JP7384853B2 - Personnel formulation support device and personnel formulation support method - Google Patents

Description

The present invention relates to a personnel formulation support device and a personnel formulation support method.

The concept of so-called combinatorial optimization problems, which search for solutions that maximize or minimize desired parameters under given conditions, is useful for optimizing the placement and operation schedules of various resources such as workers and equipment, resolving traffic congestion, and global supply chains. It can also be applied to complex problems in the real world, such as reducing logistics costs.

On the other hand, since the number of solution candidates for such problems increases explosively, it is difficult to solve the problem within a practical amount of time unless a computer with appropriate computational power such as a supercomputer or a quantum computer is used.

For example, conventional technology related to quantum computers involves a computer that enables high-speed calculations for inverse problems and combinatorial optimization problems that require exhaustive search, and uses spin as a variable in the calculations to solve them. The problem is set up using spin-spin interactions and a local field that acts on each spin, and at time t = 0, all spins are directed in one direction by an external magnetic field, and at time t = τ, the external magnetic field becomes zero. The external magnetic field is gradually reduced, and each spin is allowed to evolve over time with its direction determined according to the effective magnetic field determined by the external magnetic field of each site at time t and all the interactions between spins. Techniques have been proposed in which the system maintains almost its ground state by not completely aligning with the effective magnetic field but in a quantum mechanically corrected direction (see Patent Document 1).

In addition, as a conventional technology for generating work schedules for personnel, a communicator is used for the purpose of creating a schedule for a communicator whose skills match the anticipated inquiry content based on the communicator's skills from among communicators who can be scheduled. A method for creating a data schedule (see Patent Document 2) has been proposed.

This technique is a method for a computer to create a schedule for a plurality of communicators in a contact center, the computer storing at least data indicating the skills of each of the plurality of communicators in a storage section, and the computer a step of receiving, from a contact center administrator, a designation input of the number of communicators to be assigned for each of the plurality of skills of the communicators for each business time period; and a step of receiving data indicating the number of people who have received the designated input. The method further includes the steps of further storing data in the storage unit for each business time period, and creating a schedule for the plurality of communicators based on the data indicating the stored number of people for each business time period. It is something that

In addition, the shift information that stores the shift information in which the first person working during the target period is assigned to the target period is aimed at facilitating sudden changes in work shifts in facilities such as childcare facilities. A shift management device (see Patent Document 3) has also been proposed, which includes a storage unit and a shift creation unit that allocates a predetermined number of spare second personnel to the first personnel for the target period of the shift information. There is.

WO2016/157333 Japanese Patent Application Publication No. 2008-186203 JP2015-212881A

However, no form has been proposed in which the above-mentioned quantum computer technology is appropriately applied to a case where personnel allocation is determined for a task that is performed by many people working together in sequence.

For example, in insurance claim payment assessment work at an insurance company or call center work, it is necessary to create weekly and monthly shift schedules for a large number of people in charge and their teams. Currently, experienced personnel create schedules manually based on predetermined rules (for example, shift patterns such as early shifts and late shifts are arranged in a fixed order and frequency for the relevant period). Ta.

On the other hand, in a business that consists of a plurality of sequential processes, it is difficult to manually calculate the required number of people for each team in charge of each process for each time period. For example, in a work environment where the number of processes ranges from tens to hundreds and the number of teams is 10 or more, if the processes that can be handled by each team differ, there are a wide variety of points to consider, so the number of personnel is calculated manually. That is impossible.

In addition, if it is necessary to take into account conditions such as the number of people who can come to work or the processing capacity that differs depending on the team, the difficulty will increase significantly. These problems become even more serious when attempting to perform the above-mentioned calculations in consideration of overall optimization (eg, optimization of human costs) over a specific unit period, such as one day or one week.

On the other hand, even if a general-purpose optimization solver is used for the above calculation, for example, a huge amount of calculation time is required, and it is difficult to say that it can withstand actual operation.

SUMMARY OF THE INVENTION An object of the present invention is to provide a technology that enables efficient calculation of the number of people required for each process in each time period in a business that consists of a plurality of sequential processes.

The personnel planning support device of the present invention, which solves the above-mentioned problems, calculates the predicted workload for each time period, the number of people who can work in each team collaborating in the task, and the number of people who can work at Information on the processing capacity of each of the processes in each team, the standard throughput of each of the processes, and the time limit for completing the work, the constraints on the number of people working in each team for each time period, and the series of processes. a storage unit storing information on constraints on the amount of processing in each process when sequentially executed; the predicted amount of work, the number of people who can work, the processing capacity, the standard amount of processing, and the completion time; Regarding the objective function that includes as a term a constraint function that is the minimum when each of the constraints of the number of working people and the processing amount is satisfied, the number of staff assigned to each team is set as spin, and the function for the constraint condition is an arithmetic unit that calculates an Ising model in which the sensitivity between variables is set as the strength of the interaction between the spins; The information on the number of personnel assigned to each team is output to a predetermined device.

In addition, the staff planning support method of the present invention provides a method for determining the predicted workload for each time period for a task consisting of a series of steps to be executed sequentially, the number of people who can work in each team collaborating in the task, and the number of people in each team who can work together. Information on the processing capacity of each of the processes, the standard throughput of each of the processes, and the time limit for completion of the work, constraints on the number of people working in each of the teams for each time period, and sequential execution of the series of processes. An information processing device including a storage section storing information on constraints on processing amount in each process, the predicted workload, the number of people who can work, the processing capacity, the standard processing amount, and the completion time limit. and a constraint function that is the minimum when each of the constraints of the number of working people and the processing amount are satisfied, as a term, the number of staff assigned to each team is set as spin, and the constraint function is An Ising model is calculated in which the sensitivity between variables in the function is set as the strength of the interaction between the spins, and based on the results of the calculation, the number of personnel assigned to each team for each time period in each of the steps is calculated. It is characterized by outputting information to a predetermined device.

According to the present invention, it is possible to efficiently calculate the number of people required for each process in a business consisting of a plurality of sequential processes for each time period.

FIG. 1 is a network configuration diagram including a personnel formulation support device according to the present embodiment. It is a diagram showing an example of the hardware configuration of the personnel formulation support device in this embodiment. It is a figure showing an example of a timing chart of this embodiment. It is a figure showing an example of a flow in this embodiment. FIG. 3 is a diagram illustrating an example of predicted workload information held in the basic information DB of the present embodiment. It is a figure showing an example of process information held in basic information DB of this embodiment. It is a figure showing an example of team information (number of people) held in basic information DB of this embodiment. It is a figure showing an example of team information (processing capacity) held in basic information DB of this embodiment. FIG. 3 is a diagram showing an example of standard processing number information held in the basic information DB of the present embodiment. It is a figure showing an example of completion time limit information held in basic information DB of this embodiment. It is a figure showing an example of a flow of a personnel formulation support method of this embodiment. It is a figure which shows the example of a screen in this embodiment. It is a figure showing an example of a concept of mountain collapse in this embodiment. It is a figure showing an example of a concept of mountain collapse in this embodiment. It is a figure showing an example of a concept of mountain collapse in this embodiment. It is a figure showing an example of a concept of mountain collapse in this embodiment. It is a figure showing an example of a concept of mountain collapse in this embodiment. It is a figure showing an example of a concept of mountain collapse in this embodiment. FIG. 3 is a diagram illustrating an example of predicted workload information held in the basic information DB of the present embodiment. It is a figure showing an example of scenario information held in basic information DB of this embodiment. It is a figure showing an example of team information (number of people) held in basic information DB of this embodiment. It is a figure showing an example of team information (processing capacity) held in basic information DB of this embodiment. FIG. 3 is a diagram showing an example of standard processing number information held in the basic information DB of the present embodiment. It is a figure showing an example of completion time limit information held in basic information DB of this embodiment. It is a figure which shows the example of a screen in this embodiment. FIG. 3 is a diagram illustrating an example of a scenario correspondence concept in this embodiment. FIG. 3 is a diagram illustrating an example of a scenario correspondence concept in this embodiment. FIG. 3 is a diagram illustrating an example of a scenario correspondence concept in this embodiment. FIG. 2 is a diagram illustrating a conceptual example of inter-team personnel accommodation in the present embodiment. FIG. 3 is a diagram illustrating a conceptual example of personnel exchange between locations in the present embodiment. It is a figure which shows the example of another application form in this embodiment.

<About the annealing machine>
As shown in the above-mentioned Patent Document 1, the present applicant has developed quantum computing technology and has attempted to solve various problems in, for example, exhaustive search problems (including the concept of combinatorial optimization problems) based on big data. .

In general, there are high expectations for quantum computers to solve such exhaustive search problems. Quantum computers are made up of basic elements called quantum bits, which can simultaneously realize "0" and "1". Therefore, it is possible to simultaneously calculate all solution candidates as initial values, and there is a possibility of realizing an exhaustive search. However, quantum computers need to maintain quantum coherence throughout the entire computation time.

Among these, a method called adiabatic quantum computation has started to attract attention (Reference: E. Farhi, et al., "A quantum adiabatic
evolution al gorithm applied to random
instances of an NP-complete problem, "S
science292, 472 (2001). ).

This method transforms the problem so that the ground state of a certain physical system becomes the solution, and attempts to obtain the solution by finding the ground state.

Let H^p be the Hamiltonian of the physical system in which the problem is set. However, at the start of the calculation, instead of using H^p as the Hamiltonian, we use another Hamiltonian H^0 whose ground state is clear and easy to prepare. Next, take a sufficient amount of time to shift the Hamiltonian from H^0 to H^p. If enough time is spent, the system will remain in the ground state, and the ground state of the Hamiltonian H^p will be obtained. This is the principle of adiabatic quantum computation. If the calculation time is τ, the Hamiltonian becomes the formula (1),

[Formula 1]

A solution is obtained through time evolution based on the Schrödinger equation of equation (2).
[Formula 2]

Adiabatic quantum computation can also be applied to problems that require an exhaustive search, and the solution is reached in a unidirectional process. However, if the calculation process needs to follow the Schrödinger equation of equation (2), it is necessary to maintain quantum coherence as in a quantum computer.

However, whereas a quantum computer repeats a gate operation between one or two qubits, adiabatic quantum computation involves making the entire qubit system interact all at once, and the concept of coherence is different.

For example, consider the operation of a gate on a certain quantum bit. At this time, if there is an interaction between that qubit and other qubits, it will cause decoherence, but in adiabatic quantum computation all qubits are made to interact at the same time, so in a case like this example does not become decoherent. Reflecting this difference, adiabatic quantum computation is thought to be more robust against decoherence than quantum computers.

As mentioned above, adiabatic quantum computation is effective for difficult problems that require exhaustive search. Then, the spin is used as a variable in the calculation, and the problem to be solved is set using spin-spin interactions and local fields that act on each spin.

At time t=0, all spins are directed in one direction by an external magnetic field, and the external magnetic field is gradually reduced so that it becomes zero at time t=τ.

Each spin evolves over time with its direction determined in accordance with the effective magnetic field determined by the external magnetic field of each site at time t and all the effects of interactions between spins.

At this time, the spin direction is not completely aligned with the effective magnetic field, but is quantum mechanically corrected so that the system maintains almost its ground state.

Additionally, a term (relaxation term) that maintains each spin in its original direction during time evolution is added to the effective magnetic field to improve the convergence of the solution.

The personnel formulation support device in this embodiment is assumed to be an annealing machine that performs the above-mentioned adiabatic quantum calculation, but is of course not limited to this. Any method that can be solved is applicable.

Specifically, the annealing method includes not only hardware implemented using electronic circuits (digital circuits, etc.), but also methods implemented using superconducting circuits. Furthermore, hardware that implements the Ising model using a method other than the annealing method may be used. Examples include laser network methods (optical parametric oscillation) and quantum neural networks. Furthermore, although some of the ideas are different as described above, the present invention can also be realized in a quantum gate method in which calculations performed using the Ising model are replaced with gates such as Hadamard gates, rotation gates, and control NOT gates.

<Network configuration>
Embodiments of the present invention will be described in detail below with reference to the drawings. FIG. 1 is a network configuration diagram including the personnel formulation support device 100 of this embodiment.
The personnel planning support device 100 shown in FIG. 1 is a computer device that makes it possible to efficiently calculate the number of people required for each process in each time period in a business that consists of a plurality of sequential processes. Assume an annealing machine as .

However, the outline of the annealing machine is as already described based on Patent Document 1, and details such as its specific configuration and operation will be omitted as appropriate (the same applies hereinafter).

The personnel formulation support device 100 of this embodiment is connected to a user terminal 200 for data communication via an appropriate network 10 such as the Internet.

The above-mentioned user terminal 200 is a terminal that receives from the personnel planning support device 100 a proposal for the required number of people in each team for each process in a business consisting of a plurality of sequential processes.

Specifically, the user of this user terminal 200 is a business operator that performs insurance claim payment assessment work or call center work in an insurance company, and can be assumed to be an organization such as a financial institution, an insurance company, or a major manufacturer. Alternatively, it can also be assumed that a medical institution or a care provider has many nurses and care staff to carry out nursing and care work for patients and the like.

In any case, in a business that involves many people working together in sequence, and consists of multiple sequential processes, this is a project that attempts to calculate the number of people required for each team in charge of each process for each time period. If you are a person, you can fall under the above-mentioned user. In other words, it can be said that the present invention is applicable to the operations of such businesses.

As a concrete example, in an insurance company, for example, when formulating personnel plans for each team engaged in payment assessment work, the reality is that personnel with a certain level of experience create the plans manually. In other words, the work tends to be individualized and is a manual task. In other words, it was done at the discretion of the planners themselves, and was based solely on their intuition and experience.

The above-mentioned obstacles are due to mathematical difficulties and the absence (strictly speaking, immaturity) of technology to solve the mathematical difficulties. For example, spreadsheet software can mathematically solve linear constraints quickly, but it takes an enormous amount of time to solve nonlinear constraints.

Conventionally, when formulating a personnel plan, the amount of calculation increases rapidly due to the increase in nonlinear constraints related to elements, such as each person and process, and it takes a long time to complete the calculation, but annealing By employing the personnel formulation support device 100 that uses a machine, it becomes possible to perform calculations without depending so much on an increase in the number of elements.

<Hardware configuration>
Further, the hardware configuration of the personnel formulation support device 100 of this embodiment is shown in FIG. 2 as follows.

That is, the personnel planning support device 100 includes a storage section 101, a memory 103, a calculation section 104, and a communication section 105.

Among these, the storage unit 101 is configured with an appropriate nonvolatile storage element such as an SSD (Solid State Drive) or a hard disk drive.

Furthermore, the memory 103 is composed of a volatile storage element such as a RAM.

Further, the calculation unit 104 is a CPU that reads out and executes the program 102 held in the storage unit 101 into the memory 103, performs overall control of the apparatus itself, and performs various determinations, calculations, and control processes.

Further, the communication unit 105 is assumed to be a network interface card or the like that connects to the network 10 and handles communication processing with the user terminal 200.

In addition, when the personnel formulation support apparatus 100 is a stand-alone machine, it is preferable to further include an input device that accepts key input and voice input from the user, and an output device such as a display that displays processed data.

Furthermore, in the storage unit 101, in addition to the program 102 for implementing functions necessary for the personnel formulation support device of this embodiment, at least a basic information DB 125 and a constraint condition DB 126 are stored. However, details regarding these tables will be described later.

Further, the program 102, that is, the algorithm that implements the operation as an annealing machine, holds information on the Ising model 1021, which is the problem to be solved. This Ising model 1021 is set in advance by an administrator or the like based on the task for which information is provided, each team that executes the task, and various other information that affects them.

Note that the adiabatic quantum computation described in the overview of annealing machines is also called quantum annealing, and is an extension of the classical annealing concept to quantum mechanics. In other words, adiabatic quantum computation is originally capable of classical operation, and can be interpreted as adding quantum mechanical effects to improve performance in terms of speed and accuracy of solutions. Therefore, in the present invention, the calculation section itself is classical, and by introducing parameters determined by quantum mechanics into the calculation process, a calculation method and device that is classical but includes quantum mechanical effects is realized. However, it is of course possible to adopt a configuration in which the calculation section is configured with a quantum computer.

Based on the above concept, in the following example, we will describe a classical algorithm for obtaining a ground state as a solution and a device for realizing it, while explaining its relationship with adiabatic quantum computation.

The personnel formulation support device 100 based on this assumption assumes that N variables sj ^z (j=1, 2,..., N) take a range of -1≦sj ^z ≦1, and that the local field gj and the interaction between variables Jij The assignment is set by (i, j = 1, 2, ..., N).

In addition, the calculation unit 104 divides the time into m and discretely calculates t=t. (t.=0) to tm (tm =τ), and in finding the variable Sj ^Z (tk) at each time tk, the variable Sj ^z (tk-1) (i= 1, 2,..., N) and the coefficient 9pina or 9pinb of the relaxation term, Bj ^z (tk) = {ΣiJijSi ^z (tk-1) + gj + sgn (sj ^z (tk-1))・9pina}・tk/τ or Bj ^z (tk)={ΣiJiJSj ^z (tk-1)+gj+9pinb. Sj ^Z (tk-1)}・tk/τ is determined, and the function f is determined so that the range of the above-mentioned variable Sj ^z (tk) is -1≦sj ^z (tk)≦1, and Sj ^z (tk) = f(Bj ^z (tk), tk), and as the time step advances from t=t0 to t=tm, the above-mentioned variable Sj ^z approaches -1 or 1, and finally, if sj ^z <0, Sj If ^zd = -1 and Sj ^z >0, the solution is determined as Sj ^zd =1. However, if it is appropriate for the final solution s _j ^zd to be a real number, the solution may be determined by setting s _j ^zd as a real number whose range is [-1, 1].

The coefficient gpinb is, for example, a value between 50% and 200% of the average value of |Jij|. Furthermore, regarding the local field gj of the task setting, it is also possible to add a correction term δgj' to gj' only for a certain site j' and increase the size of gj' only for that site j'. Further, the correction term δgj' is, for example, a value from 10% to 100% of the average value of |Jij|.

Next, we will explain the basic principles of annealing machines by starting from a quantum mechanical description and moving to a classical form.

The ground state search problem for the Ising spin Hamiltonian given by Equation (3) includes a class of problems called NP-hard, and is known to be a useful problem (Reference: F. Barahona, "On the computational comp lexity of Isingspin glass models,” J. Phys. A: Math. Gen. 15, 3241 (1982).).

[Formula 3]

Jij and gj are task setting parameters, and σ^ ^Z is the z component of the Pauli spin matrix and takes an eigenvalue of ±1. i and j represent spin sites. Ising spin is a variable that can take only ±1 as a value, and in equation (3), the eigenvalue of σ^ ^z is ±1, making it an Ising spin system.

The Ising spin in Equation (3) does not need to be a literal spin, and may be any physical spin as long as the Hamiltonian is described by Equation (3).

For example, it is possible to associate the number of personnel assigned to each team in each time period with spin ±1, it is also possible to associate high and low of a logic circuit with ±1, and it is possible to associate vertical and horizontal polarization of light with ±1. It is also possible to associate the phase with 1, or to associate the phases of 0 and π with ±1.

In the method illustrated here, the calculation system is prepared in the ground state of the Hamiltonian given by equation (4) at time t=0, similar to the adiabatic quantum calculation.

[Formula 4]

γ is a proportionality constant determined by the magnitude of the external field uniformly applied to all sites j, and σ^j ^x is the x component of the Pauli spin matrix. If the calculation system is spin itself, the external field means the magnetic field.

Equation (4) corresponds to applying a transverse magnetic field, and the ground state is when all spins are oriented in the x direction (γ>0). The Hamiltonian in the problem setting was defined as an Ising spin system with only the z component, but the x component of spin appears in equation (4). Therefore, the spin in the calculation process is vector-like (Bloch vector) rather than Ising. At t=0, it starts with the Hamiltonian of Equation (4), but as time t progresses, the Hamiltonian is gradually changed, and finally the Hamiltonian described by Equation (3) is obtained, and its ground state is obtained as a solution.

[Formula 5]

Here, σ^ represents the three components of the Pauli spin matrix as a vector. The ground state is when the spin points in the direction of the magnetic field, and can be written as <σ^>=B/|B|, where <·> is the quantum mechanical expectation value. In an adiabatic process, the ground state is always maintained, so the direction of the spin always follows the direction of the magnetic field.

The above discussion can be extended to multi-spin systems. At t=0, the Hamiltonian is given by equation (4). This means that the magnetic field Bj ^x =γ is uniformly applied to all spins. At t>0, the x component of the magnetic field gradually weakens, and Bj ^X =γ(1−t/τ). Regarding the z component, since there is an interaction between spins, the effective magnetic field is expressed as equation (6).

[Formula 6]

Since the spin direction can be defined by <σ^ ^z >/<σ^ ^X >, if the spin direction follows the effective magnetic field, the spin direction is determined by equation (7).

[Formula 7]

Although equation (7) is a quantum mechanical description, it takes an expected value, so unlike equations (1) to (6), it is a relational equation regarding classical quantities.

In the classical system, there is no non-local correlation (quantum stitching) of quantum mechanics, so the direction of the spin should be completely determined by the local field at each site, and Equation (7) determines the behavior of the classical spin system. In quantum systems, equation (7) has to be modified because there is non-local correlation, but this will be discussed later.Here, in order to describe the basic form of the invention, we will discuss the classical system determined by equation (7). Describe.

FIG. 3 shows a timing chart (1) for obtaining the ground state of the spin system. Since the description in FIG. 3 relates to classical quantities, the spin at site j is expressed by sj rather than σ^j. Further, accordingly, the effective magnetic field Bj in FIG. 3 is a classical quantity. At t=0, a rightward effective magnetic field Bj is applied to all sites, and all spins Sj are initialized to the right.

As time t passes, the magnetic field in the z-axis direction and the interaction between the spins are gradually applied, and finally the spins become in the +z direction or -z direction, and the z component of the spin Sj becomes sj ^z = +1 or -1 becomes. Ideally, the time t is continuous, but in an actual calculation process, it can be made discrete to improve convenience. In the following, we will discuss the discrete case.

The spin illustrated here is a vector-like spin because not only the z component but also the x component is added. The behavior as a vector can also be understood from FIG. The y component has not appeared so far, but this is because the y component of the external field does not exist because the direction of the external field is set to the xz plane, and therefore <σ^ ^Y >=0.

The spin of the calculation system is assumed to be a three-dimensional vector of magnitude 1 (this is called a Bloch vector, and the state can be described by a point on the spherical surface), but in the example shown in the figure, the axis is taken as a two-dimensional vector. (states can be described by points on a circle).

Furthermore, since γ is constant, Bj ^x (t)>0 (γ>0) or Bj ^x (t)<0 (γ<0) holds true. In this case, the two-dimensional spin vector can be described only by a semicircle, and if Sj ^z is specified by [-1, 1], the two-dimensional spin vector is determined by one variable, Sj ^z . Therefore, in this example, spin is a two-dimensional vector, but it can also be expressed as a one-dimensional continuous variable with a range of [-1, 1].

In the timing chart of FIG. 3, the effective magnetic field is determined for each site at time t=tk, and using that value, the spin direction at t=tk is determined by equation (8).

[Formula 8]

Equation (8) is a rewrite of Equation (7) into the notation related to classical quantities, so it does not have the <・> symbol. Next, the effective magnetic field at t=tk+l is determined using the spin value at t=tk. If the effective magnetic field at each time is specifically written, it becomes Equations (9) and (10).

[Formula 9]

[Formula 10]

Hereinafter, the spin and the effective magnetic field are determined alternately according to the procedure schematically shown in the timing chart of FIG.

In the classical system, the magnitude of the spin vector is 1. In this case, each component of the spin vector is written as Sj z (tk ⁾ = sin θ, Sj ^x (tk) = COS θ using the parameter θ defined as tan θ = Bj ^z (tk)/Bj ^x (tk). Ru.

Rewriting this, Sj ^z (tk) = sin(arctan(Bj ^z (tk)/Bj ^x (tk))), Sj ^x (tk) = cos(arctan(Bj ^z (tk)/Bj ^x (tk) )).

As is clear from equation (9), the only variable in Bj ^x (tk) is tk, and τ and γ are constants. Therefore, Sj ^z (tk)=sin(arctan(Bj ^z (tk)/Bj ^x (tk))) and Sjx(tk)=cos(arctan(Bj ^z (tk)/Bj ^x (tk))) are Bj Generalizations such as Sj ^z (tk) = f1 (Bj ^z (tk), tk) and Sj ^x (tk) = f2 (Bj ^z (tk), tk) as functions with z ( ^tk ) and tk as variables You can also express

Since the spin is described as a two-dimensional vector, two components, Sj ^z (tk) and sj ^x (tk), appear, but if Bj ^z (tk) is determined based on equation (10), Sj ^x (tK) is not necessary.

This corresponds to the fact that the spin state can be described only by Sj ^z (tk) whose range is [-1, 1]. The final solution Sj ^zd must be Sj ^zd =-1or1; if Sj ^z (τ)>0, Sj ^zd =1; if Sj ^z (τ)<0, Sj ^zd =-1. However, if it is appropriate that the final solution s _j ^zd be a real number, s _j ^zd may be extracted as a real number whose range is [-1, 1].

FIG. 4 shows a flowchart summarizing the above algorithm. Here, tm=τ. Each step s1 to s9 in the flowchart in FIG. 4 corresponds to processing at a certain time in the timing chart in FIG. 3 from time t=0 to t=τ. That is, steps s2, s4, and s6 of the flowchart correspond to the above equations (9) and (10) at t=t1, tk+l, and tm, respectively. The final solution is determined in step s8 by setting Sj ^zd =-1 if sj ^z <0, and setting Sj ^zd =1 if Sj ^z >0 (s9). However, if it is appropriate for the final solution to be a real number, s _j ^zd may be determined as a real number whose range is [-1, 1]. Note that the flow in FIG. 4 shows a general example, and the description of extracting as a real number is omitted.

So far, we have shown how the problem is solved when it is expressed by equation (3). Next, a specific example will be given to explain how a specific problem is expressed by equation (3) including a local field gj and an interaction between variables Jij (i, j=1, 2, . . . , N).

The specific issues here, that is, the Ising model 1021, include, for example, the predicted workload for each time period regarding a task consisting of a series of steps to be executed sequentially, the number of people who can work in each team collaborating in the task, and the Information on the processing capacity of each of the above-mentioned processes in the team, the standard throughput of each of the processes, and the completion time of the work, the constraints on the number of people working in each team for each time period, and the series of processes described above. Regarding the objective function that includes as a term the constraint function that is the minimum when the processing amount constraints in each process are satisfied when sequentially executing Assume an Ising model in which the sensitivity between variables in the constraint function is set as the strength of interaction between spins.

In this case, the local field gj is the predicted workload for each time period on a certain day, the number of people who can work in each team collaborating on the work, the processing capacity of each of the above processes in each team, and the standard for each of the processes. It is assumed that the values of the variables in the information on the amount of processing, the time limit for completing the task, and the constraint function that is minimized when each of the above constraints are satisfied are set as the degree of influence on the objective function. Suppose.

Through the above considerations, we specifically set the inter-variable interaction Jij (related to each term of the objective function described above) and the local field gj, and search for the ground state of the Ising model 1021 expressed by equation (3). In other words, the above-mentioned predicted workload for each time period on a certain day, the number of people who can work in each team collaborating on the work, the processing capacity of each of the above processes in each team, the standard processing volume of each of the processes, In each of the series of steps, through the search for the base state where the objective function consisting of the information on the completion time of the task and the constraint function that is minimized when each of the above constraints are satisfied, is minimized. Determine the number of staff on each team for each time period.

Note that the Ising model and annealing method only calculate ``minimizing the objective function.'' Therefore, if there are constraints that need to be satisfied when minimizing the objective function, it is necessary to add them to the objective function in some way.

for example,
[Formula 11]
Let's consider the following constraint. If we convert this constraint condition into "a function that is minimized when the constraint condition is satisfied", the following formula will be obtained.

[Formula 12]

Since the squared part is always a positive value, this formula has a minimum value only when the content of the square is 0. The content becomes 0 only when ΣXi - A = 0, so if you solve the optimization problem where this function is minimized, you will automatically obtain a solution that satisfies ΣXi = A. Become.

Further, for example, in the above-described annealing method, it is necessary to include items to be used as constraints in the objective function, so the objective function and the constraints are treated with the same importance.

For example, suppose we have the following optimization problem.
[Formula 13]

Changing this to the formulation for annealing results in the following.
[Formula 14]

Here, P and Q are constants, and are factors that determine which term is minimized preferentially. For example, if you want to minimize all three terms equally (that is, solve the problem without biasing the strength of the constraints), you can change the values to balance the terms, such as making P and Q equal. Make settings.

On the other hand, if the problem is set to ``strictly follow the second term's constraint, but do not place much emphasis on the third term's constraint,'' set the value of P, which is the coefficient of the term to be emphasized, to be larger than the value of Q. By doing so, you will get the desired solution.

In this way, according to the annealing method, it is possible to prioritize constraints and set constraints that are not given much importance to be "satisfied as much as possible."

Note that various settings related to the Ising model are appropriately performed in accordance with existing general concepts in accordance with each condition and information for schedule generation in this embodiment.

<Data structure example>
Next, various types of information used by the personnel formulation support device 100 of this embodiment will be explained. 5A to 5F show examples of each piece of information held in the basic information DB 125 in this embodiment.

The predicted workload information 125A of the present embodiment shown in FIG. 5A is a table that stores information on the workload at each time of the task that is the target of personnel formulation.

The value of this workload can be assumed to be estimated using an appropriate algorithm such as machine learning. In this case, the personnel planning support device 100 stores, for example, information on the amount of past work that occurred in each time period in the storage unit 101, and based on this information, various information such as date and time, day of the week, weather, etc. A model is generated by identifying the correlation between factors and the amount of each task generated using a machine learning algorithm. For example, if a certain time period is given as input to a model that has learned this correlation, it is possible to estimate the amount of each task that is expected to occur during that time period.

Further, the process information 125B of this embodiment shown in FIG. 5B is a table that stores information on the execution order and names of a series of processes that constitute the above-mentioned business. The information for this process has been predefined by a knowledgeable person.

Further, team information (number of people) 125CA and team information (processing capacity) 125CB of this embodiment shown in FIGS. 5C and 5D contain information on each team that is in charge of, that is, performs, a series of processes that constitute the above-mentioned work. This is the stored table. It is assumed that this information is appropriately acquired from a personnel management system that manages information regarding each team. Further, the information illustrated in FIGS. 5C and 5D defines the number of members of each team and the processing capacity for each process (standard value is 1.0, and if the team does not have the capacity, it is 0).

Further, the standard processing number information 125D of the present embodiment shown in FIG. 5E is a table storing information on the processing number per unit time and per person regarding each of the series of processes that constitute the above-mentioned work. This can also be said to be the number of cases processed when the ability defined in the team information 125CB described above is standard, that is, 1.0. Such information can be assumed to be obtained from a person who has detailed knowledge regarding each process or from past performance using a process management system or the like.

Further, the completion time limit information 125E of the present embodiment shown in FIG. 5F is a table that stores information regarding the time at which the task should be completed by performing all the series of steps constituting the above-mentioned task, that is, the completion time limit. This completion time can generally be assumed to be the closing time of each day or the closing time of the task.

Note that the information held by the constraint condition DB 126 in this embodiment will be described later as each conditional expression (constraint condition function) applied to the Ising model 1021.

<Flow example>
Hereinafter, the actual procedure of the personnel formulation support method in this embodiment will be explained based on the drawings. Various operations corresponding to the personnel formulation support method described below are realized by a program read into a memory or the like and executed by the personnel formulation support apparatus 100. This program is composed of codes for performing various operations described below.

FIG. 6 is a diagram showing an example of the flow of the personnel formulation support method in this embodiment. In this case, the personnel planning support device 100 uses the Ising model 1021 to be processed as the predicted workload for a certain day (forecast workload information 125A), the number of people who can work in each team and the processing capacity (team information 125CA, 125CB). , when the standard processing amount of each process (standard processing number information 125D), the completion time limit (completion time limit information 125E), and each constraint condition of the number of workers and processing amount (Equations 15 to 21, described later) are satisfied. Regarding the objective function that includes the minimum constraint function (combination of Equations 15 to 21) as a term, the number of staff assigned to each team is defined as spin, and the sensitivity between variables in the constraint function is defined as spin It is assumed that an Ising model 1021 set as the strength of interaction is calculated.

In _addition , as shown in Equation 15 _below , Equations 16 to 21, which are constraint expressions, are shown below, with the precondition being the number of operations that can be executed per time, U _ts : the upper limit of the number of people in the team (s) at each time (t).

[Formula 15]

[Formula 16]
This equation 16 is a constraint equation that specifies that there is an upper limit to the number of people working in each team and in each time period.

[Formula 17]
This equation 17 is a constraint equation that specifies that there is an upper limit to the amount that can be processed in each time period and in each process (no work can be done beyond the remaining amount).

[Formula 18]
Equation 18 is a constraint that states that the processing amount of the first step in each time period is the sum of the remaining number of cases in the previous time period (Y_1(t-1)) and the number of new cases (y_1t). It is a formula.

[Formula 19]
Equation 19 is a constraint formula that specifies that the amount of processing from the second process onward in each time period is the sum of the remaining number of processes in question and the number of cases processed in the previous time period of the previous process. .

Furthermore, as illustrated in FIGS. 8 and 9, Equations 17 to 19 above are based on the concept of "piling up" and "piling up" excess processing capacity in each process in each time period. Yes, this "piled up" amount of work will be transferred to the subsequent time period, that is, it will be subject to "piling up". Furthermore, even if "pile-up" and "pile-up" are performed sequentially across time periods, it is necessary to ensure that there is no remaining pile by the completion time limit.

According to this embodiment, it is possible to specify the number of personnel required for each time period while satisfying these constraints.

However, in FIGS. 8 and 9, in order to simplify the explanation, an example is shown in which the present embodiment is applied to the simplest case in which the number of processes is one and the number of teams is one. Actually, as shown in FIGS. 10 to 13, this embodiment is applied to a case where the number of processes and the number of teams are plural, such as two processes and one or two teams.

[Formula 20]
This equation 20 is a constraint equation that specifies that changes to the next time period should be minimized.

[Formula 21]
This formula 21 shows that the number of remaining items in each process and final time period (T) is zero (all processing is completed in one day),
This is a constraint expression that specifies the following.

Accordingly, the personnel formulation support device 100 obtains information regarding each of the above-mentioned constraint expressions from the user terminal 200, for example, in response to an instruction from the manager of the relevant job, and uses this information as a constraint condition. It is stored in the constraint condition DB 126 (s10).

Further, the personnel formulation support device 100 converts the constraint obtained in s10 into a constraint function as described above (s11). This constraint condition function is a function that is minimized when the constraint condition is satisfied. Note that when a constraint condition is specified in s10 described above, a constraint function that has already been defined for the constraint condition is extracted from the constraint condition DB 126.

In addition, the personnel formulation support device 100, as an annealing machine, can perform settings related to at least the above-mentioned items (forecast workload on a certain day, number of people who can work in each team and processing capacity, standard processing amount of each process, and completion time limit). Using the Ising model 1021 that has been made as a task, the base state in which the objective function is minimized is calculated (s12). The search itself for such a ground state is similar to the processing in existing technology.

In other words, while satisfying the above-mentioned constraints and basic information such as the predicted workload on a certain day, the number of people who can work in each team and processing capacity, the standard processing amount of each process, and the completion time, (Theoretical based on sensitivity) Transition towards the final state of the number of personnel for each team required for each process in the relevant work on the relevant day, and search for the state where the result of the number of personnel settles down as the base state. I will do it.

In addition, the personnel formulation support device 100 transmits information on the number of personnel in each team in each time period (see FIG. 7) for each process identified in s12 as an output to the user terminal 200 (s13), and processes the information. end.

Here, a description will be given of a form that also supports the case where the above-mentioned work can be performed in a plurality of scenarios. Information held by the basic information DB 125 in this case is illustrated in FIGS. 14A to 14F. As shown in FIG. 16, specific examples of scenarios and processes assumed here include the number of scenarios: 3, the number of processes: 4, and the number of teams: 3. Furthermore, the steps to be performed and their order may differ depending on the scenario. Further, as shown in FIG. 17, it is assumed that the business cases (eg, 270 cases) are sorted by scenario, and the proportion thereof is also predicted. This prediction itself is, for example, based on the frequency of occurrence of the relevant scenario in the past.

The predicted workload information 125F of this embodiment shown in FIG. 14A is a table that stores information on the workload at each time of the task that is the target of personnel formulation. As explained in connection with FIG. 5A, the value of this workload can be assumed to be estimated by an appropriate algorithm such as machine learning.

Scenario information 125G of this embodiment shown in FIG. 14B is information that defines a pattern as one scenario when there are multiple patterns of a series of process groups that constitute the above-mentioned business. This scenario information 125G is defined in advance by a knowledgeable person. The configuration includes information such as the processing flow (character string indicating whether or not each step is executed as 1 or 0) and the number of scenarios in each scenario.

Further, team information (number of people) 125HA and team information (processing capacity) 125HB of this embodiment shown in FIGS. 14C and 14D contain information on each team that is in charge of, that is, performs, a series of processes that constitute the above-mentioned work. This is a stored table. It is assumed that this information is appropriately acquired from a personnel management system that manages information regarding each team. Further, the information illustrated in FIGS. 14C and 14D defines the number of members of each team and the processing capacity for each process (standard value is 1.0, and if the team does not have the capacity, it is 0).

Further, the standard processing number information 125I of the present embodiment shown in FIG. 14E is a table storing information on the processing number per unit time and per person for each of the series of processes that constitute the above-mentioned work. This can also be said to be the number of cases processed when the ability defined in the team information 125HB is standard, that is, 1.0. Such information can be assumed to be obtained from a person who has detailed knowledge regarding each process or from past performance using a process management system or the like.

Further, the completion time limit information 125J of this embodiment shown in FIG. 14F is a table that stores information regarding the time at which the task should be completed by performing all the series of steps constituting the above-mentioned task, that is, the completion time limit. This completion time can generally be assumed to be the closing time of each day or the closing time of the task.

In _addition , as shown in Equation 22 _below , Number of cases in which (k) can be executed per unit time, U _ts : Maximum number of people in team (s) at each time (t), S _ik : Number of cases to be processed in each scenario (i)/process (k), l _ik : Equations 23 to 27, which are constraint expressions, are shown below, with scenario (i) and the next step of step (k) as prerequisites.

[Formula 22]

[Formula 23]
This expression 23 is a constraint expression that specifies that the total number of cases in one day must be completed.

[Formula 24]
This equation 24 is a constraint equation that specifies that the maximum number of personnel for each team will not be exceeded in each time period.

[Formula 25]
This formula 25 is as follows: <Pile 1> There is an upper limit to the amount that can be processed in each time period and each process (work cannot be done beyond the remaining quantity), <Pile 2> The amount that should be processed in each time period and from the second process onward is a constraint expression that specifies that the number of processes in the previous time period of the previous process (*previous process in the scenario) is the sum of the number of cases processed in the previous time period of the relevant process.

[Formula 26]
This equation 26 is a constraint equation that specifies that changes to the next time period should be reduced.

[Formula 27]
In this formula 27, the number of remaining items in each process and final time period (T) is zero (all processing is completed in one day)
, is a constraint expression that specifies the following.

As described above, as a result of calculating the Ising model in response to the existence of multiple scenarios, the output obtained is, for example, as shown in Figure 15, the output of each team in each process for each scenario. This is a table that specifies the number of people required for each time period.

Particularly, as shown in FIGS. 16 to 18, under conditions where there are multiple scenarios, processes, and teams, it is no longer possible to manually pile up or collapse piles as appropriate. However, by applying this embodiment, it is possible to efficiently specify the required number of people for each team in each time period in each process in each scenario.

Note that the personnel planning support device 100 may generate one or more patterns in which the number of people who can attend work for each team is varied from the initial value, and execute the calculation of the Ising model 1021 for each pattern. In this case, the personnel planning support device 100 determines, among the calculation results, a pattern that satisfies at least one of the following conditions: the total number of staff assigned to all teams is the minimum, or the time required to complete the task is the shortest.

By performing such processing, it is possible to obtain information on the number of personnel assigned to each team for each time period in each process, which corresponds to the pattern identified in the above-described determination, as information on an ideal team. Of course, the personnel formulation support device 100 in this case outputs information on the ideal team to the user terminal 200. However, there are cases in which such an "ideal team" does not match the actual availability of the team's personnel. In other words, there may be cases where the ideal and reality diverge.

Therefore, the personnel formulation support device 100 uses the calculation result when the initial value of the number of people who can work is adopted without making the above-mentioned changes, and the calculation result corresponding to the "ideal team", In order to establish the number of staff assigned to an "ideal team," the number of staff assigned to a certain team in a certain time period, which is shown by the calculation result when the initial value of the number of people who can work is adopted, is Identify flexible arrangements for staffing. In other words, the content of personnel exchange between teams (Fig. 19) is determined.

In the example of FIG. 19, "Team C" in charge of "Process 2" has "10 people" in the time slot "9:00", and "Team A" in charge of "Process 1" in the same time slot We have decided on a flexible form of personnel. Through this flexibility, ``Team A,'' which will be in charge of ``Process 1'' at ``9:00,'' will have the ``20 people'' necessary for an ``ideal team.'' The personnel formulation support device 100 notifies the user terminal 200 of such information.

Note that the example of FIG. 19 described above shows a case in which personnel are exchanged between teams during the same time period. Alternatively, as shown in FIG. 20, it may be possible to accommodate personnel exchange between bases. In this case, in order to establish the number of personnel to be assigned to the "ideal team," the personnel planning support device 100 calculates the number of people who can work at a certain time period, which is indicated by the calculation result when the initial value of the number of people who can work is adopted. Specify a form in which personnel assigned to a team can be exchanged as personnel assigned to a team at another base in the same time zone. In other words, the content of personnel exchange between teams (Fig. 20) is determined between different bases.

In the example in Figure 20, ``5 people'' from ``Team A'' in ``Tokyo'' are in charge of ``Process 1'' in the time period of ``9:00,'' and ``5 people'' are in charge of ``Process 1'' in the same time period. It has been decided that he will be assigned as a member of Team B in Osaka. As a result of this flexibility, ``Team B'' in ``Osaka'', which will be in charge of ``Process 1'' at ``9:00'', will have the necessary ``10 people'' as an ``ideal team.'' The personnel formulation support device 100 notifies the user terminal 200 of such information.

Further, as yet another embodiment, a configuration that corresponds to a situation in which a new job is introduced for each time period, for example, can also be envisaged. As conceptually shown in Figure 21, these situations include tasks that include branching in the flow, such as confirmation processing and remand, tasks that require waiting, such as not being able to proceed to the next step until two processes are completed, and This may include work that may result in an increase in the number of projects, and work that controls flow and traffic related to infrastructure such as electricity and water.

In this case, the personnel planning support device 100 further holds information on the amount of work newly input for each time slot as the predicted amount of work in the basic information DB 125 of the storage unit 101.

The personnel formulation _support device 100 calculates, as shown _in the following equation 28, The number of times that one person can execute process (k) per unit time, U _ts : Maximum number of people in team (s) at each time (t), S _ik : Processing number for each scenario (i)/process (k) number of cases, l _ik : next process of scenario (i)/process (k), Y _ikt : number of cases of process (k) of scenario (i) to be processed at time (t), A _ikt : at time (t) Equations 29 to 33, which are constraint expressions, are shown below, with the number of processes (k) of input scenario (i) as a precondition.

[Formula 28]

[Formula 29]
This expression 29 is a constraint expression that specifies that the total number of cases in one day must be completed.

[Formula 30]
This equation 30 is a constraint equation that specifies that the maximum number of personnel for each team will not be exceeded in each time period.

[Formula 31]
This formula 31 is as follows: <Pile 1> There is an upper limit to the amount that can be processed in each time period and each process (work cannot be done beyond the remaining quantity), <Pile 2> The amount that should be processed in each time period and from the second process onward is the number of cases processed in the previous time period of the previous process (*previous process in the relevant scenario), the remaining amount in the previous time period of the relevant process,
This is a constraint expression that specifies that the number of arrivals of new scenarios is added.

[Formula 32]
This equation 32 is a constraint equation that specifies that changes to the next time period should be minimized.

[Formula 33]
In this formula 33, the number of remaining items in each process and final time period (T) is zero (all processing is completed in one day)
, is a constraint expression that specifies the following.

The personnel planning support device 100 calculates the Ising model based on the information on the amount of work newly introduced for each time period, as described above, and calculates work whose flow includes branching, such as confirmation processing and return. Regarding tasks that require waiting, such as not being able to proceed to the next step until one process is completed, tasks that may result in an increase in the number of tasks during the task, and tasks that control flow and traffic related to infrastructure such as electricity and water. The required number of people for each team in each time period in each process will be obtained.

Although the best mode for carrying out the present invention has been specifically described above, the present invention is not limited thereto and can be modified in various ways without departing from the gist thereof.

According to this embodiment, it is possible to efficiently calculate the number of people required for each process in each time period in a business that consists of a plurality of sequential processes.

The description of this specification clarifies at least the following. That is, in the personnel planning support device of the present embodiment, the storage unit further stores information on a plurality of scenarios consisting of different combinations of process groups assumed for the work, and the calculation unit calculates the predicted work volume. , a constraint function that is minimized when each of the constraints of the number of people who can work, the processing capacity, the standard processing amount, the scenario, and the completion time limit, and the number of working people and the processing amount are satisfied; Regarding the objective function included as a term, an Ising model is calculated in which the number of personnel assigned to each team is set as spin, and the sensitivity between variables in the constraint condition function is set as the strength of interaction between the spins, and the Based on the result, information on the number of personnel assigned to each team for each time period in each process of the process group of each of the plurality of scenarios may be output to a predetermined device.

According to this, it is possible to handle operations with a large number of viewpoints, where multiple scenarios are assumed, and to more efficiently determine the number of people required for each process in each time period in operations that consist of multiple sequential processes. Calculation becomes possible.

Further, in the personnel planning support device of the present embodiment, the calculation unit generates one or more patterns in which the number of people who can attend work is varied from the initial value, and executes the calculation of the Ising model for each pattern, Among the results of the calculation, a pattern is determined that satisfies at least one of the following conditions: the total number of staff assigned to all teams is the minimum, or the time required to complete the task is the shortest, and the pattern in each of the steps corresponding to the pattern is determined. Information on the number of personnel assigned to each team for each time period may be output.

According to this, it is possible to efficiently determine the most ideal team/number of people for each process in terms of reducing the total number of required people or speeding up the task completion time. As a result, it becomes possible to more efficiently calculate the number of people required for each process in each time period in a business that consists of a plurality of sequential processes.

Further, in the personnel planning support device of the present embodiment, the calculation unit calculates the result of the calculation when the initial value of the number of people who can attend work is adopted, and the result of the calculation corresponding to a pattern that satisfies the condition. Based on the result of the calculation when the initial value of the number of people who can work is adopted in order to establish the number of personnel assigned corresponding to the pattern that satisfies the condition, the content of personnel accommodation between teams is determined. It is also possible to further execute the processing to be performed.

According to this, by comparing "the number of people per team/process calculated based on the number of people who can work for each team" and "the most ideal number of people per team/process", it is possible to provide support/receiving for each team. It becomes possible to decide the number of people to support. As a result, it becomes possible to more efficiently calculate the number of people required for each process in each time period in a business that consists of a plurality of sequential processes.

Further, in the personnel planning support device of the present embodiment, the calculation unit may determine the content of personnel accommodation between bases where the team is located, as the content of personnel accommodation between the teams. good.

According to this, it is possible to calculate the optimal required number of people for all bases, so it is possible to determine not only the number of people to be supported/supported within a base, but also the number of people to be supported/supported between bases. As a result, it becomes possible to more efficiently calculate the number of people required for each process in each time period in a business that consists of a plurality of sequential processes.

Furthermore, in the personnel planning support device of the present embodiment, the storage unit further holds information on the amount of work newly introduced for each time period as the predicted amount of work, and the calculation unit stores information on the amount of work newly introduced for each time period. A constraint that is minimized when each constraint of the predicted workload including the workload, the number of people who can work, the processing capacity, the standard processing amount, and the completion time limit, and the number of working people and the processing amount are satisfied. Regarding the objective function that includes the condition function as a term, an Ising model is created in which the number of staff assigned to each team is the spin, and the sensitivity between variables in the constraint function is set as the strength of the interaction between the spins. It is also possible to say that it is something that is calculated.

According to this, it becomes possible to handle various business flows that cannot be calculated using a one-way processing flow. Specifically, this includes tasks that involve branching in the flow, such as confirmation processing and remand, tasks that require a wait such as not being able to proceed to the next step until two processes are completed, and tasks that involve a wait such as the number of projects (infrastructure flow rate, traffic flow etc.) during the task. It can also be applied to tasks where the amount of work (which may also include the concept of quantity) increases. As a result, it becomes possible to more efficiently calculate the number of people required for each process in each time period in a business that consists of a plurality of sequential processes.

Further, in the personnel formulation support device of this embodiment, it may be a CMOS annealing machine that solves a combinatorial optimization problem regarding the Ising model.

According to this, the operation of the Ising model is simulated by a circuit using semiconductor elements such as CMOS (Complementary Metal Oxide Semiconductor), and combinations that take into account constraints that influence each other, that is, nonlinear constraints, are proposed. It becomes possible to efficiently find practical solutions to optimization problems at room temperature. As a result, it becomes possible to efficiently calculate the number of people required for each process in each time period in a business that consists of a plurality of sequential processes.

10 Network 100 Personnel formulation support device (annealing machine)
101 Storage unit 102 Program 1021 Ising model 103 Memory 104 Arithmetic unit 105 Communication unit 125 Basic information DB
126 Constraint DB
200 User terminal

Claims (8)

For work that consists of a series of processes to be executed sequentially, the predicted work volume for each time period, the number of people who can work in each team collaborating in the work, the processing capacity of each of the processes in each team, and the processing capacity of each of the processes. Information on the standard processing amount and the time limit for completing the work, constraints on the number of people working in each team for each time period, and constraints on the processing amount in each process when sequentially executing the series of steps. a storage unit storing each piece of information;
a constraint function that is minimized when each constraint of the predicted workload, the number of people who can attend work, the processing capacity, the standard processing amount, and the completion time limit, and the number of working people and the processing amount are satisfied; a calculation unit that calculates an Ising model in which the number of personnel assigned to each team is set as a spin, and the sensitivity between variables in the constraint condition function is set as the strength of interaction between the spins, with respect to an objective function including as a term; and has
The calculation unit outputs information on the number of personnel assigned to each team for each time period in each of the processes to a predetermined device, based on the result of the calculation.
A personnel planning support device characterized by: The storage unit includes:
further storing information on a plurality of scenarios consisting of different combinations of process groups assumed regarding the business;
The arithmetic unit is
A constraint condition that is minimized when each of the predicted workload, the number of people who can attend work, the processing capacity, the standard processing amount, the scenario, and the completion time limit, and the constraints of the number of working people and the processing amount are satisfied. Regarding the objective function that includes the constraint function as a term, calculate an Ising model in which the number of personnel assigned to each team is the spin, and the sensitivity between variables in the constraint function is set as the strength of the interaction between the spins. death,
Based on the result of the calculation, information on the number of personnel assigned to each team for each time period in each process of the process group of each of the plurality of scenarios is output to a predetermined device;
The personnel planning support device according to claim 1, characterized in that: The arithmetic unit is
Generate one or more patterns in which the number of people who can attend work is varied from the initial value, and execute the calculation of the Ising model for each pattern,
Among the results of the calculation, a pattern is determined that satisfies at least one of the following conditions: the total number of staff assigned to all teams is the minimum, or the time required to complete the task is the shortest, and the pattern in each of the steps corresponding to the pattern is determined. outputting information on the number of personnel assigned to each team for each time period;
The personnel planning support device according to claim 1, characterized in that: The arithmetic unit is
Based on the result of the calculation when the initial value of the number of people who can attend work is adopted and the result of the calculation corresponding to the pattern that satisfies the condition, establish the number of personnel assigned corresponding to the pattern that satisfies the condition. Therefore, based on the result of the calculation when the initial value of the number of people who can work is adopted, a process is further executed to determine the content of personnel accommodation between teams.
The personnel planning support device according to claim 3, characterized in that: The arithmetic unit is
As the content of personnel accommodation between the teams, the content of personnel accommodation between the bases where the team is located is determined;
5. The personnel planning support device according to claim 4. The storage unit includes:
As the predicted workload, information on the amount of newly introduced workload for each time period is also maintained,
The arithmetic unit is
When the predicted workload including the newly input workload, the number of people who can work, the processing capacity, the standard processing amount, the completion time limit, and the constraint conditions of the number of working people and the processing amount are satisfied. Regarding the objective function that includes as a term a constraint function that is the minimum in It calculates the set Ising model,
The personnel planning support device according to claim 1, characterized in that: 2. The personnel planning support device according to claim 1, being a CMOS annealing machine that solves a combinatorial optimization problem regarding the Ising model. For work that consists of a series of processes to be executed sequentially, the predicted work volume for each time period, the number of people who can work in each team collaborating in the work, the processing capacity of each of the processes in each team, and the processing capacity of each of the processes. Information on the standard processing amount and the time limit for completing the work, constraints on the number of people working in each team for each time period, and constraints on the processing amount in each process when sequentially executing the series of steps. An information processing device including a storage section storing each piece of information,
a constraint function that is minimized when each constraint of the predicted workload, the number of people who can attend work, the processing capacity, the standard processing amount, and the completion time limit, and the number of working people and the processing amount are satisfied; Regarding the objective function including as a term, calculate an Ising model in which the number of personnel assigned to each team is set as spin, and the sensitivity between variables in the constraint condition function is set as the strength of interaction between the spins,
Based on the result of the calculation, outputting information on the number of personnel assigned to each team for each time period in each of the steps to a predetermined device;
A personnel planning support method characterized by the following.

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