WO2023187925A1 - 配送計画作成システム、方法およびプログラム - Google Patents

配送計画作成システム、方法およびプログラム Download PDF

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Publication number
WO2023187925A1
WO2023187925A1 PCT/JP2022/015126 JP2022015126W WO2023187925A1 WO 2023187925 A1 WO2023187925 A1 WO 2023187925A1 JP 2022015126 W JP2022015126 W JP 2022015126W WO 2023187925 A1 WO2023187925 A1 WO 2023187925A1
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Prior art keywords
delivery
request
plan
requests
additional
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English (en)
French (fr)
Japanese (ja)
Inventor
暢達 中村
門太 家志
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NEC Corp
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NEC Corp
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Priority to PCT/JP2022/015126 priority Critical patent/WO2023187925A1/ja
Priority to JP2024510739A priority patent/JP7848864B2/ja
Priority to US18/842,074 priority patent/US20250173634A1/en
Publication of WO2023187925A1 publication Critical patent/WO2023187925A1/ja
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G06Q10/083—Shipping
    • G06Q10/08355—Routing methods

Definitions

  • the present invention relates to a delivery plan creation system, a delivery plan creation method, and a delivery plan creation program that create a delivery plan.
  • Patent Document 1 describes a delivery schedule selection system aimed at user convenience and efficient delivery.
  • the system described in Patent Document 1 adds/adds future delivery schedules and future delivery schedules for other packages to each of a plurality of delivery schedule candidates that can be selected for delivering products ordered by a user. Calculate shipping costs taking into account the reduction.
  • Patent Document 2 describes a transportation management system for efficient transportation.
  • the system described in Patent Document 2 manages transportation flights that operate regularly at predetermined times (default flights) and transportation flights that operate based on application (temporary flights), and manages transportation flights that operate regularly at predetermined times (temporary flights) and responds to additional transportation requests.
  • the cost of the transportation flight in the event of a change is calculated based on the hourly fare calculation result calculated from the time required to operate the default flight and the temporary flight.
  • Patent Document 3 describes an example of an objective function used for optimizing a delivery plan.
  • Patent Document 1 simply calculates the delivery cost when an additional order is added to an already existing or predicted delivery schedule. Therefore, since the calculated delivery cost is not calculated taking the entire delivery plan into consideration, it is difficult to say that a more appropriate delivery schedule is created.
  • the delivery plan creation system includes a delivery request receiving means that receives input of an additional delivery request including designation of multiple delivery conditions, and extracts delivery requests that correspond to the input delivery conditions, including additional delivery requests.
  • the system includes a delivery request extracting means, an optimizing means for optimizing a delivery plan for the extracted delivery request for each delivery condition, and an output means for outputting the optimized delivery plan for each delivery condition.
  • a computer receives input of an additional delivery request including designation of multiple delivery conditions, and the computer extracts delivery requests that correspond to the input delivery conditions, including the additional delivery requests. Then, the computer optimizes the delivery plan for the extracted delivery request for each delivery condition, and outputs the optimized delivery plan for each delivery condition.
  • the delivery plan creation program includes a delivery request reception process in which a computer receives input of an additional delivery request including designation of a plurality of delivery conditions, a delivery request corresponding to the input delivery conditions, including the additional delivery request.
  • a delivery request extraction process, an optimization process for optimizing the delivery plan for the extracted delivery request for each delivery condition, and an output process for outputting the optimized delivery plan for each delivery condition are executed.
  • a delivery plan can be created from a plurality of viewpoints, taking into consideration situations that may change due to additional delivery requests.
  • FIG. 1 is a block diagram showing a configuration example of an embodiment of a delivery plan creation system.
  • FIG. 2 is an explanatory diagram showing an example of information regarding delivery flights.
  • FIG. 3 is a block diagram showing a configuration example of another embodiment of the delivery plan creation system.
  • FIG. 2 is an explanatory diagram showing an example of a Gantt chart representing a delivery plan.
  • FIG. 7 is an explanatory diagram illustrating an example of processing for calculating costs for an additional delivery request.
  • FIG. 2 is an explanatory diagram showing an example of the operation of the delivery plan creation system.
  • 12 is a flowchart illustrating an example of the operation of the delivery plan creation system when a delivery time is specified as a delivery condition.
  • FIG. 12 is a flowchart illustrating an example of the operation of the delivery plan creation system when the number of delivery vehicles is specified as a delivery condition. 12 is a flowchart illustrating an example of the operation of the delivery plan creation system when a delivery base attribute is specified as a delivery condition.
  • 1 is a block diagram showing an overview of a delivery plan creation system according to the present invention.
  • FIG. 1 is a schematic block diagram showing the configuration of a computer according to at least one embodiment.
  • the delivery plan creation system of this embodiment generates an optimized delivery plan based on a delivery request specifying a plurality of delivery conditions that are assumed to be achievable by the administrator who has received the user's order.
  • FIG. 1 is a block diagram showing a configuration example of an embodiment of a delivery plan creation system.
  • the delivery plan creation system 100 of this embodiment includes a storage unit 10, a delivery request reception unit 20, a delivery risk prediction unit 30, a delivery request extraction unit 40, a parameter calculation unit 50, a model generation unit 60, and an optimal
  • the image forming apparatus includes a conversion processing section 70 and an output section 80.
  • the storage unit 10 stores information that the delivery plan creation system 100 of this embodiment uses for various processes. Specifically, the storage unit 10 stores a delivery plan obtained as a result of optimization processing by an optimization processing unit 70, which will be described later. The storage unit 10 also stores information regarding delivery bases and delivery flights (time, location, items to be delivered, delivery possible time slots, etc.), predetermined parameters, and the like.
  • the storage unit 10 is realized by, for example, a magnetic disk.
  • FIG. 2 is an explanatory diagram showing an example of information regarding delivery flights.
  • identification information of the truck and driver for each delivery flight, identification information of the truck and driver, business start time and business end time, travel distance, expressway toll fee, and fee for using that delivery flight are associated with each other. Indicates that
  • the delivery request receiving unit 20 receives input of a delivery request including designation of multiple delivery conditions.
  • a new delivery request received by the delivery request reception unit 20 may be referred to as an additional delivery request in order to distinguish it from delivery requests that have already been accepted.
  • the delivery conditions include, for example, delivery time (time at the delivery point), delivery means, and attributes of the delivery base.
  • delivery means include light trucks, medium trucks, large trucks, trailers, motorcycles, bicycles, airplanes, ships, etc.
  • the delivery risk prediction unit 30 predicts delivery requests that are expected to be made in the future. Specifically, the delivery risk prediction unit 30 predicts a delivery request that corresponds to the input delivery condition as a delivery request that is expected to be made in the future. For example, if the delivery conditions include a delivery time, the delivery risk prediction unit 30 predicts delivery requests that are expected to be made at that delivery time in the future.
  • the delivery risk prediction unit 30 may predict delivery requests up to 24 hours later. In addition, in order to prevent delivery flights that depart immediately from being included, the delivery risk prediction unit 30 may predict delivery requests up to 24 hours after the time when a predetermined time t0 is added to the current time. good.
  • the delivery risk prediction unit 30 calculates, for example, the average number of delivery requests that occurred in the same delivery area (direction) during a certain time period in the past based on the history of past delivery requests, and calculates the average number of delivery requests that occurred in the same delivery area (direction) in a certain time period in the past.
  • delivery requests may be predicted.
  • the delivery risk prediction unit 30 may predict future delivery requests that meet the delivery conditions, for example, based on increases and decreases in the number of deliveries recently (the previous day, the same day of the week in the previous week).
  • the delivery risk prediction unit 30 learns in advance a prediction model that includes the input delivery conditions, requested environment, etc. as explanatory variables, and uses the number of delivery requests that correspond to the delivery conditions as the objective variable. Delivery requests may be predicted using the prediction model.
  • the delivery request extraction unit 40 extracts delivery requests that correspond to the input delivery conditions, including additional delivery requests. That is, the delivery request extraction unit 40 extracts all the existing delivery requests that correspond to the input delivery conditions and the newly input additional delivery requests. For example, the delivery request extraction unit 40 may extract delivery requests scheduled for delivery at a specified delivery time, including additional delivery requests. As a result, all delivery requests that match the input delivery conditions will be extracted.
  • the delivery request extraction unit 40 may also extract delivery requests that are predicted to be made in the future and correspond to the input delivery conditions. For example, when a delivery request specifying a delivery vehicle is input, the delivery risk prediction unit 30 predicts a delivery request that is likely to be delivered by the same vehicle as the delivery vehicle, and the delivery request extraction unit 40 predicts the delivery request.
  • the requested delivery requests may also be extracted and compiled into a list. This makes it possible to create a final delivery plan in consideration of future delivery requests at an early stage.
  • the delivery plan creation system 100 does not need to include the delivery risk prediction unit 30.
  • the optimization processing unit 70 optimizes the delivery plan for the delivery request extracted by the delivery request extraction unit 40 for each delivery condition. That is, the optimization processing unit 70 derives a new delivery plan by performing optimization processing on existing delivery requests and additional delivery requests.
  • the optimization processing unit 70 may, for example, optimize the delivery plan for the extracted delivery request for each designated delivery time.
  • the optimization processing unit 70 calculates a combination of viewpoints that minimizes the value of a model (objective function) generated by a parameter calculation unit 50 and a model generation unit 60, which will be described later.
  • a process is performed to find a delivery plan that minimizes the costs incurred.
  • the mode of the optimization processing unit 70 is arbitrary.
  • the optimization processing unit 70 may be realized by, for example, a CPU (Central Processing Unit) of a general computer.
  • a CPU Central Processing Unit
  • the delivery plan to be optimized in this embodiment is a problem of selecting an appropriate combination from packages, delivery vehicles, delivery times, etc., and can therefore be called a combinatorial optimization problem. Since it is impractical to search for the optimal combination through calculation processing such as an exhaustive search, it is common to predetermine delivery patterns and optimize only the combinations of items to be delivered and delivery patterns.
  • the optimization processing unit 70 of the present embodiment may be configured to transmit the generated objective function to a quantum computer or an annealing machine and instruct the quantum computer or annealing machine to execute the optimization process.
  • FIG. 3 is a block diagram showing a configuration example of another embodiment of the delivery plan creation system.
  • the delivery plan creation system 200 may be connected to a quantum computer 201 or an annealing machine 202. Note that although FIG. 3 illustrates a case where the delivery plan creation system 200 is connected to both the quantum computer 201 and the annealing machine 202, the delivery plan creation system 200 is connected to both the quantum computer 201 and the annealing machine 202. It may be connected to either one.
  • the other configurations are similar to the configuration illustrated in FIG. 1 .
  • the optimization processing unit 70 may transmit a model for causing a quantum computer or an annealing machine to execute the optimization process, and cause the quantum computer or annealing machine to execute the optimization process. This makes it possible to optimize delivery plans for each of a plurality of delivery conditions more efficiently than when using a general computer.
  • the main point of view is that of cost.
  • the cost is calculated as the sum of delivery distance x average fuel consumption, driver personnel costs, delivery vehicle expenses, etc.
  • the cost is calculated from the cost calculated from the freight table based on the delivery distance and cargo volume, the size of the delivery vehicle, the number of days, etc.
  • the loading rate is calculated by, for example, cargo amount (weight)/maximum loading weight of the delivery vehicle, cargo amount (capacity)/maximum loading capacity of the delivery vehicle, etc.
  • a value obtained by averaging the load on the delivery vehicle can be cited as a point of view.
  • the averaged value is calculated, for example, by 1/n ⁇ (delivery time of each delivery vehicle - average delivery time) 2 or 1/n ⁇ (loading rate of each delivery vehicle - average loading rate) 2 . Ru. Note that this value can be said to be more preferable as the variance is smaller.
  • carbon dioxide emissions can be cited as a point of view other than cost.
  • the amount of carbon dioxide emissions is calculated, for example, by (delivery distance x distance coefficient + delivery time x time coefficient) x weight x cumulative height difference x vehicle coefficient. Note that the various coefficients are determined in advance by an administrator or the like.
  • the parameter calculation unit 50 calculates parameters used in the model used by the optimization processing unit 70 for optimization. Furthermore, the model generation unit 60 generates a model using the parameters generated by the parameter calculation unit 50.
  • the model generated by the model generation unit 60 is specifically an objective function used in the optimization process.
  • the objective function is a function that defines the optimization target, and includes multiple terms that combine the above-mentioned viewpoints (hereinafter referred to as cost terms) and a term that is added when the constraint conditions are not satisfied (hereinafter referred to as penalty terms). ).
  • the objective function may be, for example, a function that defines the delivery cost required for delivery as an optimization target, or a function that defines carbon dioxide emissions caused by delivery as an optimization target. In this case, the optimization processing unit 70 optimizes the delivery plan by minimizing these objective functions.
  • the optimization target is not limited to cost or carbon dioxide emissions. Further, the optimization target is not limited to one, and for example, both the above-mentioned delivery cost and carbon dioxide emissions may be the optimization target.
  • Equation 1 the objective function is expressed by Equation 1 illustrated below.
  • the parameter calculation unit 50 may calculate weights when various viewpoints are selected as parameters.
  • the parameter calculation unit 50 calculates the weight of cost, the weight of loading rate, the weight of delivery distance, and the like. At this time, the parameter calculation unit 50 calculates the weight of the target viewpoint so that the more important the viewpoint is, the greater the weight becomes.
  • the content of the objective function is arbitrary and is not limited to the format of Equation 1 shown above.
  • the parameter calculation unit 50 and the model generation unit 60 may generate an objective function in the format described in Patent Document 3, for example, or may generate a known objective function.
  • the optimization processing section 70 can be realized with any configuration, the parameter calculation section 50 only needs to calculate the parameters of the model according to the optimization processing to be performed.
  • the parameter calculation unit 50 may calculate parameters used in the Hamiltonian equation used for optimization. Further, for example, if the optimization processing unit 70 is realized by an annealing machine and the optimization process is performed by the quantum annealing machine, the parameter calculation unit 50 calculates the parameters used in the Ising model used for optimization. good. In these cases, the model generation unit 60 may use the generated parameters to generate an objective function used for optimization using a Hamiltonian formula or an Ising model.
  • the delivery plan creation system 100 (delivery plan creation system 200) includes additional elements different from normal elements such as a general computer. I can say that there is.
  • the parameter calculation unit 50 and model generation unit 60 that generate Hamiltonian formulas and Ising models improve the functionality of the optimization processing unit 70 and perform optimization processing. This can be said to show improvement in processing.
  • each viewpoint is selected is represented by a variable x n ( ⁇ 0,1 ⁇ ).
  • Equation 2 a penalty term that becomes a penalty when the variable of the specified time frame is not 1 is expressed, for example, by Equation 2 illustrated below.
  • Equation 2 w is a weighting coefficient indicating the degree of penalty.
  • the parameter calculation unit 50 and the model generation unit 60 may calculate parameters according to the assumed viewpoint and generate a model using the calculated parameters. Note that since the method of expressing the objective function using an Ising model or a Hamiltonian equation is widely known, further explanation will be omitted.
  • the output unit 80 outputs the optimized delivery plan for each delivery condition.
  • the output unit 80 may calculate costs (fees), carbon dioxide emissions, etc. that occur in response to additional delivery requests for each delivery condition, and output the calculation results.
  • the output unit 80 may output a result optimized for each delivery time, for example.
  • the output unit 80 may output each delivery plan itself in a comparable manner, or may output comparison information obtained by comparing results optimized for each delivery condition.
  • An example of a method for outputting the delivery plan is to display the delivery plan in a Gantt chart.
  • examples of comparison information include cost differences between delivery plans.
  • the output unit 80 may output delivery conditions and delivery plans in a ranking format according to the costs incurred.
  • FIG. 4 is an explanatory diagram showing an example of a Gantt chart representing a delivery plan.
  • the movement status of a package is calculated depending on whether a delivery plan is created to deliver the package using a delivery flight at 10:00, or when a delivery plan is created to deliver the package using a delivery flight at 10:30.
  • An example of output that can be compared is shown.
  • the output unit 80 may calculate the cost based on a fee calculation formula according to the weight, size, and delivery distance of the item to be delivered according to the total cost of the delivery vehicle, or calculate the cost based on a fee list. It's okay. Furthermore, since the cost for the delivery request can be estimated based on the calculated cost, the output unit 80 outputs (displays, sends an email notification, etc.) the delivery fee according to the estimated cost to the user who placed the order. Good too.
  • FIG. 5 is an explanatory diagram showing an example of a process for calculating costs for an additional delivery request.
  • the example shown in Figure 5 shows information about the delivery source and destination of the item for which an additional delivery request has been made, which delivery service will be used, and the loading time when that delivery service is used. , unloading time and possible delivery time zone are associated with each other.
  • the example shown in Figure 5 shows the accuracy of each delivery request (whether it is an additional delivery request (this case), a confirmed delivery request, a delivery request under negotiation, or a predicted delivery request). ) is associated.
  • the output unit 80 may calculate the cost for the additional delivery request based on the value obtained by subtracting the cost for other delivery requests from the fee V for using a certain delivery service. At that time, the output unit 80 multiplies the charges other than the confirmed delivery request by a predetermined weight (for example, the weight of the delivery request under negotiation is 0.5, the weight of the predicted delivery request is 0.2, etc.). ) may be calculated.
  • a predetermined weight for example, the weight of the delivery request under negotiation is 0.5, the weight of the predicted delivery request is 0.2, etc.).
  • w is a predetermined weight value.
  • the delivery request reception unit 20, the delivery risk prediction unit 30, the delivery request extraction unit 40, the parameter calculation unit 50, the model generation unit 60, the optimization processing unit 70, and the output unit 80 include, for example, a program ( This is realized by a computer processor (for example, a CPU) that operates according to a delivery plan creation program.
  • the optimization processing unit 70 may be configured to instruct a quantum computer or an annealing machine to execute the optimization process.
  • the program is stored in the storage unit 10, the processor reads the program, and according to the program, the delivery request reception unit 20, the delivery risk prediction unit 30, the delivery request extraction unit 40, the parameter calculation unit 50, and the model generation unit 60. , may operate as the optimization processing section 70 and the output section 80. Further, the functions of the delivery plan creation system 100 may be provided in a SaaS (Software as a Service) format.
  • SaaS Software as a Service
  • the delivery request reception unit 20, the delivery risk prediction unit 30, the delivery request extraction unit 40, the parameter calculation unit 50, the model generation unit 60, the optimization processing unit 70, and the output unit 80 are each dedicated to It may be realized by hardware. Further, a part or all of each component of each device may be realized by a general-purpose or dedicated circuit, a processor, etc., or a combination thereof. These may be configured by a single chip or multiple chips connected via a bus. A part or all of each component of each device may be realized by a combination of the circuits and the like described above and a program.
  • each component of the delivery plan creation system 100 is realized by a plurality of information processing devices, circuits, etc.
  • the plurality of information processing devices, circuits, etc. may be centrally arranged. , may be distributed.
  • information processing devices, circuits, etc. may be realized as a client server system, a cloud computing system, or the like, in which each is connected via a communication network.
  • FIG. 6 is an explanatory diagram showing an example of the operation of the delivery plan creation system 100 of this embodiment.
  • the delivery request receiving unit 20 receives input of an additional delivery request including designation of a plurality of delivery conditions (step S11).
  • the delivery request extraction unit 40 extracts delivery requests that meet the input delivery conditions, including additional delivery requests (step S12).
  • the optimization processing unit 70 optimizes the delivery plan for the extracted delivery request for each delivery condition (step S13).
  • the output unit 80 outputs the optimized delivery plan for each delivery condition (step S14).
  • FIG. 7 is a flowchart illustrating an example of the operation of the delivery plan creation system 100 when a delivery time is specified as a delivery condition.
  • the delivery request receiving unit 20 receives an input of a delivery request (step S21), and further receives designations of multiple delivery times (step S22).
  • the delivery risk prediction unit 30 predicts delivery requests at the specified delivery time, and the delivery request extraction unit 40 extracts delivery requests included in the specified delivery time range (step S23).
  • the optimization processing unit 70 creates a delivery plan by combinatorial optimization calculation (step S24). Then, the output unit 80 calculates the fee for the additional delivery request (step S25).
  • step S26 If calculations have not been performed for all specified delivery times (No in step S26), the processes from step S23 onwards are repeated for other delivery times. On the other hand, if calculations have been performed for all specified delivery times (Yes in step S26), the output unit 80 outputs the delivery plan, accepts the selection by the administrator, and finalizes the delivery plan (step S27). ).
  • FIG. 8 is a flowchart illustrating an example of the operation of the delivery plan creation system 100 when the number of delivery vehicles is specified as a delivery condition.
  • the delivery request reception unit 20 receives an input of a delivery request (step S31), and further receives a designation of a plurality of numbers of delivery vehicles (step S32).
  • the optimization processing unit 70 creates a delivery plan by combinatorial optimization calculation (step S33).
  • the output unit 80 calculates the fee for the additional delivery request (step S34).
  • step S35 If calculations have not been performed for all the designated numbers of delivery vehicles (No in step S35), the processes from step S33 onwards are repeated for other numbers of delivery vehicles. On the other hand, if calculations have been performed for all the designated numbers of delivery vehicles (Yes in step S35), the output unit 80 outputs the delivery plan, accepts the selection by the administrator, and finalizes the delivery plan (step S35). S36).
  • FIG. 9 is a flowchart illustrating an example of the operation of the delivery plan creation system 100 when a delivery base attribute is specified as a delivery condition.
  • the delivery request reception unit 20 receives an input of a delivery request (step S41), and further receives designation of a plurality of delivery base attributes (step S42).
  • the optimization processing unit 70 creates a delivery plan by combinatorial optimization calculation (step S43).
  • the output unit 80 calculates the fee for the additional delivery request (step S44).
  • step S45 If calculations have not been performed for all specified delivery base attributes (No in step S45), the processes from step S43 onwards are repeated for other delivery base attributes. On the other hand, if calculations have been performed for all specified delivery base attributes (Yes in step S45), the output unit 80 outputs the delivery plan, accepts the selection by the administrator, and finalizes the delivery plan (step S45). S46).
  • the operation of the delivery plan creation system 100 of this embodiment will be described using a specific example.
  • a quantum computer is used for optimization processing, and a Hamiltonian equation is used as a model (objective function) used for optimization.
  • a delivery vehicle is specified as a delivery condition.
  • the delivery request reception unit 20 accepts the input of the delivery request regarding the delivery item.
  • the delivery request extraction unit 40 lists delivery items (that is, delivery requests) that are likely to be delivered by the same delivery vehicle as the delivery item, including predictions.
  • the optimization processing unit 70 causes the quantum computer to calculate a set of variables that minimizes a preset Hamiltonian equation, which includes variables to be delivered, a delivery vehicle, a delivery order, and a delivery time frame.
  • the parameter calculation unit 50 calculates the distance (or time, transportation cost) between delivery points, which becomes the coefficient of the variable. Furthermore, the model generation unit 60 generates a Hamiltonian formula in consideration of various conditions. For example, the conditions are that the total weight of the delivered items does not exceed the loading weight set for each vehicle, the total size or capacity of the delivered items does not exceed the vehicle loading platform size, and the total size or capacity of the delivered items does not exceed the loading weight set for each vehicle. Examples of such restrictions include not exceeding the size of the vehicle's loading platform, and limiting the size of the vehicle and the number of vehicles that can be parked at the same time depending on the parking space at the delivery location.
  • the output unit 80 determines which delivery vehicle a certain delivery item should be placed on, and in what delivery order the delivery vehicles should be placed, based on the variable set that minimizes the Hamiltonian equation. Outputs a delivery plan, such as whether to go through delivery points or not. Furthermore, by specifying multiple delivery vehicles and changing the delivery plan within the range of possible deliveries, it becomes possible to compare the total delivery costs within that range.
  • the output unit 80 outputs optimization results for a plurality of delivery conditions, making it possible to flexibly create a delivery plan that meets the needs of users and administrators. . As a result, it becomes possible to make adjustments to the additional delivery request, such as whether the price should be added to the standard delivery price or discounted.
  • the delivery request receiving unit 20 receives input of an additional delivery request including designation of multiple delivery conditions, and the delivery request extraction unit 40 selects a delivery request corresponding to the input delivery conditions. , including additional delivery requests. Then, the optimization processing unit 70 optimizes the delivery plan for the extracted delivery request for each delivery condition, and the output unit 80 outputs the optimized delivery plan for each delivery condition. Therefore, it is possible to create a delivery plan from a plurality of viewpoints, taking into consideration situations that may change due to additional delivery requests.
  • FIG. 10 is a block diagram showing an overview of the delivery plan creation system according to the present invention.
  • the delivery plan creation system 190 (for example, the delivery plan creation system 100) according to the present invention includes a delivery request receiving means 191 (for example, delivery request receiving unit 20), delivery request extraction means 192 (for example, delivery request extraction unit 40) that extracts delivery requests that correspond to the input delivery conditions, including additional delivery requests, and Optimization means 193 (for example, optimization processing unit 70) that optimizes the delivery plan for each delivery condition, and output means 194 (for example, output unit 80) that outputs the optimized delivery plan for each delivery condition.
  • Delivery request receiving means 191 for example, delivery request receiving unit 20
  • delivery request extraction means 192 for example, delivery request extraction unit 40
  • Optimization means 193 for example, optimization processing unit 70
  • output means 194 for example, output unit 80
  • the delivery plan creation system 190 may include delivery risk prediction means (for example, the delivery risk prediction unit 30) that predicts delivery requests that are expected to be made in the future that correspond to the input delivery conditions.
  • the delivery request extraction means 192 may also extract the predicted delivery request.
  • the delivery plan creation system 190 may include a model generation unit (eg, model generation unit 60, parameter calculation unit 50) that generates an objective function used for optimization using a Hamiltonian formula or an Ising model.
  • the optimization means 193 may then transmit the generated objective function to a quantum computer or an annealing machine to instruct the quantum computer or annealing machine to execute the optimization process.
  • the output means 194 may output comparison information that compares the results optimized for each delivery condition.
  • the optimization means 193 may optimize the delivery plan by minimizing an objective function that defines the cost required for delivery.
  • the optimization means 193 may optimize the delivery plan by minimizing an objective function that defines the amount of carbon dioxide emissions caused by delivery.
  • the delivery request receiving means 191 receives input of an additional delivery request including designation of a plurality of delivery times as delivery conditions, and the delivery request extracting means 192 extracts the delivery request scheduled for delivery at the specified delivery time from the additional delivery request.
  • the optimization means 193 optimizes the delivery plan for the extracted delivery requests for each specified delivery time, and the output means 194 outputs the optimized results for each delivery time. You may.
  • the delivery request receiving means 191 receives an input of an additional delivery request including designation of a plurality of delivery vehicle numbers as a delivery condition, and the optimizing means 193 responds to the extracted delivery request for each designated number of delivery vehicles.
  • the delivery plan may be optimized, and the output means 194 may output the optimized result for each number of delivery vehicles.
  • the delivery request receiving means 191 receives input of an additional delivery request including designation of a plurality of delivery base attributes as a delivery condition, and the optimizing means 193 responds to the extracted delivery request for each designated delivery base attribute.
  • the delivery plan may be optimized, and the output means 194 may output the optimized results for each delivery base attribute.
  • FIG. 11 is a schematic block diagram showing the configuration of a computer according to at least one embodiment.
  • the computer 1000 includes a processor 1001, a main memory 1002, an auxiliary memory 1003, and an interface 1004. Further, as described above, a quantum computer or an annealing machine may be connected to the computer 1000.
  • the above-described delivery plan creation system 190 is implemented on the computer 1000.
  • the operations of each processing unit described above are stored in the auxiliary storage device 1003 in the form of a program (delivery plan creation program).
  • the processor 1001 reads a program from the auxiliary storage device 1003, expands it to the main storage device 1002, and executes the above processing according to the program.
  • the auxiliary storage device 1003 is an example of a non-temporary tangible medium.
  • Other examples of non-transitory tangible media include magnetic disks, magneto-optical disks, CD-ROMs (Compact Disc Read-only memory), DVD-ROMs (Read-only memory), Examples include semiconductor memory.
  • the computer 1000 that receives the distribution may develop the program in the main storage device 1002 and execute the above processing.
  • the program may be for realizing part of the above-mentioned functions.
  • the program may be a so-called difference file (difference program) that implements the above-described functions in combination with other programs already stored in the auxiliary storage device 1003.
  • Delivery request receiving means for accepting input of additional delivery requests including specification of multiple delivery conditions; delivery request extraction means for extracting delivery requests that correspond to the input delivery conditions, including the additional delivery requests; optimizing means for optimizing a delivery plan for the extracted delivery request for each delivery condition;
  • a delivery plan creation system comprising: an output means for outputting an optimized delivery plan for each of the delivery conditions.
  • the optimization means optimizes the delivery plan by minimizing an objective function that defines the amount of carbon dioxide emissions generated by delivery.
  • the delivery request receiving means receives input of an additional delivery request including designation of multiple delivery times as delivery conditions
  • the delivery request extraction means extracts delivery requests scheduled for delivery at the specified delivery time, including the additional delivery requests
  • the optimization means optimizes the delivery plan for the extracted delivery request for each specified delivery time
  • the delivery planning system according to any one of Supplementary Notes 1 to 6, wherein the output means outputs the optimized result for each delivery time.
  • the delivery request receiving means receives input of an additional delivery request including designation of multiple delivery vehicle numbers as delivery conditions,
  • the optimization means optimizes the delivery plan for the extracted delivery request for each specified number of delivery vehicles,
  • the delivery planning system according to any one of Supplementary Notes 1 to 6, wherein the output means outputs the optimized result for each number of delivery vehicles.
  • the delivery request receiving means receives input of an additional delivery request including designation of multiple delivery base attributes as a delivery condition,
  • the optimization means optimizes the delivery plan for the extracted delivery request for each specified delivery base attribute,
  • the delivery planning system according to any one of Supplementary Notes 1 to 6, wherein the output means outputs a result optimized for each delivery base attribute.
  • the computer accepts input of an additional delivery request including specification of multiple delivery conditions, the computer extracts delivery requests that correspond to the input delivery conditions, including the additional delivery requests; the computer optimizes a delivery plan for the extracted delivery request for each delivery condition; A delivery plan creation method, wherein the computer outputs an optimized delivery plan for each of the delivery conditions.
  • Delivery request reception processing that accepts input of additional delivery requests including specification of multiple delivery conditions; delivery request extraction processing that extracts delivery requests that correspond to the input delivery conditions, including the additional delivery requests; an optimization process that optimizes a delivery plan for the extracted delivery request for each delivery condition, and A program storage medium that stores a delivery plan creation program for executing an output process of outputting an optimized delivery plan for each of the delivery conditions.
  • Delivery request reception processing that accepts input of additional delivery requests including specification of multiple delivery conditions; delivery request extraction processing that extracts delivery requests that correspond to the input delivery conditions, including the additional delivery requests; an optimization process that optimizes a delivery plan for the extracted delivery request for each delivery condition, and A delivery plan creation program for executing output processing to output an optimized delivery plan for each of the delivery conditions.
  • Storage unit 20 Delivery request reception unit 30 Delivery risk prediction unit 40 Delivery request extraction unit 50 Parameter calculation unit 60 Model generation unit 70 Optimization processing unit 80 Output unit 100,200 Delivery plan creation system 201 Quantum computer 202 Annealing machine

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