JP7621232B2 - Process planning device, production line system, and process planning method - Google Patents

Description

The present invention relates to a process planning device, a production line system, and a process planning method.

Patent Document 1 describes a design information generating device that generates process design information for a production system that manufactures products by machining target parts, the device comprising: a required operation information generating unit that generates required operation information indicating operations to be performed on the target parts to reach a target state based on geometric information, assembly information, and work process information of the product; a work constraint generating unit that determines at least one constraint condition of the position or acting force of the target parts based on the geometric information, assembly information, and work process information to generate work constraint information; a work entity selecting unit that selects multiple work entities that satisfy both the required operation ability and the constraint condition to realize the required operation information based on the required operation information, the work constraint information, and work entity information, and generates process design information including information on the selected multiple work entities; and an evaluation unit that outputs evaluation information including information evaluating the time related to the target process of the multiple work entities based on the process design information, and the work entity selecting unit reselects a work entity based on the evaluation information and a request from the user information.

International Publication No. 2019/021938

The technology described in the above-mentioned Patent Document 1 is based on the premise that assembly information is given, and the optimal solution for process design depends on the assembly information and, in turn, on the equipment and workers that determine it. Therefore, in an outsourcing environment, for example, where equipment and workers tend to be diverse, it is difficult to reuse process information. In other words, in an outsourcing environment, finding the optimal solution requires data analysis and simulation calculations for a huge number of combinations, which is not practical.

The objective of the present invention is to easily create a process plan that keeps operational costs down.

In order to solve the above problem, the present application employs, for example, the means described in the claims. The present invention includes multiple means for solving the above problem, but one example is a process planning device that includes a storage unit that stores product information of an assembly product, resource information of resources used in production, and part/resource combination information including constraints and capabilities of combinations of parts and the resources that constitute the assembly product, an assembly sequence candidate generation unit that generates candidate proposals for the assembly sequence of the parts that satisfy the constraints and according to priority criteria corresponding to the resources, a line balancing unit that generates candidates for process design information by dividing processes and allocating resources using the candidate proposals, and an optimal solution selection unit that selects the optimal candidate using a predetermined evaluation index for each candidate for the process design information.

The present invention provides a technology that allows for easy process planning with reduced operational costs.

Problems, configurations, and advantages other than those described above will become clear from the description of the embodiments below.

FIG. 1 is a diagram illustrating an example of the configuration of a production line system. FIG. 1 is a diagram showing an example of a product overview. FIG. 11 is a diagram illustrating an example of part adjacency information. FIG. 11 is a diagram illustrating an example of part specification information. FIG. 13 is a diagram illustrating an example of a main resource specification. FIG. 13 is a diagram illustrating an example of a sub-resource specification. FIG. 13 illustrates an example of resource combination constraints. FIG. 11 is a diagram illustrating an example of work time information. FIG. 13 is a diagram illustrating an example of combination constraints. FIG. 2 is a diagram illustrating an example of a hardware configuration of a process planning apparatus. FIG. 13 is a diagram illustrating an example of a flow of a process design process. FIG. 13 illustrates an example of a flow of a process for generating group order candidates for each resource. FIG. 13 is a diagram showing an example of output of group order candidates. FIG. 13 is a diagram illustrating an example of a flow of a group order candidate expansion process. FIG. 13 is a diagram illustrating an example of expanding group order candidates. FIG. 13 is a diagram showing an example of an assembly sequence candidate display screen. FIG. 13 is a diagram showing an example of a comparative display screen of process design results. FIG. 13 is a diagram showing an example of a detailed display screen of a process design result.

In the following embodiments, for convenience, when necessary, the description will be divided into multiple sections or embodiments, but unless otherwise specified, they are not unrelated to each other, and one is a partial or complete modification, detail, supplementary explanation, etc. of the other.

In addition, in the following embodiments, when the number of elements (including the number, numerical value, amount, range, etc.) is mentioned, it is not limited to that specific number, and may be more than or less than the specific number, unless otherwise specified or clearly limited in principle to a specific number.

Furthermore, it goes without saying that in the following embodiments, the components (including element steps, etc.) are not necessarily essential unless specifically stated otherwise or considered to be clearly essential in principle.

Similarly, in the following embodiments, when referring to the shapes, positional relationships, etc. of components, etc., it is intended to include shapes that are substantially similar or similar to those, unless otherwise specified or when it is considered in principle that this is not the case. The same applies to the above numerical values and ranges.

In addition, in all the drawings used to explain the embodiments, the same components are generally given the same reference numerals, and repeated explanations will be omitted. However, when there is a high possibility of confusion arising from sharing the same name with the component before the change due to an environmental change or the like, the same components may be given different reference numerals or names. Each embodiment of the present invention will be explained below with reference to the drawings.

Generally, the process design of an assembly line involves the design processes of assembly sequence generation and line balancing. In the assembly sequence generation process, the process designer determines the order of tasks for assembling the parts that make up the target assembled product, taking into account the assembly information and, ultimately, the equipment and workers that determine it. In line balancing, the process designer determines the process division that divides the assembly work into multiple processes, and the resource configuration that determines the production resources that will be in charge of the processes.

The number of possible assembly sequences is at most the factorial of the number of parts. However, it is necessary to satisfy the geometric constraints of the parts based on the part adjacency relationships. For example, when assembling a fastening part such as a screw, the part to be fastened must be assembled first.

In process division, the process designer groups one or more tasks into a unit called a process in accordance with the assembly sequence. Since each process is performed in parallel by different production resources, the process with the longest working time determines the takt time of the entire assembly line. In addition, non-operating time is minimized by reducing the variance in the working time of each process. However, the working time of each process depends on the capacity of the production resource in charge of the process.

In resource configuration, the process designer decides on the main resources, such as people or robots, who will be in charge of each process. They also decide on sub-resources (dependent resources), such as jigs and tools or robot hands, depending on the work. Using a main resource with high capacity is expected to shorten the work time, but since the investment and operating costs are generally higher for a main resource with high capacity, it is necessary to compare the operating costs of the entire process when planning the process.

In this embodiment, we will describe a process design device that generates a process design for an assembly line that meets the target takt time of the assembly line and minimizes operating costs.

JP 2019-16165 A states that "the design support device includes a receiving unit that receives, for a product that requires multiple operations for assembly, the sequence constraints for each operation, the operation time for each operation, and whether or not each operation can be performed by a robot; a first index calculation unit that calculates, for each operation that cannot be performed by a robot, the number of consecutive operations that can be performed by a robot when the robot can be used based on the sequence constraints and the operation feasibility of the robot, as a first index; a second index calculation unit that calculates, for each operation that cannot be performed by a robot, the degree of freedom in placement when the robot can be used based on the sequence constraints, the operation time, and the operation feasibility of the robot, as a second index; and a presentation unit that presents information based on the first index and the second index in association with the target operations of the first index and the second index."

However, while the technology described above describes a technique for determining a work sequence based on the number of consecutive tasks a robot can perform and for allocating tasks to multiple robots, it does not take into account the hand replacement of the robot, which depends on the shape of the parts, or human workability, and therefore cannot generate an optimal work sequence assuming a mixed production line of humans and robots.

In an embodiment of the present invention, when an assembly line includes a variety of resources, process design information is generated that reduces operating costs by combining multiple production resources such as people and robots.

In the following embodiments, the "input unit", "output unit", and "communication unit" may be one or more interface devices. The one or more interface devices may be at least one of the following:
One or more I/O (Input/Output) interface devices. The I/O interface devices are interface devices to at least one of the I/O devices and a remote display computer. The I/O interface device to the display computer may be a communications interface device. The at least one I/O device may be a user interface device, for example, either an input device such as a keyboard and a pointing device, or an output device such as a display device.
One or more communication interface devices. The one or more communication interface devices may be one or more homogeneous communication interface devices (e.g., one or more NICs (Network Interface Cards)) or two or more heterogeneous communication interface devices (e.g., a NIC and an HBA (Host Bus Adapter)).

In the following description, "memory" refers to one or more memory devices, which are an example of one or more storage devices, and may typically be a primary storage device. At least one memory device in the memory may be a volatile memory device or a non-volatile memory device.

In the following description, an "external storage device" may be one or more persistent storage devices, which are an example of one or more storage devices. A persistent storage device may typically be a non-volatile storage device (e.g., an auxiliary storage device), and more specifically, may be, for example, a hard disk drive (HDD), a solid state drive (SSD), a non-volatile memory express (NVME) drive, or a storage class memory (SCM).

In addition, in the following description, a "storage unit" or an "external storage device" may refer to either a memory or a persistent storage device, or both.

In addition, in the following description, a "processing unit" or a "processor" may be one or more processor devices. At least one processor device may typically be a microprocessor device such as a CPU (Central Processing Unit), but may also be other types of processor devices such as a GPU (Graphics Processing Unit). At least one processor device may be single-core or multi-core. At least one processor device may be a processor core. At least one processor device may be a broad processor device such as a circuit that is a collection of gate arrays written in a hardware description language that performs some or all of the processing (e.g., an FPGA (Field-Programmable Gate Array), a CPLD (Complex Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit)).

In the following description, functions are sometimes described using the expression "yyy unit", but the functions may be realized by one or more computer programs being executed by a processor, or by one or more hardware circuits (e.g., FPGAs or ASICs), or by a combination thereof. When a function is realized by a program being executed by a processor, the function may be at least a part of the processor, since the specified processing is performed using a storage device and/or an interface device, etc., as appropriate. Processing described with a function as the subject may be processing performed by a processor or a device having the processor. A program may be installed from a program source. The program source may be, for example, a program distribution computer or a computer-readable recording medium (e.g., a non-transitory recording medium). The description of each function is an example, and multiple functions may be combined into one function, or one function may be divided into multiple functions.

In addition, in the following explanation, processing may be explained using a "program" or a "processing unit" as the subject, but processing explained using a program as the subject may also be processing performed by a processor or a device having that processor. Furthermore, two or more programs may be realized as one program, and one program may be realized as two or more programs.

In the following explanation, information that gives an output for an input may be described using expressions such as "xxx table", but the information may be a table of any structure, or may be a neural network that generates an output for an input, or a learning model such as a genetic algorithm or random forest. Therefore, a "xxx table" can be called "xxx information". In the following explanation, the structure of each table is an example, and one table may be divided into two or more tables, or all or part of two or more tables may be one table.

In the following description, the "production line system" may be a system made up of one or more physical computers, or may be a system (e.g., a cloud computing system) implemented on a group of physical computing resources (e.g., a cloud platform). When the production line system "displays" display information, it may mean displaying the display information on a display device possessed by the computer, or the computer may send the display information to a display computer (in the latter case, the display information is displayed by the display computer).

Figure 1 shows an example of the configuration of a production line system. Production line system 1 is installed at a manufacturing site (area) or in a facility outside the manufacturing site. Production line system 1 includes a group of devices according to the usage environment, such as a display computer, which is communicatively connected via a network (not shown).

Although not shown, the network may be, for example, a LAN (Local Area Network), a WAN (Wide Area Network), a VPN (Virtual Private Network), a communication network that uses general public lines such as the Internet in part or in whole, a mobile phone communication network, or a combination of these. The network may also be a wireless communication network such as Wi-Fi (registered trademark) or 5G (Generation).

The process planning device 100 assigns work processes related to the assembly of assembled products on a production equipment basis, or on a production resource basis in work areas that do not use production equipment.

Figure 2 shows an example of a product overview. The assembled product 200 is a product that is made by assembling a total of 10 parts onto a base plate-shaped part. In the assembled product 200, the parts are classified into two types: flat plates (P: Plate) and cylinders (C: Cylinder).

The process planning device 100 includes a memory unit 110, a processing unit 120, a communication unit 130, an input unit 140, and an output unit 150. The memory unit 110 includes product information 111, resource information 112, part/resource combination information 113, and process design information 114. The product information 111 also includes part adjacency information 1111 and part specification information 1112.

Figure 3 is a diagram showing an example of part adjacency information. Part adjacency information 1111 is an example of holding the part adjacency of assembly product 200 as a precedence graph. The circles in the precedence graph represent parts and nodes, and the arrows connecting the nodes are edges that determine the assembly order of parts from the adjacency of parts. The precedence graph is one method of expressing constraints when determining the assembly order of parts. Nodes that are not in contact with the edge arrows are disassembleable parts. In the example of part adjacency information 1111, parts with part IDs 5, 7, 9, and 10 are nodes that are disassembleable in the initial state. For example, when part 205C with part ID 5 is disassembled, there is no longer an arrow that is in contact with part ID 4, so part ID 4 also becomes a candidate for disassembly.

After the disassembly order is determined, the reverse order can be used as the assembly order. The precedence graph information may be stored in a data structure using the DOT language. By having part adjacency relationships as product information 111, constraints in generating the assembly order can be expressed as data, making it possible to narrow down candidates for the assembly order.

Figure 4 is a diagram showing an example of part specification information. Part specification information 1112 is an example of holding the part configuration, part specifications, and part positional relationships of the assembled product 200 in a table format. As the part configuration, part ID 1112a and number 1112b of each part are stored. In addition, as the part specifications, a classification based on the shape of each part, that is, part type 1112c, and part weight 1112d are stored. In addition, as the part positional relationship, the mounting position 1112e of each part is stored. The mounting position 1112e represents the center position of each part in coordinates in the three axial directions of x, y, and z from the origin set on the assembled product 200 as the reference point. Therefore, it is possible to calculate the distance between parts.

The resource information 112 includes a main resource specification 1121, a sub-resource specification 1122, and a resource combination constraint 1123.

Figure 5 is a diagram showing an example of a main resource specification. The main resource specification 1121 is an example that defines candidates for a main resource that will be responsible for the main assembly work on an assembly line, and their specifications. The main resource specification 1121 includes a resource type 1121a, a resource name 1121b, a resource ID 1121c, a load capacity 1121d, an initial investment cost 1121e, a depreciation period 1121f, and labor costs 1121g.

Resource type 1121a stores a specific type of main resource. If the resource type is a robot, then by setting initial investment cost 1121e and depreciation period 1121f, it becomes possible to calculate the depreciation cost during the usage period as part of the operating cost of the assembly line. Also, if resource type 1121a is personnel, then by setting personnel cost 1121g, it becomes possible to calculate the personnel cost according to the human working hours as part of the operating cost of the assembly line.

The resource name 1121b is the name of the resource, but does not need to be a name that can identify an individual resource among resources with the same specifications. The resource ID 1121c is an identifier for the resource and needs to be able to identify an individual resource. The load capacity 1121d is information that specifies the load that can be held when connecting to a sub-resource.

Figure 6 is a diagram showing an example of a sub-resource specification. The sub-resource specification 1122 is an example that defines candidates for sub-resources that assist the main resource that is the main body of assembly work on an assembly line, and their specifications. The sub-resource specification 1122 includes a resource type 1122a, a resource name 1122b, a resource ID 1122c, a load capacity 1122d, an initial investment cost 1122e, and a weight 1122f.

Resource type 1122a stores a specific type of sub-resource. If the resource type is a robot hand, load capacity 1122d, initial investment cost 1122e, and weight 1122f are set as indicators of its capabilities.

The resource name 1122b is the name of the resource, but does not need to be a name that can identify an individual resource among resources with the same specifications. Alternatively, even if the resource type is a hand, if there are two types of hands with different specifications, the resource name needs to be able to identify both. The resource ID 1122c is an identifier for the resource, and needs to be able to identify an individual resource. The load capacity 1122d is information that specifies the load that can be held when handling a part. The weight 1122f is the weight of the sub-resource itself that is required from the perspective of load capacity when connected to the main resource.

The relationship between the hand and the robot (i.e., the main resource that can be connected to the sub-resource) is defined separately by the resource combination constraint 1123 described below. Also, the relationship between the hand and the parts that the hand handles is defined separately by the part/resource combination information 113 described below.

Figure 7 shows an example of a resource combination constraint. The resource combination constraint 1123 defines the main resource (combinable main resource 1123b) to which each hand can be connected, using the hand, which is a sub-resource, as the primary key (resource ID 1123a).

The part/resource combination information 113 includes work time information 1131, combination constraints 1132, and combination constraint expressions (1) and (2) described below.

Figure 8 is a diagram showing an example of work time information. Work time information 1131 is an example that defines the processing time for each part that makes up an assembly product when assembly is performed by each main resource. Work time information 1131 includes part ID 1131a, processing time (MR_Hu_01) 1131b, processing time (MR_Ro_01) 1131c, processing time (MR_Ro_04) 1131d, and processing time (MR_Ro_05) 1131e.

For example, it is defined that a part with part ID 1 takes 12.0 seconds when a human (MR_Hu_01) is in charge of the assembly work as the main resource, as defined in processing time (MR_Hu_01) 1131b, and 30.0 seconds when a robot (MR_Ro_05) is in charge of the assembly work, as defined in processing time (MR_Ro_05) 1131e. Note that the work time information 1131 may define the work time for each candidate main resource, as in the example shown in the figure, or may define a predetermined coefficient for the reference main resource (e.g., a human).

Figure 9 is a diagram showing an example of a combination constraint. Combination constraint 1132 is an example of a combination constraint between part specifications and sub-resource specifications. Combination constraint 1132 defines the sub-resources that can handle each part (handleable sub-resources 1132b) using part type 1132a as the primary key. Since the robot's hands that can handle the part differ depending on the shape of the part, the combination constraint needs to be satisfied. For example, the shapes of the hands that can grasp a P-type part and a C-type part differ, so restrictions arise on the sub-resources that can be used depending on the part.

The relationship between parts and sub-resources may be defined individually for each part. In addition, multiple sub-resources may be defined as possible combinations for one part or shape. As in the example of Figure 9, if combination constraints can be defined for part attributes such as part type, rather than for parts specific to the target product, it will be possible to store them in advance in the memory unit 110 of the process planning device 100 as information common to process design.

In addition to the above, the part and resource combination information 113 also holds combination constraints in the form of mathematical expressions. These combination constraint expressions (1) and (2) are explained below.

MR _WC > (SR _W + P _W ) ×α _MR ...Formula (1)

The above formula (1) is an example of a combination constraint of a part and a main resource. In formula (1), MR _WC is the load capacity 1121d of the main resource, SR _W is the weight 1122f of the sub-resource, P _W is the weight 1112d of the part, and α _MR is the safety factor of the main resource.

In the above formula (1), in a task in which a sub-resource hand is attached to a robot (main resource) and a part with a shape that the hand can handle is to be grasped, the constraint formula defines that the sum of the hand weight and the part weight multiplied by the safety factor of the main resource must be less than or equal to the load capacity of the main resource. When selecting a main resource for this task, it is necessary to select a main resource that satisfies the constraint of formula (1).

SR _WC > P _W × α _SR ...Formula (2)

The above formula (2) is an example of a combination constraint of parts and sub-resources. In formula (2), SR _WC is the load capacity 1122d of the sub-resource, P _W is the weight 1112d of the part, and α _SR is the safety factor of the sub-resource.

In the above formula (2), in a task in which a sub-resource hand is attached to a robot (main resource) and the hand needs to grasp a part with a shape that the hand can handle, the constraint formula defines that the part weight multiplied by the safety factor of the sub-resource must be equal to or less than the load capacity of the sub-resource. When selecting a sub-resource for this task, it is necessary to select a sub-resource that satisfies the constraint of formula (2) in addition to the constraint of combination constraint 1132.

Process design information 114 stores, in association with each other, the assembly sequence, information about the processes that divide the assembly sequence, the main resources and sub-resources used in each process, and the operating costs incurred as a result.

Returning to the explanation of FIG. 1, the processing unit 120 includes an assembly sequence candidate generating unit 121, an assembly sequence candidate expanding unit 122, a line balancing unit 123, and an optimal solution selecting unit 124.

The assembly sequence candidate generation unit 121 generates candidate proposals for the assembly sequence of parts that satisfy various constraints and follow priority criteria according to resources. Note that the assembly sequence candidate generation unit 121 prioritizes maintaining the quality of assembly work by workers as a priority criterion for worker resources, and prioritizes reducing the number of times hand members are replaced as a priority criterion for robots, which are main resources that can replace hand members, which are sub-resources.

For example, the assembly sequence candidate generation unit 121 may assemble parts in order of proximity to the installation position as a priority criterion according to the worker resource.

The assembly sequence candidate expansion unit 122 generates new candidate proposals for the assembly sequence of the parts by combining the generated candidate proposals for the assembly sequence of the parts. Specifically, the assembly sequence candidate expansion unit 122 divides each of the generated candidate proposals for the assembly sequence of the parts into processes so that the total work time included in each process is equalized, and generates new candidate proposals for the assembly sequence of the parts by combining the divided processes among the candidate proposals for the assembly sequence of the parts.

A specific example of leveling is where the assembly sequence candidate expansion unit 122 divides the parts into processes based on the part working time and the target takt time, and combines the divided processes among the part assembly sequence candidates to generate new part assembly sequence candidates. Alternatively, the assembly sequence candidate expansion unit 122 may use the target takt time to determine the maximum and minimum number of process divisions for each of the multiple part assembly sequence candidates generated, divide the parts into processes so that the process divisions fall between the maximum and minimum numbers, and combine the divided processes among the part assembly sequence candidates to generate new part assembly sequence candidates.

The line balancing unit 123 uses the candidate plans to divide processes and allocate resources to generate candidates for process design information.

The optimal solution selection unit 124 selects the optimal candidate for each candidate of the process design information using a predetermined evaluation index.

The communication unit 130 transmits and receives various information to and from other devices via the network.

The input unit 140 receives input information that is displayed and operated, for example, on a screen and that is entered by operating a keyboard or mouse.

The output unit 150 creates screen information including information to be output as a result of performing a specified process, and outputs the screen information to the display computer via the communication unit 130.

Figure 10 is a diagram showing an example of the hardware configuration of a process planning device. The process planning device 100 can be realized by a general computer 900 equipped with a processor (e.g., a CPU or GPU) 901, a memory 902 such as a RAM (Random Access Memory), an external storage device 903 such as a hard disk drive (HDD) or SSD, a reading device 905 for reading information from a portable storage medium 904 such as a CD or DVD, an input device 906 such as a keyboard, mouse, barcode reader, or touch panel, an output device 907 such as a display, and a communication device 908 for communicating with other computers via a communication network such as a LAN or the Internet, or a network system equipped with a plurality of such computers 900. It goes without saying that the reading device 905 may be capable of not only reading but also writing to the portable storage medium 904.

For example, the assembly sequence candidate generating unit 121, the assembly sequence candidate expanding unit 122, the line balancing unit 123, and the optimal solution selecting unit 124 included in the processing unit 120 can be realized by loading a predetermined program stored in the external storage device 903 into the memory 902 and executing it with the processor 901, the input unit 140 can be realized by the processor 901 using the input device 906, the output unit 150 can be realized by the processor 901 using the output device 907 or the communication device 908, the communication unit 130 can be realized by the processor 901 using the communication device 908, and the storage unit 110 can be realized by the processor 901 using the memory 902 or the external storage device 903.

This predetermined program may be downloaded to the external storage device 903 from the portable storage medium 904 via the reading device 905 or from the network via the communication device 908, and then loaded onto the memory 902 and executed by the processor 901. It may also be directly loaded onto the memory 902 from the portable storage medium 904 via the reading device 905 or from the network via the communication device 908, and then executed by the processor 901.

Figure 11 is a diagram showing an example of the flow of the process design process. The process design process starts when a start instruction is received from a user via an interface device.

First, the input unit 140 accepts input of the target takt time of the assembly line for which process design is to be performed (step S001). All subsequent processing is executed to meet the target takt time set here. If the assembly line is made up of multiple processes, the maximum value of the takt time of each process becomes the takt time of the entire assembly line. In other words, the takt time of the assembly line matches the takt time of the bottleneck process.

Then, the input unit 140 accepts the input of product information (step S002). The accepted product information is stored in the product information 111. The product information 111 is defined and stored for each assembly product that is the subject of the process design.

Then, the input unit 140 accepts the input of resource information (step S003). The accepted resource information is stored in the resource information 112. The resource information 112 is defined and stored for each assembly product that is the subject of the process design. Alternatively, the resource information 112 may be stored in advance in the storage unit 110 of the process planning device 100 as information common to the process design regardless of the subject of the process design. Furthermore, a selection input may be accepted as the specified resource information from the pre-stored information.

Then, the input unit 140 accepts the input of part and resource combination information (step S004). The accepted part and resource combination information is stored in the part and resource combination information 113. The part and resource combination information 113 is defined and stored for each process design target. Alternatively, the part and resource combination information may be stored in advance in the storage unit 110 of the process planning device 100 as information common to process design regardless of the process design target. Furthermore, a selection input may be accepted as specified resource information from the pre-stored information.

Then, the assembly order candidate generation unit 121 generates assembly order candidates using each piece of information that has been received as input (step S005). The generation of assembly order candidates is roughly divided into a process for generating assembly order candidates for each resource (step S0051) and a process for expanding assembly order candidates (step S0052).

First, the process of generating candidate assembly sequences for each resource (step S0051) will be described. Since there are a maximum of factorial number of candidate assembly sequences for the number of parts, if subsequent line balancing is calculated for all candidate assembly sequences, the number of combinations will be enormous, making it difficult to achieve. Therefore, the process designer defines assembly sequence generation rules for each resource based on the impact that the shape and arrangement of parts have on assembly time and quality. These rules are incorporated as algorithms into the process of generating candidate assembly sequences (step S0051). Alternatively, when the process of generating candidate assembly sequences (step S0051) is implemented on a rule-based basis, they are implemented as rules. Here, an example is shown in which a rule that uses robots and people as resources is incorporated as an algorithm.

Robots need to change hands to grip parts according to their shape, and in addition to the assembly work time, additional setup time is required, including the time to change hands. On the other hand, for part placement, if it is within the robot's operating range, the precision of repetitive movements is high and the impact on quality is small. Therefore, the assembly sequence generation rule when using robots as the main resource is to measure and minimize the number of times that hands need to be changed due to differences in the shapes of parts handled in the previous and next sequences.

Humans can flexibly respond to changes in part shapes. On the other hand, working continuously on parts that are spatially close allows workers to maintain concentration and ensure high quality. Therefore, the assembly sequence generation rule when humans are the main resource is to measure and minimize the amount of change in part positions.

Alternatively, assembly sequence generation rules may be established by further subdividing people and robots as candidate resource types. For example, since it is believed that the characteristics of people change depending on age and experience, the assembly sequence generation rules may be subdivided accordingly. Also, for robots, the assembly sequence generation rules may be subdivided according to the number of controlled axes and size. Furthermore, if there are candidate resources other than people and robots (such as dedicated machines), the assembly sequence generation rules may be defined individually.

In the process of generating assembly order candidates for each resource, the assembly order candidate generating unit 121 generates assembly order candidates for each resource using such assembly order generation rules. Note that, if there are multiple assembly order candidates that satisfy the assembly order generation rules, the assembly order candidate generating unit 121 may compare the candidates and select them using another index.

For example, as an assembly sequence generation rule when a robot is used as the main resource, if the shapes of the parts handled in the previous and next sequences are different and the number of hand changes required is measured and minimized, when an assembly sequence that results in the same number of hand changes is derived, the assembly sequence candidate generation unit 121 may measure the amount of change in the positions of the parts between the assembly sequences and select the one that minimizes it as a candidate assembly sequence. Alternatively, a rule for narrowing down the assembly sequence candidates using such multiple evaluation indexes may be set in advance. Details of the assembly sequence candidate generation process will be described later.

Next, we will explain the process of expanding the assembly order candidates. Assembly order candidates optimized for both humans and robots are not necessarily optimal in an environment that requires multiple resource combinations. Therefore, it is possible to generate assembly order candidates that assume a mixed human-robot production line environment, based on assembly order candidates optimized for both humans and robots.

As an example of the candidate assembly order expansion process (step S0052), a process of crossing candidate assembly orders for each resource to expand the candidate assembly orders will be described. This process divides the candidate assembly orders generated in the candidate assembly order generation process for each resource into multiple subelements, crosses and combines the divided elements, and removes overlapping work, thereby generating additional candidate assembly orders that assume combinations of multiple resources. Note that the process of expanding candidate assembly orders by crossing is just one example, and new candidates may be added using a genetic algorithm or the like.

In the process of expanding the candidate assembly order by crossover, for example, if there are candidates X, Y, and Z for the assembly order for each resource, one additional candidate is generated by taking the first process of X as the base, making the process of Y a successor, and then making the process of Z a successor, and removing the second and subsequent occurrences of the same work. Similarly, an additional candidate can be generated by taking the first process of X as the base, making the process of Y a successor, and removing the second and subsequent occurrences of the same work (without using the candidate of Z). Alternatively, an additional candidate can be generated by taking the first process of Y as the base, making the process of Z a successor, and then making the process of X a successor, and removing the second and subsequent occurrences of the same work. In this way, the assembly order candidate expansion unit 122 generates variations by combining X, Y, and Z. Details of the candidate assembly order expansion process will be described later.

Then, the line balancing unit 123 initializes a variable N (N is an integer) for selecting a candidate for the generated assembly order, and sets N = 1 (step S006).

Then, the line balancing unit 123 selects the Nth candidate from the generated assembly sequence candidates (step S007).

Then, the line balancing unit 123 uses the selected assembly order candidate N as input and generates a line balancing solution that satisfies the target takt time set in step S001 (step S008). Line balancing is roughly divided into a process division process for the assembly order candidate N (step S0081) and a resource allocation process (step S0082).

First, the process division process (step S0081) will be described. The line balancing unit 123 divides the selected assembly sequence candidate N into a plurality of processes. At this time, the number of process divisions is set to be between the maximum value _Cmax and the minimum value _Cmin of the number of process divisions. The maximum value _Cmax and the minimum value _Cmin of the number of process divisions are calculated using formulas (3) and (4) based on the takt time, which will be described later.

Each process requires its own main resource, and since the cost of the main resource dominates the line operation cost, it is necessary to divide the process into as few processes as possible. Therefore, the line balancing unit 123 divides the processes by giving priority to the smallest number of divisions. The division may be performed using the equalization of the time for each process as an evaluation index. However, the exact working time for each process can only be calculated after the resources have been allocated.

Next, the resource allocation process (step S0082) will be described. The line balancing unit 123 allocates a main resource suitable for each divided process. Furthermore, based on the part/resource combination information 113, it allocates sub-resources to each part as necessary. For example, if a robot is allocated as the main resource, it is necessary to allocate a hand according to the shape of the part as a sub-resource.

The work time for each process is calculated based on the work time and setup time for each part. The line balancing unit 123 calculates the work time for each part according to the main resource assigned to each part. In addition, the line balancing unit 123 calculates the setup time as the additional time required when a sub-resource needs to be replaced within a process.

If the process design solution does not satisfy the target takt time, the line balancing unit 123 repeatedly executes different process division processes and resource allocation processes until a process design solution that satisfies the target takt time is generated. As an example of regenerating the process division, the line balancing unit 123 may maintain the number of divisions and re-create the process division by moving some of the parts of a process that exceeds the target takt time to an adjacent process. The line balancing unit 123 may also change the number of process divisions to generate a new division plan.

When a process design solution that satisfies the target takt time is generated, the line balancing unit 123 stores the process design solution in the process design information 114. By generating all process division plans for the selected assembly order candidates and performing optimal resource allocation for each process division plan, it is possible to derive an optimal process design solution for the selected assembly order candidate N. In addition, the calculation time may be set as a threshold value, and the line balancing unit 123 may derive the best solution that can be reached within the time as the process design solution.

Once the resource allocation for each process has been determined, the operating costs of the assembly line can be calculated. For robots as main resources and sub-resources, the line balancing unit 123 calculates the operating costs for the relevant work period for each process design solution using the initial investment cost and depreciation period. For people as main resources, the line balancing unit 123 calculates the operating costs using labor costs.

Then, the line balancing unit 123 determines whether all the generated assembly sequence candidates have been processed (step S009). If unprocessed candidates remain (step S009: No), the line balancing unit 123 adds 1 to the variable N (step S010), returns control to step S007, and selects the next assembly sequence candidate.

When all grouping candidates have been processed (step S009: "Yes"), the optimal solution selection unit 124 selects the process design solution with the lowest operating cost (step S011) and ends the process design process. In step S011, the optimal solution selection unit 124 may select a solution using an evaluation formula other than operating cost (e.g., initial investment cost). In addition, the optimal solution selection unit 124 may display the results in a list and accept the user's input to select a solution.

The above is an example of the process design processing flow. The process design processing makes it possible to easily create a process plan that keeps operational costs down.

Figure 12 is a diagram showing an example of the flow of the process of generating group order candidates for each resource. The process of generating group order candidates for each resource is performed in step S0051 of the process design process.

First, the assembly sequence candidate generating unit 121 selects one of the main resource candidates (referred to as resource candidate R) from the resource information 112 (step S0511). For example, in the case of the main resource specification 1121, the assembly sequence candidate generating unit 121 selects the resource whose resource ID 1121c, which is the data at the beginning of the line, is MR_Ro_01 as resource candidate R.

Then, the assembly sequence candidate generation unit 121 determines whether the type of the selected resource candidate R is a robot (step S0512). Specifically, the assembly sequence candidate generation unit 121 determines whether the resource type 1121a of the selected resource candidate R is a Robot.

If the type of the selected resource candidate R is a robot ("Yes" in step S0512), the assembly sequence candidate generation unit 121 generates an assembly sequence that minimizes the number of hand changes defined as the assembly sequence generation rule for the robot (step S0513). The need for hand changes can be determined each time the part type in the assembly sequence of the parts is changed, using the combination constraint 1132.

If the type of the selected resource candidate R is not a robot (No in step S0512), the assembly sequence candidate generation unit 121 generates an assembly sequence that minimizes the amount of change in part position defined as the manual assembly sequence generation rule (step S0514). The amount of change in part position can be calculated using the part specification information 1112 and the distance of the mounting position 1112e.

Then, the assembly sequence candidate generation unit 121 stores the assembly sequence generated in step S0513 or step S0514 as a candidate in the process design information 114 (step S0515).

Then, the assembly sequence candidate generation unit 121 judges whether all main resource candidates in the resource information 112 have been processed (step S0516). If unprocessed candidates remain ("No" in step S0516), the assembly sequence candidate generation unit 121 returns control to step S0511 and selects the next resource candidate. If all candidates have been processed ("Yes" in step S0516), the assembly sequence candidate generation unit 121 ends the assembly sequence candidate generation process for each resource.

The above is an example of the flow of the process for generating group order candidates for each resource. By using the process for generating group order candidates for each resource, it is possible to generate group order candidates that are suitable for the resource.

Figure 13 is a diagram showing an example of an output of assembly sequence candidates. It shows an example of an assembly sequence generated by applying a human assembly sequence generation rule that minimizes the amount of change in part position to an assembly product 200, and an example of an assembly sequence generated by applying a robot assembly sequence generation rule that minimizes the number of hand changes. It can be said that the assembly sequence generated by applying the human assembly sequence generation rule is efficient by focusing on the position of the parts, while the assembly sequence generated by applying the robot assembly sequence generation rule is efficient by focusing on the consecutive handling of parts of the same type, rather than the position of the parts.

Figure 14 shows an example of the flow of the group order candidate expansion process. The group order candidate expansion process is performed in step S0052 of the process design process.

First, the assembly sequence candidate expansion unit 122 calculates the maximum value (C _max ) and the minimum value (C _min ) of the process division number for generating subelements (step S0521). Here, an example is shown in which the calculation is performed using the work time of each part and the target takt time.

The maximum process division number (C _max ) can be calculated using the following formula (3) on the assumption that hand changes occur for all parts using the slowest resource for each part.

C _max = Roundup (Σ(OT(max)i+CT)/T)...Equation (3)

The minimum process division number (C _min ) can be calculated using the following formula (4) by assuming that the fastest resource for each part is used and that there is no hand change.

C _min =Roundup(Σ(OT(min)i/T)...Formula (4)

In the above formulas (3) and (4), Roundup() is a function that rounds up the parameters to integers, OT(max)i is the operation time (longest operation time) when the slowest resource is selected for part i, OT(min)i is the operation time (shortest operation time) when the fastest resource is selected for part i, CT is the tool change time, and T is the target takt time.

Then, the assembly sequence candidate expansion unit 122 selects one of the assembly sequence candidates generated by the assembly sequence candidate generation unit 121 and stored in the process design information 114 (step S0522). The assembly sequence candidate selected here is set as assembly sequence candidate I.

Then, the assembly sequence candidate expansion unit 122 selects one process division number C within the range of the maximum process division number (C _max ) to the minimum process division number (C _min ), and divides the selected assembly sequence candidate I into the selected process division number C (step S0523). Here, the assembly sequence candidate expansion unit 122 may divide the processes so that the total operation time included in each process is equalized.

Then, the assembly sequence candidate expansion unit 122 combines the first process of the assembly sequence candidate I with other assembly sequence candidates and deletes the operations of overlapping parts (step S0524). In other words, after combining other assembly sequence candidates with the divided partial solution of the assembly sequence candidate I, the parts included in the first partial solution overlap with the later assembly sequence candidates, so by deleting the overlaps, crossover is established and a new assembly sequence candidate is generated.

In addition, when expanding the assembly order candidates by crossover, the assembly order candidate expansion unit 122 may randomly combine and cross over partial solutions. In addition, the assembly order candidate expansion unit 122 may generate a new assembly order candidate by randomly combining partial solutions of not only two assembly order candidates, but also three or more assembly order candidates.

Then, if the generated assembly sequence candidate is different from the assembly sequence candidates generated so far, the assembly sequence candidate expansion unit 122 registers it as an additional candidate in the process design information 114 (step S0525).

Then, the assembly sequence candidate expansion unit 122 judges whether or not candidates for all process division numbers C have been processed (step S0526). If unprocessed process division number C candidates remain (step S0526: No), the assembly sequence candidate expansion unit 122 returns control to step S0523 and selects another process division number C to divide the assembly sequence candidate I.

When all process division numbers C have been processed (step S0526: "Yes"), the assembly order candidate expansion unit 122 determines whether all candidate assembly orders I have been processed (step S0527). When unprocessed candidate assembly orders remain (step S0527: "No"), the assembly order candidate expansion unit 122 returns control to step S0522 and selects another candidate assembly order I. When all candidate assembly orders have been processed (step S0527: "Yes"), the assembly order candidate expansion unit 122 ends the candidate assembly order expansion process.

The above is an example of the flow of the candidate group order extension process. The candidate group order extension process makes it possible to generate candidate group orders that are suitable for resource combinations.

Figure 15 is a diagram showing an example of expanding the assembly order candidates. For the assembled product 200, an example of process division in step S0523 is candidate X when humans are used as resources, and candidate Y when robots are used as resources. The assembly order candidates for humans and robots are each divided into three processes. Here, the candidate for human assembly order is candidate X, and the candidate for robot assembly order is candidate Y.

In extended candidate X×Y, the top of the partial solution of candidate X is combined with the entire candidate Y, and because the parts included in the top of the partial solution of candidate X (1P, 2C, 3P, 4P) overlap with candidate Y, the overlapping work is deleted from candidate Y to generate a new assembly sequence candidate. In extended candidate Y×X, the top of the partial solution of candidate Y is combined with the entire candidate X, and because the parts included in the top of the partial solution of candidate Y (1P, 6P, 8P) overlap with candidate X, the overlapping work is deleted from candidate X to generate a new assembly sequence candidate.

In this way, by crossing the subelements of the candidate orderings generated for each resource candidate, it is possible to generate additional candidate orderings that assume combinations of multiple resources. This increases the number of candidates for subsequent line balancing near the optimal solution, making it possible to derive a more optimal solution.

Figure 16 is a diagram showing an example of an assembly sequence candidate display screen. An example assembly sequence candidate display screen 1200 shows that multiple assembly sequence candidates have been generated. Specifically, in the example assembly sequence candidate display screen 1200, for each assembly sequence candidate, the assembly sequence, the calculation unit that generated it, and the selected resource are displayed in association with each other. Note that for the selected resource candidates generated by the assembly sequence candidate generation unit 121 for each resource candidate, the resource candidate selected during the calculation, i.e., the assembly sequence generation rule used, is displayed.

Figure 17 is a diagram showing an example of a comparative display screen of process design results. The example 1300 of the comparative display screen of process design results includes a pull-down 1301 for selecting the display order of the results, an evaluation index graph 1302, and a process detail display button 1303. The pull-down 1301 accepts the selection of a predetermined criterion for the display order of the process design results. The evaluation index graph 1302 is a graph with the evaluation index selected in the pull-down 1301 such as operating cost on the vertical axis and the grouping ID on the horizontal axis, and a graph such as a bar graph of the index value is displayed so that the process design results can be compared for each grouping order of the process design. In the evaluation index graph 1302, if the evaluation index is operating cost, the bar graph also displays the breakdown such as labor cost and depreciation cost. When the process detail display button 1303 accepts an input, it transitions to an example 1400 of a detailed display screen of the process design results described later for the grouping ID selected in the evaluation index graph 1302.

Figure 18 is a diagram showing an example of a detailed display screen of the process design results. The example 1400 of the detailed display screen of the process design results includes a display 1401 of the group order ID of the target for which details are displayed, a display 1402 of process design information of the process division and resource configuration, i.e., the result of line balancing, and a display 1403 of operating costs.

For example, the process design information display 1402 displays process design information with a group sequence ID of 3. The process is divided into three, with a person (MR_Hu_01) assigned as the main resource to the first process, and a robot (MR_Ro_01) assigned to each of the second and third processes. In addition, the parts in the second process (part IDs are 6, 8, and 9, respectively) are all of type P (flat plate: Plate), so a flat plate compatible hand (SR_Ha_02) is assigned as the corresponding sub-resource. Similarly, the parts in the third process (part IDs are 7, 10, and 5, respectively) are all of type C (cylinder: Cylinder), so a cylinder compatible hand (SR_Ha_01) is assigned as the corresponding sub-resource.

The operational cost display 1403 displays the depreciation cost and labor cost for a specified period (e.g., one month) when the process is carried out in the given order.

The above is the configuration of the production line system 1 according to this embodiment. The production line system 1 makes it possible to create a process design that minimizes operational costs in a short time without relying on the know-how of engineers and eliminating rework. In other words, it is possible to easily create a process plan that keeps operational costs down.

In the above technology, an example is shown in which a person, who is the main resource, does not have a sub-resource, but this is not limited to the above. For example, by setting a jig as a sub-resource and setting the load capacity of the person as well, it becomes possible to allocate resources that include sub-resources, just like the robot in this example.

The above-mentioned embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to having all of the configurations described. It is possible to replace part of the configuration of the embodiment with another configuration. It is also possible to delete part of the configuration of the embodiment.

Furthermore, the above-mentioned parts, configurations, functions, processing units, etc. may be realized in hardware, in part or in whole, for example by designing them as integrated circuits. Further, the above-mentioned parts, configurations, functions, etc. may be realized in software, by a processor interpreting and executing a program that realizes each function. Information on the programs, tables, files, etc. that realize each function can be stored in a memory, a recording device such as a hard disk, or a storage medium such as an IC card, SD card, or DVD.

The control lines and information lines in the above-mentioned embodiment are those considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be considered that almost all components are connected to each other. The present invention has been explained above, focusing on the embodiment.

1: Production line system, 100: Process planning device, 110: Memory unit, 111: Product information, 112: Resource information, 113: Part/resource combination information, 114: Process design information, 120: Processing unit, 121: Assembly sequence candidate generation unit, 122: Assembly sequence candidate expansion unit, 123: Line balancing unit, 124: Optimal solution selection unit, 130: Communication unit, 140: Input unit, 150: Output unit, 1111: Part adjacency information, 1112: Part specification information, 1121: Main resource specification, 1122: Sub-resource specification, 1123: Resource combination constraint, 1131: Work time information, 1132: Combination constraint.

Claims (11)

a storage unit that stores product information of an assembly product, resource information of resources used in production, and part/resource combination information including constraints and capabilities of combinations of parts and the resources that constitute the assembly product;
an assembly sequence candidate generating unit that generates a candidate proposal for an assembly sequence of the parts according to a priority criterion that satisfies the constraints and corresponds to the resources;
a line balancing unit that performs process division and resource allocation using the candidate plan to generate candidates for process design information;
an optimal solution selection unit that selects an optimal candidate for each candidate of the process design information by using a predetermined evaluation index;
A process planning device comprising: 2. The process planning apparatus according to claim 1,
the assembly sequence candidate generation unit sets the priority criterion for the resource being a worker to prioritize maintaining quality of assembly work by the worker;
A process planning device comprising: 2. The process planning apparatus according to claim 1,
the assembly sequence candidate generation unit prioritizes reducing the number of times the hand member is replaced as the priority criterion according to the robot capable of replacing the hand member as the resource;
A process planning device comprising: 2. The process planning apparatus according to claim 1,
the product information includes an installation position for each part of the assembly product;
the assembly sequence candidate generation unit determines, as the priority criterion according to the resource being a worker, that the parts are assembled in order of proximity to each other in the installation position of the parts;
A process planning device comprising: 2. The process planning apparatus according to claim 1,
The part-resource combination information includes the constraints modeled by a table and a constraint expression.
A process planning device comprising: 2. The process planning apparatus according to claim 1,
an assembly sequence candidate expansion unit that divides each of the generated candidate plans for the assembly sequence of the parts into processes so as to equalize the total working time included in each process, and combines the divided processes among the candidate plans for the assembly sequence of the parts to generate new candidate plans for the assembly sequence of the parts;
A process planning device comprising: 2. The process planning apparatus according to claim 1,
an assembly sequence candidate expansion unit that divides each of the generated candidate plans for the assembly sequence of the parts into processes based on the work time of each part and the target takt time so as to equalize the total work time included in each process, and combines the divided processes among the candidate plans for the assembly sequence of the parts to generate new candidate plans for the assembly sequence of the parts;
A process planning device comprising: 2. The process planning apparatus according to claim 1,
an assembly sequence candidate expansion unit that uses a target takt time to determine a maximum number and a minimum number of process divisions for each of the generated assembly sequence candidates for the parts, divides the process into processes so that the process divisions fall between the maximum number and the minimum number, and combines the divided processes among the assembly sequence candidates for the parts to generate new assembly sequence candidates for the parts;
A process planning device comprising: 2. The process planning apparatus according to claim 1,
an assembly sequence candidate expansion unit that generates a new candidate for the assembly sequence of the parts by combining the generated candidate for the assembly sequence of the parts;
A process planning device comprising: A production line system using a process planning device,
The process planning device includes:
a storage unit that stores product information of an assembly product, resource information of resources used in production, and part/resource combination information including constraints and capabilities of combinations of parts constituting the assembly product and the resources; and a processor;
The processor,
an assembly sequence candidate generating step of generating a candidate for an assembly sequence of the parts according to a priority criterion that satisfies the constraints and corresponds to the resources;
a line balancing step of dividing processes and allocating resources using the candidate plans to generate candidates for process design information;
an optimal solution selection step of selecting an optimal candidate for each candidate of the process design information by using a predetermined evaluation index;
A production line system comprising: A process planning method using a process planning device,
The process planning device includes:
a storage unit that stores product information of an assembly product, resource information of resources used in production, and part/resource combination information including constraints and capabilities of combinations of parts of the assembly product and the resources; and a processor;
The processor,
an assembly sequence candidate generating step of generating a candidate for an assembly sequence of the parts according to a priority criterion that satisfies the constraints and corresponds to the resources;
a line balancing step of dividing processes and allocating resources using the candidate plans to generate candidates for process design information;
an optimal solution selection step of selecting an optimal candidate for each candidate of the process design information by using a predetermined evaluation index;
A process planning method comprising the steps of:

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