JP2012093865A - Production schedule computation method and production schedule computing device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To obtain an optimized production schedule when producing multiple kinds of products using processes commonly.SOLUTION: A storage unit 16 is used to store: worker data 18 including the total number of workers N; process data 20 including required processing time and the required number of workers for each product and the available number of products to be processed simultaneously, with respect to each process; product data 22 including process order required to produce each product; and a list of products to be produced by a due date. Production schedule data generation means 36 generates production schedule data 28 through a step for allocating each product to each process in process order, a step for allocating required workers to each process and a step for arranging states of allocating products to all processes in chronological order. Optimization means 38 searches for the earliest final process finish time 30 through a step for selecting products in random order, a step for delivering data about the selected products to the production schedule data generation means 36, and a step for changing product selection order and worker allocation order. Then, the production schedule is formed.

Description

  The present invention uses a computer to calculate the order of process input and the assignment of workers to each process to efficiently manufacture multiple types of products, each of which is manufactured through a plurality of processes that are partly or entirely in common. The present invention relates to a manufacturing plan calculation method, a manufacturing plan calculation device, a computer program, and a program recording medium that are processed and optimized.

  Developed technology to increase productivity by storing the manufacturing process of products, the necessary personnel for each, and the number of personnel that can be put in each time zone as data in the storage device, and optimally arranging the personnel for each process ing.

JP 2001-249712 A JP 2007-156990 A

The known prior art has the following problems to be solved.
In factories that manufacture housing components such as joinery, stairs, and storage, all undergo a process of cutting materials, a process of assembling, a process of painting, an inspection process, and the like. That is, some or all of the manufacturing processes of a plurality of products are common. In each process, several kinds of products are accepted and processed in order. However, the time required for the process differs for each type of product. Accordingly, the work efficiency varies depending on the order in which the products are received in each process. In addition, if there are processes that require several workers and processes that are processed unattended, it is not easy to optimize a production plan that includes worker assignments.

  The present invention has been made paying attention to the above points, and optimizes the order of acceptance of each product into the manufacturing process and the assignment of workers, calculates the production end time of each product, and provides reasonable work instructions. An object of the present invention is to provide a manufacturing plan calculation method, a manufacturing plan calculation device, a computer program, and a program recording medium that are automatically generated and used for the purpose of reducing the manufacturing cost.

The following configurations are means for solving the above-described problems.
<Configuration 1>
When manufacturing multiple types of products that commonly use any process or all of a plurality of processes, the identification code and the total number of workers who can engage in the manufacturing of the storage device Worker data including N, for each process, the identification code of the process, the required processing time for each product, the number of workers required for that process, and the number of products that can be processed simultaneously in that process Process data, product data including identification codes for each product, order of processes required for the manufacture of each product, and a list of products whose production is to be terminated by a specified date. When a product is selected from the list of products in order, the data generation means refers to the product data, and each product is received in each step so that each product is received in each step in the order of selection in the order of the steps. Assigned to Referring to the process data, the operator detects necessary processes, and all the workers are moved from the previous process to the subsequent process or sequentially from the subsequent process to the previous process. When there is a process with insufficient workers, the corresponding worker is assigned after the process that ends most quickly, and the product is assigned to all the processes. Are arranged in time series, generate time series allocation data including the worker's allocation result, and the optimization means selects the products in a random order from the list of products, and performs the production. Causing the plan data generation means to generate the time-series allocation data, detect the final process end time from the generated time-series allocation data, and then change the order of selecting products from the list of products, Total production It repeats the process of generating the time series allocation data in the data generation means, searches for the earliest final process end time, and selects the product selection order of the production plan data in which the earliest final process end time is detected. Fixed, change the order of assigning the workers to the process, repeat the process of generating the time-series allocation data in the production plan data generation means, search for the earliest final process end time A production plan calculation method characterized by outputting optimized production plan data including time-series allocation data in which the earliest final process end time is detected.

<Configuration 2>
In the manufacturing plan calculation method according to Configuration 1, the work-in-process storage location determining means includes a work-in-process storage space for temporarily storing work-in-process items of the product until the product that has been processed in each step moves to the next step. After a set and optimized production plan is acquired, a work plan calculation method is characterized in that only the work in process place used in the production plan is determined as the work in process place with the minimum necessary capacity. .

<Configuration 3>
In the manufacturing plan calculation method according to Configuration 2, the work in process place is defined as zero processing required time for the product, zero workers required for the process, and unlimited number of products that can be stored simultaneously, The time series data is generated, and the work-in-process storage area used for storage and the maximum number of products stored in the work-in-process storage area are acquired during any process, and the minimum required capacity is obtained. A manufacturing plan calculation method characterized by determining a work-in-process storage area.

<Configuration 4>
4. The manufacturing plan calculation method according to any one of configurations 1 to 3, wherein a product that has not been manufactured and a product that has already been manufactured are mixed in the product list. Method.

<Configuration 5>
In the manufacturing plan calculation method according to any one of Configurations 1 to 4, the process data includes an operator code for specifying an operator who can work in the process and data indicating a processing time required for each operator. A manufacturing plan calculation method characterized by that.

<Configuration 6>
In the manufacturing plan calculation method according to any one of the configurations 1 to 5, the optimization unit allocates a sufficient number of workers necessary for all the processes, executes the calculation process, and calculates the number of workers operating simultaneously. A manufacturing plan calculation method characterized in that a maximum value is obtained and included in the production plan data.

<Configuration 7>
In the manufacturing plan calculation method according to any one of the configurations 1 to 6, an initial value of the total number of workers N is set so as to cause a process of waiting for worker assignment due to a shortage of workers. A manufacturing plan calculation method characterized in that a calculation value with a changed number of workers N is repeated to obtain a minimum value of the number of workers capable of finishing the manufacturing by the designated date.

<Configuration 8>
An apparatus that performs computer processing on a production plan for manufacturing a plurality of types of products that commonly use any process or all of a plurality of processes, and an identification code for all workers who can engage in manufacturing Worker data including the total number of workers N, and for each process, the process identification code, the required processing time for each product, the number of workers required for the process, and the process can be processed simultaneously. Stores process data including the number of products, product data including the identification code for each product, the order of processes required for manufacturing each product, and a list of products that will be discontinued by a specified date When a product is selected in order from the storage device and the list of products, each product is referred to by referring to the product data so that each product is accepted in each step in the order of selection in the order of the process. Referring to the process data, the worker detects necessary processes and assigns all workers in order from the previous process to the subsequent process or from the subsequent process to the previous process. , Assigning the number of workers required for each process, and when there is a process with insufficient workers, wait for the end of the process to be completed earliest, assign the appropriate workers, and The product allocation state is arranged in chronological order, production plan data generation means for generating production plan data including the worker allocation results, and products in a random order are selected from the product list. , By causing the production plan data generating means to generate the production plan data, detecting a final process end time from the generated production plan data, and then changing the order of selecting products from the list of products, The process of generating the production plan data in the production plan data generation unit is repeated to search for the earliest final process end time, and the product selection of the production plan data in which the earliest final process end time is detected The order of fixing the order, changing the order in which the worker is assigned to the process, and repeating the process of generating the production plan data in the production plan data generating means, so that the earliest final process end time is detected. A production plan calculation device comprising an optimization means for outputting the production plan data as optimized production plan data.

<Configuration 9>
A manufacturing plan calculation program for causing a computer to function as each means described in Configuration 7.

<Configuration 10>
A computer-readable recording medium on which a manufacturing plan calculation program for causing a computer to function as each means described in Configuration 7 is recorded.

<Effect of Configuration 1>
Even in cases where the combination of the order of introduction of multiple types of products into each process and the assignment of workers becomes very complicated, an optimized production plan can be processed at high speed by a computer.
<Effect of Configuration 2>
By providing a work-in-process storage area, the production plan can be optimized more efficiently and the minimum required work-in-process storage area can be automatically determined.
<Effect of Configuration 3>
By defining the work-in-process storage area as a kind of process, it is possible to perform the work-in-process storage area allocation calculation simultaneously with the process allocation calculation.
<Effect of Configuration 4>
Products that have not yet been manufactured can be mixed with semi-finished products that have been manufactured and suspended, and the production plan can be processed.
<Effect of Configuration 5>
Depending on the ability of the operator, the required time may be different even in the same process. Even in this case, automatic calculation is possible.
<Effect of Configuration 6>
It is possible to generate a production plan that completely eliminates the process that the operator lacks.
<Effect of Configuration 7>
If production can be completed by a specified date, it may be economical to have a minimum number of workers in charge. The calculation process can be executed.

It is a block diagram of the manufacturing plan calculating apparatus suitable for implementation of this invention. It is a hardware block diagram of a computer. 4 is an explanatory diagram illustrating a specific configuration example of data stored in a storage device 16; FIG. FIG. 3 is a schematic explanatory diagram of a manufacturing process for Example 1. It is explanatory drawing which shows the specific example of process data. (A) is a comparative example of a product list, and (b) is an example of a product list. It is explanatory drawing which shows an example of time series allocation data. It is explanatory drawing which shows an example of the time series allocation data after optimization. It is process data explanatory drawing of the Example which provided the work-in-process place. It is an Example explanatory drawing of the product list which optimized reading order. It is explanatory drawing which shows an example of the time series allocation data after optimization. It is the schematic of the process in the case of the product from which some manufacture was already started being mixed. It is principal part explanatory drawing of process data. The time-series allocation data in the case of Example 3, where (a) is before improvement and (b) is after improvement. It is Example explanatory drawing of the output worker shift data. It is Example explanatory drawing of the output production plan data. It is a flowchart which shows schematic operation | movement of the system of this invention. It is a search process operation | movement flowchart of the front | former stage of an optimization means. It is a search process operation | movement flowchart of the latter part of an optimization means. It is an operation | movement flowchart of a production plan data generation means.

  The production plan calculation device of the present invention is used, for example, for creating a plan when a plurality of types of products are produced by a designated date. Alternatively, it is used for creating a plan when manufacturing a plurality of types of products as soon as possible. Furthermore, it is used to create a plan for efficiently operating the minimum number of workers. The product is, for example, a member such as a fitting for building a house, a staircase, or a storage. Each product is manufactured in a certain process order. The processes are, for example, processes such as cutting, assembly, painting, and inspection.

  In the cutting process, equipment for cutting a material into a specified shape or opening a hole is provided. In the assembling process, for example, facilities for screw tightening and adhesion are provided. For example, at least one worker is assigned to these processes. In the painting process, facilities for receiving products collected from the previous process and spraying and drying the paint are provided. In this step, for example, a plurality of products are processed fully automatically. No workers are needed. In the inspection process, for example, at least one worker performs a predetermined completion inspection.

  In each process as described above, a plurality of types of products can be processed based on design documents. That is, different types of products can use any process or all the processes in common. For example, even if the processing contents are different for each product and the working time is different, the same worker works using the same tool.

  Production plan data shows a specific time flow of a product when a plurality of products are manufactured using such a process. In order to automatically generate the production plan data, conditions for calculation are input to the computer in advance. The total number of workers who can be engaged in manufacturing is, for example, N (a positive integer) and is constant. Production plan data is generated on the premise that N workers are always secured until a designated date.

  When a plurality of single-type products are manufactured, each product may be processed in order from the first step. If the processing operations in each process are optimized, the processing order for each product has no influence on the production end time. However, if a plurality of types of products are mixed, the production end time changes if the order in which each product is put into the process is changed. In the present invention, the production order data is generated by searching for the order in which the production ends most quickly.

  Furthermore, if necessary and sufficient workers are arranged in all processes, no wasteful waiting time is generated when any product proceeds from one process to the next process. However, since the end timing of each process varies, there is a waiting time for the worker in charge of each process. Therefore, it is not always economical to arrange necessary and sufficient workers for all processes. When the total number of workers N to be put into the process is insufficient and necessary, the efficiency as a whole changes depending on when and where the workers are arranged, and the production end time also changes.

  In the present invention, while optimizing the order in which each product is put into the process, the arrangement of workers is optimized, and production plan data is generated by efficiently using fewer workers than the necessary and sufficient number of workers. . For this purpose, a computer program and data are set as follows, and arithmetic processing is executed in the following procedure.

(Hardware)
FIG. 1 is a block diagram of a manufacturing plan calculation apparatus suitable for implementing the present invention.
The manufacturing plan calculation device 12 of the present invention is realized by a single computer as shown in the figure. In the figure, this computer is provided with man-machine interface hardware such as a display 2, a keyboard 4, and a mouse 5 in addition to a main body control unit 3 that executes arithmetic processing. The display 2 is a display output device having an arbitrary configuration for outputting a result of processing by a computer. CRT displays, liquid crystal displays, projectors, etc. can be used. A keyboard 4 and a mouse 5 are devices for inputting instructions to the computer. Since all of these man-machine interface hardware can be used, illustration and specific description of each function are omitted.

FIG. 2 is a hardware block diagram of the computer.
In the main body controller 3 of the computer, an internal bus 110 includes a CPU (Central Processing Unit) 111, a ROM (Read Only Memory) 112, a RAM (Random Access Memory) 113, an HDD (Hard Disk) 114, An input / output interface 115 is connected. A display 2, a keyboard 4, and a mouse 5 are connected to the input / output interface 115.

  The storage device 16 illustrated in FIG. 1 includes a ROM 112, a RAM 113, and an HDD 114. The arithmetic processing unit 14 illustrated in FIG. 1 includes a CPU 111, a ROM 112, a RAM 113, and the like. Various data are mainly stored and stored in the HDD 114. A computer program executed by the CPU 111 is stored in the ROM 112 or loaded into the RAM 113 as appropriate.

  As shown in FIG. 1, the computer includes an arithmetic processing device 14 and a storage device 16. As shown in the figure, the computer processing unit 14 includes computer program modules such as a production plan data generation unit 36, an optimization unit 38, and a work-in-process storage place determination unit 40. The computer functions as each means constituting the arithmetic processing unit 14 at a predetermined timing by executing a computer program installed in advance. During execution of this computer program, the computer uses the storage device 16 to read necessary data stored in advance. Alternatively, new data is written and stored in the storage device 16.

(Data used)
FIG. 3 is an explanatory diagram of a specific configuration example of data stored in the storage device 16.
The storage device 16 (FIG. 1) stores worker data 18, process data 20, product data 22, and a product list 24. The worker data 18 includes an identification code 42 and a total number of workers (N) 44 of all workers who can engage in manufacturing. The number of workers N is not necessarily a number that is sufficient to assign to all processes. In order to reduce the worker's useless waiting time and efficiently use the worker, N is selected to be a number equal to or less than the necessary and sufficient number of workers.

  When a necessary and sufficient number of workers are assigned to all the processes, if a maximum value of the number of workers operating simultaneously is obtained, the process of waiting for worker assignment due to lack of workers can be eliminated. In addition, even when there is a process of waiting for worker assignment with fewer workers, a production plan that satisfies the requested work end time can be generated. When the number of workers is changed and the above calculation process is repeated, the minimum number of workers can be obtained. This calculation process is executed by the optimization means 38, and the result is preferably included in the production plan data 28 and output. In addition, when there is a capability difference among workers, the processing time of each process differs for each assigned worker. Optimization that takes this into account is also possible. At this time, the worker data 18 may include evaluation data ranking the workers. The evaluation data is an extra rate of processing time for each process.

  The process data 20 includes, for each process, the process identification code 46, the required processing time 48 for each product, the number of workers 50 required for the process, and the number 52 of products that can be processed simultaneously in the process. Including. Even in the same process, the time required varies depending on the type of product. This required time becomes an important parameter for determining the timing of receiving each product from the previous process and sending it to the subsequent process. In addition, in order to simplify description, the conveyance time of the product between processes was abbreviate | omitted. Furthermore, the setup change time which is necessary in some cases is omitted. In addition, in a process that can process multiple products at the same time, for example, a process that does not start unless three products are available, or three or more products can be processed simultaneously, and processing starts at a specified timing. The process of doing may be included.

  The product data 22 includes an identification code 54 for each product and a process order 56 necessary for manufacturing each product. Each product is always sent to each process in this order. In addition, what is completed through all the steps is a product, and those in the previous step should be called work-in-process or semi-finished products, but since there is no problem in proceeding with the explanation of the present invention, all products and Call for explanation.

  The product list 24 is a list of product identification codes 54 of products whose production is finished by a designated date. The manufacturing target includes, for example, three types of products A, B, and C. Assume that product A is 5, product B is 3, and product C is 3. Each product is input separately in each process, and it is necessary to always be aware of which process these products exist in. Therefore, a number indicating the process input order is added to the product identification code, and all products are added. To be able to distinguish. An example of this will be described with reference to FIG. In addition to this, the product list 24 also includes a production end date 60.

(Description of means (program module))
Next, each means of the arithmetic processing unit will be described. The production plan data generating unit 36 has a function of generating a specific production plan under given conditions. The optimization unit 38 has a function of changing the conditions to be given to the production plan data generation unit 36, causing the production plan data generation unit 36 to repeatedly perform arithmetic processing, and searching for an optimum value. The work-in-process storage location determining means 40 has a function of obtaining an optimum value by removing unnecessary work-in-process storage space from the generated production plan data.

  That is, the production plan data generation unit 36 first reads the product list 24 from the storage device 16. The product list 24 is a list of products whose production is finished by a designated date. The optimization unit 38 determines the order of the product identification codes 54 read from the product list 24. That is, the optimization unit 38 selects and reads the product identification codes 54 from the product list 24 in a predetermined order and passes them to the production plan data generation unit 36. The production plan data generation means 36 refers to the product data 22 of the product whose code matches the read product identification code 54 so that each product is sequentially accepted in each process shown in the necessary process order 56. Each product is assigned to each process.

  Subsequently, workers are assigned. For each process, the process data 20 of the corresponding process identification code 46 stored in the storage device 16 is referenced to detect a process in which the required number of workers 50 is not “0”. All workers are assigned in a certain order according to certain rules. For example, the processes that proceed simultaneously on the time axis are detected and assigned in order from the subsequent process to the previous process so as to satisfy the minimum number of workers required for each process. When a process with insufficient workers remains, the process waits for the worker who has finished the work in the process and assigns the corresponding worker. You may make it allocate in order toward a back process from a pre-process. A certain rule is set for the priority of allocation. The product identification code 54 is read one by one, time series assignment is executed, and the process of assigning workers is repeated to generate time series assignment data 26.

  In this way, the production plan data generation means 36 arranges the state where the products are allocated to all the processes in time series, and generates the time series allocation data 26 including the worker allocation results. A specific example of the time series allocation data 26 will be described later. For example, the optimization unit 38 generates a random number every time, determines the product data reading order 32 for reading the product identification code 54 from the product list 24, and passes it to the production plan data generation unit 36. Repeat the process. That is, the production plan data generation unit 36 assigns products selected in a random order to processes in that order, and repeatedly generates time-series allocation data 26. From the result, the last process end time 30 that is earlier than the manufacturing end date 60 at which the manufacture of all the products ends and is earliest is searched.

  That is, the optimizing means 38 detects the final process end time 30 from the generated time series allocation data 26, and then changes the product data reading order 32 for reading the identification code 54 from the product list 24, thereby producing the production plan data. The process of causing the generation means 36 to generate new time series allocation data 26 is repeated. At this time, for example, a search for an optimum value is performed using various known search methods such as a hill-climbing method and an annealing method.

Here, the product selection order of the time-series allocation data 26 in which the earliest final process end time 30 is detected is fixed. Then, this time, the process of assigning workers to processes, changing the worker assignment order, and causing the production plan data generation means 36 to generate the time-series assignment data 26 is repeated. Also in this case, the earliest final process end time 30 that is earlier than the designated date and by the method such as the hill climbing method or the annealing method is searched. In this way, production plan data 28 including the optimized time series allocation data 26 is generated. The production plan data 28 includes time-series allocation data 26, data indicating the worker allocation order 34, instructions to the workers, and other data for printing various materials necessary at the start of actual production. To do.
Hereinafter, embodiments of the present invention will be described in detail for each example.

FIG. 4 is a schematic explanatory diagram of a manufacturing process for the first embodiment. FIG. 5 is an explanatory diagram of process data.
As shown in FIG. 4, in order to manufacture the planned product, a total of seven steps from the A step to the G step are provided. The A process, the C process, and the G process are manual operations, and require one worker as shown in FIG. B process is a processing machine which processes a product automatically. The D process and the F process are spaces in which three products can be put on standby in preparation for the next process. There are two types of products to be manufactured. Six products AA1 and AA2 are manufactured. The production end dates 60 (FIG. 3) of all products are all the same. The number of workers required in all processes is three, but the number of workers who can actually work is two.

  In addition, in the process that requires an operator, the processing that has been started is surely completed. Further, the process B and the process E are not started unless three products are prepared. If three products are thrown in, processing starts and the next three products cannot be thrown in unless the three products after machining are taken out. In order to simplify the description, intermediate work time such as transfer time and setup change time is omitted. In the product data of the products AA1 and AA2, it is assumed that the necessary process order 56 (FIG. 3) is A-B-C-D-E-F-G.

FIG. 5 is an explanatory diagram showing a specific example of process data.
As shown in the figure, the machining required time 48 (working time column) for each product AA1 and AA2 and the required number of workers 50 are displayed from the A process to the G process, respectively. In the remarks portion, the number 52 of simultaneously processed products is displayed. The “workpiece” is a work in progress during processing. In the column of work time per piece at the right end of this chart, the average value of work time per piece is shown.

6A is a comparative example of the product list, and FIG. 6B is an example of the product list. Product identification codes are arranged in the order of reading.
In FIG. 5A, there are two types of products, AA1 and AA2, and product identification codes are arranged so that six products AA1 are produced in succession and then six products AA2 are produced in succession. . Here, in order to distinguish the same products that are input into the process in different orders, serial numbers are given in the order of input. This serial number may be added to the product identification code. These products may be manufactured in any order. Therefore, in the embodiment, the order of manufacturing is changed, and a condition for ending manufacturing earlier is searched. The production end date 60 is included in, for example, the attribute data of the list in this figure.

(Production plan data)
FIG. 7 is an explanatory diagram showing an example of time-series allocation data.
This is a time-series arrangement of products assigned to all processes. That is, for example, the first product (product identification code is AA1) W1 waits for the processing of W2 and W3 to continue after the B process after 330 seconds of processing in the A process. When W1, W2, and W3 are ready after 990 seconds from the beginning, 120 seconds of processing in the B process is started. When the processing in the B process is completed, the processing for 330 seconds in the C process is advanced in the order of W1, W2, and W3.

  W1 is stored in the D step after the processing in the C step, and waits for the subsequent processing of W2 and W3 to end. When the processing of the C process of W1, W2, and W3 is completed after 2100 seconds from the beginning, these are sent from the D process to the E process, and the processing of the E process of 780 seconds is started. When the processing in the E process is finished, W1 is immediately put into the G process. W2 and W3 stand by at the work-in-process storage area in the F process. In this way, when all the products are processed in the G process in order and the processing is completed, all the operations are completed. In this example, the final process end time is 6530 seconds after the start.

Next, shortening of the end time by rationalizing the order of product introduction into each product process will be described.
Using the product input order shown in FIG. 6A as an initial value, the input order of two products is randomly switched. Then, a new product list as shown in FIG. 6B is obtained. The following arithmetic processing is executed in this order. Then, the final process end time is obtained. The final process end time 30 obtained as a result of the previous arithmetic processing is stored in the storage device 16 shown in FIG. 1, and the arithmetic processing is performed each time.
The final process end time obtained as a result is compared.

  The operation is adopted when the final process end time decreases, and when the final process end time is not decreased, the operation of not adopting is repeated to obtain the minimum end time. This is a search method called a hill-climbing method. The hill-climbing method sometimes finds a local solution depending on how the initial value is selected. Therefore, for example, the process of changing the initial value by exchanging AA1 and AA2 in FIG. In this way, the minimum value may be obtained repeatedly.

(Assignment of workers)
Next, shortening of the end time by rationalizing the allocation to the worker's process will be described.
As a result of the above calculation, for example, it is assumed that the minimum value is obtained in the order of input shown in FIG. This time, the assignment method to each process of the worker is randomly changed while the product reading order of the product list is fixed.

  Until now, the process which progresses simultaneously on a time axis was detected, and it allocated so that the minimum number of workers required for each process might be satisfied in order from the post process to the previous process. For example, in FIG. 7, the processing of W1 is started in the G process after 2880 seconds from the beginning, and then W2 and W3 are processed in order. At the start of processing of W1, the A process, the C process, and the G process are requesting the worker at the same time. However, the total number of workers is two. Therefore, an operator was assigned to the G process and the C process in accordance with the above-mentioned fixed rule. In the A process, the process of W8 was started after the completion of the C process of processing W6.

  Therefore, the process A is in a pause state from the beginning until 3540 seconds, and then the processing of W8 is started. However, for example, after 3230 seconds from the beginning, the worker who finished the G process can move to the A process and start the processing of W8. In this way, the operator's assignment is changed at random, and when the final process end time is reduced, the operation is adopted, and when it is not reduced, the operation of not adopting is repeated to obtain the minimum end time.

FIG. 8 is an explanatory diagram illustrating an example of time-series allocation data after optimization.
As a result of the above processing, the allocation of FIG. 7 could be improved. 320 seconds (= 6530-6210) could be shortened at the end time of the final process (shortening ratio 5%). In addition, regarding the order of assignment to the processes of the workers, it is possible to avoid obtaining a local solution by searching by changing the initial value. In this case, the initial value can be changed if the storage device stores a solution different from the optimal solution that is close to the optimal solution in the process of optimizing the product input order.

  In addition, if the optimization unit 38 allocates a sufficient number of workers for all processes and executes the above-described arithmetic processing, and obtains the maximum value Nmax of the number of workers operating at the same time, a process in which workers are insufficient. It is possible to generate a production plan that eliminates all of the above. This is likely to result in the fastest production of all products. On the other hand, if the initial value of the total number of workers N is set to be smaller than Nmax and the calculation process with the total number of workers N changed is repeated, the production can be completed and the case where it cannot be completed by the designated date. I understand. The limit number N of workers who can finish the production by the designated date becomes the minimum value of the appropriate number N of workers. Economic production is possible with the minimum number of workers.

FIG. 9 is an explanatory diagram of the process data of the embodiment in which the work-in-process storage area is provided.
In this embodiment, a method for obtaining an appropriate value for the storage capacity when a work-in-process storage space is provided between the processes will be described. In the examples so far, it is a process to process 3 pieces at the same time, and a work place storage place that seems to be sufficient from the beginning is set up to wait for the product to wait for processing or before being put into the next process did. Examples are the D process and F process before and after the E process shown in FIG.

  On the other hand, for example, when the process of processing one product at a time is continuous, during the processing of the product in the subsequent process, the product cannot be put into the subsequent process even if the processing of the product is completed in the previous process. At this time, in the previous process, even if the product to be processed next is on standby, the process cannot be started. For this purpose, an in-process storage area is provided between the processes. This problem is eliminated if there is enough work in process storage, but there is a problem of reducing the effective space of the factory.

  In this embodiment, in the same manner as in the other steps, the work in process place is arbitrarily set, and the above arithmetic processing is executed. The time required for processing a product is defined as zero, the number of workers required for the process is defined as zero, and the number of products that can be stored simultaneously is defined as unlimited. In the work in process storage area, the product is transferred to the next process as soon as it is ready for the next process. Under the condition that the product is introduced from the A process to the B process in the shortest time, if the B process is not completed, the condition is set to hold the product in the a process until the B process is completed. Execute.

  In the above calculation process, when the calculation process was executed by temporarily placing a work area with a sufficient capacity between all processes, the result was that a maximum of two work items were placed between process A and process B. . Therefore, in the example of FIG. 9, for example, the work-in-place storage place where two products can be placed between the A process and the B process is set as the a process. In other words, by temporarily allocating a work-in-process storage area with a sufficient capacity and executing calculation processing, the work-in-process storage area used for storage and the number of products stored in the work-in-process storage area during any of the processes It is possible to determine a work place storage area with a minimum necessary capacity by obtaining the maximum value of. Therefore, the product can be manufactured by effectively using the factory space.

FIG. 10 is an explanatory diagram of an embodiment of a product list in which the reading order is optimized. FIG. 11 is an explanatory diagram showing an example of time-series allocation data after optimization.
As a result of the above arithmetic processing, optimization was realized by putting products in the order shown in FIG. As a result, time-series allocation data was generated as shown in FIG. When this was compared with the thing of FIG. 7, it was shortened by 830 seconds (= 6530-5700) in the last process end time (shortening ratio 12.7%).

  According to this time-series allocation data, it is possible to know how many products should be waiting in the a process at which timing in order to operate the entire manufacturing process with maximum efficiency. The maximum value is the appropriate capacity of the work-in-process storage area in step a.

FIG. 12 is a schematic diagram of a process when products that have already been partially manufactured are mixed, and FIG. 13 is an explanatory diagram of a main part of process data.
In the above-described embodiment, description has been made on the assumption that all products are manufactured from the beginning. However, even if a product that has not yet been manufactured and a product that has already been manufactured are mixed, the same arithmetic processing is possible. This is also effective when reviewing a production plan for the reason that, for example, the delivery date of some products has been changed after starting production with a predetermined production plan for the first time.

FIG. 14 shows time-series allocation data in the case of Example 3, where (a) is before improvement and (b) is after improvement.
As shown in FIG. 12, it is assumed that one worker completes these machining operations with the products W1 remaining in the S step, W2 remaining in the B step, and W3 remaining in the C step. Further, the remaining machining time of the S process is 10 seconds, and the remaining machining time of the C process is 0 seconds. In this case, the waiting time is minimized if processing is started immediately after the product that has been processed in the C process is put into the E process. If there is no place for the D process, the E process must be started immediately.

  However, if the assignment of the worker is changed from the S process to the A process, the final process end time is calculated earlier. At the end of the immediately preceding C process, the final process end time changes in this way simply by changing the selection of the process for assigning one worker. In addition, the work-in-process storage place where at least one product is placed between the C process and the E process functions effectively, and the maximum number of products that can be kept in the storage place is 1 in this case, etc. However, the result of the arithmetic processing is revealed.

FIG. 15 is an explanatory diagram of an example of the output worker shift data. FIG. 16 is an explanatory diagram of an embodiment of the output production plan data.
As shown in FIG. 15, each worker is listed with the name of the worker, instructions on what process to process from what time to what time, and what product to process in each process. Output a form. Further, as shown in FIG. 16, the production plan data is generated so that the manager can see which product is processed by which worker in what process from what time to what time. . Either output can be generated using the time series allocation data described above.

FIG. 17 is a flowchart showing a schematic operation of the system of the present invention.
Hereinafter, a specific example of a program flowchart for executing the above arithmetic processing will be described. In FIG. 17, calculation processing conditions are set in advance in step S11. Specifically, the operator sets data such as worker data 18, process data 20, product data 22, and product list 24 as initial values in the storage device. After the storage device 16 accepts these data, the production plan data generation means 36 and the optimization means 38 are activated. In step S12, the production plan data generation unit 36 and the optimization unit 38 execute a calculation process for optimizing the production plan. As a result, production plan data 28 is generated.

  In step S13, it is evaluated whether or not the generated production plan data 28 may be output. This evaluation is a judgment as to whether or not the final process end time 30 satisfies the specified delivery date. If the specified delivery date is not satisfied, the process returns to step S11 and the conditions are set again. When the designated delivery date is satisfied, the process proceeds to step S15.

  In step S15, the work-in-process storage place determination means 40 deletes the surplus work-in-process storage place included in the production plan data 28 and determines the work-in-process storage capacity. In step S16, the generated production plan data 28 is output. The production plan data 28 is printed out by the printer 116 (FIG. 2) with the content shown in FIG. 15, for example. In step S17, worker shift data is output. The worker shift data is printed out by the printer 116 (FIG. 2) with the contents shown in FIG. 16, for example.

FIG. 18 is a flowchart of the pre-processing operation of the optimization unit.
In the pre-stage process, the optimum value is searched by changing the order of product introduction at random. In the subsequent process, the optimal order value is searched by changing the worker assignment order at random while keeping the product input order fixed. In FIG. 18, the pre-processing will be described. Further, in FIG. 19, the post-processing will be described.

  In step S21 of FIG. 18, the optimization means 38 reads the worker data 18, the process data 20, and the product data 22 from the storage device and accepts them. In step S22, the worker assignment order is fixed to a certain rule such as priority on the post-process as described above. In step S23, the initial value of the product input order (product data reading order 32) is set. A random number should be used here. In step S24, the production plan data generation means 36 is activated to execute arithmetic processing. A specific example will be described with reference to FIG. In step S25, the optimization means 38 receives the calculation processing result of the production plan data generation means 36 and stores the final process end time 30.

  In step S26, the product input order (product data reading order 32) is changed. In step S27, the production plan data generating means 36 is activated again to execute the arithmetic processing. In step S 28, the optimization unit 38 receives the calculation processing result of the production plan data generation unit 36 and compares the new final process end time with the final process end time 30 stored in the storage device 16. Then, it is determined whether the final process end time has been shortened. When the result of this determination is yes, the process proceeds to step S29, and when the result is no, the process proceeds to step S26, and the calculation process of the production plan data generating means 36 is repeated.

  In step S29, the product loading order product data reading order 32) adopted when the current arithmetic processing is executed is stored in the storage device 16 and updated. Further, in step S30, the new final process end time 30 shortened by the current arithmetic processing is stored in the storage device 16 to update the data. In step S31, it is determined whether the calculation processing result has converged. For this determination, a convergence criterion for the final process end time 30 may be determined in advance. If the result of this determination is yes, the process is terminated. If no, the process returns to step S26, and the calculation process of the production plan data generating means 36 is repeated.

FIG. 19 is a flowchart of the subsequent process operation of the optimization means.
In step S41 in this figure, the optimization means 38 reads the worker data 18, the process data 20, and the product data 22 from the storage device and accepts them. Next, in step S42, the order of product introduction is determined. Fix in the order of product input obtained as a result of the previous process. In step S43, the worker allocation order 34 (initial value) is set. For example, in the former stage, workers were assigned with priority to the subsequent process. However, here, for example, the priorities of the second and third steps from the most subsequent step are switched. In step S44, the production plan data generating means 36 is activated to execute arithmetic processing.

  In step S45, the optimization means 38 receives the calculation processing result of the production plan data generation means 36, and stores the final process end time 30. In step S46, the worker assignment order is changed again. For example, the priority of the third process and the fourth process from the most subsequent process is switched. Such a priority switching method is randomly selected each time. In step S47, the production plan data generating means 36 is activated again to execute arithmetic processing. In step S <b> 48, the optimization unit 38 receives the calculation processing result of the production plan data generation unit 36 and compares the new final process end time with the final process end time 30 stored in the storage device 16. Then, it is determined whether the final process end time has been shortened. When the result of this determination is yes, the process proceeds to step S49, and when the result is no, the process proceeds to step S46, and the calculation process of the production plan data generating means 36 is repeated.

  In step S49, the operator assignment order 34 employed when the current arithmetic processing is executed is stored in the storage device 16 and updated. Further, in step S50, the new final process end time 30 shortened by the current arithmetic processing is stored in the storage device 16 to update the data. In step S31, it is determined whether the calculation processing result has converged. For this determination, a convergence criterion for the final process end time 30 may be determined in advance. If the result of this determination is yes, the process is terminated. If no, the process returns to step S26, and the calculation process of the production plan data generating means 36 is repeated.

FIG. 20 is a flowchart illustrating an operation example of the production plan data generation unit.
The production plan data generation unit 36 receives the product identification code of the product to be input to the next process and the rule of the worker allocation order from the optimization unit 38 in the above process, and starts this process. In step S61, time = 0 is set to start generation of time-series data. After that, each product is assigned to a process while shifting the time by unit time. In step S62, the received product is put into an empty process.

  In step S63, it is determined whether the input process is a manual operation. When the result of this determination is yes, the process proceeds to step S64, and when no, the process proceeds to step S67. In step S64, it is determined whether there is an empty worker. If the result of this determination is yes, the process proceeds to step S65, and if no, the process proceeds to step S67. In step S65, workers are assigned according to the rules. In step S66, the time unit time is advanced. This is repeated until the time-series allocation data 26 is generated. That is, in step S67, it is determined whether all products have been processed and all products have been completed. If the result of this determination is yes, the process proceeds to step S68, and if no, the process returns to step S62. In step S67, the time series allocation data 26 is generated and the process is terminated. The result is passed to the optimization means 38.

  Note that the computer program executed by the arithmetic processing unit may be modularized in units illustrated in functional blocks, or may be integrated by combining a plurality of functional blocks. Further, the above computer program may be used by being incorporated into an existing application program. The computer program for realizing the present invention can be recorded on a computer-readable recording medium such as a CD-ROM and installed in any information processing apparatus for use.

DESCRIPTION OF SYMBOLS 12 Manufacturing plan calculating apparatus 14 Arithmetic processing apparatus 16 Memory | storage device 18 Worker data 20 Process data 22 Product data 24 Product list 26 Time series allocation data 28 Production plan data 30 Final process end time 32 Product data reading order 34 Worker allocation order 36 Production plan data generation means 38 Optimization means 40 Work-in-process storage place determination means 42 Worker identification code 44 Total number of workers N
46 Process identification code 48 Time required for machining per product 50 Number of required workers 52 Number of simultaneously processed products 54 Product identification code 56 Order of required processes 58 Product code 60

Claims (10)

When manufacturing multiple types of products that use any process or all multiple processes in common,
For storage devices
Identification data of all workers who can engage in manufacturing, worker data including the total number N of workers, identification code of each process for each process, processing time required for each product, and necessary for that process Process data including the number of workers and the number of products that can be processed simultaneously in the process,
Product data including an identification code for each product and the sequence of steps required to manufacture each product;
Remember the list of products that will be discontinued by the specified date,
Production plan data generation means
When products are selected in order from the list of products,
Referring to the product data, assign each product to each step so that each product is accepted in each step in the order of selection in the order of selection,
Referring to the process data, the worker detects necessary processes, and all the workers are moved from the previous process to the subsequent process or from the subsequent process to the previous process in each process. When there is a process with insufficient workers, assign the appropriate workers after the end of the process that finishes the earliest.
Arranging the state in which the products are allocated to all the processes in time series, and generating time series allocation data including the worker allocation results,
Optimization means
Select products in a random order from the list of products, let the production plan data generation means generate the time series allocation data,
Detecting the final process end time from the generated time series allocation data,
Then, change the order of selecting products from the list of products, repeat the process of generating the time series allocation data in the production plan data generation means, search for the earliest final process end time,
The production plan data in which the earliest final process end time is detected is fixed in the selection order of the products, the order in which the workers are assigned to the processes is changed, and the production plan data generating means Repeat the process of generating the series allocation data, search for the earliest final process end time,
A production plan calculation method characterized by outputting optimized production plan data including time series allocation data in which the earliest final process end time is detected. In the manufacturing plan calculation method according to claim 1,
The work in process storage location determination means
Set the work in process storage area to temporarily store the work in progress of the product until the product finished processing in each step moves to the next process,
A manufacturing plan calculation method characterized in that after an optimized production plan is acquired, only a work in process place used in the production plan is determined as a work in process place with a minimum necessary capacity. In the manufacturing plan calculation method according to claim 2,
The time-series data is generated by defining the work-in-process storage area as zero processing time for the product, defining the number of workers required for the process as zero, and limiting the number of products that can be stored at the same time, It is characterized by obtaining the maximum value of the work in process storage used for storage and the maximum number of products stored in the work in progress storage between processes, and determining the minimum work in process storage. Manufacturing plan calculation method. In the manufacturing plan calculation method according to any one of claims 1 to 3,
A manufacturing plan calculation method characterized in that a product that has not been manufactured and a product that has already been manufactured are mixed in the list of products. In the manufacturing plan calculation method according to any one of claims 1 to 4,
A manufacturing plan calculation method characterized in that the process data includes an operator code for specifying an operator who can perform an operation in the process and data indicating a processing time required for each operator. In the manufacturing plan calculation method according to any one of claims 1 to 5,
The optimization means allocates a sufficient number of workers necessary for all processes and executes the calculation process, obtains a maximum value of the number of workers operating simultaneously, and includes it in the production plan data. Manufacturing plan calculation method. In the manufacturing plan calculation method according to any one of claims 1 to 6,
An initial value of the total number of workers N is set so as to cause a process of waiting for workers to be assigned due to a shortage of workers, and the arithmetic processing with the total number of workers N being changed is repeated to specify the specified value. A method for calculating a production plan, characterized in that a minimum value of the number of workers capable of finishing the production by a predetermined date is obtained. A device for computing a production plan for manufacturing a plurality of types of products that commonly use any process or all of a plurality of processes by a computer,
Identification data of all workers who can engage in manufacturing, worker data including the total number N of workers, identification code of each process for each process, processing time required for each product, and necessary for that process Process data including the number of workers and the number of products that can be processed simultaneously in the process,
A storage device for storing product data including an identification code of each product, a process order necessary for manufacturing each product, and a list of products whose production is to be terminated by a specified date;
When products are selected in order from the list of products, each product is assigned to each step so that each product is accepted in each step in the selection order in the order of the steps with reference to the product data. Referring to the data, the operator detects the necessary steps, and all the workers are required for each step from the previous step to the subsequent step or from the subsequent step to the previous step. When there was a process with insufficient workers, the number of workers was allocated to meet the number of workers, and the corresponding workers were allocated after the process that ended first was completed, and the product was allocated to all the processes. Production plan data generating means for arranging the states in time series and generating production plan data including the worker's allocation results;
Select products in a random order from the list of products, let the production plan data generation means generate the production plan data, detect the final process end time from the generated production plan data, Change the order of selecting products from the list of products, repeat the process of causing the production plan data generation means to generate the production plan data, search for the earliest final process end time, and end the earliest final process The production plan data from which the time is detected is fixed in the selection order of the products, the order in which the workers are assigned to the processes is changed, and the production plan data generation means generates the production plan data. Optimized means to output the production plan data with the earliest final process end time detected as optimized production plan data by repeating the process Production planning calculation apparatus characterized by a.   A manufacturing plan calculation program for causing a computer to function as each means according to claim 7.   A computer-readable recording medium having recorded thereon a manufacturing plan calculation program that causes the computer to function as each means according to claim 7.

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