Information processing apparatus, information processing method, and program

Abstract

To provide an information processing apparatus, method and program for determining a rental reservation (schedule) so as to reduce deadhead mobile bodies by using an Ising machine.SOLUTION: An information processing apparatus includes: a Hamiltonian generation unit which generates, by using reservation information, mobile body quantity information, reservation quantity information, allocation slot quantity information and a decision variable for deciding an allocation slot of a mobile body to which a reservation is to be allocated, a one reservation-to-one mobile body constraint in which one reservation is allocated to one mobile body, and a deadhead time cost for reducing a deadhead time for deadheading a mobile body when an arrival position for a preceding reservation is different from a departure position of a following reservation for adjacent reservations that are allocated to the reservation slots of the same mobile body, and generates a Hamiltonian by using the one reservation-to-one mobile body constraint and the deadhead time cost; and a schedule generation unit which inputs the generated Hamiltonian to an Ising machine to generate a schedule with reduced deadhead, based on result information output by the Ising machine.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to an information processing device, an information processing method, and a program that determine a reservation using an Ising machine. [Background technology]

[0002] When reserving a rental car, the current system determines the reservation based on the user's desired reservation period and rental car class (model and class). However, if the store you are starting to use does not have the rental car you want in stock, a rental car must be sent from another store to a time when no reservations are available.

[0003] As a related technique, Patent Document 1 discloses a schedule creation support device that efficiently creates a schedule using nonlinear constraints for many personnel working together on a specified task. According to the schedule creation support device of Patent Document 1, an Ising model is calculated in which the presence or absence of each personnel's attendance is set as a spin and the sensitivity between variables in the constraint function is set as the strength of interaction between the spins, with respect to an objective function whose terms are the total work length of each personnel working together on a specified task over a specified period, the number of personnel required at each timing within the period, and a constraint function that is minimized when the constraint conditions for assigning each personnel to the task are satisfied. The schedule creation support device then includes a calculation unit that outputs a schedule that specifies the presence or absence of each personnel's attendance at each timing within the specified period based on the calculation results. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2020-170422 Summary of the Invention [Problem to be solved by the invention]

[0005] However, Patent Document 1 uses an Ising model to create a schedule that specifies whether each employee will be at work at each timing during a predetermined period, and does not relate to reservations for rental of moving objects such as rental cars, nor does it relate to reservations that reduce the delivery time of rental cars.

[0006] An example of an objective of the present disclosure is to use an Ising machine to determine a rental reservation (schedule) that reduces the number of trips of moving objects. [Means for solving the problem]

[0007] In order to achieve the above object, an information processing device according to one aspect of the present disclosure includes: a Hamiltonian generator that generates a Hamiltonian using the one-reservation-one-trip constraint that allocates one reservation to one mobile object as a constraint equation, and a path time cost for reducing the path time for forwarding the mobile object when the arrival location of an earlier reservation and the departure location of a later reservation are different between adjacent reservations allocated to the same mobile object's path slots, using reservation identifiers that identify reservations, reservation information including start and end times representing a rental period for the mobile object reserved by the user, departure and arrival locations of the mobile object, and desired conditions for the mobile object of the user, mobile object number information representing the number of mobile objects to be rented, reservation number information representing the number of reservations set by multiple users, allocation slot number information representing the number of allocation slots representing the chronological order in which reservations are allocated to the mobile objects, and decision variables for determining to which of the allocation slots of the mobile object the reservation should be allocated; and A schedule generation unit that inputs the generated Hamiltonian into an Ising machine and generates a schedule that combines the reservation in which the forwarding is reduced and the mobile body based on result information output by the Ising machine; The present invention is characterized by having the following.

[0008] In order to achieve the above object, an information processing method according to one aspect of the present disclosure includes: The information processing device and generating a Hamiltonian using the reservation identifier for identifying the reservation, reservation information including a start time and end time representing the rental period of the mobile object reserved by the user, the departure and arrival locations of the mobile object, and the user's desired conditions for the mobile object, mobile object number information representing the number of mobile objects to be rented, reservation number information representing the number of reservations set by multiple users, allocation slot number information representing the number of allocation slots representing the chronological order in which reservations are allocated to the mobile objects, and a decision variable for determining to which of the allocation slots of the mobile object the reservation should be allocated, to generate, as constraint equations, a one-reservation-one-trip constraint for allocating one reservation to one mobile object and a dead-travel time cost for reducing the dead-travel time for forwarding the mobile object when the arrival location of an earlier reservation and the departure location of a later reservation differ between adjacent reservations allocated to the allocation slots of the same mobile object; and generating a Hamiltonian using the one-reservation-one-trip constraint and the dead-travel time cost. The generated Hamiltonian is input to an Ising machine, and a schedule is generated that combines the reservation with the reduced forwarding and the mobile body based on the result information output by the Ising machine. The present invention is characterized by the fact that it executes processing.

[0009] Furthermore, in order to achieve the above object, a program according to one aspect of the present disclosure comprises: On the computer, and generating a Hamiltonian using the reservation identifier for identifying the reservation, reservation information including a start time and end time representing the rental period of the mobile object reserved by the user, the departure and arrival locations of the mobile object, and the user's desired conditions for the mobile object, mobile object number information representing the number of mobile objects to be rented, reservation number information representing the number of reservations set by multiple users, allocation slot number information representing the number of allocation slots representing the chronological order in which reservations are allocated to the mobile objects, and a decision variable for determining to which of the allocation slots of the mobile object the reservation should be allocated, to generate, as constraint equations, a one-reservation-one-trip constraint for allocating one reservation to one mobile object and a dead-travel time cost for reducing the dead-travel time for forwarding the mobile object when the arrival location of an earlier reservation and the departure location of a later reservation differ between adjacent reservations allocated to the allocation slots of the same mobile object; and generating a Hamiltonian using the one-reservation-one-trip constraint and the dead-travel time cost. The generated Hamiltonian is input to an Ising machine, and a schedule is generated that combines the reservation with the reduced forwarding and the mobile body based on the result information output by the Ising machine. The present invention is characterized in that it causes the processing to be executed. [Effects of the Invention]

[0010] As described above, according to the present disclosure, it is possible to use an Ising machine to determine a rental reservation (schedule) that reduces the number of forward trips of moving objects. [Brief explanation of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating an example of an information processing device. [Figure 2] FIG. 2 is a diagram illustrating an example of a system including an information processing device. [Figure 3] FIG. 3 is a diagram illustrating the constraints of each of the constraint condition expressions (H1 to H9). [Figure 4] FIG. 4 is a diagram illustrating an example of the data structure of the mobile reservation information. [Figure 5] FIG. 5 is a diagram illustrating an example of a data structure of the mobile object information. [Figure 6] FIG. 6 is a diagram illustrating an example of the data structure of reservation information. [Figure 7] FIG. 7 is a diagram for explaining the decision variables. [Figure 8] FIG. 8 is a diagram for explaining the one-reservation-one-move agreement. [Figure 9] FIG. 9 is a diagram for explaining allocation filling constraints. [Figure 10] FIG. 10 is a diagram for explaining the initial unusable constraint. [Figure 11] FIG. 11 is a diagram for explaining the allocation order constraint. [Figure 12] FIG. 12 is a diagram for explaining the moving object condition constraint. [Figure 13] FIG. 13 is a diagram for explaining the overlap time cost. [Figure 14] FIG. 14 is a diagram for explaining the idle time cost. [Figure 15] FIG. 15 is a diagram for explaining the deadhead time cost. [Figure 16] FIG. 16 is a diagram for explaining the assigned mobile object change cost. [Figure 17] FIG. 17 is a diagram for explaining allocation of decision variables, quantum annealing, and decoding. [Figure 18] FIG. 18 is a diagram illustrating an example of the operation of the information processing device. [Figure 19] FIG. 19 is a diagram illustrating an example of a computer that realizes the information processing device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0012] Hereinafter, embodiments will be described with reference to the drawings. In the drawings described below, elements having the same or corresponding functions are denoted by the same reference numerals, and repeated description thereof may be omitted.

[0013] (Embodiment) The configuration of an information processing device in an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram illustrating an example of an information processing device.

[0014] [Device configuration] The information processing device 10 shown in FIG. 1 is a device (a schedule generation device using an Ising machine) that uses an Ising machine (for example, a quantum annealing machine, a pseudo quantum annealing machine, or the like) to determine rental reservations for moving objects. Here, the moving object is, for example, a moving object that can be rented, such as a vehicle, an aircraft, or a ship. However, the moving object is not limited to the above-mentioned vehicle, aircraft, or ship. Note that hereinafter, the moving object may be described as a vehicle such as a rental car.

[0015] Next, the information processing device 10 shown in FIG. 1 includes a Hamiltonian generating unit 11 and a schedule generating unit 12.

[0016] The Hamiltonian generation unit 11 generates a one reservation one movement agreement (H1) that allocates one reservation to one moving object using reservation identifiers that identify the reservation, reservation information including start and end times that represent the rental period of the moving object reserved by the user, departure and arrival positions of the moving object, and the user's desired conditions for the moving object, moving object number information that represents the number of moving objects to be rented, reservation number information that represents the number of reservations set by multiple users, allocation slot number information that represents the number of allocation slots that represent the chronological order in which reservations are allocated to moving objects, and decision variables that determine to which allocation slot of a moving object the reservation should be allocated.

[0017] Furthermore, the Hamiltonian generation unit 11 generates a delivery time cost (H8) for reducing the delivery time for delivering the mobile body when the arrival position of the earlier reservation and the departure position of the later reservation are different between adjacent reservations allocated to the same mobile body allocation slot.

[0018] Thereafter, the Hamiltonian generating unit 11 generates a Hamiltonian (H) using the one reservation one movement constraint (H1) and the deadhead time cost (H8).

[0019] The schedule generation unit 12 inputs the generated Hamiltonian (H) into an Ising machine, and generates a schedule that combines reservations and mobile objects with reduced forwarding based on the result information output by the Ising machine (determines rental reservations).

[0020] In this way, in the embodiment, by providing a formulation method for solving an optimization problem for determining rental reservations for moving objects, it is possible to use an Ising machine to determine a schedule (rental reservation) that reduces the number of moving object trips.

[0021] [System Configuration] The configuration of the information processing device 10 in the embodiment will be described more specifically with reference to Fig. 2. Fig. 2 is a diagram illustrating an example of a system including an information processing device.

[0022] 2 includes an information processing device 10, a quantum computer 20, a terminal device 30, and an output device 40. The information processing device 10, the quantum computer 20, the terminal device 30, and the output device 40 are connected via a network.

[0023] The network is a general network constructed using communication lines such as the Internet, LAN (Local Area Network), dedicated lines, telephone lines, corporate networks, mobile communication networks, Bluetooth (registered trademark), and Wi-Fi (Wireless Fidelity) (registered trademark).

[0024] The processing device 10 is, for example, an information processing device such as a CPU (Central Processing Unit), a programmable device such as an FPGA (Field-Programmable Gate Array), a GPU (Graphics Processing Unit), or a circuit equipped with one or more of these, or a server computer.

[0025] The quantum computer (Ising machine) 20 is hardware for solving the optimization problem of determining the breakdown. The optimization problem of determining the breakdown is solved using, for example, a QUBO (Quadratic Unconstrained Binary Optimization) solver. Examples of QUBO solvers include quantum annealing and pseudo-quantum annealing.

[0026] The terminal device 30 is, for example, an information processing device such as a CPU, a programmable device such as an FPGA, a GPU, or a circuit equipped with any one or more of these, a personal computer, or a mobile terminal.

[0027] The output device 40 acquires output information (described later) converted into an outputtable format, and outputs generated images, sounds, etc. based on the output information. The output device 40 is, for example, an image display device using a liquid crystal, an organic EL (Electro Luminescence), or a CRT (Cathode Ray Tube). Furthermore, the image display device may also include an audio output device such as a speaker. The output device 40 may also be a printing device such as a printer.

[0028] The information processing device 10 will be described in detail. The information processing device 10 includes an acquisition unit 13 , a Hamiltonian generation unit 11 , a schedule generation unit 12 , an adjustment unit 14 , and an output information generation unit 15 .

[0029] <Acquisition part> The acquisition unit 13 acquires reservation information transmitted by the user using the terminal device 30, and stores the acquired reservation information in a storage device (not shown).

[0030] Specifically, first, the user determines a reservation (at least the rental start time, end time, departure location, arrival location, and desired conditions) using the user interface of the terminal device 30. Next, the terminal device 30 generates reservation information and transmits the generated reservation information from a communication unit (not shown) of the terminal device 30 to the processing device 10. Next, the acquisition unit 13 acquires the reservation information via the communication unit (not shown) of the information processing device 10 and stores the reservation information in a storage device. Note that the above-mentioned storage device may be provided inside or outside the information processing device 10. Furthermore, the reservation is made using the terminal device 30 at a timing of the user's choice.

[0031] The reservation information includes, for example, a reservation number (reservation identifier) ​​that identifies the reservation, start and end times indicating the rental period of the vehicle reserved by the user, the departure and arrival locations of the vehicle, and information indicating the user's desired conditions for the vehicle.

[0032] The reservation number (reservation identifier) ​​can be expressed, for example, as reservation 0, reservation 1, reservation 2, reservation 3, etc. However, it is not limited to the reservation number example mentioned above. The start time and end time can be expressed, for example, as year, month, date, and time. However, it is not limited to year, month, date, and time. The departure location can be expressed, for example, as the name of the store from which the mobile object departs. Furthermore, the arrival location can be expressed, for example, as the name of the store to which the user returns the mobile object after the rental ends. However, the departure location and arrival location are not limited to the store name. For example, in the case of a vehicle, the desired conditions can be expressed, for example, as the model, class, and color of the mobile object desired by the user. However, the desired conditions are not limited to the conditions mentioned above.

[0033] <Hamiltonian generation part> First, the Hamiltonian generating unit 11 acquires information (reservation information, moving object number information, reservation number information, allocated slot number information, etc.) required to generate a Hamiltonian (H) from a storage device.

[0034] Next, the Hamiltonian generation unit 11 generates constraint equations based on the acquired information. Specifically, the Hamiltonian generation unit 11 generates a one-reservation-one-move constraint (H1: first constraint equation) and a dead-end time cost (H8: eighth constraint equation). The Hamiltonian generation unit 11 may also generate one or more of an allocation filling constraint (H2: second constraint equation), an initial unavailability constraint (H3: third constraint equation), an allocation order constraint (H4: fourth constraint equation), a mobile object condition constraint (H5: fifth constraint equation), an overlap time cost (H6: sixth constraint equation), a vacant time cost (H7: seventh constraint equation), and an allocated mobile object change cost (H9: ninth constraint equation).

[0035] Figure 3 is a diagram that explains the constraints of each of the constraint condition formulas (H1 to H9). A detailed explanation of the constraint condition formulas (H1 to H9) will be given later. Note that the above-mentioned constraints are conditions that must be observed absolutely. The smaller the cost (the closer to 0), the better.

[0036] Next, the Hamiltonian generation unit 11 generates a Hamiltonian (H=K1×H1+K8×H8) using at least a coefficient K1 for adjusting the one reservation one movement agreement (H1) and a coefficient K8 for adjusting the transit time cost (H8).

[0037] Furthermore, the Hamiltonian generating unit 11 may generate an allocation filling constraint (H2) and generate a Hamiltonian (H=K1×H1+K2×H2+K8×H8) including the allocation filling constraint (H2).

[0038] Furthermore, the Hamiltonian generation unit 11 may generate a Hamiltonian (H) by using the above-mentioned Hamiltonian (H=K1×H1+K2×H2+K8×H8) along with constraint equations (one or more of H3, H4, H5, H6, H7, and H9) and their corresponding coefficients (K3, K4, K5, K6, K7, and K9).

[0039] For example, when all constraints (constraint condition equations: H1 to H9) are used, the Hamiltonian generation unit 11 calculates a coefficient K1 for adjusting the one reservation one movement constraint (H1), a coefficient K2 for adjusting the allocation filling constraint (H2), a coefficient K3 for adjusting the initial unavailability constraint (H3), a coefficient K4 for adjusting the allocation order constraint (H4), a coefficient K5 for adjusting the mobile unit condition constraint (H5), and an overlap time cost (H6). A Hamiltonian (H=(K1×H1)+(K2×H2)+(K3×H3)+(K4×H4)+(K5×H5)+(K6×H6)+(K7×H7)+(K8×H8)+(K9×H9)) is generated using coefficient K6 for adjusting the idle time cost (H7), coefficient K7 for adjusting the deadhead time cost (H8), and coefficient K9 for adjusting the assigned mobile unit change cost (H9).

[0040] <<Explanation of constraint formulas H1 to H9>> In the following, for each of the constraint formulas H1 to H9, (i) an explanation of the constraints, (ii) the constraint formula (general formula), and (iii) the constraint formula based on the embodiment will be explained. In the embodiment, the reservation of a rental car will be explained.

[0041] Also, to make the explanation easier to understand, we will explain the case where the number of stores is 3 (Store 1, Store 2, Store 3), the number of reservations is 4 (Reservation 0, Reservation 1, Reservation 2, Reservation 3), the number of rental cars (number of vehicles) is 2 (Vehicle 1, Vehicle 2), and the number of allocation slots is 4 (Allocation slot 0, Allocation slot 1, Allocation slot 2, Allocation slot 3).

[0042] FIG. 4 shows mobile object reservation information that indicates the relationship (allocation) between reservations and mobile objects (rental cars (vehicles)). FIG. 4 is a diagram showing an example of the data structure of mobile object reservation information. In the example of FIG. 4, reservations 0 and 2 are allocated to vehicle 1, and reservations 1 and 3 are allocated to vehicle 2. When a reservation is made by a user, the information processing device 10 generates mobile object reservation information and stores it in the storage device.

[0043] Furthermore, mobile object information indicating the specifications of the rental car (vehicle) required for reservation is stored in advance in a storage device. FIG. 5 is a diagram showing an example of the data structure of mobile object information. In the example of FIG. 5, the color of vehicle 1 is shown to be red, and the color of vehicle 2 is shown to be blue. Note that in the example of FIG. 5, only the color of the vehicle is shown for ease of explanation, but in reality, for example, the class of the vehicle and other information may also be included.

[0044] An example of reservation information showing the details of the above-mentioned reservations is shown in Fig. 6. Fig. 6 is a diagram showing an example of the data structure of reservation information. In the example of Fig. 6, the reservation details (start time, end time, departure location, arrival location, desired conditions) for each of four reservations (reservation 0 to reservation 3) are shown.

[0045] An example of the decision variables for determining whether or not to allocate a reservation to which allocation slot of a mobile object is shown in FIG. 7. FIG. 7 is a diagram for explaining the decision variables. In the example of FIG. 7, the decision variables x for each of four reservations (reservation 0 to reservation 3) are i,j,k is constructed as a three-dimensional array using reservations, vehicles, and allocation frames. The decision variables x shown in Fig. 7 i,j,k where i represents a reservation, j represents a vehicle, and k represents an allocation slot. That is, the decision variable x i,j,k determines whether to allocate reservation i to the k-th allocation slot of vehicle j. In the example of Figure 7, since allocation slots 0 and 1 of vehicle 1 have flags set to "1", reservation 0 is allocated to allocation slot 0 of vehicle 1, and reservation 2 is allocated to allocation slot 1. Since allocation slots 0 and 1 of vehicle 2 have flags set to "1", reservation 3 is allocated to allocation slot 0 of vehicle 1, and reservation 1 is allocated to allocation slot 1.

[0046] ●1 reservation 1 travel arrangement (H1) (i) Explanation of constraints The one reservation one movement constraint (H1: first constraint) is a constraint that assigns one reservation to one moving object. The one reservation one movement constraint (H1: first constraint) calculates the cost.

[0047] Figure 8 is a diagram to explain the one-reservation-one-move agreement. In Figure 8A, each reservation is assigned to one vehicle, so the cost is 0. In Figure 8B, reservation 0 is assigned to two vehicles (allocation slot 0 for vehicle 1 and allocation slot 2 for vehicle 2), so the cost increases.

[0048] (ii) Constraint expression (general expression) The one-reservation, one-move constraint (H1: first constraint) can be expressed as in Equation 1.

[0049]

number

[0050] Note that carNum represents the number of moving objects (number of moving objects), rsvNum represents the number of reservations (number of reservations), and orderNum represents the number of allocation slots (number of allocation slots). ijk represents the decision variable that indicates whether reservation i should be assigned to the kth mobile j.

[0051] (iii) Constraint equations based on the example In the above-described embodiment, the one-reservation-one-movement rule (H1) is expressed as in Equation 2.

[0052]

number

[0053] ●Allocation filling constraint (H2) (i) Explanation of constraints The allocation filling constraint (H2: second constraint) is a constraint that the allocation slots for the same mobile object are filled with reservations one by one, starting from the first allocation slot (allocation slot 0).The allocation filling constraint (H2: second constraint) calculates the cost.

[0054] Figure 9 is a diagram for explaining allocation filling constraints. In Figure 9A, reservations 0 to 4 are filled one by one, starting from allocation slot 0, so the cost is 0. Also, in Figure 9B, for vehicle 1, allocation slots 0 and 2 are 1 (filled), but allocation slot 1 is 0, so the cost increases. Also, for vehicle 2, allocation slot 0 is filled with two reservations, reservation 1 and reservation 3, so the cost increases.

[0055] (ii) Constraint expression (general expression) The allocation fill constraint (H2: second constraint formula) can be expressed as in Formula 3. The two items in the curly brackets of H2 take the difference between the previous and next allocations, but the first and third items have special terms (virtual allocation differences). Also, the -1 in the fourth item in the curly brackets of H2 is provided to adjust the minimum value of the overall formula to 0.

[0056]

number

[0057] (iii) Constraint equations based on the example In the above embodiment, the allocation filling constraint (H2) is expressed as in Equation 4.

[0058]

number

[0059] ●Initial unavailability restriction (H3) (i) Explanation of constraints The initial unavailable constraint (H3: third constraint condition formula) is a constraint that prevents reservations allocated to the first allocation slot (allocation slot 0) from being allocated to a mobile object outside of the available time period, including the out-of-service time and rental preparation time. The initial unavailable constraint (H3: third constraint condition formula) calculates the cost.

[0060] FIG. 10 is a diagram illustrating the initial unavailability constraint. For example, if the start times of the reservations are 12:00 for reservation 0, 4:00 for reservation 1, 2:00 for reservation 2, and 2:00 for reservation 3, and the end time (return time) of the currently rented vehicle 2 is 1:00, then in A of FIG. 10, available reservation 0 is assigned to allocation slot 0 of vehicle 1. Also, available reservation 3 is assigned to allocation slot 0 of vehicle 2. Therefore, the cost is 0. In contrast, in B of FIG. 10, vehicle 2, which has reservation 1 assigned to allocation slot 0, will not make it in time for the start time of the reservations, so the cost increases.

[0061] (ii) Constraint expression (general expression) The initial unavailability constraint can be expressed as Equation 5.

[0062]

number

[0063] In addition, δ1 of H3 (i,j) is a function that is set to 1 when either condition (1) or (2) is met, and is set to 0 when neither condition (1) or (2) is met. (i,j) The above-mentioned conditions (1) and (2) that determine the above are as follows:

[0064] (1) If the rental vehicle j does not arrive in time for the reservation start time, taking into account the return time, transportation time, and rental preparation time. (2) If the vehicle j is in stock and cannot make it in time for the reservation start time, taking into account the transportation time and rental preparation time.

[0065] (iii) Constraint equations based on the example In the above embodiment, the initial unavailability constraint (H3) is expressed as in Equation 6.

[0066]

number

[0067] ● Allocation order constraint (H4) (i) Explanation of constraints The allocation order constraint (H4: fourth constraint condition formula) is a constraint that fills the reservations allocated to the allocation slot of the same mobile object in order of start time. The allocation order constraint (H4: fourth constraint condition formula) calculates the cost.

[0068] Figure 11 is a diagram for explaining allocation order constraints. For example, if the start times of each reservation are as follows: reservation 0 is midnight, reservation 1 is 4 o'clock, reservation 2 is 2 o'clock, and reservation 3 is 2 o'clock, in Figure 11A, the reservations for each vehicle are in ascending order of start time, so the cost is 0. In contrast, in Figure 11B, vehicle 1 is allocated in the order of reservation 2 → reservation 0, but the start times are not in ascending order, so the cost increases.

[0069] (ii) Constraint expression (general expression) The allocation order constraint can be expressed as in Equation 7.

[0070]

number

[0071] In addition, δ2 of H4 (i1,j2) is a function that is 1 in the case of condition (3) and 0 in the case of condition (4). (i1,j2) The above-mentioned conditions (3) and (4) that determine the above are as follows:

[0072] (3) If reservation i2 starts earlier than reservation i1 (4) If the start time of reservation i2 is later than reservation i1

[0073] (iii) Constraint equations based on the example In the above embodiment, the allocation order constraint (H4) is expressed as in Equation 8.

[0074]

number

[0075] ●Moving object condition constraints (H5) (i) Explanation of constraints The mobile unit condition constraint (H5: fifth constraint) is a constraint that limits the mobile units assigned to each reservation to those that meet the user's desired conditions. The mobile unit condition constraint (H5: fifth constraint) calculates the cost.

[0076] Figure 12 is a diagram for explaining mobile object condition constraints. For example, if the color of vehicle 1 is red, the color of vehicle 2 is blue, and the desired conditions for each reservation are red for reservation 0, blue for reservation 1, red for reservation 2, and no specification for reservation 3, then in A of Figure 12, the color of the vehicle assigned to each reservation satisfies the desired color of the reservation, so the cost is 0. In contrast, in B of Figure 12, vehicle 2, assigned to reservation 0, does not satisfy the desired color, so the cost increases.

[0077] (ii) Constraint expression (general expression) The mobile condition constraint can be expressed as follows:

[0078]

number

[0079] In addition, δ3 of H5 (i,j) is a function that is 1 if condition (5) is met and 0 if condition (6) is met. (i,j) The above-mentioned conditions (5) and (6) that determine the above are as follows:

[0080] (5) If mobile j does not meet the desired conditions of reservation i (6) If mobile j satisfies the desired conditions of reservation i

[0081] (iii) Constraint equations based on the example In the above-described embodiment, the moving object condition constraint (H5) is expressed as in Equation 10.

[0082]

number

[0083] ●Overlap time cost (H6) (i) Explanation of constraints The overlap time cost (H6: sixth constraint) is a constraint that reduces the overlap time (including the outgoing time and rental preparation time) between adjacent reservations assigned to the same mobile unit's allocation slot. The overlap time cost (H6: sixth constraint) calculates the cost. Specifically, the overlap time cost is calculated by adding up the squared cost of the overlap time between adjacent reservations assigned to the same mobile unit's allocation slot.

[0084] Figure 13 is a diagram to explain overlapping time costs. For example, if the start and end times of each reservation are 12:00-1:00 for reservation 0, 4:00-5:00 for reservation 1, 2:00-5:00 for reservation 2, and 2:00-3:00 for reservation 3, then in Figure 13A, there is no overlapping time between reservations assigned to adjacent slots on the same vehicle, so the cost is 0. In contrast, in Figure 13B, there is a 1-hour overlap between reservation 3 and reservation 2, both assigned to vehicle 2, so the cost increases.

[0085] (ii) Constraint expression (general expression) The overlap time cost can be expressed as follows:

[0086]

number

[0087] The Overlap1 function takes reservations i1 and i2 as arguments and returns the overlap value of the usage time of the two reservations i1 and i2. The MIN and MAX functions used in the Overlap1 function compare the arguments and return the smaller and larger arguments, respectively.

[0088] Specifically, the Overlap1 function first calculates MIN(end time of reservation i1, end time of reservation i2) - MAX(start time of reservation i1, start time of reservation i1). Next, if the calculation result is positive, there is overlap time, so the calculated value is returned, otherwise it returns 0. Note that the end time is calculated not only based on the reserved usage time, but also on the rental preparation time, etc.

[0089] (iii) Constraint equations based on the example In the above embodiment, the overlap time cost (H6) is expressed as in Equation 12.

[0090]

number

[0091] In the example of reservations 2 and 3, Overlap1(2,3) = 1 (= MIN(5,3) - MAX(2,2) = 3 - 2). Note that the actual calculation also takes into account the rental preparation time and delivery time.

[0092] ●Idle time cost (H7) (i) Explanation of constraints The idle time cost (H7: seventh constraint condition formula) is a constraint for keeping the idle time (including the outgoing time and rental preparation time) of adjacent reservations allocated to the same mobile unit's allocation slot within a pre-set range. The idle time cost (H7: seventh constraint condition formula) calculates the cost. Specifically, the idle time cost is calculated by adding up the cost according to the idle time of adjacent reservations allocated to the same mobile unit's allocation slot.

[0093] Figure 14 is a diagram to explain the cost of available time. For example, if the start time and end time of each reservation are 12:00-1:00 for reservation 0, 4:00-5:00 for reservation 1, 2:00-5:00 for reservation 2, and 2:00-3:00 for reservation 3, in A of Figure 14, there is no available time for reservations allocated to the slots before and after the same vehicle, so the cost is 0. In contrast, in B of Figure 14, there is available time (2:00, 3:00: undesirable available time) between reservation 0 and reservation 1 allocated to vehicle 1, so the cost increases.

[0094] (ii) Constraint expression (general expression) The idle time cost can be expressed as follows:

[0095]

number

[0096] The IntervalCost function returns the cost according to the value of the free time calculated by the Overlap2 function. The IntervalCost function can be expressed as in Equation 14.

number

[0097] In Expression 13, interval_min represents the minimum value of the range of undesirable free time that is set in advance. Interval_max represents the maximum value of the range of undesirable free time that is set in advance. The minimum value interval_min and maximum value interval_max of the undesirable free time are set in advance by the user using the terminal device 30.

[0098] Additionally, the Overlap2 function in equation 13 first calculates MIN(end time of reservation i1, end time of reservation i2) - MAX(start time of reservation i1, start time of reservation i1). Next, if the calculation result is negative, there is available time (negative number), so the calculated value is returned. If the value is non-negative, 0 is returned. Note that the end time is calculated not only including the reserved usage time, but also the rental preparation time, etc.

[0099] (iii) Constraint equations based on the example In the above-described embodiment, the idle time cost (H7) is expressed as in Equation 15.

[0100]

number

[0101] In the example of reservations 0 and 1, Overlap2(0,1) = -3 (= MIN(1,5) - MAX(0,4) = 1 - 4). Note that the actual calculation also takes into account the rental preparation time and delivery time.

[0102] For example, if an undesirable interval is set in advance as 90 to 180 minutes (1.5 to 3.0 hours), the cost will increase from 0 to less than 1 for the period from 0 to 90 minutes after the previous reservation (when there is no available time), and will be 1 for the period from 90 to 180 minutes (undesirable time). Once it exceeds 180 minutes (when the available time diverges to infinity), the cost will gradually decrease towards 0.

[0103] ●Transportation time cost (H8) (i) Explanation of constraints The deadhead time cost (H8: eighth constraint condition formula) is a constraint for reducing the deadhead time for moving a mobile object when the arrival location of the previous reservation and the departure location of the subsequent reservation are different for adjacent reservations assigned to the same mobile object allocation slot. The deadhead time cost (H8: eighth constraint condition formula) calculates the cost. Specifically, the deadhead time cost is calculated by adding the cost according to the deadhead time that occurs when the arrival location of the previous reservation and the departure location of the subsequent reservation are different.

[0104] Figure 15 is a diagram to explain the deadhead time cost. For example, if the departure location-arrival location for each reservation is store 1-store 1 for reservation 0, store 2-store 2 for reservation 1, store 1-store 2 for reservation 2, and store 3-store 1 for reservation 3, then in A of Figure 15, the arrival store for vehicle 1 for reservation 0 is the same as the departure store for reservation 2, and the arrival store for vehicle 2 for reservation 3 is the same as the departure store for reservation 1. Therefore, the cost is 0. In contrast, in B of Figure 15, reservation 1 → reservation 2 assigned to vehicle 1 requires a deadhead from store 2 to store 1, so the cost increases.

[0105] (ii) Constraint expression (general expression) The idle time cost can be expressed as follows:

[0106]

number

[0107] The OutSRV function of the formula 16 calculates the forwarding time from reservation i1 to reservation i2 when reservations i1 and i2 are given as arguments.

[0108] (iii) Constraint equations based on the example In the above-described embodiment, the deadhead time cost (H8) is expressed as in Equation 17.

[0109]

number

[0110] ●Allocated mobile unit change cost (H9) (i) Explanation of constraints The assigned mobile object change cost (H9: ninth constraint) is a constraint to reduce the allocation of a mobile object different from the mobile object previously assigned to the allocation slot. The assigned mobile object change cost (H9: ninth constraint) calculates the cost. Specifically, the assigned mobile object change cost is added when a mobile object different from the existing allocation is assigned.

[0111] Figure 16 is a diagram for explaining the cost of changing assigned mobile units. For example, if reservations 0 and 2 are assigned to vehicle 1 and reservation 1 is assigned to vehicle 2 in the existing schedule, the cost is 0 in Figure 16A because the same vehicles are assigned as in the existing schedule. In Figure 16B, the cost increases because the vehicles used for reservations 0 and 3 have changed.

[0112] (ii) Constraint expression (general expression) The assigned mobile unit change cost can be expressed as in Equation 18.

[0113]

number

[0114] In addition, δ4 of H9 (i,j) is a function that is 1 under condition (7) and 0 under conditions (8) and (9). (i,j)The above-mentioned conditions (7), (8), and (9) that determine

[0115] (7) When the combination of reservation i and mobile j is changed from the allocation table (8) If the combination of reservation i and mobile j remains unchanged from the previous allocation (9) If the combination of reservation i and mobile j does not exist in the previous allocation

[0116] The schedule generating unit 12 will now be described. The schedule generation unit 12 first inputs the generated Hamiltonian (H) to the quantum computer (Ising machine) 20. Next, the schedule generation unit 12 acquires the result information output by the quantum computer (Ising machine) 20. Next, the schedule generation unit 12 generates (decodes) a schedule (rental reservation) based on the acquired result information.

[0117] (iii) Constraint equations based on the example In the above-described embodiment, the deadhead time cost (H8) is expressed as in Equation 19.

[0118]

number

[0119] <Schedule Generation Section> The schedule generation unit 12 first inputs the Hamiltonian (H) to the Ising machine. Next, the schedule generation unit 12 decodes the result information output by the Ising machine and generates a schedule that reduces the number of forwardings of moving objects (determines rental reservations).

[0120] Specifically, as shown in Fig. 17, first, (A) a decision variable allocation process is executed, and (B) quantum annealing is executed, and the result information is used for decoding to generate a schedule. Fig. 17 is a diagram for explaining the allocation of decision variables, quantum annealing, and decoding.

[0121] In the decision variable assignment process in Figure 17 (A), first, all reservations are numbered consecutively starting from 0. Similarly, all vehicles are numbered consecutively starting from 0. Next, all combinations of reservation numbering numbers and vehicle numbering numbers are assigned as decision variables. Here, each decision variable takes one of two values ​​{0, 1}, and when it is 1, that combination is realized.

[0122] In quantum annealing (B) in Figure 17, the values ​​{0, 1} of all decision variables are determined (result information is obtained). Here, the combination where the decision variable is 1 is the optimal combination to be realized.

[0123] Note that quantum annealing and other processing is not performed at the time of reservation. The reason is that as reservations are made repeatedly, the combination of reservation and rental car becomes suboptimal, meaning that the number of rental car deliveries increases and there is no time available for new reservations. In other words, when the combination of reservation and rental car becomes suboptimal, quantum annealing and other processing is performed to resolve the suboptimal state.

[0124] Therefore, the timing of quantum annealing and other processes is determined based on when the combination of reservations and rental cars becomes suboptimal, the amount of reservation and rental car data, and the performance of the machine that executes quantum annealing and other processes. For example, once every 30 minutes, or when 10 reservations increase.

[0125] In the decoding of Figure 17 (C), a combination where the decision variable takes the value 1 is detected, and the reservation and vehicle are restored from the reservation numbering number and vehicle numbering number of that combination. That is, (reservation 0, vehicle 0) ⇒ (reservation No. 195, vehicle No. 103), (reservation 1, vehicle 2) ⇒ (reservation No. 235, vehicle No. 233), (reservation 2, vehicle 1) ⇒ (reservation No. 345, vehicle No. 203).

[0126] <Adjustment section> The adjustment unit 14 adjusts one or more of the current coefficients K1 to K9 when the Hamiltonian (H) does not satisfy the constraints or when it is desired to further reduce costs. A case where the constraints are not satisfied is when the value of at least one of H1 to H5 is greater than 0. In this case, it is sufficient to increase the coefficient corresponding to the term whose value is greater than 0 in the positive direction. A case where it is desired to reduce costs is when the value of any of H6 to H9 is larger than expected. In this case, it is sufficient to increase the coefficient corresponding to the term whose value is desired to be reduced in the positive direction.

[0127] The coefficients K1 to K9 may be adjusted manually by the user or automatically. In the manual case, the user adjusts the coefficients using the user interface of the terminal device 30 or the like. In the automatic case, there are two methods for adjusting the coefficients. One is a method of adjusting the coefficients using a hyperparameter optimization framework that automates hyperparameter search, and the other is a method of performing grid search that searches for good coefficients by trying all the coefficients to be searched for.

[0128] Next, the adjustment unit 14 replaces the current coefficients with the adjusted coefficients. For example, the adjustment unit 14 generates coefficients K1' to K9' and replaces the current coefficients K1 to K9 with the coefficients K1' to K9'. Note that coefficients that do not need adjustment do not need to be replaced.

[0129] <Output information generation unit> The output information generation unit 15 generates output information for causing the output device 40 (or the output device of the terminal device 30) to output, for example, the display of (A) the allocation of decision variables, (B) the result of quantum annealing (result information), (C) the decoding result, etc. in Fig. 17. Thereafter, the output device 40 (or the output device of the terminal device 30) acquires the output information output by the output information generation unit 15 and displays based on the output information.

[0130] [Device operation] Next, the operation of the information processing device in the embodiment will be described with reference to FIG. 18. FIG. 18 is a diagram for explaining an example of the operation of the information processing device. In the following description, the diagram will be referenced as appropriate. Also, in the embodiment, an information processing method is implemented by operating the information processing device. Therefore, the description of the information processing method in the embodiment will be replaced with the description of the operation of the information processing device below.

[0131] As shown in FIG. 18, first, in the information processing device 10, when it is time to generate a schedule (step A1: Yes), the Hamiltonian generating unit 11 generates a Hamiltonian (H) (step A2).

[0132] Specifically, in step A2, first, information required to generate a Hamiltonian (H) (such as information on the number of moving objects, information on the number of reservations, information on the number of allocated slots, and reservation information) is acquired from a storage device. Next, in step A2, the Hamiltonian generation unit 11 generates a one-reservation-one-movement constraint (H1: first constraint equation) and a deadhead time cost (H8: eighth constraint equation). Also, in step A2, the Hamiltonian generation unit 11 may generate one or more of an allocation filling constraint (H2: second constraint equation), an initial unavailability constraint (H3: third constraint equation), an allocation order constraint (H4: fourth constraint equation), a moving object condition constraint (H5: fifth constraint equation), an overlap time cost (H6: sixth constraint equation), a vacant time cost (H7: seventh constraint equation), and an assigned moving object change cost (H9: ninth constraint equation).

[0133] Next, in step A2, the Hamiltonian generation unit 11 generates a Hamiltonian (H=K1×H1+K8×H8) using at least a coefficient K1 for adjusting the one reservation one movement agreement (H1) and a coefficient K8 for adjusting the deadhead time cost (H8). Also, in step A2, the Hamiltonian generation unit 11 may generate a Hamiltonian (H) by using constraint condition equations (one or more of H2, H3, H4, H5, H6, H7, and H9) and their corresponding coefficients (K2, K3, K4, K5, K6, K7, and K9) in the above-mentioned Hamiltonian (H=K1×H1+K8×H8).

[0134] Next, the schedule generating unit 12 inputs the generated Hamiltonian (H) to the Ising machine (step A3). Next, the schedule generating unit 12 acquires the result information output by the Ising machine (step A4).

[0135] Next, if the Hamiltonian (H) satisfies the constraints (step A5: Yes), the schedule generation unit 12 generates a schedule based on the acquired result information (determines rental reservations) (step A6). If the Hamiltonian (H) does not satisfy the constraints (step A5: No), the adjustment unit 14 adjusts the current coefficients (one or more of K1 to K9) (step A7: No). Thereafter, the process returns to step A2, and the processes of steps A2 to A4 are executed.

[0136] Next, the output information generation unit 15 generates output information for outputting to the output device 40 (or the output device of the terminal device 30) a display of, for example, (A) the allocation of decision variables, (B) the result of quantum annealing (result information), (C) the decoding result, etc. in Fig. 17, and outputs the output information to the output device 40 (or the output device of the terminal device 30) (step A8). The output information output by the output information generation unit 15 is acquired, and a display is made based on the output information.

[0137] [Effects of the embodiment] As described above, according to the embodiment, by providing a formulation method for solving an optimization problem for determining rental reservations for moving objects, it is possible to use an Ising machine to determine a schedule (rental reservation) that reduces the number of times that moving objects are sent out.

[0138] [program] The program in the embodiment may be any program that causes a computer to execute steps A1 to A8 shown in Fig. 18. By installing and executing this program in a computer, the information processing device and information processing method in the embodiment can be realized. In this case, the processor of the computer functions as an acquisition unit 13, a Hamiltonian generation unit 11, a schedule generation unit 12, an adjustment unit 14, and an output information generation unit 15, and performs processing.

[0139] The program in the embodiment may be executed by a computer system constructed by a plurality of computers, in which case, for example, each computer may function as any one of the acquisition unit 13, Hamiltonian generation unit 11, schedule generation unit 12, adjustment unit 14, and output information generation unit 15.

[0140] [Physical configuration] A computer that realizes the information processing device by executing the program in the embodiment will now be described with reference to Fig. 19. Fig. 19 is a diagram for explaining an example of a computer that realizes the information processing device in the embodiment.

[0141] 19, the computer 110 includes a CPU (Central Processing Unit) 111, a main memory 112, a storage device 113, an input interface 114, a display controller 115, a data reader / writer 116, and a communication interface 117. These components are connected to each other via a bus 121 so as to be able to communicate data with each other. Note that the computer 110 may include a GPU or an FPGA in addition to or instead of the CPU 111.

[0142] The CPU 111 loads a program in the embodiment, which is composed of a group of codes and stored in the storage device 113, into the main memory 112 and executes each code in a predetermined order to perform various calculations. The main memory 112 is typically a volatile storage device such as a DRAM (Dynamic Random Access Memory).

[0143] The program in the embodiment is provided in a state stored in a computer-readable recording medium 120. The program in the embodiment may be distributed over the Internet connected via the communication interface 117.

[0144] Specific examples of the storage device 113 include a hard disk drive and a semiconductor storage device such as a flash memory. The input interface 114 mediates data transmission between the CPU 111 and input devices 118 such as a keyboard and a mouse. The display controller 115 is connected to a display device 119 and controls the display on the display device 119.

[0145] The data reader / writer 116 mediates data transmission between the CPU 111 and the recording medium 120, reads programs from the recording medium 120, and writes processing results from the computer 110 to the recording medium 120. The communication interface 117 mediates data transmission between the CPU 111 and other computers.

[0146] Specific examples of the recording medium 120 include general-purpose semiconductor storage devices such as CF (Compact Flash (registered trademark)) and SD (Secure Digital), magnetic recording media such as flexible disks, or optical recording media such as CD-ROMs (Compact Disk Read Only Memory).

[0147] Note that the information processing device 10 in the embodiment can be realized not by a computer on which a program is installed, but by hardware corresponding to each unit, for example, an electronic circuit. Furthermore, the information processing device 10 may be partially realized by a program and the remaining unit by hardware. In the embodiment, the computer is not limited to the computer shown in FIG. 19. [Note] The following supplementary notes are further provided with respect to the above-described embodiments. Some or all of the above-described embodiments can be expressed by (Supplementary Note 1) to (Supplementary Note 12) described below, but are not limited to the following descriptions.

[0148] (Appendix 1) a Hamiltonian generator that generates a Hamiltonian using the one-reservation-one-trip constraint that allocates one reservation to one mobile object as a constraint equation, and a path time cost for reducing the path time for forwarding the mobile object when the arrival location of an earlier reservation and the departure location of a later reservation are different between adjacent reservations allocated to the same mobile object's path slots, using reservation identifiers that identify reservations, reservation information including start and end times representing a rental period for the mobile object reserved by the user, departure and arrival locations of the mobile object, and desired conditions for the mobile object of the user, mobile object number information representing the number of mobile objects to be rented, reservation number information representing the number of reservations set by multiple users, allocation slot number information representing the number of allocation slots representing the chronological order in which reservations are allocated to the mobile objects, and decision variables for determining to which of the allocation slots of the mobile object the reservation should be allocated; and A schedule generation unit that inputs the generated Hamiltonian into an Ising machine and generates a schedule that combines the reservation in which the forwarding is reduced and the mobile body based on result information output by the Ising machine; An information processing device having the above.

[0149] (Appendix 2) the Hamiltonian generation unit further generates, as a constraint condition formula, an allocation filling constraint that allocates one reservation to one of the allocation slots in order from the first allocation slot of the same mobile body, and generates the Hamiltonian including the generated allocation filling constraint; the schedule generation unit inputs the generated Hamiltonian into the Ising machine and calculates the schedule based on result information output by the Ising machine. 2. The information processing device according to claim 1.

[0150] (Appendix 3) The Hamiltonian generation unit further generates, as constraint condition expressions, any one or more of the following: an initial unavailability constraint that prevents the reservation to be allocated to the initial allocation slot from being allocated to the mobile object outside of an available time period including the forwarding time and rental preparation time; an allocation order constraint that fills reservations to be allocated to the allocation slot of the same mobile object in order of start time; a mobile object condition constraint that limits the reservations to the mobile object that meets the user's desired conditions; an overlap time cost that reduces overlap time between adjacent reservations allocated to the allocation slot of the same mobile object; a vacant time cost that reduces the occurrence of vacant times being within a predetermined undesirable vacant time range between adjacent reservations allocated to the allocation slot of the same mobile object; and an assigned mobile object change cost that reduces the occurrence of a mobile object being assigned that is different from the mobile object previously allocated to the allocation slot, and generates the Hamiltonian including the generated constraint condition expressions; the schedule generation unit inputs the generated Hamiltonian into the Ising machine and calculates the schedule based on result information output by the Ising machine. 3. The information processing device according to claim 2.

[0151] (Appendix 4) an adjusting unit that adjusts, based on the result information, each of coefficients to be multiplied to the one-reservation-one-movement constraint, the deadhead time cost, the allocation filling constraint, the initial unavailability constraint, the allocation order constraint, the mobile unit condition constraint, the overlap time cost, the vacant time cost, and the allocated mobile unit change cost; 4. The information processing device according to claim 3,

[0152] (Appendix 5) The information processing device and generating a Hamiltonian using the reservation identifier for identifying the reservation, reservation information including a start time and end time representing the rental period of the mobile object reserved by the user, the departure and arrival locations of the mobile object, and the user's desired conditions for the mobile object, mobile object number information representing the number of mobile objects to be rented, reservation number information representing the number of reservations set by multiple users, allocation slot number information representing the number of allocation slots representing the chronological order in which reservations are allocated to the mobile objects, and a decision variable for determining to which of the allocation slots of the mobile object the reservation should be allocated, to generate, as constraint equations, a one-reservation-one-trip constraint for allocating one reservation to one mobile object and a dead-travel time cost for reducing the dead-travel time for forwarding the mobile object when the arrival location of an earlier reservation and the departure location of a later reservation differ between adjacent reservations allocated to the allocation slots of the same mobile object; and generating a Hamiltonian using the one-reservation-one-trip constraint and the dead-travel time cost. The generated Hamiltonian is input to an Ising machine, and a schedule is generated that combines the reservation with the reduced forwarding and the mobile body based on the result information output by the Ising machine. An information processing method that performs processing.

[0153] (Appendix 6) Furthermore, as a constraint condition formula, an allocation filling constraint is generated that allocates one reservation to one of the allocation slots in order from the first allocation slot of the same mobile body, and the Hamiltonian is generated including the generated allocation filling constraint; The generated Hamiltonian is input to the Ising machine, and the schedule is calculated based on result information output by the Ising machine. 6. The information processing method according to claim 5, wherein the processing is performed.

[0154] (Appendix 7) Furthermore, as constraint condition expressions, one or more of the following are generated: an initial unavailability constraint that prevents the reservation to be assigned to the initial allocation slot from being assigned to the mobile object outside of the available time period including the forwarding time and rental preparation time; an allocation order constraint that fills the reservations to be assigned to the allocation slot of the same mobile object in order of start time; a mobile object condition constraint that limits the reservations to the mobile object that meets the user's desired conditions; an overlap time cost that reduces overlap time between adjacent reservations assigned to the allocation slot of the same mobile object; a vacant time cost that reduces the occurrence of vacant times being within a predetermined undesirable vacant time range between adjacent reservations assigned to the allocation slot of the same mobile object; and an assigned mobile object change cost that reduces the occurrence of a mobile object being assigned that is different from the mobile object previously assigned to the allocation slot; and generate the Hamiltonian including the generated constraint condition expressions, The generated Hamiltonian is input to the Ising machine, and the schedule is calculated based on result information output by the Ising machine. 7. The information processing method according to claim 6, wherein the processing is performed.

[0155] (Appendix 8) Based on the result information, adjust the coefficients multiplied to the one-reservation-one-movement constraint, the deadhead time cost, the allocation filling constraint, the initial unavailability constraint, the allocation order constraint, the mobile unit condition constraint, the overlap time cost, the vacant time cost, and the allocated mobile unit change cost. 8. The information processing method according to claim 7, wherein the processing is performed.

[0156] (Appendix 9) On the computer, and generating a Hamiltonian using the reservation identifier for identifying the reservation, reservation information including a start time and end time representing the rental period of the mobile object reserved by the user, the departure and arrival locations of the mobile object, and the user's desired conditions for the mobile object, mobile object number information representing the number of mobile objects to be rented, reservation number information representing the number of reservations set by multiple users, allocation slot number information representing the number of allocation slots representing the chronological order in which reservations are allocated to the mobile objects, and a decision variable for determining to which of the allocation slots of the mobile object the reservation should be allocated, to generate, as constraint equations, a one-reservation-one-trip constraint for allocating one reservation to one mobile object and a dead-travel time cost for reducing the dead-travel time for forwarding the mobile object when the arrival location of an earlier reservation and the departure location of a later reservation differ between adjacent reservations allocated to the allocation slots of the same mobile object; and generating a Hamiltonian using the one-reservation-one-trip constraint and the dead-travel time cost. The generated Hamiltonian is input to an Ising machine, and a schedule is generated that combines the reservation with the reduced forwarding and the mobile body based on the result information output by the Ising machine. A program that executes a process.

[0157] (Appendix 10) Furthermore, as a constraint condition formula, an allocation filling constraint is generated that allocates one reservation to one of the allocation slots in order from the first allocation slot of the same mobile body, and the Hamiltonian is generated including the generated allocation filling constraint; The generated Hamiltonian is input to the Ising machine, and the schedule is calculated based on result information output by the Ising machine. 10. The program according to claim 9, which executes the process.

[0158] (Appendix 11) Furthermore, as constraint condition expressions, one or more of the following are generated: an initial unavailability constraint that prevents the reservation to be assigned to the initial allocation slot from being assigned to the mobile object outside of the available time period including the forwarding time and rental preparation time; an allocation order constraint that fills the reservations to be assigned to the allocation slot of the same mobile object in order of start time; a mobile object condition constraint that limits the reservations to the mobile object that meets the user's desired conditions; an overlap time cost that reduces overlap time between adjacent reservations assigned to the allocation slot of the same mobile object; a vacant time cost that reduces the occurrence of vacant times being within a predetermined undesirable vacant time range between adjacent reservations assigned to the allocation slot of the same mobile object; and an assigned mobile object change cost that reduces the occurrence of a mobile object being assigned that is different from the mobile object previously assigned to the allocation slot; and generate the Hamiltonian including the generated constraint condition expressions, The generated Hamiltonian is input to the Ising machine, and the schedule is calculated based on result information output by the Ising machine. 11. The program according to claim 10, which executes processing.

[0159] (Appendix 12) Based on the result information, adjust the coefficients multiplied to the one-reservation-one-movement constraint, the deadhead time cost, the allocation filling constraint, the initial unavailability constraint, the allocation order constraint, the mobile unit condition constraint, the overlap time cost, the vacant time cost, and the allocated mobile unit change cost. 12. The program according to claim 11, which causes the processing to be executed.

[0160] Although the present invention has been described above with reference to the embodiments, the present invention is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. [Industrial Applicability]

[0161] According to the above description, it is possible to use an Ising machine to determine a rental reservation (schedule) that reduces the number of trips of moving objects. This is also useful in fields where moving object rental is required. [Explanation of symbols]

[0162] 10. Information processing equipment 11 Hamiltonian generator 12 Schedule Generation Unit 13 Acquisition Department 14 Adjustment part 15 Output information generation unit 20 Quantum Computer 30 Terminal Equipment 40 Output Devices 100 systems 110 Computer 111 CPU 112 main memory 113 Storage device 114 Input Interface 115 Display Controller 116 Data Reader / Writer 117 Communication Interface 118 Input Devices 119 Display Device 120 Recording Media 121 Bus

Claims

1. a Hamiltonian generating means for generating a Hamiltonian using the one-reservation-one-trip constraint for allocating one reservation to one mobile object as a constraint equation, and a dead-time cost for reducing the dead-time for forwarding the mobile object when the arrival location of an earlier reservation and the departure location of a later reservation differ between adjacent reservations allocated to the same mobile object's allocation slots, using reservation identifiers for identifying reservations, reservation information including start and end times representing a rental period for the mobile object reserved by the user, departure and arrival locations of the mobile object, and desired conditions for the mobile object of the user, mobile object number information representing the number of mobile objects to be rented, reservation number information representing the number of reservations set by multiple users, allocation slot number information representing the number of allocation slots representing the chronological order in which reservations are allocated to the mobile objects, and decision variables for determining to which allocation slot of the mobile object the reservation should be allocated; and a schedule generating means for inputting the generated Hamiltonian into an Ising machine and generating a schedule that combines the reservation with the reduced forwarding and the mobile body based on result information output by the Ising machine; An information processing device having the above.

2. the Hamiltonian generation means further generates, as a constraint expression, an allocation filling constraint that allocates one reservation to one of the allocation slots in order from the first allocation slot of the same mobile body, and generates the Hamiltonian including the generated allocation filling constraint; the schedule generation means inputs the generated Hamiltonian into the Ising machine and calculates the schedule based on result information output by the Ising machine. The information processing device according to claim 1 .

3. the Hamiltonian generation means further generates, as constraint condition expressions, any one or more of the following: an initial unavailability constraint that prevents the reservation to be allocated to the initial allocation slot from being allocated to the mobile object outside of an available time period including the forwarding time and rental preparation time; an allocation order constraint that fills the reservations to be allocated to the allocation slot of the same mobile object in order of start time; a mobile object condition constraint that limits the reservations to the mobile object that meets the user's desired conditions; an overlap time cost that reduces overlap time between adjacent reservations allocated to the allocation slot of the same mobile object; a vacant time cost that reduces the occurrence of vacant times being within a predetermined undesirable vacant time range between adjacent reservations allocated to the allocation slot of the same mobile object; and an assigned mobile object change cost that reduces the occurrence of a mobile object being assigned that is different from the mobile object previously allocated to the allocation slot; and generates the Hamiltonian including the generated constraint condition expressions; the schedule generation means inputs the generated Hamiltonian into the Ising machine and calculates the schedule based on result information output by the Ising machine. The information processing device according to claim 2 .

4. an adjusting means for adjusting, based on the result information, each of coefficients to be multiplied to the one-reservation-one-movement constraint, the deadhead time cost, the allocation filling constraint, the initial unavailability constraint, the allocation order constraint, the mobile unit condition constraint, the overlap time cost, the vacant time cost, and the assigned mobile unit change cost; 4. The information processing apparatus according to claim 3,

5. The information processing device and generating a Hamiltonian using the reservation identifier for identifying the reservation, reservation information including a start time and end time representing the rental period of the mobile object reserved by the user, the departure location and arrival location of the mobile object, and the user's desired conditions for the mobile object, mobile object number information representing the number of mobile objects to be rented, reservation number information representing the number of reservations set by multiple users, allocation slot number information representing the number of allocation slots representing the chronological order in which reservations are allocated to the mobile objects, and a decision variable for determining to which of the allocation slots of the mobile object the reservation should be allocated, to generate, as constraint equations, a one-reservation-one-trip constraint for allocating one reservation to one mobile object and a dead-travel time cost for reducing the dead-travel time for forwarding the mobile object when the arrival location of an earlier reservation and the departure location of a later reservation differ between adjacent reservations allocated to the allocation slots of the same mobile object; and generating a Hamiltonian using the one-reservation-one-trip constraint and the dead-travel time cost. The generated Hamiltonian is input to an Ising machine, and a schedule is generated that combines the reservation with the reduced forwarding and the mobile body based on the result information output by the Ising machine. An information processing method that performs processing.

6. On the computer, and generating a Hamiltonian using the reservation identifier for identifying the reservation, reservation information including a start time and end time representing the rental period of the mobile object reserved by the user, the departure location and arrival location of the mobile object, and the user's desired conditions for the mobile object, mobile object number information representing the number of mobile objects to be rented, reservation number information representing the number of reservations set by multiple users, allocation slot number information representing the number of allocation slots representing the chronological order in which reservations are allocated to the mobile objects, and a decision variable for determining to which of the allocation slots of the mobile object the reservation should be allocated, to generate, as constraint equations, a one-reservation-one-trip constraint for allocating one reservation to one mobile object and a dead-travel time cost for reducing the dead-travel time for forwarding the mobile object when the arrival location of an earlier reservation and the departure location of a later reservation differ between adjacent reservations allocated to the allocation slots of the same mobile object; and generating a Hamiltonian using the one-reservation-one-trip constraint and the dead-travel time cost. The generated Hamiltonian is input to an Ising machine, and a schedule is generated that combines the reservation with the reduced forwarding and the mobile body based on the result information output by the Ising machine. A program that executes a process.

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