Work plan optimization device, 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

Work plan optimization device, method, and program

[0001] The present invention relates to a work plan optimization device, a work plan optimization method, and a work plan optimization program for optimizing a work plan for assigning workers to a plurality of work processes.

[0002] The task of assigning workers to each task is a routine one. Tasks are generally assigned to workers simply in the order in which they occur, or by experienced managers who assign them empirically. However, because these methods are highly dependent on the individual, they often involve waste, bias, and losses compared to ideal assignments.

[0003] Furthermore, with conventional methods, it often takes several tens of minutes for a manager to create a plan, and if an event occurs that requires a change to the plan, the manager must spend time creating the plan again. Furthermore, there are many issues, such as a deterioration in quality when an experienced manager is absent and the need to train experienced managers. Therefore, various methods have been proposed that use computers or other tools to create work plans that eliminate personal dependency as much as possible and ensure efficient production.

[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 that sets the order of each process for processing and assembling a product and assigns equipment to each process, and a personnel allocation plan is created that assigns workers to each process based on the contents of the process plan and equipment layout plan. Then, a production plan is created that schedules the operation of each production resource based on the contents of the process plan, equipment layout plan, and personnel allocation plan so as to satisfy the production demands for the production system.

[0005] Japanese Patent Application Laid-Open No. 2003-162313

[0006] The method described in Patent Document 1 selects workers to be assigned to each process on the assumption that there are workers who can fulfill the work process requirements. Therefore, when the process plan and equipment layout plan are set using the method described in Patent Document 1, constraints on the workers to be assigned are not taken into consideration. This can lead to unrealistic work schedules.

[0007] Furthermore, the optimization of work schedule formulation and worker allocation to that schedule, known as work schedule formulation, is mathematically an NP-hard problem. Therefore, calculations using ordinary simple algorithms are difficult, making computer-based automation difficult. While the method described in Patent Document 1 can create a production plan that satisfies production requirements, it is difficult to say that the production plan is necessarily optimal.

[0008] Therefore, an object of the present invention is to provide a work plan optimization device, a work plan optimization method, and a work plan optimization program that can optimize a work plan to be assigned while taking into account worker constraints.

[0009] The work plan optimization device according to the present invention is a work plan optimization device that optimizes a plan for allocating each task in a plurality of work processes to a target worker, and is characterized by comprising: a task selection optimization means that optimizes the selection of tasks to be prioritized at each time based on the skills of the workers and the number of workers while progressing the time for allocating tasks in the work process; an assignment optimization means that optimizes the workers to be assigned to the optimized task selection, taking into account worker constraints; and an output means that outputs a schedule of the tasks to which the workers have been assigned as a work plan.

[0010] The work plan optimization method according to the present invention is a work plan optimization method that optimizes a plan for assigning each task in a plurality of work processes to a target worker, and is characterized by the following: as the time for assigning tasks in a work process progresses, 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; for the optimized selection of tasks, the workers to be assigned are optimized taking into account worker constraints; and a schedule of the tasks to which the workers have been assigned is output as a work plan.

[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 a target worker, and is characterized by having the computer execute a work selection optimization process that optimizes the selection of tasks to be prioritized at each time based on the skills of the workers and the number of workers while progressing the time for assigning tasks in the work process; an assignment optimization process that optimizes the workers to be assigned to the optimized task selection, taking into account worker constraints; and an output process that outputs a schedule of the tasks to which the workers have been assigned as a work plan.

[0012] According to the present invention, it is possible to optimize the work plan to be assigned taking into account the constraints of workers.

[0013] FIG. 1 is a block diagram showing an example of the configuration of an embodiment of a work plan optimization device of the present invention. FIG. 2 is an explanatory diagram showing an example of work schedule data. FIG. 3 is an explanatory diagram showing an example of information held in a worker database. FIG. 4 is an explanatory diagram showing an example of information held in a work location distance database. FIG. 5 is an explanatory diagram showing a specific example of processing to optimize work selection. FIG. 6 is a flowchart showing an example of operation of the work plan optimization device. FIG. 7 is an explanatory diagram showing an example of visualization of work schedule data. FIG. 8 is an explanatory diagram showing an example of visualization of selected work. FIG. 9 is an explanatory diagram showing an example of a schedule to which workers are assigned. FIG. 10 is a flowchart showing an example of processing to optimize work selection. FIG. 11 is a flowchart showing an example of processing to optimize worker assignment. FIG. 12 is a block diagram showing an overview of a work plan optimization device according to the present invention.

[0014] An embodiment of the present invention will be described below with reference to the drawings. In the following embodiment, an optimal worker work plan is created for various tasks that occur simultaneously on a manufacturing line, which is a multi-step process, using a combinatorial optimization solver such as quantum annealing technology. Note that in this embodiment, the order of tasks occurring on the same line remains unchanged. Furthermore, in this embodiment, it is assumed that the skill sets possessed by each worker are known, and a worker with the skills required to perform a target task is assigned to that task.

[0015] 1 is a block diagram showing an example of the configuration of an embodiment of a 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 work location distance database 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 a plan for allocating tasks in a plurality of work processes to target workers, and is realized, for example, as a typical computer system. An example of a work process is a production line in a factory.

[0017] Furthermore, the work plan optimization device 100 is communicably connected to a quantum annealing means 180. Note that the work plan optimization device 100 may be configured to include the quantum annealing means 180.

[0018] The quantum annealing means 180 is a dedicated device for finding 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., Hamiltonian) of an Ising model that takes binary variables as arguments. The binary variables may be realized by classical bits or quantum bits. The quantum annealing means 180 of this embodiment may take any form. The quantum annealing means 180 may be configured by any hardware that probabilistically determines the value of a binary variable that minimizes or maximizes the objective function that takes binary variables as arguments. The quantum annealing means 180 may be, for example, a non-von Neumann computer in which the objective function is implemented by hardware in the form of an Ising model. The quantum annealing means 180 may also be a device that performs pseudo-quantum annealing or quantum-inspired annealing.

[0020] Note that if the task selection optimization means 160 and the worker assignment optimization means 170 described below do not use quantum annealing in the optimization process, 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 realized by, for example, a display device.

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

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

[0025] 2 is an explanatory diagram showing an example of work schedule data. Data D1 shown in Fig. 2 indicates work data for the day, data D2 indicates information about workers for the day, and data D3 indicates line information for the day.

[0026] Specifically, in data D1, "Line" is information identifying a work line indicating a work process, "Place" is the work location, "Work" is the work content, "Estimate Time" is the estimated occurrence time, and "Work Time" is the work time. The work content corresponds to the skills of the worker.

[0027] Data D2 is a list of workers on that day, which allows the number of workers to be ascertained. Furthermore, "Priority" in data D3 indicates the priority of the work process (line), with smaller numbers indicating higher priorities. Line priorities are set in advance, for example, according to losses.

[0028] The worker database 140 acquires various information about the workers to be assigned. In this embodiment, the worker database 140 stores the skills and work process priorities of each worker. The priorities are set in advance based on the worker's strengths and weaknesses in a task, how well the task balances with other work, and so on. The worker database 140 is realized 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, for example.

[0029] Fig. 3 is an explanatory diagram showing an example of information held in the operator database 140. The example shown in Fig. 3 shows an example in which, for each operator, the skills held by the operator and the priority of the work process for that operator are associated and held.

[0030] The work location distance database 150 stores 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 means the distance between the location where one work is performed and the location where another work is performed. The work location distance database 150 is also realized by a device such as a magnetic disk, and has a function of extracting and returning necessary data in response to a query to the work location distance database 150, etc.

[0031] Fig. 4 is an explanatory diagram showing an example of information stored in the work location distance database 150. The example shown in Fig. 4 shows an example in which distances between two work locations are associated with each other. 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, the worker assignment optimization means 170, which will be described later, performs optimization taking into account worker constraints, so the task selection optimization means 160 only determines whether a task can be assigned based on the number and skills of workers.

[0033] Specifically, the task selection optimization means 160 optimizes the selection of tasks to be prioritized at each time based on the skills and number of workers while advancing the time t at which tasks are assigned in the work process at predetermined intervals. That is, the task selection optimization means 160 focuses on each moment of time t and optimizes the task selection at that time t.

[0034] FIG. 5 is an explanatory diagram showing a specific example of a process for optimizing task selection. In the example shown in FIG. 5, there are three workers to be assigned, each with the skills indicated by {Skill A, Skill B}, {Skill B, Skill C}, and {Skill C}. The rectangular shapes shown in FIG. 5 indicate tasks a to f to be assigned. Here, tasks a, d, and e are assumed to require skill A, tasks b and f are assumed to require skill B, and task c is assumed to require skill C. Furthermore, line 1, on which tasks a, b, c, e, and f are performed, is assumed to be a high-priority line, and line d, on which task d is performed, is assumed to be a low-priority line.

[0035] First, at time t, there are tasks a, b, c, and d to be assigned. When assigning one task to one worker, since there are three workers, it is not possible to assign a worker to all tasks. Therefore, as a result of optimization (here, the result of selecting a task on a line with a high priority), task d, which has a low priority, 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 allocation. There are three tasks and three workers, but only one worker is capable of performing a task requiring skill A. As a result of optimization (here, a task on a high-priority line is selected), task d, which has a lower priority, is postponed even further (i.e., it becomes a candidate for allocation at time t+2).

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

[0038] The objective function used for optimization by the task selection optimization means 160 is determined depending on the optimization method. For example, when optimization is performed by quantum annealing, an Ising model may be used as the objective function. In the following explanation, an example is given in which optimization is performed by quantum annealing. However, the optimization method is not limited to quantum annealing, and optimization may be performed using, for example, a mathematical optimization solver.

[0039] The objective function (hereinafter, sometimes referred to as an optimization model) used to optimize the selection of tasks at time t is expressed, for example, by the following equation 1. In equation 1, x ｗ，ｗｒ is a binary variable that indicates whether or not the work w is to be performed by the worker wr, where a value of 1 indicates that the work is to be performed, and a value of 0 indicates that the work is not to be performed. ｗ represents the loss when operation w is not selected. ｗ can be said to be the priority of the work. For example, the "Priority" of the data D3 shown in FIG. ｗ That is, the operation selection optimization means 160 may optimize the operation selection based on the priority of the operation process.

[0040]

[0041] Furthermore, it is preferable that the task selection optimization means 160 optimizes the objective function so as to satisfy constraints related to the allocation of workers. Specifically, it is preferable that the task selection optimization means 160 optimizes the selection of tasks so as to satisfy a constraint that limits the assignment of multiple workers to one task (hereinafter referred to as a first constraint), a constraint that limits the assignment of multiple tasks to one worker (hereinafter referred to as a second constraint), and a constraint that limits the assignment of a worker to a task that requires skills that the worker does not have (hereinafter referred to as a third constraint).

[0042] The first constraint, the second constraint, and the third constraint are, for example, the binary variable x ｗ，ｗｒ These are expressed as the following formulas 2, 3, and 4, respectively, using SKILL. ｗｒ indicates a list of workers who have each skill.

[0043]

[0044] As described above, the task selection optimization means 160 may perform optimization based on the objective function and constraint conditions using a mathematical optimization solver such as linear programming, or may have the quantum annealing means 180 perform the optimization. When performing optimization using a mathematical optimization solver such as linear programming, it is sufficient to set an objective function and constraint conditions according to the optimization solver instead of the Ising model.

[0045] The worker assignment optimization means 170 optimizes the workers to be assigned to the optimized task selection, taking into account worker constraints. The worker assignment optimization means 170 then outputs a schedule of the tasks to which the workers have been assigned as a task plan. As described above, in this embodiment, the task selection optimization means 160 optimizes the task selection in advance, and the worker assignment optimization means 170 only needs to focus on optimizing the worker assignment, thereby reducing the costs required for optimization.

[0046] The objective function used for optimization by the worker assignment optimization means 170 is also determined depending on the optimization method. The following explanation will be given assuming that optimization is performed by quantum annealing. However, as with the task selection optimization means 160, the optimization method is not limited to quantum annealing, and optimization may be performed using, for example, a mathematical optimization solver.

[0047] The objective function (hereinafter sometimes referred to as the optimization model) used to optimize worker allocation is set according to the worker constraints to be considered, such as worker priority, equalization of worker workloads, and suppression of long-distance travel by workers in a short period of time.

[0048] The objective function (hereinafter referred to as a first function) whose value decreases as the priority of the worker is satisfied is expressed, for example, by the following formula 5. In formula 5, priority ｗ，ｗｒ indicates the priority of worker wr performing task w, with a smaller number indicating a higher priority.

[0049]

[0050] The objective function (hereinafter referred to as the second function) whose value decreases as the workload of the workers is equalized is expressed by, for example, the following equation 6. ｗ indicates the work time of work w.

[0051]

[0052] An objective function (hereinafter referred to as a third function) whose value decreases as the worker's long-distance movement in a short time is suppressed is expressed, for example, by the following formula 7. ｗ１，ｗ２ indicates the travel distance between the location where the work w1 is performed and the location where the work w2 is performed.

[0053]

[0054] The worker assignment optimization means 170 may optimize the workers to be assigned so as to minimize an objective function that includes at least one of the objective functions (first function, second function, and third function) shown above. For example, an objective function that includes the first function, second function, and third function shown above is expressed as in Equation 8 below. w in Equation 8 １ , w ２ and w ３ are adjusted and set by an administrator or the like to produce the desired results.

[0055] Objective function = w １ First function + w ２ Second function + w ３ Third function (Equation 8)

[0056] Furthermore, it is preferable that the worker assignment optimization means 170 optimizes the objective function so as to satisfy constraints related to worker assignment. For example, it is preferable that the worker assignment optimization means 170 optimizes worker assignment so as to satisfy, in addition to the first and third constraints described above, a constraint (hereinafter referred to as a fourth constraint) that restricts the assignment of multiple overlapping tasks to one worker.

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

[0058]

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

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

[0061] More specifically, the worker allocation optimization means 170 may perform control to output information accompanying the work, such as the deadline for completing each work and detailed information about each work.

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

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

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

[0065] Furthermore, when some or all of the components of the work plan optimization device 100 are realized by multiple information processing devices, circuits, etc., the multiple information processing devices, circuits, etc. may be centrally or decentralized. For example, the information processing devices, circuits, etc. may be realized as a client-server system, a cloud computing system, or the like, in a form in which they are connected via a communication network.

[0066] Next, a description will be given of the operation of the work plan optimization device 100 of this embodiment. Fig. 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 input of the data (step S101).

[0067] 7 is an explanatory diagram showing an example of visualized work schedule data. In the example shown in Fig. 7, work data scheduled to occur on all lines (such as work content, work location, time of occurrence, and work duration), data on work to be performed on that day, and line information data for that day are input, and the data is visualized by changing the display format for each work.

[0068] The task selection optimization means 160 optimizes the selection of tasks to be prioritized at each time based on the skills and number of workers while progressing through the time to assign tasks in the work process (step S102). More specifically, the task selection optimization means 160 selects tasks that are feasible and minimize the loss of the objective function, based on the skills and number of workers and the priority of the line. Note that, as described above, no actual assignment of workers is made at this stage.

[0069] Fig. 8 is an explanatory diagram showing an example of visualization of selected tasks. The example shown in Fig. 8 shows that the time to perform each task has been changed as a result of optimizing the selection of tasks based on the line information data shown in Fig. 7.

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

[0071] Fig. 9 is an explanatory diagram showing an example of a schedule to which workers are assigned. In the example shown in Fig. 9, workers are assigned to the selected tasks shown in Fig. 8, and the schedule is visualized in a different display mode for each worker.

[0072] The worker allocation optimization means 170 then outputs a schedule of the work to which the workers have been assigned as a work plan (work plan table) (step S104). The worker allocation optimization means 170 may output the work plan in a format such as a Gantt chart as shown in FIG.

[0073] Next, the operation of the task selection optimization means 160 of this embodiment will be described. Fig. 10 is a flowchart showing an example of the process for optimizing task selection. The process shown in Fig. 10 corresponds to the process of step S102 in Fig. 6. Here, the optimization process is performed with the task start time set to t and a predetermined interval set to 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 the above formula 1 and the constraints shown in the above formulas 2 to 4.

[0075] Next, the task selection optimization means 160 executes the optimization process (step S202). The task selection optimization means 160 may, for example, input the generated optimization model into the quantum annealing means 180 to cause the quantum annealing means 180 to execute the optimization process.

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

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

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

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

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

[0081] As described above, in this embodiment, the task selection optimization means 160 optimizes the selection of tasks to be prioritized at each time based on the skills and number of workers while progressing through the time t at which tasks are assigned in the work process. The worker assignment optimization means 170 then optimizes the workers to be assigned to the optimized task selection, taking into account worker constraints, and outputs a schedule of the tasks to which the workers have been assigned as a task plan. Thus, the task assignment plan can be optimized taking into account worker constraints.

[0082] In other words, in this embodiment, by creating a work plan by dividing it into work selection optimization, which determines when work should start, and worker assignment optimization, which determines who should be in charge, it is possible to significantly reduce the time required for work planning. This makes it possible to obtain and compare multiple optimal schedules for various cases in which input conditions such as worker and line loss values ​​are changed, which can contribute to management decisions.

[0083] For example, if a work plan is created simply using quantum annealing or mathematical optimization, the model becomes too large in practical cases, and it is often impossible to solve it in a realistic amount of time, resulting in insufficient accuracy. For example, if there are eight lines, 30 tasks per line, a total work time of 600 minutes, and five workers assigned to the line, 720,000 variables would be required. In contrast, this embodiment divides the problem and performs optimization without compromising overall optimization as much as possible, thereby significantly reducing the time required for work planning while eliminating waste, bias, and losses due to personal differences and manual labor.

[0084] Furthermore, for example, when an unexpected task occurs, workers may start work without understanding the priority, causing the line to be stopped for longer than necessary and reducing operation. On the other hand, in this embodiment, the time required for work planning can be shortened, thereby suppressing the reduction in operation.

[0085] Next, an overview of the present invention will be described. FIG. 12 is a block diagram showing an overview of a 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 to assign each task in a plurality of work processes (e.g., a production line) to a target worker. The work plan optimization device includes: a task selection optimization means 81 (e.g., task selection optimization means 160) that optimizes the selection of a task to be prioritized at each time point based on the skills and number of workers of the worker while advancing the time for assigning the tasks in the work process; an assignment optimization means 82 (e.g., worker assignment optimization means 170) that optimizes the workers to be assigned to the optimized task selection in consideration of worker constraints; and an output means 83 (e.g., worker assignment optimization means 170) that outputs a schedule of the tasks to which the workers have been assigned as a work plan.

[0086] Such a configuration allows for an optimized work plan to be assigned taking into account the constraints of workers.

[0087] In addition, the allocation optimization means 82 may optimize the workers to be assigned so as to minimize an objective function (e.g., Equation 8 shown above) that includes at least one of a first function (e.g., Equation 5 shown above) whose value decreases as the worker priorities are met, a second function (e.g., Equation 6 shown above) whose value decreases as the burden on workers is equalized, and a third function (e.g., Equation 7 shown above) whose value decreases as long-distance travel by workers in a short period of time is suppressed.

[0088] In addition, the task selection optimization means 81 may optimize the task selection so as to satisfy a first constraint (e.g., Equation 2 shown above) that limits the assignment of multiple workers to one task, a second constraint (e.g., Equation 3 shown above) that limits the assignment of multiple tasks to one worker, and a third constraint (e.g., Equation 4 shown above) that limits the assignment of a worker to a task that requires skills that the worker does not have.

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

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

[0091] A part or all of the above-described embodiments can be described as, but not limited to, the following supplementary notes.

[0092] (Supplementary Note 1) A work plan optimization device that optimizes a plan to assign each task in a plurality of work processes to a target worker, comprising: a task selection optimization means that optimizes a selection of tasks to be prioritized at each time point based on the skills and number of workers of the worker while progressing the time to assign tasks in the work process; an assignment optimization means that optimizes the workers to be assigned to the optimized task selection in consideration of the constraints of the worker; and an output means that outputs a schedule of the tasks to which the workers are assigned as a work plan.

[0093] (Supplementary Note 2) The work plan optimization device described in Supplementary Note 1, wherein the allocation optimization means optimizes the workers to be assigned so as to minimize an objective function including at least one of a first function whose value decreases as the priority of the workers is satisfied, a second function whose value decreases as the burden on the workers is equalized, and a third function whose value decreases as long-distance movement of the workers in a short period of time is suppressed.

[0094] (Supplementary Note 3) The work plan optimization device according to Supplementary Note 2, wherein the allocation optimization means optimizes the workers to be assigned so as to satisfy a constraint that restricts the assignment of multiple overlapping tasks to one worker.

[0095] (Supplementary Note 4) The work plan optimization device according to Supplementary Note 2 or Supplementary Note 3, wherein the priority of each worker is set in advance based on at least one of the worker's strengths or weaknesses in a task and the balance with other tasks.

[0096] (Supplementary Note 5) The work plan optimization device described in any one of Supplementary Note 1 to Supplementary Note 4, wherein the work selection optimization means optimizes the selection of work so as to satisfy a first constraint that limits the assignment of multiple workers to one work, a second constraint that limits the assignment of multiple works to one worker, and a third constraint that limits the assignment of a worker to a work that requires skills that the worker does not have.

[0097] (Supplementary Note 6) The work plan optimization device according to any one of Supplementary Note 1 to Supplementary Note 5, wherein the work selection optimization means optimizes the selection of the work based on the priority of the work process.

[0098] (Supplementary Note 7) The work plan optimization device described in any one of Supplementary Note 1 to Supplementary Note 6, wherein the work selection optimization means advances the time to assign work in the work process at predetermined intervals, and optimizes the selection of work to be prioritized at each time based on the skills of workers and the number of workers.

[0099] (Supplementary Note 8) The work plan optimization device according to any one of Supplementary Note 1 to Supplementary Note 7, wherein the work selection optimization means selects the work so as to minimize an objective function that represents a total of losses that would occur if each worker did not perform each work.

[0100] (Supplementary Note 9) The work plan optimization device according to Supplementary Note 8, wherein the work selection optimization means selects work that is feasible and minimizes loss of the objective function, based on the skills and number of workers and the priority of the line.

[0101] (Supplementary Note 10) The work plan optimization device according to any one of Supplementary Note 1 to Supplementary Note 9, wherein one or both of the work selection optimization means and the assignment optimization means cause a quantum annealing machine to perform the optimization.

[0102] (Supplementary Note 11) The work plan optimization device according to any one of Supplementary Note 1 to Supplementary Note 10, wherein the allocation optimization means controls the display means to display the allocation of workers.

[0103] (Supplementary Note 12) The work plan optimization device according to Supplementary Note 11, wherein the allocation optimization means performs control to display the allocation of workers in a Gantt chart format.

[0104] (Supplementary Note 13) The work plan optimization device according to Supplementary Note 11 or Supplementary Note 12, wherein the allocation optimization means performs control to change the display mode for each work.

[0105] (Supplementary Note 14) The work plan optimization device described in any one of Supplementary Note 1 to Supplementary Note 13, wherein the allocation optimization means performs control to notify the start of work by sending a message to the mobile device carried by the worker in charge of each work, a predetermined time before the work start, based on the worker allocation.

[0106] (Supplementary Note 15) The work plan optimization device described in any one of Supplementary Note 1 to Supplementary Note 14, wherein the allocation optimization means controls output of a deadline for performing each task to a mobile terminal carried by a worker in charge of each task as information accompanying the assigned task.

[0107] (Supplementary Note 16) The work plan optimization device described in any one of Supplementary Note 1 to Supplementary Note 15, wherein the allocation optimization means controls output of detailed information about each task to a mobile terminal carried by a worker in charge of each task as information accompanying the assigned task.

[0108] (Supplementary Note 17) A work plan optimization method for optimizing a plan to assign each task in a plurality of work processes to a target worker, the work plan optimization method comprising: optimizing a selection of tasks to be prioritized at each time based on the skills and number of workers of the task while advancing the time to assign tasks in the task process; optimizing the workers to be assigned to the optimized task selection in consideration of the constraints of the workers; and outputting a schedule of the tasks to which the workers are assigned as a work plan.

[0109] (Supplementary Note 18) The work plan optimization method according to Supplementary Note 17, wherein the workers to be assigned are optimized so as to minimize an objective function including at least one of a first function whose value decreases as the priority of the workers is satisfied, a second function whose value decreases as the burden on the workers is equalized, and a third function whose value decreases as long-distance travel of the workers in a short period of time is suppressed.

[0110] (Supplementary Note 19) A program storage medium storing a work plan optimization program applied to a computer that optimizes a plan for allocating each task in a plurality of work processes to a target worker, the program causing the computer to execute: a work schedule optimization process that optimizes selection of tasks to be prioritized at each time based on the skills and number of workers while advancing the time for allocating tasks in the work process; an assignment optimization process that optimizes the workers to be assigned to the optimized selection of tasks, taking into consideration constraints on the workers; and an output process that outputs a schedule of tasks to which the workers are assigned as a work plan.

[0111] (Supplementary Note 20) The program storage medium according to Supplementary Note 19 stores a work plan optimization program in a computer, which optimizes the workers to be assigned so as to minimize an objective function including at least one of a first function whose value decreases as the priority of the workers is satisfied, a second function whose value decreases as the burden on the workers is equalized, and a third function whose value decreases as the movement of long distances in a short period of time of the workers is suppressed.

[0112] (Supplementary Note 21) A work plan optimization program applied to a computer that optimizes a plan to assign each task in a plurality of work processes to a target worker, the work plan optimization program causing the computer to execute: a work schedule optimization process that optimizes a selection of tasks to be prioritized at each time based on the skills and number of workers of the task while advancing the time to assign tasks in the task process; an assignment optimization process that optimizes the workers to be assigned to the optimized selection of tasks, taking into consideration constraints on the workers; and an output process that outputs a schedule of tasks to which the workers are assigned as a work plan.

[0113] (Supplementary Note 22) The work plan optimization program according to Supplementary Note 21, wherein the computer optimizes the workers to be assigned so as to minimize, in the assignment optimization process, an objective function including at least one of a first function whose value decreases as the priority of the workers is satisfied, a second function whose value decreases as the burden on the workers is equalized, and a third function whose value decreases as long-distance movement of the workers in a short period of time is suppressed.

[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-described embodiments and examples. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention.

[0115] This application claims priority based on Japanese Patent Application No. 2023-21319, filed February 15, 2023, the disclosure of which is incorporated herein in its entirety.

[0116] The present invention is suitable for use in a work plan optimization device that optimizes work plans for allocating workers to multiple work processes. For example, the present invention can be applied to cases in which multiple manual tasks occur simultaneously in a factory with multiple production lines.

[0117] 100 Work plan optimization device 110 Control means 120 Display means 130 Operation / input means 140 Worker database 150 Work location distance database 160 Work selection optimization means 170 Worker allocation optimization means 180 Quantum annealing means

Claims

1. A work plan optimization device that optimizes a plan to assign each work in a plurality of work processes to a target worker, a task selection optimization means for optimizing the selection of a task to be prioritized at each time point based on the skills of the workers and the number of the workers while advancing the time points for assigning tasks in the task process; an allocation optimization means for optimizing the workers to be assigned to the optimized selection of tasks, taking into consideration constraints on the workers; an output means for outputting a schedule of the work to which the workers are assigned as a work plan; A work plan optimization device characterized by:

2. The allocation optimization means optimizes the workers to be assigned so as to minimize an objective function including at least one of a first function whose value decreases as the priority of the workers is satisfied, a second function whose value decreases as the burden on the workers is equalized, and a third function whose value decreases as the movement of long distances in a short period of time of the workers is suppressed. The work plan optimization device according to claim 1 .

3. The allocation optimization means optimizes the workers to be assigned so as to satisfy the constraints that restrict the assignment of multiple overlapping tasks to one worker. The work plan optimization device according to claim 2.

4. The priority of each worker is set in advance based on at least one of their strengths and weaknesses in the task and the balance with other tasks.

4. The work plan optimization device according to claim 2 or 3.

5. The task selection optimization means optimizes task selection so as to satisfy a first constraint that limits the assignment of multiple workers to one task, a second constraint that limits the assignment of multiple tasks to one worker, and a third constraint that limits the assignment of a worker to a task that requires skills that the worker does not have.

3. The work plan optimization device according to claim 1 or 2.

6. The task selection optimization means optimizes task selection based on the priority of the task process.

3. The work plan optimization device according to claim 1 or 2.

7. The task selection optimization means optimizes the selection of tasks to be prioritized at each time point based on the skills and number of workers while advancing the time to assign tasks in the work process at predetermined intervals.

3. The work plan optimization device according to claim 1 or 2.

8. The task selection optimization means selects tasks so as to minimize an objective function that represents the total loss that occurs if each worker does not perform each task.

3. The work plan optimization device according to claim 1 or 2.

9. A work plan optimization method for optimizing a plan for assigning each work in a plurality of work processes to a target worker, comprising: optimizing the selection of the work to be prioritized at each time based on the skills of the workers and the number of workers while advancing the time at which the work is assigned in the work process; optimizing the workers to be assigned to the optimized selection of tasks, taking into account the constraints of the workers; The schedule of the work to which the workers are assigned is output as a work plan. A work plan optimization method comprising:

10. On the computer, A work plan optimization program applied to a computer that optimizes a plan for assigning each work in a plurality of work processes to a target worker, comprising: The computer, a work schedule optimization process for optimizing the selection of a work to be prioritized at each time point based on the skills of the workers and the number of workers, while advancing the time points at which the work is assigned in the work process; an allocation optimization process that optimizes the workers to be assigned to the optimized selection of tasks, taking into account the constraints on the workers; and An output process for outputting a schedule of the work to which the workers are assigned as a work plan. A work plan optimization program for executing the above.