belong to the same shipping group.

  Further, the transfer device work amount prediction model creation unit 407 extracts a shipping group including orders (products 70) having different contents of “customer presence”. Then, the transport device work amount prediction model creation unit 407 divides the extracted shipping groups into two so that orders (product 70) having the same content of “customer presence” become one shipping group. For example, product no. 10 orders (product 70) and product no. The contents of the “customer presence” of the order 11 (product 70) is “Yes”, whereas the product No. The content of “customer presence” of 12 orders (product 70) is “none”. Therefore, the transfer device work amount prediction model creation unit 407 has the product No. One shipping group including orders 10 to 12 (product 70) is assigned a product number. 10 and 11 (product 70) shipping group and product No. Divide into 12 orders (product 70) shipping groups.

  When a customer witnesses the confirmation of the product 70 shipped from the warehouse 10, the product 70 is once transported to a special place in the warehouse 10, and then transported to a delivery position (that is, the special place Will be present). Therefore, unless orders that the customer witnesses (product 70) and orders that do not witness (product 70) are not divided into different shipping groups, there is a risk of greatly affecting the prediction accuracy of the work amount of the transfer device described later. Therefore, in the present embodiment, as described above, after grouping for each order (product 70) having the same “transportation form” and “destination”, those shipping groups are set to the contents of “customer presence”. Depending on the situation, it is further divided into two shipping groups.

(Calculation of transfer results data for each transfer device for each shipment group)
The transport device work amount prediction model creation unit 407 generates each shipment group based on the transport record data 600 stored in the transport record data storage unit 404 and the shipment group information obtained as described above. The transport result data of the transport device (total value of the number of times each transport device is used for each shipment group) is calculated and temporarily stored in the RAM 103 or the like.
FIG. 8 is a diagram illustrating an example of the transfer record data 800 of each transfer device for each shipment group.
In FIG. 1 (product 70) and product no. Since the order 2 (product 70) is the same shipping group (shipping group A), the number of products in the shipping group A is “2” (= 1 + 1). Therefore, the transfer device work amount prediction model creation unit 407 writes “2” in the “number of products” column of “shipping group A” in FIG.
Further, in FIG. 1 (product 70) and product no. The total number of times of use of the crane 30a (crane 1) of order 2 (product 70) is “4” (= 2 + 2). Therefore, the transfer device work amount prediction model creation unit 407 writes “4” in the “crane 1” column of “shipping group A” in FIG.
The values in the other columns in FIG. 8 are also obtained by performing the same calculation as the above calculation.

(Calculation of transport equipment work occurrence probability for each shipping group)
The transport device work amount prediction model creation unit 407 calculates the transport device work occurrence probability for each shipping group based on the transport result data 800 of each transport device for each shipping group obtained as described above. In the present embodiment, the transport device work occurrence probability is a value obtained by dividing the number of times each transport device is used in each shipping group by the number of orders (products 70) belonging to the shipping group (the number of products in FIG. 8). That is, the conveyance device work occurrence probability is the number of times each conveyance device is used in each shipping group per unit order (unit product).
FIG. 9 is a diagram illustrating an example of the transfer device work occurrence probability 900 for each shipment group.
In FIG. 8, the number of uses of the crane 30a (crane 1) in the shipping group A is “4”. Further, the number (number of products) of orders (product 70) belonging to the shipping group A is “2”. Accordingly, the transfer device work amount prediction model creation unit 407 sets the number of uses “4” of the crane 30a (crane 1) in the shipping group A to “2” as the number (number of products) of orders (products 70) belonging to the shipping group A. To obtain “2” as the work occurrence probability of the crane 30a in the shipping group A.
The values in the other columns in FIG. 9 can also be obtained by performing the same calculation as the above calculation.
The work occurrence probability may be acquired based on an operation input of the PD 104 by the operator.

Then, the transport device work amount prediction model creating unit 407 stores the transport device work occurrence probability 900 for each shipment group calculated as described above in the transport device work amount prediction model storage unit 408 as a transport device work amount prediction model. Remember.
The transport device work amount prediction model creation unit 407 is realized by using, for example, the CPU 101, the ROM 102, and the RAM 103. Further, the transport device work amount prediction model storage unit 408 is realized by using, for example, the HD 105.
In the present embodiment, for example, an example of the transport device work occurrence probability acquisition unit is realized by the processing of the transport device work amount prediction model creation unit 407 as described above.

<< Shipping Plan >>
The shipment plan acquisition unit 409 is realized by using, for example, the CPU 101, the ROM 102, the RAM 103, and the PD 104 (or the communication I / F 108), and the shipment by product that represents the shipment amount in each predetermined period for each order (product 70). The planned amount is acquired and stored in the shipping plan data storage unit 410. Here, the shipment plan acquisition unit 409 acquires the planned shipment amount for all the products 70 having the order attribute 500 acquired by the order attribute acquisition unit 401.
FIG. 10 is a diagram showing an example of the planned shipment quantity 1000 for each product. In FIG. 10, the planned shipment amount 1000 for each product 70 is represented by the weight [ton] of the steel sheet to be shipped.
Note that the planned shipment quantity by product 1000 can be derived by a known method, and the deriving method is not particularly limited, and thus detailed description thereof is omitted here. Further, the shipping plan acquisition unit 409 acquires (as it is), for example, the product-specific shipping plan amount created by the operator based on the operation input by the operator without performing the process of acquiring the product-specific shipping plan amount. You can also.
The shipping plan data storage unit 410 is realized by using the HD 105, for example.
In this embodiment, for example, an example of a product-specific shipping plan acquisition unit is realized by the processing of the shipping plan acquisition unit 409 as described above.

<< Prediction of work volume of transport equipment >>
The transport device work amount prediction unit 411 stores the order attribute 501 (order attribute related to the order to be shipped to be transported by the transport device that is the work amount prediction target, FIG. 5B) stored in the order attribute storage unit 402. Read), and the shipment group definition logic stored in the shipment group definition logic storage unit 406 is read. Then, the transfer device work amount prediction unit 411 classifies each order (product 70) of the order attribute 501 into a shipping group according to the shipping group definition logic. The classification method to the shipping group is as described above. As a result, product no. Orders for a, b, product no. C. Order of D, Product No. Order from E, Product No. Order, product no. Order, product no. Nu, Le order, Product No. Orders are classified into shipping groups A, B, C, D, E, F, and G, respectively.
Further, the transport device work amount prediction unit 411 reads the product-specific shipment plan amount 1000 (see FIG. 10) stored in the shipment plan data storage unit 410.
Then, the transfer device work amount prediction unit 411 uses these pieces of information to calculate a planned shipment amount (shipment amount) of the product 70 in each predetermined period for each shipment group.
FIG. 11 is a diagram illustrating an example of the planned shipping amount 1100 of the product 70 in each predetermined period for each shipping group. In FIG. 11, the planned shipping amount 1100 of the product 70 in each predetermined period for each shipping group is represented by the weight [ton] of the steel plate to be shipped.

As described above, the order (product 70) belonging to the shipping group A has the product No. Order (product 70) and product no. B order (product 70). Further, as shown in FIG. Order (product 70) and product no. The planned shipping quantities in the period N of the B order (product 70) are 120 [ton] and 0 [ton], respectively. Therefore, the transfer device work amount predicting unit 411 has the product No. Order (product 70) and product no. The planned shipping amount for the period N of the B order (product 70) is added to obtain 120 [ton] (= 120 + 0) as the planned shipping amount for the period N in the shipping group A.
The values in other columns in FIG. 11 are also obtained by performing the same calculation as the above calculation.
In the present embodiment, for example, an example of a shipment group-specific shipment plan calculation unit is realized by the processing of the transport device work amount prediction unit 411 as described above.

Next, the transport device work amount predicting unit 411 reads the “transport device work occurrence probability by shipment group 900 (see FIG. 9)” stored in the transport device work amount prediction model storage unit 408. Then, the transport device work amount prediction unit 411 reads the read “transport device work occurrence probability 900 for each shipment group” and “shipment plan of the product 70 in each predetermined period for each shipment group obtained as described above. Using the amount 1100 (see FIG. 11), the work amount for each predetermined period in each transport device is calculated.
FIG. 12 is a diagram illustrating an example of a work amount 1200 for each predetermined period in each transport device. In FIG. 12, the work amount 1200 for each predetermined period in each transport device is represented by the weight [ton] of the steel plate transported by the transport device.

As shown in FIG. 9, the work occurrence probability of the crane 30a (crane 1) in the shipping group A is “2”, and the work occurrence probability of the crane 30a (crane 1) in the shipment group B is “1”. In addition, the work occurrence probability of the crane 30a (crane 1) in the other shipping groups C to G is also “1”. Further, as shown in FIG. 11, the shipment planned quantities of the shipment groups A, B, C, D, E, F, and G in the period N are “120”, “240”, “160”, “0”, respectively. ”,“ 250 ”,“ 300 ”, and“ 400 ”. Therefore, the transfer device work amount prediction unit 411 adds the product of the shipment plan amount of each shipment group in the period N to the work occurrence probability of the crane 30a (crane 1) in the shipment group, and adds the crane 30a (crane The work amount for the period N of 1) is “1590” (= [(120 × 2) + (240 × 1) + (160 × 1) + (0 × 1) + (250 × 1) + (300 × 1) ) + (400 × 1)]).
The values in the other columns in FIG. 12 are also obtained by performing the same calculation as the above calculation.
The transport apparatus work amount prediction unit 411 is realized by using, for example, the CPU 101, the ROM 102, and the RAM 103.
In the present embodiment, for example, an example of the transport device work amount calculation unit is realized by the processing of the transport device work amount prediction unit 411 as described above.

<< Displaying the prediction result of the work amount of the transfer equipment >>
The transport device work amount display unit 412 uses the “work amount 1200 for each predetermined period in each transport device” calculated by the transport device work amount prediction unit 411 as the predicted result of the work amount for each predetermined period in each transport device. Display as.
FIG. 13 is a diagram illustrating a display example of a predicted work amount for each predetermined period in each transport device.
As shown in FIG. 13, in the present embodiment, the transport device work amount display unit 412 displays the work amount prediction result display area 1301, calculated by the transport device work amount prediction unit 411, for each predetermined period in each transport device. "Work volume" is displayed in a table format.
Further, the transfer device work amount display unit 412 displays an OK button 1302 and an NG button 1303 below the work amount prediction result display area 1301.

When the OK button 1302 is pressed by the operation of the PD 104 by the operator, the “work amount for each predetermined period (predicted result) in each transport device” displayed in the work amount prediction result display area 1301 is acknowledged. Shall be.
On the other hand, when the NG button 1303 is pressed by the operation of the PD 104 by the operator, the “work amount for each predetermined period (predicted result) in each transport device” displayed in the work amount prediction result display area 1301. It is assumed that it was not approved. In this case, the shipment plan correction unit 413 determines that the product-specific shipment plan amount 1000 is different from that stored in the shipment plan data storage unit 410, for example, the work amount prediction result is based on the operation input of the PD 104 by the operator. If the capacity is larger than the capacity of the transport device, the contents of the shipment plan data storage unit 410 are rewritten by changing the ship date of the order belonging to the shipping group using the transport device to another date.

The transport device work amount prediction unit 411 performs the same calculation as described above using the product-by-product shipment plan amount 1000 rewritten in this manner, and obtains the work amount 1200 for each predetermined period in each transport device. . The transport device work amount display unit 412 displays the “work amount 1200 for each predetermined period in each transport device” calculated by the transport device work amount prediction unit 411 in the work amount prediction result display area 1301. The above processing is repeated until the operator presses the OK button 1302.
In the present embodiment, for example, an example of a display unit is realized by the processing of the transport device work amount display unit 412 as described above.

<Operation Process of Product Conveyance Work Quantity Prediction Device 100>
Next, an example of operation processing of the product transport work amount prediction apparatus 100 will be described with reference to the flowchart of FIG. Here, the order attribute 500, the delivery record data 600, the shipment group definition logic, and the shipment planned quantity 1000 for each product are respectively an order attribute storage unit 402, a delivery record data storage unit 404, a shipment group definition logic storage unit 406, The description will be made assuming that the shipping plan data storage unit 410 has already been stored.

First, in step S1401, the transport device work amount prediction model creation unit 407 reads the order attribute 500 (order attribute related to an already manufactured / shipped order) stored in the order attribute storage unit 402 (FIG. 5A). )).
Next, in step S1402, the transport device work amount prediction model creation unit 407 reads the shipping group definition logic stored in the shipping group definition logic storage unit 406 (see FIG. 7).
Next, in step S1403, the transport device work amount prediction model creation unit 407 classifies each order (product 70) of the order attribute 500 read in step S1401 into a shipping group according to the shipping group definition logic read in step S1402. Shipment group discrimination processing is performed. Details of the shipping group determination process will be described later.

Next, in step S1404, the transport device work amount prediction model creation unit 407 reads the transport result data 600 stored in the transport result data storage unit 404 (see FIG. 6).
Next, in step S1405, the transfer device work amount prediction model creation unit 407 determines each transfer device for each shipment group based on the transfer record data 600 read in step S1404 and the shipment group information obtained in step S1403. Is calculated (see FIG. 8).

  Next, in step S1406, the transport device work amount prediction model creation unit 407 generates transport device work for each shipping group based on the “transport result data 800 of each transport device for each shipping group” obtained in step S1405. Probability 900 is calculated (see FIG. 9). As described above, the transfer device work occurrence probability 900 for each shipment group is obtained by dividing the number of times each transfer device is used in each shipment group by the number of orders (products 70) belonging to the shipment group (the number of products in FIG. 8). It is the value.

Next, in step S1407, the transport device work amount prediction unit 411 determines the order attribute 501 (order attribute related to the order to be shipped to be transported by the transport device whose work amount is to be predicted) stored in the order attribute storage unit 402. ) Is read (see FIG. 5B).
Next, in step S1408, the transfer device work amount prediction unit 411 reads the shipping group definition logic stored in the shipping group definition logic storage unit 406.
Next, in step S1409, the transport device work amount prediction unit 411 classifies each order (product 70) of the order attribute 501 read in step S1408 into a shipping group according to the shipping group definition logic read in step S1402. Perform discrimination processing. Details of the shipping group determination process will be described later.
Next, in step S1410, the product-specific shipment plan quantity 1000 (see FIG. 10) stored in the shipment plan data storage unit 410 is read.
Next, in step S1411, the transport device work amount prediction unit 411 obtains the result of the shipment group determination process performed in step S1409 and the “product-specific shipment plan amount 1000 (see FIG. 10)” read in step S1407. Is used to calculate the planned shipping amount 1100 for each predetermined period for each shipping group (see FIG. 11). As described above, the transfer device work amount prediction unit 411 first determines the order (product 70) belonging to the target shipping group from the result of the shipping group determination process performed in step S1409, and determines the shipping planned amount 1000 for each product. From this, the planned shipment amount for each period in the determined order (product 70) is read out. Then, the transfer device work amount prediction unit 411 adds the read shipment plan amounts for the same period. Such a process is performed for all the shipping groups to obtain a planned shipping amount 1100 for each predetermined period for each shipping group.

  Next, in step S1412, the transport device work amount predicting unit 411 calculates the “transport device work occurrence probability by shipment group 900” calculated in step S1406 and the “predetermined each shipment group work probability by step S1411”. The work amount 1200 for each predetermined period in each transport device is calculated using the “planned shipment amount 1100 in the period”. As described above, the transfer device work amount prediction unit 411 reads out the planned shipment amount for the target period of each shipment group from the planned shipment amount 1100 for each predetermined period for each shipment group, and the transfer device work for each shipment group. From the occurrence probability 900, the transfer device work occurrence probability in each shipping group of the target transfer device is read. Then, the transfer device work amount prediction unit 411 multiplies the shipment plan amount of the target period by the transfer device work occurrence probability of the target transfer device for the same shipment group, and adds the multiplied values. The work amount of the target period of the target transport device is obtained. Then, such a process is performed for all the conveying devices and periods, and a work amount 1200 for each predetermined period in each conveying device is obtained.

Next, in step S1413, the transport device work amount display unit 412 displays a work amount prediction result for each predetermined period in each transport device (see FIG. 13).
Next, in step S1414, the transfer device work amount display unit 412 determines which of the OK button 1302 and the NG button 1303 is pressed by the operator. If the operator presses the OK button 1302 as a result of this determination, it is determined that the prediction result of the work amount for each predetermined period in each transport device has been approved by the operator, and the processing according to the flowchart of FIG. 14 ends. To do.
On the other hand, when the NG button 1303 is pressed by the operator, it is determined that the prediction result of the work amount for each predetermined period in each transport device is not approved by the operator, and the process proceeds to step S1415. In step S1415, the shipping plan correction unit 413 changes the product-specific shipping plan amount 1000. Then, the process proceeds to step S1407, and the processes in and after step S1407 are executed using the changed shipment planned amount 1000 for each product.

Next, an example of the shipping group determination process in step S1403 in FIG. 14 will be described with reference to the flowchart in FIG. Note that the shipping group determination process in step S1409 of FIG. 14 is also realized by the transfer device work amount prediction unit 411 executing the flowchart of FIG.
First, in step S1501, the transport device work amount prediction model creation unit 407 refers to the order attribute 500 and collects orders (product 70) having the same “transportation form” and “destination” as one shipping group. .
Next, in step S1502, the transport device work amount prediction model creation unit 407 has the same “transportation mode” and the same “exporting country” for all shipping groups obtained in step S1501. Determine whether there are multiple shipping groups in foreign countries.

As a result of the determination, if there are no plural shipping groups having the same “transportation mode” and “exporting country”, step S1503 is omitted and the process proceeds to step S1504 described later.
On the other hand, if there are a plurality of groups having the same “transportation mode” and “exporting country”, the process advances to step S1503. In step S1503, the transfer device work amount prediction model creation unit 407 collects a plurality of shipping groups having the same “transportation mode” and “exporting country” as one shipping group. Then, the process proceeds to step S1504.

In step S1504, the transfer device work amount prediction model creation unit 407 determines whether there is a shipping group including orders (products 70) having different contents of “customer presence”. As a result of the determination, if there is no shipping group including orders (products 70) having different contents of “customer presence”, step S1505 is omitted, and the process proceeds to step S1506 described later.
On the other hand, if there is a shipping group including orders (products 70) with different contents of “customer presence”, the process proceeds to step S1505. In step S1505, the transfer device work amount prediction model creation unit 407 determines that the contents of the “customer witness” are mutual so that orders (product 70) having the same “customer witness” content are included in one shipping group. The shipping group including different orders (product 70) is divided into two. Then, the process proceeds to step S1506.

  In step S1506, the transfer device work amount prediction model creation unit 407 determines a shipment group to which each order (product 70) belongs, based on the results of the processing so far. And it returns to the flowchart of FIG.

  As described above, in this embodiment, an order (product 70) having the same “transportation form” and “destination” included in the order attribute 500 is defined as one shipping group. Based on the transfer result data 600, the number of times of use of each transfer device in each shipment group (transfer device work occurrence probability for each shipment group) per unit order (unit product) is calculated. In addition, based on the planned shipment quantity by product 1000, the planned shipping quantity 1100 for each predetermined period for each shipping group is calculated. Then, the product of the shipment schedule amount of the corresponding period and the transfer device work occurrence probability of the corresponding transfer device are multiplied for each shipment group, and the work amount 1200 for each predetermined period of each transfer device is calculated.

  Therefore, among the order attributes, the position at which the product 70 is shipped (shipping position, in the example shown in FIG. 1, the second address of the ridge 2 of the warehouse 10a, the second address of the ridge 3 of the warehouse 10b, and the first address of the warehouse 10b). The products 70 having the same two order attributes that affect the same can be grouped as one shipping group. In other words, the product 70 in which the usage of the transport device in the warehouse 10 may be similar (at least at the time of shipment) does not decrease the prediction accuracy of the work amount of the transport device and has a large calculation processing amount. It can be classified into a reasonable number of shipping groups. Therefore, it is possible to easily predict the work amount of the transfer device without constructing a complicated physical distribution simulator. In addition, since the work amount of the transfer device can be predicted without the information regarding the actual arrangement of the product 70 in the warehouse 10, the work amount of the transfer device over a long period of time (for example, prediction for each day of the month) can be performed. It can be carried out. Further, since the shipment definition group logic is constructed by order attributes, it does not depend on past operation results. Therefore, the shipment definition group logic can be continuously used, and it is not necessary to frequently change the shipment definition group logic. As described above, in the present embodiment, when planning a product shipment plan for a manufacturing industry that manufactures a large amount of products, it is easy and reliable to quantitatively predict the work amount of the transfer device in the warehouse 10. This makes it possible to devise a shipping plan that does not become a bottleneck for the work of transport equipment in the warehouse.

  In the present embodiment, orders (product 70) having the same “transportation form” and “destination” included in the order attribute 500 are grouped into one shipping group, and then each order (product) belonging to those shipping groups 70), according to the contents of “exporting country” and “customer presence” included in the order attribute 500, a plurality of shipping groups are combined into one, or one shipping group is divided into two. That is, for all orders (products 70), a shipping group having the same “transportation mode” and the content of “exporting country” is the same overseas country is extracted. And multiple shipping groups with the same “exporting country”. In addition, for a shipment group including orders (product 70) having different contents of “customer witness”, the shipment group includes orders (product 70) having the same content of “customer witness” as one shipment group. Is divided into two.

  Therefore, the products 70 whose arrangement in the warehouse 10 of the products 70 may be greatly different can be divided into separate shipping groups. Further, the products 70 that are close to each other before the products 70 are shipped and before the products are shipped and that may have the same (near) delivery position are more reliably collected into one shipping group. be able to.

<< Modification >>
In this embodiment, the case where an order (product 70) having the same “transportation form” and “destination” is set as one shipping group is described as an example. However, if an order (product 70) having a plurality of order attributes including one or more order attributes that affect the position (shipment position) at which the product 70 is issued is a single shipping group, this is not necessarily the case. The order attribute 500 is not limited to that described above. For example, if the order attribute 500 includes a “delivery deadline date (the date on which the product 70 must be shipped from the warehouse 10)”, an order having the same “destination” and “shipment deadline date”. (Product 70) may be one shipping group. Further, an order (product 70) having the same “transportation mode”, “destination”, and “delivery date” may be set as one shipping group. However, it is preferable that the products 70 having the same two order attributes be one shipping group so that the number of shipping groups obtained is not too large. Further, the order attribute that affects the delivery position of the product 70 includes the final destination of the product 70 in addition to the “transportation mode” and the “destination”. The order attribute that affects the delivery position of the product 70 is an order attribute that can change the delivery position of the product 70 depending on the content.

  In the present embodiment, for all orders (product 70), a shipping group having the same “transportation mode” and the content of “exporting country” is the same overseas country is extracted. The case where a plurality of shipping groups having the same “transportation mode” and “exporting country” are combined into one has been described as an example. However, this is not necessarily necessary if a plurality of shipping groups are combined into one based on information that affects the arrangement of the product 70 before it is shipped from the warehouse 10 and before it is shipped. Absent. For example, “size” may be adopted instead of “exporting country”, and a plurality of shipping groups whose “size” is a predetermined size or more may be combined into one. This is because there is a possibility that the product 70 having a large “size” is close to the place in the warehouse 10. Further, the information that affects the arrangement of the product 70 during the period from entering the warehouse 10 to before shipping may not be the order attribute. Note that the information that affects the arrangement of the product 70 before it is shipped from the warehouse 10 and before it is shipped is the arrangement of the product 70 before it is shipped from the warehouse 10 according to its contents. Refers to information that can change.

  Further, in the present embodiment, for a shipping group including orders (product 70) having different contents of “customer witness”, orders (product 70) having the same content of “customer witness” are combined into one shipping group. The case where the shipping group is divided into two as described above is described as an example. However, this is not always necessary if the shipping group is divided into a plurality of groups based on information that affects the arrangement of the product 70 before it is shipped from the warehouse 10 and before it is shipped. For example, instead of “customer presence”, “size” is adopted, an order (product 70) in which “size” is equal to or larger than a predetermined size, and an order (product 70) in which “size” is less than the predetermined size. May be divided into a plurality of groups according to “size”. This is because the product 70 having a large “size” may be placed only at a specific location in the warehouse 10. Further, as described above, the information that affects the arrangement of the product 70 between the time when it is received at the warehouse 10 and before it is shipped may not be the order attribute.

  Further, in the present embodiment, the case where the processes of steps S1504 and S1505 are performed after steps S1502 and S1503 has been described as an example, but this is not necessarily required. For example, the processes in steps S1502 and S1503 may be performed after the processes in steps S1504 and S1505. Further, it is preferable to perform the processes in steps S1502 to S1505 because the above-described effects can be obtained. However, only one of steps S1502 and S1503 and steps S1504 and S1505 may be performed. Further, the processing in steps S1502 to S1505 may not be performed.

Further, after obtaining the shipping group as described above (after the processing of step S1505 in FIG. 15 is completed), if the inventory amount in the warehouse 10 is equal to or greater than the threshold, the shipping group is selected according to the inventory amount. It may be further subdivided. This is because when the amount of inventory in the warehouse 10 is large, the place where the product 70 can be placed is limited, and therefore, the accuracy of predicting the work amount of the transporting device increases when the shipping group is subdivided.
Further, the warehouse 10 and the transfer device and the product 70 are not limited to those shown in FIGS.
In addition, the transport device work occurrence probability 900 for each shipping group is calculated based on the transport result data 800 of each transport device for each shipping group, but the operator inputs the transport device work occurrence probability for each shipping group. You may make it do.

[Second Embodiment]
Next, a second embodiment of the present invention will be described. In the first embodiment described above, the case where the work amount of the transfer device is individually estimated for each transfer device has been described as an example. In contrast, in the present embodiment, a case will be described in which a plurality of transfer devices that do not need to be distinguished and handled are set as one transfer device group, and the work amount of the transfer device group is predicted. As described above, the present embodiment and the first embodiment described above are the same except for the handling of the transport device. Therefore, in the description of the present embodiment, the same parts as those in the first embodiment described above are denoted by the same reference numerals as those in FIGS.

  In the warehouse 10 shown in FIG. 1, it is assumed that the freight cars 40a and 40b have the same ability. In such a case, what is necessary is just to verify whether the product 70 can be conveyed by at least one of the freight cars 40a and 40b rather than by which of the freight cars 40a and 40b the product 70 is conveyed. Accordingly, the freight cars 40a and 40b (freight cars 1 and 2) are regarded as one transport device and are defined as a transport device group, and the work amount of the transport device group is predicted. In this case, the “Freight car 1” field and the “Freight car 2” field of the “Transport equipment work occurrence probability 900 by shipping group” shown in FIG. The values for G are “1”, “1”, “2”, “1”, “2”, “2”, and “2”, respectively. In addition, the “freight car 1” column and the “freight car 2” field of “the work amount 1200 for each predetermined period in each transport device” shown in FIG. The value of N + 5 is a value obtained by adding the value of the column “Freight car 1” and the value of the field “Freight car 2” in FIG. 12 (for example, “2580” (= 1440 + 1140) for the period N). Become).

  As described above, in this embodiment, a plurality of transfer devices that do not need to be distinguished and handled are set as one transfer device group, and the work amount of the transfer device group is predicted. Therefore, in addition to the effects described in the first embodiment, the scale of the transport device work amount prediction model can be reduced more than that described in the first embodiment, and the calculation load can be reduced. Is obtained.

  Note that the predicted value of the transfer device work amount described in each of the above-described embodiments is the total value of the transfer device work amount associated with both the warehousing and shipping operations. Therefore, if there is a product that has already been received, the value obtained by subtracting the work amount of the transfer device generated at the time of receiving the product from the predicted value of the transfer device work amount will be the net required transfer device work amount in the future. What is necessary is just to calculate as a predicted value.

  Further, in each of the embodiments described above, the example in which the transfer device work occurrence probability for each shipment group is calculated without distinguishing between entering the warehouse and shipping from the warehouse has been described. It is good also as a structure which classify | categorizes into a shipment work and calculates separately the conveyance apparatus work generation | occurrence | production probability which concerns on a warehousing work, and the conveyance apparatus work generation | occurrence | production probability which concerns on a shipment work. In this case, if there is a product that has already been received, the work volume of the transfer device is predicted using only the probability of occurrence of the shipment for the received product, and for the product that has not been received, The work amount of the transfer device is predicted using the occurrence probability of both operations, and the total value may be set as the work amount of the transfer device of the product.

Each embodiment of the present invention described above can be realized by a computer executing a program. Further, a means for supplying the program to the computer, for example, a computer-readable recording medium such as a CD-ROM recording such a program, or a transmission medium for transmitting such a program may be applied as an embodiment of the present invention. it can. A program product such as a computer-readable recording medium that records the program can also be applied as an embodiment of the present invention. The programs, computer-readable recording media, transmission media, and program products are included in the scope of the present invention.
In addition, each of the embodiments of the present invention described above is merely an example of implementation in practicing the present invention, and the technical scope of the present invention should not be construed as being limited thereto. It will not be. That is, the present invention can be implemented in various forms without departing from the technical idea or the main features thereof.

DESCRIPTION OF SYMBOLS 10 Warehouse 20 Track 30 Crane 40 Freight car 50 Cart 60 Truck 70 Product 71 Product pile 100 Product conveyance work amount prediction device 401 Order attribute acquisition unit 402 Order attribute storage unit 403 Transfer result data acquisition unit 404 Transfer result data storage unit 405 Shipping group definition Logic acquisition unit 406 Shipment group definition logic storage unit 407 Transport device work amount prediction model creation unit 408 Transport device work amount prediction model storage unit 409 Shipment plan acquisition unit 410 Shipment plan data storage unit 411 Transport device work amount prediction unit 412 Transport device work Quantity display part 413 Shipment plan correction part

Claims (13)

The product before entering the warehouse is used for receiving goods from the warehouse and shipping from the warehouse, and the products already received in the warehouse are used for shipping from the warehouse. A product transfer work amount prediction device for predicting a work amount,
Order attribute acquisition means for acquiring an order attribute which is information relating to an order of a plurality of the products;
Shipping group generating means for generating a shipping group by grouping products for which a plurality of predetermined order attributes including the order attribute affecting the delivery position of the product in the warehouse are the same among the order attributes; ,
A conveying device work occurrence probability obtaining means for obtaining by the shipping group handling equipment work occurrence probability of the number of times of use per unit product of each conveying devices that are used to ship the product,
And by product shipment schedule acquisition means for acquiring the product-specific delivery schedule is shipments in the plurality of product-specific future predetermined period,
A shipment group-specific shipment plan calculation means for calculating a shipment amount in the predetermined period for each shipment group based on the product-specific shipment plan;
A transport device that calculates a work amount of each transport device in the predetermined period based on a transport device work occurrence probability in each transport device by the shipment group and a shipment amount in the predetermined period by the shipment group. Work amount calculation means;
A product conveyance work amount prediction device, comprising: a display unit configured to display, on a display device, information on a work amount of each of the transfer devices in the predetermined period. The shipment group generation means has an order attribute different from the order attribute in which at least one of the predetermined order attributes affecting the delivery position of the product is the same for each of the generated products belonging to the plurality of shipment groups In the case where the same information is set as an order attribute that affects the placement of the product in the warehouse, the plurality of shipping groups are combined into one. Product conveyance work amount prediction device as described in 1. The shipment group generation means has an order attribute different from a predetermined order attribute that affects a delivery position of the product for products belonging to the generated shipment group, and affects the placement of the product in the warehouse in the warehouse. 3. The product transfer work amount prediction according to claim 1, wherein, when different information is set as an order attribute for providing the product, the shipping group is divided into a plurality of shipping groups according to the information. apparatus. It further has a transport record acquisition means for acquiring a transport record of the transport device when shipping a product already shipped from the warehouse,
The transfer device work occurrence probability acquisition means calculates the transfer device work occurrence probability for each shipment group based on a transfer result of the transfer device when a product already shipped from the warehouse is shipped. The product conveyance work amount prediction apparatus according to any one of claims 1 to 3. The transfer device work occurrence probability acquisition means, for each of a plurality of transfer devices used for shipping the product, sets the number of uses per unit product of the entire transfer devices. Calculate as the work equipment work occurrence probability,
5. The product transfer work amount prediction apparatus according to claim 4, wherein the transfer device work amount calculation means calculates the work amount of the plurality of transfer devices as a whole for the predetermined plurality of transfer devices. As the plurality of order attributes, two order attributes that affect the delivery position of the product in the warehouse , a transport mode, and a destination that is a place where the product is first unloaded after being delivered from the warehouse The product conveyance work amount prediction apparatus according to any one of claims 1 to 5, wherein The product before entering the warehouse is used for receiving goods from the warehouse and shipping from the warehouse, and the products already received in the warehouse are used for shipping from the warehouse. A method for predicting the amount of work carried by a product, wherein the amount of work is predicted by a computer ,
An order attribute obtaining step for obtaining an order attribute which is information relating to an order of a plurality of the products;
A shipping group generation step of generating a shipping group by grouping for each product having a plurality of predetermined order attributes including the order attribute that affects the delivery position of the product in the warehouse among the order attributes; ,
A conveying device work occurrence probability obtaining step of conveying equipment work occurrence probability of the number of uses to get by the shipping group per unit product of each conveying devices that are used to ship the product,
And by product shipment schedule acquisition step of acquiring the product-specific delivery schedule is shipments in the plurality of product-specific future predetermined period,
A shipment group-specific shipment plan calculation step for calculating a shipment amount in the predetermined period for each shipment group based on the product-specific shipment plan;
A transport device that calculates a work amount of each transport device in the predetermined period based on a transport device work occurrence probability in each transport device by the shipment group and a shipment amount in the predetermined period by the shipment group. A workload calculation step;
And a display step of displaying information on the work amount of each of the transport devices in the predetermined period on a display device. In the shipping group generation step, for each product belonging to the plurality of generated shipping groups, at least one of the predetermined order attributes that affect the delivery position of the product is the same, and the order attributes that are different from the order attributes a is, if the same information is set as an order attribute that affects the positioning of said product warehouse in the warehouse, claim 7, characterized in that put together the plurality of shipping groups into one Product transportation work amount prediction method described in 1. The shipping group generation step is an order attribute different from a predetermined order attribute that affects a delivery position of the product for products belonging to the generated shipping group, and affects the placement of the product in the warehouse in the warehouse. 9. The product transfer work amount prediction according to claim 7 or 8, wherein, when different information is set as an order attribute for providing the product, the shipping group is divided into a plurality of shipping groups according to the information. Method. A transport record acquisition step of acquiring a transport record of the transport device when shipping a product that has already been shipped from the warehouse;
The transport device work occurrence probability obtaining step calculates the transport device work occurrence probability for each shipment group based on the transport performance of the transport device when the product already shipped from the warehouse is shipped. The product conveyance work amount prediction method according to any one of claims 7 to 9. In the transfer device work occurrence probability acquisition step, for each of a plurality of transfer devices used for shipping the product, the number of uses per unit product of the plurality of transfer devices as a whole is calculated. Calculate as the work equipment work occurrence probability,
11. The product transfer work amount prediction method according to claim 10, wherein the transfer device work amount calculation step calculates a work amount of the entire plurality of transfer devices for the predetermined plurality of transfer devices. As the plurality of order attributes, there are two order attributes that affect the delivery position of the product in the warehouse, and a destination that is a place where the product is first unloaded after being delivered from the warehouse. The product conveyance work amount prediction method according to any one of claims 7 to 11, further comprising: The product before entering the warehouse is used for receiving goods from the warehouse and shipping from the warehouse, and the products already received in the warehouse are used for shipping from the warehouse. A computer program for causing a computer to predict a workload,
An order attribute obtaining step for obtaining an order attribute which is information relating to an order of a plurality of the products;
A shipping group generation step of generating a shipping group by grouping for each product having a plurality of predetermined order attributes including the order attribute that affects the delivery position of the product in the warehouse among the order attributes; ,
A conveying device work occurrence probability obtaining step of conveying equipment work occurrence probability of the number of uses to get by the shipping group per unit product of each conveying devices that are used to ship the product,
And by product shipment schedule acquisition step of acquiring the product-specific delivery schedule is shipments in the plurality of product-specific future predetermined period,
A shipment group-specific shipment plan calculation step for calculating a shipment amount in the predetermined period for each shipment group based on the product-specific shipment plan;
A transport device that calculates a work amount of each transport device in the predetermined period based on a transport device work occurrence probability in each transport device by the shipment group and a shipment amount in the predetermined period by the shipment group. A workload calculation step;
A computer program that causes a computer to execute a display step of displaying information on a work amount of each of the transport devices in the predetermined period on a display device.

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