Work plan optimization apparatus, method, and program

Abstract

A work plan optimization device 80 optimizes a plan in which each work in a plurality of work processes is assigned to a targeted worker. A work selection optimization means 81 optimizes, on the basis of the skill of the worker and the number of workers, the selection of work to be prioritized at each time while advancing the time for assigning work in each work process. An assignment optimization means 82 optimizes the worker to be assigned for the optimized selection of work in consideration of the constraints of the workers. An output means 83 outputs, as a work plan, a schedule of work to which workers have been assigned.

Description

[Technical Field]

[0001] The present invention relates to a work plan optimization device, a work plan optimization method, and a work plan optimization program that optimize work plans to assign to workers for multiple work processes. [Background technology]

[0002] The task of assigning workers to various tasks is a daily occurrence. Common methods of assigning tasks include simply assigning them in the order they arise or having experienced managers assign them based on their experience. However, these methods are highly dependent on individual skills and often contain waste, bias, and losses compared to ideal assignments.

[0003] Furthermore, with conventional methods, managers often spend several tens of minutes creating plans, and if events arise that necessitate changes to the plan, they have to spend time creating the plan again. In addition, there are many challenges, such as the deterioration of quality when skilled managers are absent and the problem of training skilled managers. Therefore, various methods have been proposed to create work plans using computers and other means to eliminate reliance on individual expertise as much as possible and to carry out production efficiently.

[0004] Patent Document 1 describes a production system planning method for planning an optimized production system. In the method described in Patent Document 1, a process plan is created by setting the sequence of each process for processing and assembling a product and allocating equipment to each process. Based on the contents of the process plan and equipment layout plan, a staffing plan is created to assign workers to each process. Then, based on the contents of the process plan, equipment layout plan and staffing plan, a production plan is created that schedules the operation of each production resource in order to satisfy the production requirements for the production system. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2003-162313 [Overview of the project] [Problems that the invention aims to solve]

[0006] The method described in Patent Document 1 assumes that there are enough workers to complete the work process, and then selects the workers to be assigned to each process. Therefore, when creating the process plan and equipment layout plan using the method described in Patent Document 1, constraints on the assigned workers are not considered. As a result, there is a risk that the work schedule itself may become unreasonable.

[0007] Furthermore, the process of formulating work schedules and optimizing their allocation to those schedules—the so-called work planning problem—is mathematically an NP-hard problem. Therefore, it is difficult to compute using ordinary, simple algorithms, making computer automation challenging. While the method described in Patent Document 1 can create a production plan that satisfies production requirements, it cannot necessarily be said that this production plan is optimal.

[0008] Therefore, the present invention aims to provide a work plan optimization device, a work plan optimization method, and a work plan optimization program that can optimize work plans assigned while taking into account the constraints of workers. [Means for solving the problem]

[0009] The work plan optimization device according to the present invention is a work plan optimization device that optimizes the plan for assigning each task in a plurality of work processes to a target worker, and comprises: a work selection optimization means that advances the time for assigning tasks in the work process and optimizes the selection of tasks that should be prioritized at each time based on the worker's skills and the number of workers; an assignment optimization means that optimizes the workers to be assigned to the optimized selection of tasks, taking into account the constraints of the workers; and an output means that outputs the schedule of the tasks to which workers have been assigned as a work plan. The work selection optimization means selects tasks in such a way that it minimizes an objective function representing the total loss that would occur if each worker did not perform each task. It is characterized by the following:

[0010] The work plan optimization method according to the present invention is a work plan optimization method that optimizes the plan for assigning each task in multiple work processes to target workers, and while advancing the time for assigning tasks in the work process, the selection of tasks that should be prioritized at each time is optimized based on the skills of the workers and the number of workers, the assignment of workers is optimized considering the constraints of the workers based on the optimized selection of tasks, and the schedule of the tasks to which workers have been assigned is output as a work plan. In optimization, tasks are selected to minimize an objective function that represents the total loss incurred if each worker does not perform each task. It is characterized by the following:

[0011] The work plan optimization program according to the present invention is a work plan optimization program applied to a computer that optimizes a plan for assigning each task in a plurality of work processes to target workers, and causes the computer to perform a work selection optimization process that optimizes the selection of tasks to be prioritized at each time while advancing the time for assigning tasks in the work process, based on the skills of the workers and the number of workers; an assignment optimization process that optimizes the assignment of workers to the optimized selection of tasks, taking into account the constraints of the workers; and an output process that outputs the schedule of the tasks to which workers have been assigned as a work plan. In the task selection optimization process, the system selects tasks in a way that minimizes an objective function representing the total loss incurred if each worker does not perform each task. It is characterized by the following: [Effects of the Invention]

[0012] According to the present invention, it is possible to optimize the work schedule assigned to workers, taking into account their constraints. [Brief explanation of the drawing]

[0013] [Figure 1] This is a block diagram showing an example configuration of one embodiment of the work plan optimization device of the present invention. [Figure 2] This is an explanatory diagram showing an example of work schedule data. [Figure 3] This is an explanatory diagram showing an example of the information held in the worker database. [Figure 4] This is an explanatory diagram illustrating an example of the information held by the work location distance database. [Figure 5]It is an explanatory diagram showing a specific example of a process for optimizing the selection of operations. [Figure 6] It is a flowchart showing an operation example of the work plan optimization device. [Figure 7] It is an explanatory diagram showing an example of visualizing work schedule data. [Figure 8] It is an explanatory diagram showing an example of visualizing the selected operations. [Figure 9] It is an explanatory diagram showing an example of a schedule with workers assigned. [Figure 10] It is a flowchart showing an example of a process for optimizing the selection of operations. [Figure 11] It is a flowchart showing an example of a process for optimizing worker assignment. [Figure 12] It is a block diagram showing an overview of the work plan optimization device according to the present invention.

Mode for Carrying Out the Invention

[0014] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following embodiments, for various operations that occur simultaneously in a production line, which is a plurality of work processes, an optimal work plan for workers is created using a combinatorial optimization solver such as a quantum annealing technique. In this embodiment, it is assumed that the precedence relationship of the operations occurring on the same line does not change. Also, in this embodiment, it is assumed that the skill set possessed by each worker is known, and a worker having the skill to execute the target operation is assigned to that operation.

[0015] FIG. 1 is a block diagram showing a configuration example of an embodiment of the work plan optimization device of the present invention. The work plan optimization device 100 of this embodiment includes a control means 110, a display means 120, an operation / input means 130, a worker database 140, a distance database between work locations 150, a work selection optimization means 160, and a worker assignment optimization means 170.

[0016] The production plan optimization device 100 is a device that optimizes the plan for assigning each task in multiple work processes to the target worker, and is implemented, for example, as a typical computer system. An example of a work process is a manufacturing line in a factory.

[0017] Furthermore, the work plan optimization device 100 is communicated with the quantum annealing means 180. The work plan optimization device 100 may also be configured to include the quantum annealing means 180.

[0018] The quantum annealing means 180 is a dedicated device for determining the ground state of the Hamiltonian of the Ising model, and is a device that performs annealing based on the Ising model generated by the work selection optimization means 160 and the worker assignment optimization means 170, which will be described later.

[0019] More specifically, an annealing machine is a device that probabilistically determines the value of a binary variable that minimizes or maximizes the objective function (i.e., the Hamiltonian) of an Ising model with a binary variable as an argument. The binary variable may be implemented using classical bits or quantum bits. The configuration of the quantum annealing means 180 in this embodiment is arbitrary. The quantum annealing means 180 may be configured with any hardware that probabilistically determines the value of a binary variable that minimizes or maximizes the objective function with a binary variable as an argument. For example, the quantum annealing means 180 may be a non-von Neumann computer in which the objective function is implemented in hardware in the form of an Ising model. Alternatively, the quantum annealing means 180 may be a device that performs pseudo-quantum annealing or quantum-inspired computing.

[0020] Furthermore, if the work selection optimization means 160 and the worker assignment optimization means 170, which will be described later, do not use quantum annealing for their optimization processes, the quantum annealing means 180 does not need to be connected to the work plan optimization device 100.

[0021] The control means 110 is a means for controlling various processes performed by the work plan optimization device 100.

[0022] The display means 120 displays the progress and results of the processing performed by the work plan optimization device 100. The display means 120 is implemented, for example, by a display device.

[0023] The operation / input means 130 receives instructions for various operations to the work plan optimization device 100. The operation / input means 130 also receives input of information necessary for various processes performed by the work plan optimization device 100. In this embodiment, the operation / input means 130 receives input of work schedule data for an allocated period (for example, one day).

[0024] Work schedule data includes work data (e.g., work content, work location, time of occurrence, work duration, etc.), information on workers assigned to the assignment period (e.g., workers on duty), and line information for the assignment period.

[0025] Figure 2 is an explanatory diagram showing an example of work schedule data. In Figure 2, data D1 shows the work data for the day, data D2 shows the worker information for the day, and data D3 shows the line information for the day.

[0026] Specifically, in data D1, "Line" identifies the work line indicating the work process, "Place" indicates the work location, "Work" indicates the work content, "Estimate Time" indicates the estimated time of occurrence, and "Work Time" indicates the work duration. The work content corresponds to the worker's skills.

[0027] Data D2 is a list of workers for the day, allowing us to understand the number of workers. Data D3's "Priority" indicates the priority of the work process (line), with a smaller number indicating higher priority. Line priorities are predetermined, for example, based on losses.

[0028] The worker database 140 acquires various information about the workers to be assigned. In this embodiment, the worker database 140 maintains the skills and work process priorities for each worker. Priorities are set in advance according to the worker's strengths and weaknesses in a task, and considerations regarding other tasks. The worker database 140 is implemented by a device such as a magnetic disk and has the function of extracting and returning necessary data in response to queries to the worker database 140.

[0029] Figure 3 is an explanatory diagram illustrating an example of the information held by the worker database 140. The example shown in Figure 3 illustrates how the skills held by each worker and the priority of that worker's work process are associated and stored.

[0030] The work location distance database 150 holds information indicating the distance between work locations. For example, in a production line, which is one of the work processes, the distance between work locations refers to the distance between a location where one task is performed and a location where another task is performed. The work location distance database 150 is also implemented by a device such as a magnetic disk and has the function of extracting and returning necessary data in response to queries against the work location distance database 150.

[0031] Figure 4 is an explanatory diagram illustrating an example of the information held by the work location distance database 150. The example shown in Figure 4 illustrates the correspondence between the distances between two work locations. The unit of distance is, for example, meters.

[0032] The task selection optimization means 160 optimizes the selection of tasks that should be prioritized. In this embodiment, since the worker assignment optimization means 170, which will be described later, performs optimization considering worker constraints, the task selection optimization means 160 only determines whether an assignment is possible based on the number and skills of the workers.

[0033] Specifically, the work selection optimization means 160 advances the time t for assigning tasks in the work process at predetermined intervals, and optimizes the selection of tasks that should be prioritized at each time based on the skills of the workers and the number of workers. In other words, the work selection optimization means 160 focuses on the moment at each time t and optimizes the selection of tasks at that time t.

[0034] Figure 5 is an explanatory diagram illustrating a specific example of a process for optimizing task selection. In the example shown in Figure 5, there are three workers to be assigned, each possessing the skills indicated by {Skill A, Skill B}, {Skill B, Skill C}, and {Skill C}, respectively. The rectangular shapes illustrated in Figure 5 represent the assigned tasks a to f. Tasks a, d, and e require Skill A, tasks b and f require Skill B, and task c requires Skill C. Furthermore, line 1, where tasks a, b, c, e, and f are performed, is considered a high-priority line, while line d is considered a low-priority line.

[0035] First, focusing on time t, there are tasks a, b, c, and d that can be assigned. If one worker is assigned one task, and there are three workers, it is not possible to assign a worker to all tasks. Therefore, as a result of optimization (in this case, the tasks on the higher priority line are selected), the lower priority task d is postponed (i.e., it becomes a candidate for assignment at time t+1).

[0036] Next, at time t+1, there are tasks d, e, and f available for assignment. There are three tasks and three workers, but only one worker is capable of performing a task requiring skill A. Therefore, as a result of optimization (in this case, selecting tasks on the higher priority line), the lower priority task d is further postponed (i.e., it becomes a candidate for assignment at time t+2).

[0037] Next, at time t+2, only task d exists as a task to be assigned. Since there is a worker capable of performing the task requiring skill A, task d is selected at time t+2. However, the actual assignment of workers is not performed at this stage because it is necessary to consider the balance of the entire time.

[0038] The objective function used for optimization by the task selection optimization means 160 is determined according to the optimization method. For example, when optimization is performed by quantum annealing, the Ising model may be used as the objective function. The following explanation illustrates the case where optimization is performed by quantum annealing. However, the optimization method is not limited to quantum annealing; for example, optimization may be performed using a mathematical optimization solver.

[0039] The objective function used to optimize the selection of the task at time t (hereinafter sometimes referred to as the optimization model) can be expressed, for example, by Equation 1 shown below. In Equation 1, x w,wr This is a binary variable that indicates whether worker wr will perform task w, with a value of 1 indicating that the task will be performed and a value of 0 indicating that it will not be performed. w This represents the loss if you do not select task w. cost w This can be said to be the priority of the task, so for example, the “Priority” of data D3 shown in Figure 2 is cost w It may be used as such. That is, the work selection optimization means 160 may optimize the selection of work based on the priority of the work process.

[0040]

number

[0041] Furthermore, it is preferable that the work selection optimization means 160 optimizes the above objective function to satisfy the constraints regarding worker assignment. Specifically, it is preferable that the work selection optimization means 160 optimizes work selection to satisfy the following constraints: a constraint that restricts assigning multiple workers to a single work (hereinafter referred to as the first constraint), a constraint that restricts assigning multiple work to a single worker (hereinafter referred to as the second constraint), and a constraint that restricts assigning a worker to a work that requires skills that the worker does not possess (hereinafter referred to as the third constraint).

[0042] The first constraint, second constraint, and third constraint are, for example, the binary variable x mentioned above. w,wr Using these, they can be expressed as shown in Equations 2, 3, and 4 below. Note that in Equation 4, SKILL wr This shows a list of workers who possess each skill.

[0043]

number

[0044] As described above, the task selection optimization means 160 may perform the optimization based on the above objective function and constraints using a mathematical optimization solver such as linear programming, or it may be performed by the quantum annealing means 180. When performing optimization using a mathematical optimization solver such as linear programming, instead of the above Ising model, an objective function and constraints appropriate to the optimization solver may be set.

[0045] The worker assignment optimization means 170 optimizes the assignment of workers to the selected optimized tasks, taking into account the constraints of the workers. The worker assignment optimization means 170 then outputs the schedule of the assigned tasks as a work plan. As described above, in this embodiment, the task selection optimization means 160 has already optimized the selection of tasks, and the worker assignment optimization means 170 only needs to optimize the assignment of workers, thus reducing the cost required for optimization.

[0046] The objective function used for optimization by the operator allocation optimization means 170 is also determined according to the optimization method. In the following description, the case where optimization is performed by quantum annealing is exemplified. However, similar to the operation selection optimization means 160, the optimization method is not limited to quantum annealing, and for example, optimization may be performed using a mathematical optimization solver.

[0047] The objective function (hereinafter, may also be referred to as an optimization model) used for optimizing the operator allocation is set according to the constraints of the operators to be considered. Examples of the constraints of the operators to be considered include considering operator priorities, leveling the workload of the operators, suppressing long-distance movement of the operators in a short time, and the like.

[0048] The objective function (hereinafter, referred to as the first function) for which the value becomes smaller as the priority of the operator is satisfied is represented by, for example, Equation 5 shown below. In Equation 5, priority w,wr indicates the priority for the operator wr to perform the work w, and the smaller the number, the higher the priority.

[0049]

Equation

[0050] In addition, the objective function (hereinafter, referred to as the second function) for which the value becomes smaller as the workload of the operator is leveled is represented by, for example, Equation 6 shown below. In Equation 6, l w indicates the working time of the work w.

[0051]

Equation

[0052] In addition, the objective function (hereinafter, referred to as the third function) for which the value becomes smaller as the long-distance movement of the operator in a short time is suppressed is represented by, for example, Equation 7 shown below. In Equation 7, distance w1,w2This indicates the distance traveled between the location where task w1 is performed and the location where task w2 is performed.

[0053]

number

[0054] The worker assignment optimization means 170 may optimize the assignment of workers to minimize an objective function that includes at least one of the objective functions (first function, second function, and third function) as shown above. For example, an objective function that includes the first function, second function, and third function as shown above can be expressed as shown in Equation 8 below. w1, w2, and w3 in Equation 8 are adjusted and set by a manager or the like to obtain the desired result.

[0055] Objective function = w1 first function + w2 second function + w3 third function (Equation 8)

[0056] Furthermore, it is preferable that the worker assignment optimization means 170 optimizes the objective function in such a way that it satisfies the constraints regarding worker assignment. For example, it is preferable that the worker assignment optimization means 170 optimizes worker assignment in such a way that, in addition to the first and third constraints described above, it satisfies a constraint (hereinafter referred to as the fourth constraint) that restricts assigning multiple tasks with overlapping timeframes to a single worker.

[0057] The fourth constraint is, for example, the binary variable x mentioned above. w,wr Using this, it can be expressed as shown in Equation 9 below. In Equation 8, all of w1 and w2 that overlap in time are considered.

[0058]

number

[0059] As described above, the worker assignment optimization means 170 may perform the optimization based on the above objective function and constraints using a mathematical optimization solver such as linear programming, or it may be performed by the quantum annealing means 180. When performing optimization using a mathematical optimization solver such as linear programming, instead of the above Ising model, an objective function and constraints appropriate to the optimization solver may be set.

[0060] The worker assignment optimization means 170 may control the display means 120 to display the schedule (work plan) of the work to which the workers have been assigned. The worker assignment optimization means 170 may also notify the mobile terminal (not shown) of the worker in charge of each task of a message to control the start of work when the work time is approaching (for example, 10 minutes before the work starts).

[0061] More specifically, the worker assignment optimization means 170 may also perform control to output information associated with the work, such as the deadline for each task to be performed and detailed information about each task.

[0062] The control means 110, the display means 120, the operation / input means 130, the work selection optimization means 160, and the worker assignment optimization means 170 are implemented by a computer processor (e.g., CPU (Central Processing Unit), GPU) that operates according to a program (work plan optimization program).

[0063] For example, the program may be stored in the memory unit (not shown) of the work plan optimization device 100, and the processor may read the program and operate according to the program as a control means 110, a display means 120, an operation / input means 130, a work selection optimization means 160, and a worker assignment optimization means 170. Alternatively, the functions of the work plan optimization device 100 may be provided in SaaS (Software as a Service) format.

[0064] Furthermore, the control means 110, the display means 120, the operation / input means 130, the work selection optimization means 160, and the worker assignment optimization means 170 may each be implemented with dedicated hardware. Also, some or all of the components of each device may be implemented by general-purpose or dedicated circuits, processors, etc., or combinations thereof. These may be composed of a single chip or multiple chips connected via a bus. Some or all of the components of each device may be implemented by a combination of the above-mentioned circuits, etc., and programs.

[0065] Furthermore, if some or all of the components of the work plan optimization device 100 are realized by multiple information processing devices or circuits, these multiple information processing devices or circuits may be centrally located or distributed. For example, the information processing devices or circuits may be realized in a form in which each is connected via a communication network, such as a client-server system or a cloud computing system.

[0066] Next, the operation of the work plan optimization device 100 of this embodiment will be described. Figure 6 is a flowchart showing an example of the operation of the work plan optimization device 100 of this embodiment. First, the operation / input means 130 reads the work schedule data and accepts the input of that data (step S101).

[0067] Figure 7 is an explanatory diagram showing an example of visualizing scheduled work data. In the example shown in Figure 7, it is shown that data on scheduled work (work content, work location, time of occurrence, work duration, etc.) that will occur on all lines, work data for the day, and line information data for the day have been input and visualized with different display methods for each work.

[0068] The work selection optimization means 160 advances the time for assigning tasks in the work process, and at each time, it optimizes the selection of tasks that should be prioritized based on the skills and number of workers (step S102). More specifically, the work selection optimization means 160 selects tasks that are feasible and minimize the loss in the objective function, based on the skills and number of workers, as well as the priority of the line. As mentioned above, no actual assignment of workers takes place at this stage.

[0069] Figure 8 is an explanatory diagram illustrating an example of visualizing selected tasks. The example in Figure 8 shows that the time spent on each task changed as a result of optimizing task selection based on the line information data illustrated in Figure 7.

[0070] The worker assignment optimization means 170 optimizes the assignment of workers to the selected optimized tasks, taking into account worker constraints (step S103). More specifically, the worker assignment optimization means 170 optimizes the assignment of workers to all selected tasks, taking into account worker priority, workload equalization, suppression of long-distance travel in a short time, etc.

[0071] Figure 9 is an explanatory diagram showing an example of a schedule with assigned workers. The example shown in Figure 9 shows how, for each of the tasks exemplified in Figure 8, workers are assigned and the display method is changed for each worker to visualize the schedule.

[0072] The worker assignment optimization means 170 then outputs the schedule of the assigned tasks as a work plan (work plan table) (step S104). The worker assignment optimization means 170 may also output the work plan in a format such as a Gantt chart, as illustrated in Figure 9.

[0073] Next, the operation of the work selection optimization means 160 of this embodiment will be described. Figure 10 is a flowchart of an example of a process for optimizing work selection. The process illustrated in Figure 10 corresponds to the process in step S102 in Figure 6. Here, the work start time is set to t, and the optimization process is performed with a predetermined interval of 1 (unit time).

[0074] First, the task selection optimization means 160 creates an optimization model for time t (step S201). The task selection optimization means 160 creates an optimization model that includes, for example, the objective function shown in equation 1 and the constraints shown in equations 2 to 4.

[0075] Next, the task selection optimization means 160 performs the optimization process (step S202). The task selection optimization means 160 may, for example, input the generated optimization model to the quantum annealing means 180 and have it perform the optimization process.

[0076] Next, the work selection optimization means 160 delays the scheduled time of all tasks that were not selected as a result of the optimization process by 1 (step S203), and adds 1 to t (step S204).

[0077] If there are remaining tasks (no in step S205), the process from step S201 onwards is repeated. On the other hand, if there are no remaining tasks (Yes in step S205), the process of optimizing task selection ends.

[0078] Next, the operation of the worker assignment optimization means 170 of this embodiment will be described. Figure 11 is a flowchart showing an example of the process for optimizing worker assignment. The process illustrated in Figure 11 corresponds to the process in step S103 in Figure 6.

[0079] The worker assignment optimization means 170 creates an optimization model (step S301). The worker assignment optimization means 170 creates an optimization model that includes, for example, the objective function shown in Equation 8 and the constraints shown in Equations 2, 4, and 9.

[0080] Then, the worker assignment optimization means 170 executes the optimization process (step S302). The worker assignment optimization means 170 may, for example, input the generated optimization model to the quantum annealing means 180 and have it execute the optimization process.

[0081] As described above, in this embodiment, the task selection optimization means 160 advances the time t for assigning tasks in the work process, and optimizes the selection of tasks that should be prioritized at each time based on the skills of the workers and the number of workers. Then, the worker assignment optimization means 170 optimizes the assignment of workers to the optimized task selection, taking into account the constraints of the workers, and outputs the schedule of the assigned tasks as a work plan. Thus, the work plan for assignment can be optimized while taking into account the constraints of the workers.

[0082] In other words, in this embodiment, by dividing the work plan into optimizing the selection of tasks (determining when to start the work) and optimizing the assignment of workers (determining who should be in charge), the time required for work planning can be significantly reduced. This makes it possible to find and compare multiple optimal schedules for various cases with varying input conditions such as the number of workers and loss values ​​for each line, thereby contributing to management decisions.

[0083] For example, if one attempts to create a work plan using quantum annealing or mathematical optimization methods, in practical cases the model becomes large, often making it impossible to solve within a realistic timeframe and resulting in insufficient accuracy. For instance, if there are 8 lines, 30 tasks per line, a total work time of 600 minutes, and 5 workers assigned, 720,000 variables would be required. In contrast, this embodiment divides the problem and performs optimization while minimizing the loss of overall optimality, thereby eliminating reliance on individuals, human error, bias, and losses, and significantly reducing the time required for work planning.

[0084] Furthermore, for example, if an unexpected task arises, workers may perform the task without understanding its priority, resulting in unnecessarily long line downtime and reduced productivity. In contrast, this embodiment allows for a reduction in the time required for work planning, thereby suppressing the decline in productivity.

[0085] Next, an overview of the present invention will be described. Figure 12 is a block diagram illustrating an overview of the work plan optimization device according to the present invention. The work plan optimization device according to the present invention is a work plan optimization device 80 (e.g., work plan optimization device 100) that optimizes a plan for assigning each task in a plurality of work processes (e.g., a manufacturing line) to a target worker, and comprises: a work selection optimization means 81 (e.g., work selection optimization means 160) that advances the time for assigning tasks in the work process and optimizes the selection of tasks that should be prioritized at each time based on the worker's skills and the number of workers; an assignment optimization means 82 (e.g., worker assignment optimization means 170) that optimizes the workers to be assigned to the optimized task selection, taking into account the constraints of the workers; and an output means 83 (e.g., worker assignment optimization means 170) that outputs the schedule of the tasks to which workers have been assigned as a work plan.

[0086] Such a configuration allows for the optimization of work assignments, taking into account the constraints of the workers.

[0087] Furthermore, the assignment optimization means 82 may optimize the assignment of workers to minimize an objective function (e.g., Equation 8 shown above) that includes at least one of the following: a first function whose value decreases as the worker's priority is met (e.g., Equation 5 shown above); a second function whose value decreases as the worker's workload is leveled out (e.g., Equation 6 shown above); and a third function whose value decreases as the worker's short-time long-distance travel is suppressed (e.g., Equation 7 shown above).

[0088] Furthermore, the work selection optimization means 81 may optimize work selection to satisfy a first constraint (e.g., Equation 2 shown above) that restricts assigning multiple workers to a single work, a second constraint (e.g., Equation 3 shown above) that restricts assigning multiple work to a single worker, and a third constraint (e.g., Equation 4 shown above) that restricts assigning workers to work that requires skills not possessed by the worker.

[0089] Furthermore, the work selection optimization means 81 may optimize the selection of work based on the priority of the work process (for example, Equation 1).

[0090] Furthermore, either or both of the task selection optimization means 81 and the assignment optimization means 82 may cause a quantum annealing machine (e.g., quantum annealing means 180) to perform optimization (e.g., pseudo-quantum annealing, quantum-inspired, etc.).

[0091] Some or all of the above embodiments may also be described as follows, but are not limited to the following:

[0092] (Note 1) A work plan optimization device that optimizes the plan for assigning each task in multiple work processes to a target worker, A work selection optimization means that, while advancing the time for assigning tasks in the aforementioned work process, optimizes the selection of tasks to be prioritized at each time based on the skills of the workers and the number of workers, An assignment optimization means that optimizes the assignment of workers to the optimized selection of the work, taking into account the constraints of the workers, The system includes an output means that outputs the schedule of the work to which the aforementioned workers have been assigned as a work plan. A work plan optimization device characterized by the following features.

[0093] (Note 2) The assignment optimization means optimizes the assigned workers to minimize an objective function that includes at least one of the following: a first function whose value decreases as the worker's priority is met; a second function whose value decreases as the worker's workload is leveled; and a third function whose value decreases as the worker's short-time long-distance travel is suppressed. Work plan optimization device as described in Appendix 1.

[0094] (Note 3) The assignment optimization means optimizes the workers to be assigned so as to satisfy the constraint that limits assigning multiple tasks with overlapping time to a single worker. Work plan optimization device as described in Appendix 2.

[0095] (Note 4) The priority of workers is predetermined based on at least one of the following: their strengths or weaknesses in a task, and the balance with other tasks. Work plan optimization device as described in Appendix 2 or Appendix 3.

[0096] (Note 5) The work selection optimization means optimizes work selection to satisfy a first constraint that restricts assigning multiple workers to a single work, a second constraint that restricts assigning multiple work to a single worker, and a third constraint that restricts assigning a worker to a work that requires skills the worker does not possess. A work plan optimization device as described in any one of the appendices 1 through 4.

[0097] (Note 6) The work selection optimization means optimizes the selection of work based on the priority of the work process. A work plan optimization device as described in any one of the appendices 1 through 5.

[0098] (Note 7) The work selection optimization means advances the time for assigning tasks in the work process at predetermined intervals, and at each time, optimizes the selection of tasks that should be prioritized based on the skills of the workers and the number of workers. A work plan optimization device as described in any one of the appendices 1 through 6.

[0099] (Note 8) The work selection optimization means selects tasks in such a way that it minimizes an objective function that represents the total loss incurred if each worker does not perform each task. A work plan optimization device as described in any one of the appendices 1 through 7.

[0100] (Note 9) The work selection optimization means selects work that is feasible and minimizes the loss of the objective function, based on the skills and number of workers, as well as the priority of the line. Work plan optimization device as described in Appendix 8.

[0101] (Note 10) Either the task selection optimization means or the assignment optimization means, or both, cause the quantum annealing machine to perform the optimization. A work plan optimization device as described in any one of the appendices 1 through 9.

[0102] (Note 11) The assignment optimization means controls the display means to display the worker assignment. A work plan optimization device as described in any one of the appendices 1 through 10.

[0103] (Note 12) The assignment optimization means controls the display of worker assignments in a Gantt chart format. Work plan optimization device as described in Appendix 11.

[0104] (Note 13) The assignment optimization means controls the display to change the display mode for each task. A work plan optimization device as described in Appendix 11 or Appendix 12.

[0105] (Note 14) The assignment optimization means controls the mobile terminals of workers assigned to each task to notify them of the start of work by sending a message a predetermined time before the scheduled work time, based on the worker assignment. A work plan optimization device as described in any one of the appendices 1 through 13.

[0106] (Note 15) The assignment optimization means controls the mobile terminals of the workers assigned to each task to output the deadline for each task as information associated with that task. A work plan optimization device as described in any one of the appendices 1 through 14.

[0107] (Note 16) The assignment optimization means controls the output of detailed information about each task as information associated with the assigned task to the mobile terminal held by the worker in charge of each task. A work plan optimization device as described in any one of the appendices 1 through 15.

[0108] (Note 17) A work plan optimization method that optimizes the plan for assigning each task in multiple work processes to the target worker, As the work process progresses and the time for assigning tasks advances, the selection of tasks that should be prioritized at each time is optimized based on the skills of the workers and the number of workers. With regard to the optimized selection of the work, the assignment of the worker is optimized, taking into account the constraints of the worker. The schedule for the work assigned to the aforementioned worker is output as a work plan. A method for optimizing work plans, characterized by the following features.

[0109] (Note 18) Optimize the assigned workers to minimize an objective function that includes at least one of the following: a first function whose value decreases as worker priority is met; a second function whose value decreases as the worker workload is leveled out; and a third function whose value decreases as worker long-distance travel in a short time is suppressed. Work plan optimization method as described in Appendix 17.

[0110] (Note 19) A work plan optimization program applied to a computer that optimizes the plan for assigning each task in multiple work processes to a target worker, To the aforementioned computer, A work schedule optimization process that, while advancing the time for assigning tasks in the aforementioned work process, optimizes the selection of tasks to be prioritized at each time based on the skills of the workers and the number of workers. An assignment optimization process that optimizes the assignment of workers to the optimized selection of the work, taking into account the constraints of the workers, and Output process that outputs the schedule of the work to which the aforementioned workers have been assigned as a work plan. A program storage medium that stores a work plan optimization program for executing a task.

[0111] (Note 20) The system stores a work plan optimization program that optimizes the assignment of workers to minimize an objective function that includes at least one of the following: a first function whose value decreases as worker priority is met; a second function whose value decreases as the worker workload is leveled out; and a third function whose value decreases as workers' short-time, long-distance travel is suppressed. The program storage medium described in Appendix 19.

[0112] (Note 21) To the computer, A work plan optimization program applied to a computer that optimizes the plan for assigning each task in multiple work processes to a target worker, To the aforementioned computer, A work schedule optimization process that, while advancing the time for assigning tasks in the aforementioned work process, optimizes the selection of tasks to be prioritized at each time based on the skills of the workers and the number of workers. An assignment optimization process that optimizes the assignment of workers to the optimized selection of the work, taking into account the constraints of the workers, and Output process that outputs the schedule of the work to which the aforementioned workers have been assigned as a work plan. A work plan optimization program to execute tasks.

[0113] (Note 22) To the computer, The assignment optimization process optimizes the assignment of workers to minimize an objective function that includes at least one of the following: a first function whose value decreases as worker priority is met; a second function whose value decreases as the worker workload is leveled out; and a third function whose value decreases as workers' short-duration long-distance travel is suppressed. The work plan optimization program described in Appendix 21.

[0114] Although the present invention has been described above with reference to the embodiments and examples, the present invention is not limited to the above embodiments and examples. Various modifications to the structure and details of the present invention can be made, as can be understood by those skilled in the art within the scope of the present invention.

[0115] This application claims priority based on Japanese Patent Application No. 2023-21319, filed on 15 February 2023, and incorporates all of its disclosures herein. [Industrial applicability]

[0116] The present invention is suitably applied to a work plan optimization device that optimizes the work plan assigned to workers for multiple work processes. For example, the present invention can be applied to cases in factories with multiple lines where manual work occurs simultaneously in multiple locations. [Explanation of Symbols]

[0117] 100 Work Plan Optimization Device 110 Control means 120 Display means 130 Operation / Input Methods 140 Worker Database 150 Workplace Distance Database 160 Task Selection Optimization Method 170 Worker Assignment Optimization Method 180 Quantum Annealing Methods

Claims

1. A work plan optimization device that optimizes the plan for assigning each task in multiple work processes to a target worker, A work selection optimization means that, while advancing the time for assigning tasks in the aforementioned work process, optimizes the selection of tasks to be prioritized at each time based on the skills of the workers and the number of workers, An assignment optimization means that optimizes the assignment of workers to the optimized selection of the work, taking into account the constraints of the workers, The system includes an output means that outputs the schedule of the work to which the aforementioned workers are assigned as a work plan, The aforementioned work selection optimization means selects tasks in such a way that it minimizes an objective function representing the total loss that would occur if each worker did not perform each task. A work plan optimization device characterized by the following features.

2. The assignment optimization means optimizes the assigned workers to minimize an objective function that includes at least one of the following: a first function whose value decreases as worker priority is met; a second function whose value decreases as the worker workload is leveled out; and a third function whose value decreases as worker long-distance travel in a short time is suppressed. The work plan optimization apparatus according to claim 1.

3. The assignment optimization means optimizes the assigned workers to satisfy constraints that limit assigning multiple overlapping tasks to a single worker. The work plan optimization apparatus according to claim 2.

4. Worker priorities are predetermined based on at least one of the following: their strengths or weaknesses in a task, and how it fits with other tasks. A work plan optimization device according to claim 2 or claim 3.

5. The work selection optimization means optimizes work selection to satisfy a first constraint that restricts assigning multiple workers to a single work, a second constraint that restricts assigning multiple work to a single worker, and a third constraint that restricts assigning a worker to a work that requires skills the worker does not possess. A work plan optimization device according to claim 1 or claim 2.

6. The work selection optimization means optimizes the selection of work based on the priority of the work process. A work plan optimization device according to claim 1 or claim 2.

7. The work selection optimization means optimizes the selection of the work that should be prioritized at each predetermined time, based on the worker's skills and the number of workers, while advancing the time at which tasks are assigned in the work process. A work plan optimization device according to claim 1 or claim 2.

8. The work selection optimization means selects work from the worker's skills and number, as well as the line priority, in such a way that it is feasible and minimizes the loss of the objective function. A work plan optimization device according to claim 1 or claim 2.

9. A work plan optimization method that optimizes the plan for assigning each task in multiple work processes to target workers, As the work process progresses and the time for assigning tasks advances, the selection of tasks that should be prioritized at each time is optimized based on the skills of the workers and the number of workers. With regard to the optimized selection of the work, the assignment of the worker is optimized, taking into account the constraints of the worker. The schedule for the work to which the aforementioned workers are assigned is output as a work plan. In the optimization described above, tasks are selected in such a way that an objective function representing the total loss incurred if each worker does not perform each task is minimized. A method for optimizing work plans, characterized by the following features.

10. On the computer, A work plan optimization program applied to a computer that optimizes the plan for assigning each task in multiple work processes to a target worker, To the aforementioned computer, A work selection optimization process that, while advancing the time for assigning tasks in the aforementioned work process, optimizes the selection of tasks to be prioritized at each time based on the skills of the workers and the number of workers. An assignment optimization process that optimizes the assignment of workers to the optimized selection of the work, taking into account the constraints of the workers, and The system then executes an output process that outputs the schedule of the work to which the aforementioned workers are assigned as a work plan. The aforementioned task selection optimization process selects tasks in such a way that it minimizes an objective function representing the total loss incurred if each worker does not perform each task. A work plan optimization program for this purpose.

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