Information processing device, information processing method, and program

Abstract

To provide an information processing device, an information processing method, and a program that can optimize resources related to delivery. [Solution] The information processing device of the embodiment includes: an acquisition unit that acquires package information relating to a package and vehicle conditions relating to a vehicle that delivers the package; a generation unit that generates a daily delivery plan for the package based on the package information and the vehicle conditions; a calculation unit that calculates the vehicle's operational end time from the final delivery time of the package included in the delivery plan, calculates the vehicle's operational time from the calculated operational end time, calculates the number of packages that can be delivered from the operational time; and an output unit that outputs the calculated number of packages that can be delivered.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and a program.

Background Art

[0002] In the logistics industry, there is a demand for automatically and efficiently formulating a delivery plan for goods. Regarding this, for example, technologies for creating a transportation schedule for goods from goods information, transport vehicle information, time zone information, etc. input by a user, and technologies for optimizing the delivery route of goods are known (see, for example, Patent Documents 1 and 2).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] In an enterprise where goods to be delivered are added over time, it is important to determine whether the added goods can be delivered when they are added. By calculating in advance the number of goods that can be delivered (hereinafter also referred to as the deliverable quantity), if the number of added goods is less than or equal to the deliverable quantity, it can be determined that delivery is possible. As a conventional technique, there is a method of determining the average delivery time per piece, taking the number obtained by dividing the daily operating time of the vehicle by the average delivery time as the maximum deliverable quantity, and subtracting it each time a new good is added. However, in the conventional technique, when the delivery time of the goods actually delivered deviates from the average delivery time, it is impossible to optimize (utilize without excess or deficiency) resources related to delivery such as delivery vehicles and delivery personnel, resulting in a decrease in service level due to delayed delivery of goods and an increase in delivery costs due to securing excessive resources.

[0005] This invention has been made in consideration of these circumstances, and one of its objectives is to provide an information processing device, an information processing method, and a program that can optimize resources related to delivery. [Means for solving the problem]

[0006] One aspect of the present invention is an information processing device comprising: an acquisition unit that acquires cargo information relating to cargo and vehicle conditions relating to a vehicle that delivers the cargo; a generation unit that generates a daily delivery plan for the cargo based on the cargo information and the vehicle conditions; a calculation unit that calculates the vehicle's operational end time from the final delivery time of the cargo included in the delivery plan, calculates the vehicle's operational time from the calculated operational end time, calculates the number of cargo deliveries that can be made from the operational time; and an output unit that outputs the calculated number of deliveries that can be made. [Effects of the Invention]

[0007] According to one aspect of the present invention, by optimizing resources related to delivery, it is possible to improve service levels and reduce delivery costs. [Brief explanation of the drawing]

[0008] [Figure 1] This figure shows an example of the configuration of the information processing device 100 according to the embodiment. [Figure 2] This is a flowchart showing the sequence of processes of the processing unit 120 according to the embodiment. [Figure 3] This is a diagram illustrating an example of a delivery plan. [Figure 4] This is a diagram showing an excerpt from the delivery plan. [Figure 5] This is a diagram showing an excerpt from the delivery plan. [Figure 6] This is a diagram showing an excerpt from the delivery plan. [Figure 7] This is a diagram showing an excerpt from the delivery plan. [Figure 8]This diagram shows an example of the screen of a store terminal 10 or a vehicle terminal 20. [Modes for carrying out the invention]

[0009] The information processing apparatus, information processing method, and program of the embodiment will be described below with reference to the drawings.

[0010] [Configuration of the information processing device] Figure 1 is a diagram showing an example of the configuration of an information processing device 100 according to an embodiment. The information processing device 100 includes, for example, a communication interface 111, an input interface 112, an output interface 113, a storage unit 114, and a processing unit 120.

[0011] The communication interface 111 communicates with external devices via a communication network NW. The communication network NW includes, for example, a LAN (Local Area Network) or a WAN (Wide Area Network). External devices include, for example, a store terminal 10 or a vehicle terminal 20.

[0012] The store terminal 10 is a store terminal device that stores packages scheduled to be delivered to customers as inventory. The vehicle terminal 20 is a terminal device for delivery personnel that deliver packages from the store to customers. External devices may further include customer terminal devices, etc. The store terminal 10, vehicle terminal 20, and customer terminal devices may be, for example, smartphones, tablet devices, laptop computers, etc. The communication interface 111 includes, for example, a NIC (Network Interface Card) or an antenna for wireless communication.

[0013] The input interface 112 receives various input operations from the user, converts the received input operations into electrical signals, and outputs them to the processing unit 120. For example, the input interface 112 includes a mouse, keyboard, trackball, switch, button, joystick, touch panel, etc. The input interface 112 may also be a voice user interface that accepts audio input, such as from a microphone.

[0014] The output interface 113 includes, for example, a display and a speaker. The display shows images generated by the processing unit 120, as well as a GUI (Graphical User Interface) for accepting various input operations from the user. For example, the display may be an LCD (Liquid Crystal Display) or an organic EL (Electro Luminescence) display. The speaker outputs the information input from the processing unit 120 as sound.

[0015] The storage unit 114 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, a hard disk, or an optical disc. These non-transient storage media may also be implemented by other storage devices connected via a communication network NW, such as a NAS (Network Attached Storage) or an external storage server device. The storage unit 114 may also include non-transient storage media such as ROM (Read Only Memory) or registers. The storage unit 114 stores programs executed by the hardware processor of the processing unit 120, as well as various calculation results from the processing unit 120.

[0016] The processing unit 120 includes, for example, an acquisition unit 121, a generation unit 122, a calculation unit 123, and an output control unit 124. The output control unit 124 and the output interface 113 together constitute one example of an "output unit." Another example of an "output unit" is the output control unit 124, the store terminal 10, and the vehicle terminal 20 together.

[0017] The components of these processing units 120 are realized by a processor such as a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit) executing a program stored in the storage unit 114. Some or all of the components of the processing unit 120 may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), a GPU (Graphics Processing Unit), or a SOC (System On Chip), or may be realized by the cooperation of software and hardware.

[0018] Also, the processing unit 120 may be realized by a processor based on quantum physics such as a QPU (Quantum Processing Unit), that is, a quantum processor, instead of a processor based on classical physics such as a CPU or a GPU. That is, the information processing apparatus 100 may be implemented using a classical computer or may be implemented using a quantum computer.

[0019] [Processing Flow of Information Processing Apparatus] Hereinafter, a series of processes performed by the processing unit 120 of the information processing apparatus 100 will be described while referring to a flowchart. FIG. 2 is a flowchart showing the flow of a series of processes of the processing unit 120 according to the embodiment.

[0020] First, the acquisition unit 121 acquires package information and vehicle conditions (step S100).

[0021] Package information refers to information about a package scheduled to be delivered to a customer. For example, package information may include the weight (i.e., load) of the package to be delivered and the time slot (i.e., delivery time slot) requested by the customer. The delivery time slot is the time period in which the customer wishes to receive the package, such as 8:00-12:00, 14:00-16:00, 16:00-18:00, or 18:00-20:00.

[0022] Vehicle conditions refer to the specifications of the vehicle used to transport goods from the store to the customer when delivering goods to the customer. For example, vehicle conditions may include the vehicle's load capacity.

[0023] For example, suppose a store employee inputs package information and vehicle conditions into the store terminal 10. In this case, the acquisition unit 121 may access the store terminal 10 via the communication interface 111 and acquire the package information and vehicle conditions from the store terminal 10.

[0024] Furthermore, some of the cargo information and vehicle conditions (for example, delivery time slots) may be entered by the customer into their own terminal device. In this case, the acquisition unit 121 may access the customer's terminal device via the communication interface 111 and acquire some of the cargo information and vehicle conditions from the terminal device.

[0025] Furthermore, some or all of the cargo information and vehicle conditions may be input to the input interface 112 of the information processing device 100. In this case, the acquisition unit 121 may acquire the cargo information and vehicle conditions from the input interface 112.

[0026] Next, the generation unit 122 generates a delivery plan for the packages (step S102). A delivery plan is, for example, a plan of the order in which packages (goods) stored as inventory in one or more stores will be collected, and further, the order in which the collected packages will be delivered to one or more customers. In other words, a delivery plan can have a 1:N store:customer ratio, or an N:N ratio. A delivery plan can be considered, for example, as a combination of customers and stores.

[0027] For example, the generation unit 122 may generate an optimal delivery plan by solving a combinatorial optimization problem related to delivery, such as the one described in Reference 1.

[0028] Reference 1: Kenji Takai, Eiichi Taniguchi, Tadashi Yamada, "Analysis of the Impact of e-commerce on Urban Traffic Using a Vehicle Dispatch and Delivery Planning Model," Journal of the Japan Society of Civil Engineers, Vol. 26, 2009, pp. 725-730, https: / / doi.org / 10.2208 / journalip.26.725

[0029] The generation unit 122 may generate a delivery plan (a combination of customer and store delivery order) based on an evaluation function (also called an objective function) aimed at minimizing delivery time or maximizing the number of deliveries possible. Such an evaluation function can be expressed, for example, by formula (1).

[0030]

number

[0031] x1 is the number of deliverable items, and α is the weighting coefficient for the number of deliverable items. The number of deliverable items is calculated by subtracting the time required to deliver all packages received up to the time of processing from the total operating time of the vehicle per day, dividing the remaining time by the average delivery time per package, and rounding down the result to an integer value. x2 is the total travel time, and β is the weighting coefficient for the total travel time. The total travel time is the total time expected to be required for the vehicle to move when delivering packages. x3 is the number of vehicles, and γ is the weighting coefficient for the number of vehicles. The number of vehicles is the number of vehicles used for delivering packages.

[0032] If the processing unit 120 is implemented by a QPU, the generation unit 122 may generate a delivery plan by solving an optimization problem using a combination of quantum annealing and rule-based heuristics.

[0033] For example, when using quantum annealing, the generation unit 122 may generate a delivery plan by rearranging the delivery order of packages, which are represented by qubits that take values ​​of 0 or 1, so as to minimize the delivery time of packages (i.e., total travel time x 2), or so as to maximize the number of deliverable packages x 1.

[0034] Figure 3 illustrates an example of a delivery plan. For example, as part of a dominant strategy, a company might open multiple stores (A, B, and C in the example shown) in a concentrated area. In such a case, it is conceivable to collect packages from store A and deliver them to multiple customers from there. However, it may be more efficient to collect packages from store B at the same time that packages from store A have been delivered to multiple customers, or at some point while packages from store A are being delivered to multiple customers, and then deliver packages from store B to multiple customers from there.

[0035] Therefore, the generation unit 122 assigns a number to each of the stores A to C and customers c1 to c7, and further determines the delivery order by combining the 10 assigned numbers, and optimizes this delivery order. Specifically, the generation unit 122 rearranges the combinations of numbers from 1 to 10 so as to minimize the delivery time of the packages (i.e., total travel time x 2), or so as to maximize the number of deliverables x 1. This determines a delivery plan (delivery order) as shown in Figure 3, for example.

[0036] Figures 4 to 7 are diagrams showing excerpts from a delivery plan. As shown in Figure 4, let's assume that the delivery time slot for store A is 10:00 to 13:00 and the delivery time slot for store B is 10:00 to 12:00. In such a case, as shown in Figure 5, if packages are delivered to customers at store A first, and then to customers at store B, the number of deliveries that can be made will decrease compared to Figure 4. Therefore, as in this embodiment, by rearranging the delivery order to minimize the delivery time (i.e., total travel time x 2) while maximizing the number of deliveries that can be made x 1, it is possible to check the delivery completion time for each store and deliveries to stores in order of the earliest delivery completion time.

[0037] On the other hand, simply arranging deliveries in order of earliest completion time may result in longer travel times between stores. For example, suppose the delivery time slot for store C and store D is 10:00-12:00, the delivery time slot for store A and store B is 10:00-13:00, the travel time from store C to store D is 30 minutes, the travel time from store A to store B is 30 minutes, and the travel time from store C to store A and from store C to store B are both 60 minutes. In such a case, arranging deliveries in order of delivery time slot results in longer travel times, as shown in Figure 7. Conversely, shortening travel times reduces the number of deliveries that can be made, as shown in Figure 6. Thus, there is a trade-off relationship between travel time and the number of deliveries that can be made. In this embodiment, considering this trade-off relationship, the delivery order is rearranged to minimize the delivery time of packages (i.e., total travel time x 2) while maximizing the number of deliveries that can be made x 1.

[0038] Returning to the flowchart explanation, the calculation unit 123 then calculates the vehicle's operational time (step S104). For example, the calculation unit 123 calculates the vehicle's operational end time from the final delivery time of the cargo included in the delivery plan.

[0039] The final delivery time for a package is the time of the last delivery time slot of the day. For example, if the last delivery time slot of the day is from 18:00 to 20:00, the final delivery time for the package will be 20:00. The calculation unit 123 calculates the time required to travel from the final customer to the return store by dividing the distance from the customer to whom the package is delivered last (hereinafter referred to as the final customer) to the store to which the package returns (hereinafter referred to as the return store) by the average vehicle speed (for example, 60 [km / h]). Then, the calculation unit 123 calculates the end time of the vehicle's operation based on the final delivery time for the package and the time required to travel from the final customer to the return store. If the final delivery time for the package is 20:00 and the time required to travel from the final customer to the return store is 1 hour, the end time of the vehicle's operation will be 21:00.

[0040] Similarly, the calculation unit 123 calculates the vehicle's start time from the fastest delivery time of the cargo included in the delivery plan.

[0041] The fastest delivery time for a package is the time of the first delivery time slot of the day. For example, if the first delivery time slot of the day is from 8:00 to 12:00, the fastest delivery time for a package will be 8:00. The calculation unit 123 calculates the time required to travel from the departure store (hereinafter referred to as the departure store) to the first customer to whom the package will be delivered that day (hereinafter referred to as the first customer) by dividing the distance by the average vehicle speed (for example, 60 km / h). Then, the calculation unit 123 calculates the start time of the vehicle's operation based on the fastest delivery time for the package and the time required to travel from the departure store to the first customer. If the fastest delivery time for a package is 8:00 and the time required to travel from the departure store to the first customer is 1 hour, the start time of the vehicle's operation will be 7:00.

[0042] The calculation unit 123 then calculates the operating time of the vehicle as the time from the start time of operation (e.g., 7:00) to the end time of operation (e.g., 21:00) (e.g., 14 hours).

[0043] Next, the calculation unit 123 calculates the remaining number of deliveries that can be made based on the calculated operating time of the vehicle (step S106).

[0044] For example, the calculation unit 123 may calculate the remaining number of deliveries by dividing the vehicle's available operating time by the estimated delivery time required for each delivery (hereinafter referred to as the unit delivery time). The unit delivery time may be determined based on statistical indicators (e.g., the mean or median) of the delivery time required when deliveries were made in the past.

[0045] Next, the output control unit 124 outputs the calculated remaining number of deliverable items (step S108).

[0046] For example, the output control unit 124 transmits the remaining number of deliverable items to the store terminal 10 or vehicle terminal 20 via the communication interface 111. This displays the remaining number of deliverable items on the screens of the store terminal 10 or vehicle terminal 20. Alternatively, the output control unit 124 may output the remaining number of deliverable items via the output interface 113. This completes the processing shown in this flowchart.

[0047] Figure 8 shows an example of the screen of a store terminal 10 or vehicle terminal 20. The screen of the store terminal 10 or vehicle terminal 20 may display the number of remaining deliverables for each store. This allows store employees and delivery personnel to know the number of packages that can be delivered from their store. If there are many deliverables, delivery resources can be effectively utilized by encouraging customers to use the service, and if there are few deliverables, delivery delays can be prevented by encouraging changes in delivery times, etc.

[0048] According to the embodiment described above, the processing unit 120 of the information processing device 100 acquires cargo information and vehicle conditions. Based on the cargo information and vehicle conditions, the processing unit 120 generates a daily cargo delivery plan. The processing unit 120 calculates the vehicle's end time from the final delivery time of the cargo included in the delivery plan, and calculates the vehicle's start time from the fastest delivery time of the cargo included in the delivery plan, and calculates the time from the start time to the end time as the vehicle's available operating time. The processing unit 120 calculates the remaining number of deliveries that can be made from the vehicle's available operating time and outputs it. With this configuration, it is possible to optimize resources related to delivery (such as delivery vehicles and delivery personnel). As a result, it is possible to improve the delivery service level and reduce delivery costs.

[0049] While several embodiments have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of symbols]

[0050] 100... Information processing device, 111... Communication interface, 112... Input interface, 113... Output interface, 114... Storage unit, 120... Processing unit, 121... Acquisition unit, 122... Generation unit, 123... Calculation unit, 124... Output control unit

Claims

1. An acquisition unit that acquires cargo information related to the cargo and vehicle conditions related to the vehicle that delivers the cargo, A generation unit that generates a daily delivery plan for the cargo based on the cargo information and the vehicle conditions, A calculation unit calculates the vehicle's end-of-operation time from the final delivery time of the cargo included in the delivery plan, calculates the vehicle's available operating time from the calculated end-of-operation time, and calculates the number of cargo deliveries that can be made from the available operating time. An output unit that outputs the calculated number of deliverable items, An information processing device equipped with the following features.

2. The calculation unit calculates the number of deliveries that can be made by dividing the available operating time by the estimated delivery time required for each delivery of the package. The information processing apparatus according to claim 1.

3. The aforementioned delivery time is determined based on a statistical indicator of the time required when the aforementioned package was delivered in the past. The information processing apparatus according to claim 2.

4. The generation unit generates the delivery plan using at least quantum annealing. The information processing apparatus according to claim 1 or 2.

5. The generation unit generates the delivery plan by rearranging the delivery order of the packages, which are represented by qubits that take values ​​of 0 or 1, in such a way that the delivery time of the packages is minimized or the number of deliverable packages is maximized. The information processing apparatus according to claim 4.

6. Computers We obtain cargo information regarding the cargo and vehicle conditions regarding the vehicle that delivers the cargo. Based on the aforementioned cargo information and vehicle conditions, a daily delivery plan for the cargo is generated. The end time of operation of the vehicle is calculated from the final delivery time of the cargo included in the delivery plan. The operating time of the vehicle is calculated from the calculated end time of operation. The number of deliveries that can be made is calculated from the aforementioned operating time. Output the calculated number of deliverable items. Information processing methods.

7. On the computer, To obtain cargo information regarding the cargo and vehicle conditions regarding the vehicle that delivers the cargo, Based on the aforementioned cargo information and vehicle conditions, generate a daily delivery plan for the cargo. Calculate the end time of operation of the vehicle from the final delivery time of the cargo included in the delivery plan. The operating time of the vehicle is calculated from the calculated end time of operation. Calculate the number of deliveries that can be made from the aforementioned operating time. Output the calculated number of deliverable items. A program to execute.

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