Information processing device, assignment program, and assignment method

Abstract

The challenge is to optimize the allocation of workers to specific tasks. [Solution] The information processing device includes an estimation unit that estimates the future elapsed time and processing speed of a target worker by inputting the target worker's past elapsed time and processing speed into an estimation model trained on the elapsed time since the worker started work and the processing speed that indicates the amount of work per unit time, and an assignment unit that assigns the target worker to the target work based on the elapsed time and processing speed of the target worker estimated by the estimation unit and the total amount of work for the target work.

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an assignment program, and an assignment method. [Background technology]

[0002] When a new project is ordered, workers are assigned based on the details of the project and the workers' past performance. For example, prior art related to worker assignment is disclosed in Patent Documents 1 and 2.

[0003] Patent Document 1 discloses a technique that determines the work period requiring workers based on the start and end dates of the work, searches for workers with available tasks during the work period using a predetermined calculation formula, and then assigns the workers with the skills and experience best suited to the work to the searched workers.

[0004] Patent Document 2 discloses a technique for selecting workers based on their skill level, work performance, and the difficulty level of the work described in the work request form. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2006-318331 [Patent Document 2] Japanese Patent Publication No. 2014-191377 [Overview of the project] [Problems that the invention aims to solve]

[0006] For example, during the process of a worker actually performing a new task, their proficiency may improve, and their work speed may change. In this case, a discrepancy may arise between the worker's initially assumed skills and their actual skills.

[0007] In contrast, in the above-described prior art, the allocation of workers is based on the past performance of past workers and cannot follow the skills of workers that change over time as described above, and the allocation of workers has not been optimized.

[0008] One aspect aims to provide an information processing apparatus, an allocation program, and an allocation method that can optimize the allocation of workers to tasks.

Means for Solving the Problem

[0009] An information processing apparatus according to one aspect includes an estimation unit that estimates the future elapsed time and processing speed of a target worker by inputting the past elapsed time and processing speed of the target worker into an estimation model trained based on the elapsed time since the worker started working and the performance of the processing speed indicating the amount of work per unit time, and an allocation unit that allocates the target worker to the target task based on the elapsed time and processing speed of the target worker estimated by the estimation unit and the total amount of work of the target task.

[0010] An allocation program according to one aspect causes a computer to estimate the future elapsed time and processing speed of a target worker by inputting the past elapsed time and processing speed of the target worker into an estimation model trained based on the elapsed time since the worker started working and the performance of the processing speed indicating the amount of work per unit time, and execute a process of allocating the target worker to the target task based on the elapsed time and processing speed of the estimated target worker and the total amount of work of the target task.

[0011] An allocation method according to one aspect includes a process in which a computer estimates the future elapsed time and processing speed of a target worker by inputting the past elapsed time and processing speed of the target worker into an estimation model trained based on the elapsed time since the worker started working and the performance of the processing speed indicating the amount of work per unit time, and executes a process of allocating the target worker to the target task based on the elapsed time and processing speed of the estimated target worker and the total amount of work of the target task.

Effect of the Invention

[0012] According to one embodiment, the allocation of workers to tasks can be optimized.

Brief Description of the Drawings

[0013] [Figure 1] It is a diagram for explaining the processing of the training phase. [Figure 2] It is a diagram showing an example of teacher data. [Figure 3] It is a diagram for explaining the processing of the estimation phase. [Figure 4] It is a diagram for explaining the processing of the allocation phase. [Figure 5] It is a diagram (1) showing an example of schedule information. [Figure 6] It is a diagram (2) showing an example of schedule information. [Figure 7] It is a functional block diagram showing the configuration of the information processing apparatus according to this embodiment. [Figure 8] It is a flowchart showing the processing flow of the information processing apparatus of this embodiment. [Figure 9] It is a diagram for explaining an example of hardware configuration.

Mode for Carrying Out the Invention

[0014] Hereinafter, embodiments of the information processing apparatus, allocation program, and allocation method disclosed in the present application will be described in detail based on the drawings. Note that the invention is not limited by this embodiment. Also, the same elements are denoted by the same reference numerals, and duplicate descriptions are omitted as appropriate, and each embodiment can be appropriately combined within a non - contradictory range.

[0015] (First Embodiment) (Explanation of Processing of Information Processing Apparatus) The processing of the information processing device according to this embodiment will now be described. In the following description, the information processing device according to this embodiment will be referred to as "information processing device 100". For example, the information processing device 100 performs the training phase processing, the estimation phase processing, the assignment phase processing, and the schedule information review processing, respectively.

[0016] (Processing during the training phase) First, an example of the training phase processing performed by the information processing device 100 will be described. Figure 1 is a diagram illustrating the training phase processing. The information processing device 100 trains the estimation model 50 using the training dataset 141.

[0017] The estimated model 50 is a machine learning model such as a neural network, support vector machine, random forest, or logistic regression. In this embodiment, the estimated model 50 is described as a neural network, but it is not limited to this.

[0018] The training dataset 141 contains multiple training data points. The training data is historical data showing the relationship between the elapsed time from the start of a task and the processing speed when a particular worker has performed a single task in the past. Processing speed is the amount of work a worker can process per unit of time, and the unit is "workload / hour". For example, the training dataset 141 contains training data for all workers.

[0019] Figure 2 shows an example of training data. The vertical axis of graph G1 corresponds to processing speed, and the horizontal axis corresponds to elapsed time. An elapsed time of "0" represents the start time of the task in past performance. In the example shown in Figure 2, the end time of the task in past performance is set to "20", but this is not the only option. For example, training data L1 is training data generated based on the past performance of worker U1. Training data L2 is training data generated based on the past performance of worker U2. Training data L3 is training data generated based on the past performance of worker U3. Training data L4 is training data generated based on the past performance of worker U4. Training data L5 is training data generated based on the past performance of worker U5. Training data L6 is training data generated based on the past performance of worker U6. Training data L7 is training data generated based on the past performance of worker U7.

[0020] In Figure 2, for the sake of explanation, only training data L1 to L7 are shown, but other training data will be registered in training dataset 141.

[0021] Here, the information processing device 100 may use any existing technology to train the estimation model 50, but one example is shown below. For example, the information processing device 100 trains the estimation model 50 based on backpropagation. In backpropagation, when input data (explanatory variables) is input to the estimation model 50, the parameters of the estimation model 50 are updated so that the values ​​output from the estimation model 50 approach the correct data (target variable).

[0022] For example, the information processing device 100 uses the relationship between the elapsed time from the start time to a predetermined time and the processing speed, which is part of the relationship between the elapsed time from the start time to a predetermined time and the processing speed included in the training data of the training dataset 141, as an explanatory variable, and the relationship between the elapsed time after the predetermined time and the processing time as the dependent variable. In this way, by inputting the relationship between the elapsed time from the start time to a predetermined time and the processing speed into the estimation model 50, the estimation model 50 can be trained to estimate the relationship between the elapsed time after the predetermined time and the processing time.

[0023] (Processing during the estimation phase) Next, an example of the estimation phase processing performed by the information processing device 100 will be described. Here, workers A, B, C, and D will be used for the explanation. Workers A to D may be the same workers as workers U1 to U7 mentioned above, or they may be different workers. The information processing device 100 uses the trained estimation model 50 to estimate the relationship between future elapsed time and processing time for workers A, B, C, and D, based on the relationship between past elapsed time and processing time for each worker. For example, the relationship between past elapsed time and processing time for workers A, B, C, and D is specified in advance.

[0024] Figure 3 is a diagram illustrating the processing of the estimation phase. In the example shown in Figure 3, the information processing device 100 inputs data on the relationship LA-1 between worker A's past elapsed time and processing speed into the estimation model 50, thereby obtaining the relationship LA-2 between worker A's future elapsed time and processing speed.

[0025] The relationship LA-1 between worker A's past elapsed time and processing speed, and the relationship LA-2 between worker A's future elapsed time and processing speed, will be explained using graph G2. The explanation of the vertical and horizontal axes of graph G2 is the same as for graph G1. For example, the line segment from elapsed time 0 to 20 represents the relationship LA-1 between worker A's past elapsed time and processing speed. The line segment from elapsed time 20 onwards represents the relationship LA-2 between worker A's future elapsed time and processing speed.

[0026] Although illustration is omitted, the process of estimating the relationship between the elapsed time and processing time of workers B to D in the future is the same as the process of estimating the relationship between the elapsed time and processing time of worker A in the future. That is, the information processing apparatus 100 estimates the relationship between the elapsed time and processing time of workers B to D in the future by inputting the relationship between the past elapsed time and processing time of workers B to D into the estimation model 50, respectively.

[0027] (Process of Assignment Phase) Next, an example of the process of the assignment phase executed by the information processing apparatus 100 will be described. The information processing apparatus 100 acquires data of projects (tasks) to be assigned to workers. For example, the project data includes the total workload of the project, the start date of the project, and the like.

[0028] The information processing apparatus 100 calculates the workload from the start time to the end time of the work of workers A to D using the estimation result of the estimation phase, and calculates the total workload of the projects of workers A to D. Here, the start time in the assignment phase is defined as "start time t s ". The end time in the assignment phase is defined as "end time t e ". When the end time of the work in the past performance as the teacher data is 20, the start time t s is 20. The information processing apparatus 100 sets the start time t s to match the past performance work end time, and repeatedly calculates the total workload of workers A to D while gradually changing the end time t e . For each worker, different values are set for the start time t s . For example, the information processing apparatus 100 sets the initial value of the end time t e as "t ' s ", and calculates the total workload of workers A to D while increasing the value by "1" each time. As a result, the relationship between the start time t s , the end time t e , and the total workload of workers A to D is different for different end times t eIt is calculated each time. The information processing device 100 determines that the sum of the workloads of workers A to D equals the total workload of the project by the end time t. e The information processing device 100 identifies the end time t. e The process of calculating the total workload of workers A-D while modifying the parameters is repeatedly executed until the total workload of workers A-D equals the total workload of the project by the end time t. e Identify the total workload of workers A through D, which will be the total workload of the project by the end time t. e This will be referred to as the "minimum completion time."

[0029] Furthermore, the information processing device 100 will determine the end time t when the sum of the workloads of workers A to D is equal to or greater than the total workload of the project. e The minimum completion time t e This may be identified as the shortest completion time.

[0030] Figure 4 is a diagram illustrating the processing of the assignment phase. In Figure 4, the start time of worker A's work t s From the end time of the work e This section explains how to calculate the amount of work up to a certain point. The relationship LA-1 between worker A's past elapsed time and processing speed, and the relationship LA-2 between worker A's future elapsed time and processing speed, are combined and expressed as line segment LA. The information processing device 100 calculates the amount of work up to a certain point. s From end time t e By integrating the line segment LA up to the start time t, we can obtain the result. s From end time t e Calculate the amount of work done by worker A up to t. s This is set to match the worker's past work completion time.

[0031] Although not shown in the diagram, the information processing device 100 has a start time t s From end time t e The workload of workers B through D up to that point is calculated in the same way as the workload of worker A.

[0032] For example, the information processing device 100 has a termination time t. eIf the value is set to "60", and the sum of the workloads of workers A through D equals the total workload of the project, the shortest completion time will be determined as "60".

[0033] The information processing device 100 generates schedule information for workers A to D, as shown in Figure 5, based on the identified shortest completion time. Figure 5 is Figure (1), which shows an example of schedule information. The schedule information corresponds to the assignment results of workers A to D to the project.

[0034] In Figure 5, the schedule information 30 is set with the total workload, start date, completion date, and workload for workers A to D. The information processing device 100 sets data to be notified in advance for the total workload and start date. The information processing device 100 sets the completion date based on the start date, the shortest completion time, and information outside of working hours such as holidays and break times.

[0035] The information processing device 100 sets the workload for workers A to D from the start time to the shortest possible completion time. For example, the workloads for workers A, B, C, and D from the start date to the completion date are 600, 300, 300, and 800, respectively. In the example shown in Figure 5, the sum of the workloads for workers A to D (2000) is the same as the total workload of 2000. The start time corresponds to the start date mentioned above. The shortest possible completion time corresponds to the completion date mentioned above. The completion date is merely the planned completion date.

[0036] (Reviewing schedule information) Incidentally, the information processing device 100 may collect the relationship between elapsed time and processing speed during the process in which workers A to D proceed with the project according to the schedule information 30, and may revise the schedule information after a predetermined time has elapsed since the start of the project. In the following, the information processing device 100 will start at t s Next, we will explain the case in which the schedule information 30 is reviewed at the elapsed time t1, after a predetermined time has elapsed.

[0037] First, the information processing device 100 inputs the relationship between worker A's past elapsed time and processing speed into the estimation model 50 to estimate the relationship between worker A's future elapsed time and processing speed. Similarly, the information processing device 100 inputs the relationship between workers B to D's future elapsed time and processing time into the estimation model 50 to estimate the relationship between workers B to D's future elapsed time and processing time. For example, past elapsed time is calculated from the start time t s This corresponds to the elapsed time t1. Future elapsed time refers to the period after elapsed time t1.

[0038] The explanation of other processes by which the information processing device 100 estimates the relationship between the elapsed time and processing time for workers A to D is the same as the process described in the estimation phase above.

[0039] Next, the information processing device 100 calculates the start time t from the total workload of the project. s The remaining total workload for the project is calculated by subtracting the sum of the workloads of workers A through D at elapsed time t1.

[0040] The information processing device 100 calculates the completion time t of the work of workers A to D from the elapsed time t1. e The process of calculating the amount of work up to the end time t e The process is repeated while making adjustments, and the total amount of work done by workers A-D equals the remaining total amount of work for the project by the end time t. e Identify the end time t. e This corresponds to the shortest possible completion time.

[0041] The description of other processes by which the information processing device 100 determines the shortest completion time which will be the total remaining work for the project is the same as the process of the allocation phase described above.

[0042] The information processing device 100 generates schedule information for workers A to D, as shown in Figure 6, based on the shortest completion time identified again. Figure 6 is Figure (2), which shows an example of schedule information.

[0043] In Figure 6, the schedule information 31 includes the total workload, start date, review date, completion date, and the workload of workers A to D. The explanation for the total workload and start date is the same as explained in Figure 5. The review date is the day t1 elapsed from the start date, taking into account information outside of working hours. The information processing device 100 resets the completion date based on the review date, the newly calculated shortest completion time, and information outside of working hours.

[0044] The information processing device 100 sets the workload for workers A to D from the start time to the elapsed time t1, and the workload from the elapsed time t1 to the shortest completion time. For example, the workloads for workers A, B, C, and D from the start date to the review date are 50, 20, 30, and 100, respectively. The workloads for workers A, B, C, and D from the review date to the completion date are 550, 380, 170, and 700, respectively. The elapsed time t1 corresponds to the review date.

[0045] The information processing device 100 repeatedly performs the above-mentioned review of the schedule information at predetermined intervals.

[0046] (Functional configuration of the information processing device 100) Next, an example of the configuration of the information processing device 100 will be described. Figure 7 is a functional block diagram showing the configuration of the information processing device according to this embodiment. As shown in Figure 7, the information processing device 100 has a communication unit 110, an input unit 120, a display unit 130, a storage unit 140, and a control unit 150.

[0047] The communication unit 110 performs data communication with external devices, etc.

[0048] The input unit 120 inputs various types of information to the control unit 150 of the information processing device 100. The input unit 120 can be a keyboard, mouse, touch panel, etc. For example, the user operates the input unit 120 to input data regarding the relationship between each worker's past elapsed time and processing time. The user also operates the input unit 120 to input data on the projects (tasks) to which the workers are assigned.

[0049] The display unit 130 displays information output from the control unit 150 of the information processing device 100. For example, the display unit 130 displays schedule information 30, 31, etc., as described in Figures 5 and 6.

[0050] The memory unit 140 stores the estimated model 50 and the training dataset 141. The memory unit 140 is implemented using memory, a hard disk, or the like.

[0051] Estimated Model 50 is a machine learning model such as a neural network, support vector machine, random forest, or logistic regression. Further explanation of Estimated Model 50 is the same as the explanation of Estimated Model 50 above.

[0052] The training dataset 141 contains multiple training data points. The training data is historical data showing the relationship between the elapsed time from the start of a task and the processing speed when a particular worker has performed a single task in the past. The explanation of the training dataset 141 is the same as the explanation of the training dataset 141 described above.

[0053] The control unit 150 is a processing unit that oversees the entire information processing device 100 and is implemented by, for example, a processor. The control unit 150 includes a training unit 151, an estimation unit 152, an assignment unit 153, and a work progress management unit 154.

[0054] The training unit 151 trains the estimated model 50 based on the training dataset 141. Further details regarding the training unit 151 are the same as those for the training phase described above. In the following description, the estimated model 50 trained by the training unit 151 will simply be referred to as the estimated model 50.

[0055] The estimation unit 152 obtains data from the input unit 120 that shows the relationship between the past elapsed time and processing time of the target workers (for example, workers A to D). The estimation unit 152 inputs the data showing the relationship between the past elapsed time and processing time of the target workers into the estimation model 50 to estimate data on the relationship between the future elapsed time and processing speed of the target workers.

[0056] The estimation unit 152 outputs to the allocation unit 153, as estimation result data, data that integrates the relationship between the target worker's past elapsed time and processing time, and the relationship between the target worker's future elapsed time and processing time. Further explanation of the estimation unit 152 is the same as the explanation of the processing in the estimation phase described above.

[0057] The assignment unit 153 acquires data on the project (task) to which the worker will be assigned. The assignment unit 153 acquires the start time t of the task for the target worker. s From the end time of the work e The process of calculating the amount of work up to the end time t e The process is repeated while making adjustments, and the total amount of work done by the target workers reaches the total amount of work for the project by the end time t e Identify the end time t. e This corresponds to the shortest possible completion time.

[0058] The allocation unit 153 generates schedule information based on the identified shortest completion time and outputs the generated schedule information to the display unit 130 for display. Further details regarding the allocation unit 153 are the same as those described above for the processing of the allocation phase.

[0059] Furthermore, the estimation unit 152 and the assignment unit 153 revise the schedule information by executing the estimation phase processing and the assignment phase processing again after a predetermined time has elapsed since the start of the project, as the target worker proceeds with the project according to the schedule information. The explanation of the process by which the estimation unit 152 and the assignment unit 153 revise the schedule information is the same as the explanation of the schedule information revision process described above.

[0060] The work progress management unit 154 manages the workload and progress of the target project. For example, the work progress management unit 154 acquires data on the relationship between the elapsed time since the start of the project and the processing speed of the target worker from the target worker's terminal device or the terminal device of the manager who manages the target worker, and stores the acquired data in the storage unit 140. The work progress management unit 154 may also acquire data on the relationship between the elapsed time since the start of the project and the processing speed of the target worker from the input unit 120 or the like.

[0061] Furthermore, the work progress management unit 154 may identify the timing for reviewing the schedule information and request the estimation unit 152 and the assignment unit 153 to review the schedule information. The work progress management unit 154 may also refer to a timer (not shown) and determine that the timing for reviewing the schedule information is when a predetermined time has elapsed since the assignment unit 153 last generated the schedule information.

[0062] The generation unit 155 generates schedule information for workers A to D, as shown in Figure 5, based on the final completion time identified by the above process. The generation unit 155 may also output the generated schedule information to the display unit 130 for display.

[0063] (Process flow) Next, an example of the processing flow of the information processing device according to this embodiment will be described. Figure 8 is a flowchart of the processing flow of the information processing device according to this embodiment. As shown in Figure 8, the training unit 151 of the information processing device 100 trains the estimation model 50 based on the training dataset 141 (step S101).

[0064] The information processing device 100 acquires data showing the relationship between the target worker's past elapsed time and processing time, as well as project data (step S102). The estimation unit 152 of the information processing device 100 inputs the data showing the relationship between the target worker's past elapsed time and processing time into the estimation model 50 to estimate the relationship between the target worker's future elapsed time and processing time (step S103).

[0065] The allocation unit 153 of the information processing device 100 identifies the shortest completion time in which the sum of the workloads of the target workers equals the total workload of the project, based on the estimated result data and the total workload of the project (step S104).

[0066] The allocation unit 153 generates schedule information based on the shortest completion time (step S105). The allocation unit 153 displays the schedule information on the display unit 130 (step S106).

[0067] If the completion date has passed (step S107, Yes), the information processing device 100 terminates the process. On the other hand, if the completion date has not passed (step 107, No), the information processing device 100 proceeds to step S108.

[0068] The work progress management unit 154 of the information processing device 100 determines whether a predetermined time has elapsed since the time the previous schedule information was generated (step S108). If the predetermined time has not elapsed since the time the previous schedule information was generated (step S108, No), the work progress management unit 154 proceeds to step S107. On the other hand, if the predetermined time has elapsed since the time the previous schedule information was generated (step S108, Yes), the work progress management unit 154 proceeds to step S109.

[0069] The estimation unit 152 acquires data showing the relationship between the target worker's past elapsed time and processing speed (step S109), and proceeds to step S103. The target worker's past refers to the period from the start of the project to the present.

[0070] (effect) Next, the effects of the information processing device 100 according to this embodiment will be described. The information processing device 100 inputs the past elapsed time and processing speed of target workers into an estimation model 50 trained on the training dataset 141, estimates the future elapsed time and processing speed of target workers, and assigns target workers to the target project based on the estimated elapsed time and processing speed of target workers and the total workload of the target work. This makes it possible to optimize the assignment of workers to work in accordance with the processing speed of workers that changes over time.

[0071] The information processing device 100 repeatedly performs a process to calculate the workload of each target worker by integrating the relationship between the elapsed time and processing speed of each target worker from the start time to the end time, while adjusting the end time. The end time at which the sum of the workloads of the target workers equals the total workload is identified as the time until the target work is completed. This makes it possible to identify the shortest possible completion time for the project.

[0072] (Other processing) The processing performed by the information processing device 100 described above is just one example, and the information processing device 100 may perform other processing. Other processing (1) to (4) of the information processing device 100 will be described below.

[0073] First, let's describe the other processing (1) of the information processing device 100. The information processing device 100 may also perform training phase processing and estimation phase processing, taking into further consideration the difficulty of the work.

[0074] The training phase in other processing (1) will now be explained. The training dataset 141 used in this training phase has a task difficulty level assigned to it, in addition to the training data explained in Figure 2. For example, the task difficulty level is a numerical value from 1 to 10, with a higher number indicating a higher task difficulty level.

[0075] The training unit 151 of the information processing device 100 trains an estimation model 50 by backpropagation, using the following data as explanatory and dependent variables from the relationship between the elapsed time over all time periods and the processing speed included in the training data of the training dataset 141. Explanatory variables: Relationship between elapsed time from start time to predetermined time and processing speed, and difficulty of the task. Dependent variable: Relationship between elapsed time after a predetermined time and processing time.

[0076] The inference phase in other processing (1) will now be described. The estimation unit 152 of the information processing device 100 acquires data showing the relationship between the past elapsed time and processing time for the target workers (for example, workers A to D), and the difficulty level of the project.

[0077] The estimation unit 152 inputs data showing the relationship between the target worker's past elapsed time and processing time, as well as the difficulty level of the task, into the estimation model 50 to estimate data showing the relationship between the target worker's future elapsed time and processing speed according to the difficulty level of the task.

[0078] According to the other process (1), it becomes possible to perform estimations that take into account the difficulty of the work.

[0079] Next, the other processing (2) of the information processing device 100 will be described. The information processing device 100 accepts the setting of the cost per unit amount of work for the target worker and the threshold for the shortest completion time. There may be multiple target workers. Based on the cost per unit amount set for each target worker and the amount of work of the multiple target workers, the information processing device 100 calculates the cost for each of the multiple target workers and selects the target worker to perform the project under the condition that the total cost is minimized and the shortest completion time does not exceed the threshold.

[0080] For example, the estimation unit 152 of the information processing device 100 outputs to the assignment unit 153, as estimation result data, which integrates the relationship between the past elapsed time and processing time of each of the M target workers, and the relationship between the future elapsed time and processing time of each target worker.

[0081] The allocation unit 153 of the information processing device 100 selects N (M>N) target workers from M target workers randomly or according to a predetermined selection algorithm. The allocation unit 153 determines the workload and shortest completion time for each of the N target workers by performing the same processing as in the allocation phase described above. The allocation unit 153 calculates the total cost based on the workload of each of the N target workers and the cost per unit of workload for each worker.

[0082] The allocation unit 153 repeatedly performs the above process to calculate the total cost and the shortest completion time for N target workers in different combinations. Based on the calculation results, the allocation unit 153 selects the workers to perform the project under the conditions that the total cost is minimized and the shortest completion time does not exceed a threshold.

[0083] Furthermore, the allocation unit 153 may calculate the workload by integration, taking into account the periods during which the selected N target workers can join and leave, and identify the cost and shortest completion time for each target worker. In this case, when the allocation unit 153 calculates the workload for a certain target worker, the start time t s From the end time of the work e Up to that point, the period during which they can merge should be included in the scope of integration, and the period during which they separate should be excluded from the scope of integration.

[0084] According to the other process (2), under the condition that the shortest completion time does not exceed a threshold, a worker with lower costs can be selected.

[0085] Next, other processing (3) of the information processing device 100 will be described. In the information processing device 100 described above, the schedule information was reviewed at predetermined intervals after the start of the project, but this is not the only processing it can perform.

[0086] The work progress management unit 154 of the information processing device 100 may identify the timing for reviewing the schedule information according to the progress of the project. For example, the information processing device 100 may acquire project progress information from an external device, etc., and identify the timing for reviewing the schedule information as the point when the progress rate reaches 30%, 60%, and 90%, respectively.

[0087] Furthermore, the work progress management unit 154 may identify the timing for reviewing the schedule information as the timing when the difference between the amount of work obtained from the relationship between the worker's future elapsed time and processing time estimated by the estimation unit 152 and the worker's actual amount of work exceeds a predetermined amount.

[0088] According to other processing (3), the schedule information can be reviewed at the optimization timing.

[0089] Next, other processing (4) of the information processing device 100 will be described. In the information processing device 100 described above, the future elapsed time and processing time for workers A to D, who are experienced in the work, was estimated, but it is not limited to this. For example, even for worker α, who is inexperienced in the work, it is possible to estimate the relationship between the future elapsed time and processing time for worker α by performing the following processing.

[0090] For example, the information processing device 100 calculates the average or median values ​​of the past elapsed time and processing speed of workers A to D as the relationship between the past elapsed time and processing time of worker α. The information processing device 100 inputs these past elapsed time and processing time of worker α into the estimation model 50 to calculate the relationship between the future elapsed time and processing speed of worker α. The processing after the information processing device 100 has calculated the relationship between the future elapsed time and processing speed of worker α is the same as the processing in the assignment phase described above.

[0091] (Hardware) Next, an example of the hardware configuration of the information processing device 100 will be described. Figure 9 is a diagram illustrating an example of the hardware configuration. As shown in Figure 9, the information processing device 100 includes a communication device 6a, an HDD (Hard Disk Drive) 6b, memory 6c, and a processor 6d. Furthermore, each of the parts shown in Figure 9 is interconnected by a bus or the like.

[0092] The communication device 6a communicates with external devices, etc. The HDD 6b stores programs and databases that operate the functions shown in Figure 7.

[0093] The processor 6d operates the processes that perform the functions described in Figure 7 by reading programs that perform the same processing as each processing unit shown in Figure 7 from the HDD 6b or the like and loading them into memory 6c. For example, this process performs the same functions as each processing unit of the information processing device 100. Specifically, the processor 6d executes processes that perform the same processing as the training unit 151, estimation unit 152, allocation unit 153, work progress management unit 154, etc.

[0094] Thus, the information processing device 100 operates as an information processing device that performs an information provision method by reading and executing a program. Furthermore, the information processing device 100 can also achieve the same functionality as the embodiment described above by reading the program from a recording medium using a media reader and executing the read program. It should be noted that the program referred to in this other embodiment is not limited to being executed by the information processing device 100. For example, the present invention can be similarly applied when another computer or server executes the program, or when they collaborate to execute the program.

[0095] This program can be distributed via networks such as the Internet. Furthermore, this program can be recorded on computer-readable storage media such as hard disks, flexible disks (FDs), CD-ROMs, MO (Magneto-Optical disks), and DVDs (Digital Versatile Discs), and executed by reading the program from these media using a computer.

[0096] (others) Some examples of the combinations of technical features that will be disclosed are listed below.

[0097] (1) An estimation unit that estimates the future elapsed time and processing speed of a target worker by inputting the target worker's past elapsed time and processing speed into an estimation model trained on the elapsed time since the worker started work and the processing speed that indicates the amount of work per unit time, An assignment unit assigns the target worker to the target work based on the elapsed time and processing speed of the target worker estimated by the estimation unit and the total workload of the target work. An information processing device characterized by having the following features.

[0098] (2) The information processing apparatus according to (1), characterized in that the estimation unit estimates the elapsed time and processing speed of the multiple target workers in the future by inputting the past elapsed time and processing speed of the multiple target workers.

[0099] (3) The information processing apparatus according to (1), characterized in that the estimation unit estimates the elapsed time and processing speed of the target worker after the predetermined time by inputting the past elapsed time and processing speed of the target worker up to the predetermined time each time a predetermined time has elapsed.

[0100] (4) The information processing apparatus according to (2), characterized in that the allocation unit allocates the multiple target workers to the target work based on the estimated elapsed time and processing speed of the multiple target workers and the total workload of the target work.

[0101] (5) The information processing apparatus according to (2), characterized in that the allocation unit repeatedly performs a process to calculate the workload of each of the multiple target workers by integrating the relationship between the elapsed time and the processing speed of each of the multiple target workers from the start time to the end time, while adjusting the end time, and further performs a process to identify the end time at which the sum of the workloads of the multiple target workers becomes the total workload as the time until the target work is completed.

[0102] (6) The information processing apparatus according to (1), wherein the results used when training the estimation model include the difficulty level of the work, and the estimation unit further inputs the difficulty level of the target work into the estimation model to estimate the future elapsed time and processing speed of the target worker according to the difficulty level of the target work.

[0103] (7) The information processing apparatus according to (2), further comprising a generation unit that generates a schedule including the amount of work performed by the plurality of target workers until the target work is completed and the time until the target work is completed.

[0104] (8) The information processing apparatus according to (4), wherein the allocation unit further performs a process to select a target worker to perform the target work, based on the cost per unit amount set for each of the multiple target workers and the amount of work performed by each of the multiple target workers, and under the conditions that the sum of the costs is minimized and the time to complete the target work does not exceed a threshold.

[0105] (9) Computers, By inputting the past elapsed time and processing speed of the target worker into an estimation model trained on the elapsed time since the worker started work and the processing speed indicating the amount of work per unit time, the future elapsed time and processing speed of the target worker are estimated. Based on the estimated elapsed time and processing speed of the target worker, and the total workload of the target task, the target worker is assigned to the target task. A program that assigns a process to be executed.

[0106] (10) The computer By inputting the past elapsed time and processing speed of the target worker into an estimation model trained on the elapsed time since the worker started work and the processing speed indicating the amount of work per unit time, the future elapsed time and processing speed of the target worker are estimated. Based on the estimated elapsed time and processing speed of the target worker, and the total workload of the target task, the target worker is assigned to the target task. The method for assigning the execution of a process. [Explanation of symbols]

[0107] 50 Estimated Models 100 Information Processing Devices 110 Communications Department 120 Input section 130 Display section 140 Storage section 141 training datasets 150 Control Unit 151 Training Department 152 Estimation Department 153 Allocation Section 154 Work Progress Management Department 155 Generation part

Claims

1. An estimation unit estimates the future elapsed time and processing speed of a target worker by inputting the target worker's past elapsed time and processing speed into an estimation model trained based on the elapsed time since the worker started work and the processing speed that indicates the amount of work per unit time. An assignment unit assigns the target worker to the target work based on the elapsed time and processing speed of the target worker estimated by the estimation unit and the total workload of the target work. An information processing device characterized by having the following features.

2. The information processing apparatus according to claim 1, characterized in that the estimation unit estimates the future elapsed time and processing speed of a plurality of target workers by inputting the past elapsed time and processing speed of each of the plurality of target workers.

3. The information processing apparatus according to claim 1, characterized in that the estimation unit estimates the elapsed time and processing speed of the target worker after the predetermined time by inputting the past elapsed time and processing speed of the target worker up to the predetermined time each time a predetermined time has elapsed.

4. The information processing apparatus according to claim 2, characterized in that the allocation unit allocates the multiple target workers to the target work based on the estimated elapsed time and processing speed of the multiple target workers and the total workload of the target work.

5. The information processing apparatus according to claim 2, characterized in that the allocation unit repeatedly performs a process to calculate the workload of each of the multiple target workers by integrating the relationship between the elapsed time and the processing speed of each of the multiple target workers from the start time to the end time, while adjusting the end time, and further performs a process to identify the end time at which the sum of the workloads of the multiple target workers becomes the total workload as the time until the target work is completed.

6. The information processing apparatus according to claim 1, wherein the results used when training the estimation model include the difficulty level of the work, and the estimation unit further inputs the difficulty level of the target work into the estimation model to estimate the future elapsed time and processing speed of the target worker according to the difficulty level of the target work.

7. The information processing apparatus according to claim 2, further comprising a generation unit that generates a schedule including the amount of work performed by the plurality of target workers until the target work is completed, and the time required to complete the target work.

8. The information processing apparatus according to claim 4, wherein the allocation unit further calculates the cost for each of the multiple target workers based on the cost per unit of work set for each of the multiple target workers and the amount of work each of the multiple target workers performs, and selects a target worker to perform the target work under the conditions that the sum of the costs is minimized and the time to complete the target work does not exceed a threshold.

9. On the computer, By inputting the past elapsed time and processing speed of the target worker into an estimation model trained on the elapsed time since the worker started work and the processing speed indicating the amount of work per unit time, the future elapsed time and processing speed of the target worker are estimated. Based on the estimated elapsed time and processing speed of the target worker, and the total workload of the target task, the target worker is assigned to the target task. A program that assigns a task to execute a process.

10. Computers By inputting the past elapsed time and processing speed of the target worker into an estimation model trained on the elapsed time since the worker started work and the processing speed indicating the amount of work per unit time, the future elapsed time and processing speed of the target worker are estimated. Based on the estimated elapsed time and processing speed of the target worker, and the total workload of the target task, the target worker is assigned to the target task. The method for assigning the execution of a process.

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