Delivery planning device, delivery planning system, delivery planning method, and program

Abstract

To optimize a delivery plan with a small calculation amount.SOLUTION: A delivery planning device comprises: an information acquisition unit that acquires delivery information related to a delivery object to be delivered to a delivery destination and delivery person information regarding a delivery person that can deliver the delivery object; a function generation unit that, based on the delivery information and delivery person information, generates an objective function to minimize the maximum waiting time until the delivery object reaches the delivery destination, the total delivery distance for the delivery person to deliver the delivery object and the number of delivery persons that deliver the delivery objects; an optimization unit that optimizes a delivery route for each delivery person on the basis of the objective function; and a plan output unit that outputs a delivery plan including information about the delivery route and the delivery object delivered in the delivery route to the delivery person.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a delivery planning device, a delivery planning system, a delivery planning method, and a program. [Background technology]

[0002] There is known a technique for optimizing a route for delivering items to their destinations. For example, Patent Document 1 discloses a collection and delivery plan optimization device that divides a plurality of collection and delivery destination points into a plurality of groups, calculates the shortest route for visiting the plurality of collection and delivery destination points included in each group using annealing, and repeats the process of decreasing the number of groups to which the items are assigned if the required time for visiting falls within the shift time, and increasing the number of groups to which the items are assigned if the required time for visiting falls outside the shift time. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2022-032678 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the conventional technology, the number of delivery personnel is minimized by sequence processing, which has the problem of a large amount of calculation. For example, the delivery plan optimization device disclosed in Patent Document 1 needs to repeatedly group delivery destinations and optimize the routes for each group in order to deliver with as few personnel or vehicles as possible.

[0005] In view of the above technical problems, an object of the present invention is to optimize a delivery plan with a small amount of calculation. [Means for solving the problem]

[0006] A delivery planning device according to one aspect of the present invention includes an information acquisition unit that acquires delivery information regarding items to be delivered to a destination and deliverer information regarding deliverers capable of delivering the items to be delivered, a function generation unit that generates an objective function based on the delivery information and the deliverer information to minimize the maximum waiting time until the items to be delivered to the destination, the total delivery distance a deliverer must deliver the items to be delivered, and the number of deliverers delivering the items to be delivered, an optimization unit that optimizes delivery routes for each deliverer based on the objective function, and a plan output unit that outputs a delivery plan to a deliverer including information regarding the delivery route and the items to be delivered along the delivery route. Effect of the Invention

[0007] According to one aspect of the present invention, a delivery plan can be optimized with a small amount of calculation. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram illustrating an example of an overall configuration of a delivery planning system. [Diagram 2] FIG. 2 illustrates an example of a hardware configuration of a computer. [Diagram 3] FIG. 2 is a diagram illustrating an example of a functional configuration of a delivery planning system. [Figure 4] FIG. 1 illustrates an example of a delivery planning method. [Diagram 5] FIG. 4 is a diagram showing an example of deliverer information in the first embodiment. [Figure 6] FIG. 4 is a diagram showing an example of delivery information in the first embodiment. [Figure 7] FIG. 2 is a diagram showing an example of map information in the first embodiment. [Figure 8] FIG. 4 is a diagram illustrating an example of a distance matrix in the first embodiment. [Figure 9] FIG. 4 is a diagram illustrating an example of an Ising variable in the first embodiment. [Figure 10] FIG. 2 is a diagram showing an example of a delivery plan in the first embodiment. [Figure 11] FIG. 1 is a diagram showing an example of a delivery plan according to the prior art. [Figure 12] FIG. 2 is a diagram showing an example of a delivery plan according to the first embodiment. [Figure 13] FIG. 1 is a diagram showing an example of waiting time distribution according to the prior art. [Figure 14] FIG. 4 is a diagram illustrating an example of waiting time distribution according to the first embodiment. [Figure 15] FIG. 11 is a diagram showing an example of delivery information in the second embodiment. [Figure 16] FIG. 11 is a diagram showing an example of map information in the second embodiment. [Figure 17] FIG. 11 is a diagram illustrating an example of a first distance matrix in the second embodiment. [Figure 18] FIG. 11 is a diagram illustrating an example of a second distance matrix in the second embodiment. [Figure 19] FIG. 11 is a diagram illustrating an example of a third distance matrix in the second embodiment. [Figure 20] FIG. 11 is a diagram illustrating an example of an Ising variable in the second embodiment. [Figure 21] FIG. 11 is a diagram showing an example of a delivery plan in the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are denoted by the same reference numerals, and redundant description will be omitted.

[0010] [First embodiment] One embodiment of the present invention is a delivery planning system that creates an optimal delivery plan for delivering a plurality of items sent from a delivery source. The delivery planning system in this embodiment aims to achieve high speed delivery at low cost.

[0011] In recent years, the logistics industry has been facing a serious labor shortage, and delivery costs have continued to rise. Meanwhile, the EC (Electronic Commerce) market is expanding, and the demand for deliveries is growing rapidly. Furthermore, demographic changes and the aging of the population are causing an increase in the number of residents who find it difficult to do their daily shopping, and it is expected that the demand for all kinds of deliveries will increase.

[0012] In order to address these challenges in the logistics industry and improve the convenience of everyday life, there is a demand for a delivery system that can provide new added value, namely "immediate delivery" after an order is placed. To achieve "immediate delivery," it is important to build a local delivery network. A local delivery network is a system that allocates couriers sequentially within a limited local area, realizing short wait times between ordering and receiving the product.

[0013] The value of "immediate delivery" is in high demand, especially for fresh foods or pre-cooked foods (boxed lunches or side dishes). Some companies that have incorporated existing local delivery networks have seen a significant increase in service usage due to improved convenience.

[0014] However, in conventional local delivery networks, "immediate delivery" is achieved by mobilizing a large number of delivery personnel at the expense of labor costs and delivering each item quickly. Therefore, conventional local delivery networks have the problem that the cost increases in proportion to the high delivery cost and the expansion of the service scale. To address this problem, it is possible to minimize the number of delivery personnel by having a delivery person deliver to multiple locations in one delivery.

[0015] Conventionally developed delivery optimization algorithms for home delivery services and the like perform optimization under the premise that the delivery destinations and the number of deliverers are known in advance. For example, in Patent Document 1, grouping of overall delivery needs and optimization of the delivery order for each group are sequentially processed, which requires a large amount of calculation and processing time. Therefore, conventional delivery optimization algorithms are not suitable for sequentially optimizing a local delivery network while satisfying constraints on delivery time and deliverable weight.

[0016] In this embodiment, simultaneous optimization is performed taking into account multiple conditions to achieve faster and cheaper delivery than existing delivery methods. In this embodiment, optimization is performed to minimize the total delivery distance and the number of deliverers under constraints tailored to each individual deliverer, as well as to reduce the maximum waiting time of consumers. In one aspect, this embodiment makes it possible to optimize a delivery plan with a small amount of calculation, and to realize a local delivery network with reduced delivery costs.

[0017] <Overall composition> The overall configuration of the delivery planning system in this embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the overall configuration of the delivery planning system in this embodiment.

[0018] 1, a delivery planning system 1000 in this embodiment includes an order receiving device 10, a delivery planning device 20, an annealing machine 30, and n terminal devices 40 (40-1, . . . , 40-n), where n is an integer equal to or greater than 2. The order receiving device 10, the delivery planning device 20, the annealing machine 30, and the terminal devices 40 are connected to each other so as to be able to communicate data with each other via a communication network N1 such as a LAN (Local Area Network) or the Internet.

[0019] Hereinafter, when distinguishing between the multiple terminal devices 40, they will be referred to using sub-numbers such as "terminal device 40-1" and "terminal device 40-2."

[0020] The order receiving device 10 is an information processing device such as a personal computer, a workstation, or a server that receives orders involving delivery of an item to be delivered. The order receiving device 10 receives orders from consumers and transmits delivery instructions to the delivery planning device 20 for the ordered items to be the items to be delivered. The order receiving device 10 may receive orders through a communication line such as the Internet, or may receive orders through communication means such as telephone or FAX. In this embodiment, the item to be delivered may be any object that can be transported. Examples of the item to be delivered include fresh food, cooked food, daily necessities, books, furniture, home appliances, etc.

[0021] The delivery planning device 20 is an information processing device such as a personal computer, a workstation, or a server that creates a delivery plan. The delivery planning device 20 receives status information indicating the status of a deliverer from the terminal device 40, and manages deliverer information related to the deliverer. The delivery planning device 20 receives a delivery instruction from the order receiving device 10, and acquires delivery information related to the item to be delivered. The delivery planning device 20 creates a delivery plan for delivering the item to be delivered to the destination by performing simultaneous optimization taking into account multiple conditions using an annealing machine 30.

[0022] The annealing machine 30 is a computer specialized in solving combinatorial optimization problems. The annealing machine 30 can efficiently solve combinatorial optimization problems by performing annealing to search for the ground state of the Ising model, which is one of the magnetic models in physics. The annealing machine 30 in this embodiment may be a quantum annealing machine that realizes annealing using a quantum device, or a digital annealing machine that realizes annealing using a digital circuit.

[0023] The terminal device 40 is an information processing terminal such as a personal computer, a tablet terminal, or a smartphone operated by the deliverer. The terminal device 40 may be a wearable device such as a smart watch or smart glasses worn by the deliverer. The terminal device 40 transmits status information indicating the status of the deliverer to the delivery planning device 20. The terminal device 40 outputs the delivery plan received from the delivery planning device 20 to the deliverer.

[0024] The overall configuration of the delivery planning system 1000 shown in Fig. 1 is an example, and various system configuration examples are possible depending on the application and purpose. For example, the delivery planning system 1000 may include multiple units of one or more of the order receiving device 10, the delivery planning device 20, the annealing machine 30, and the terminal device 40. For example, the order receiving device 10, the delivery planning device 20, and the annealing machine 30 may be realized by multiple computers, or may be realized as a cloud computing service. The division of devices such as the order receiving device 10, the delivery planning device 20, the annealing machine 30, and the terminal device 40 shown in Fig. 1 is an example.

[0025] <Hardware configuration> The hardware configuration of the delivery planning system in this embodiment will be described with reference to FIG.

[0026] <Computer> The order receiving device 10, the delivery planning device 20, and the terminal device 40 in this embodiment are realized by, for example, a computer. Fig. 2 is a block diagram showing an example of the hardware configuration of the computer in this embodiment.

[0027] 2, the computer 500 includes a central processing unit (CPU) 501, a read only memory (ROM) 502, a random access memory (RAM) 503, a hard disk drive (HDD) 504, an input device 505, a display device 506, a communication interface (I / F) 507, and an external I / F 508. The CPU 501, the ROM 502, and the RAM 503 form a so-called computer. Each piece of hardware of the computer 500 is connected to each other via a bus line 509. The input device 505 and the display device 506 may be connected to the external I / F 508 for use.

[0028] The CPU 501 is a calculation device that reads out programs and data from a storage device such as the ROM 502 or the HDD 504 onto the RAM 503 and executes processing to realize the overall control and functions of the computer 500. The computer 500 may have a GPU (Graphics Processing Unit) in addition to or instead of the CPU 501.

[0029] The ROM 502 is an example of a non-volatile semiconductor memory (storage device) that can retain programs and data even when the power is turned off. The ROM 502 functions as a main storage device that stores various programs and data required for the CPU 501 to execute various programs installed in the HDD 504. Specifically, the ROM 502 stores boot programs such as a Basic Input / Output System (BIOS) and an Extensible Firmware Interface (EFI) that are executed when the computer 500 is started, and data such as an Operating System (OS) setting and a network setting.

[0030] The RAM 503 is an example of a volatile semiconductor memory (storage device) in which programs and data are erased when the power is turned off. The RAM 503 is, for example, a dynamic random access memory (DRAM) or a static random access memory (SRAM). The RAM 503 provides a working area in which various programs installed in the HDD 504 are expanded when the CPU 501 executes them.

[0031] The HDD 504 is an example of a non-volatile storage device that stores programs and data. The programs and data stored in the HDD 504 include an OS, which is basic software that controls the entire computer 500, and applications that provide various functions on the OS. Note that the computer 500 may use a storage device that uses a flash memory as a storage medium (e.g., an SSD (Solid State Drive) or the like) instead of the HDD 504.

[0032] The input device 505 includes a touch panel, operation keys or buttons, a keyboard or mouse, a microphone for inputting sound data such as voice, etc., which are used by the user to input various signals.

[0033] The display device 506 is composed of a display such as a liquid crystal display or an organic EL (Electro-Luminescence) display for displaying a screen, a speaker for outputting sound data such as voice, and the like.

[0034] The communication I / F 507 is an interface that connects to a communication network and enables the computer 500 to perform data communication.

[0035] The external I / F 508 is an interface with external devices, such as a drive device 510.

[0036] The drive device 510 is a device for setting the recording medium 511. The recording medium 511 here includes media that record information optically, electrically, or magnetically, such as a CD-ROM, a flexible disk, or a magneto-optical disk. The recording medium 511 may also include semiconductor memories that record information electrically, such as a ROM or a flash memory. This allows the computer 500 to read and / or write data from and to the recording medium 511 via the external I / F 508.

[0037] The various programs to be installed in the HDD 504 are installed, for example, by setting the distributed recording medium 511 in a drive device 510 connected to the external I / F 508 and reading out the various programs recorded in the recording medium 511 by the drive device 510. Alternatively, the various programs to be installed in the HDD 504 may be installed by being downloaded from a network different from the communication network via the communication I / F 507.

[0038] <Functional configuration> The functional configuration of the delivery planning system in this embodiment will be described with reference to Fig. 3. Fig. 3 is a block diagram showing an example of the functional configuration of the delivery planning system in this embodiment.

[0039] <Distribution planning device> As shown in FIG. 3 , the delivery planning device 20 in this embodiment includes a status update unit 201, a deliverer information storage unit 202, an information acquisition unit 203, a map information storage unit 204, a distance calculation unit 205, a function generation unit 206, an optimization unit 207, and a plan output unit 208.

[0040] The state update unit 201, the information acquisition unit 203, the distance calculation unit 205, the function generation unit 206, the optimization unit 207 and the plan output unit 208 are realized by processing executed by the CPU 501 of a program expanded from the HDD 504 shown in FIG. 2 onto the RAM 503.

[0041] The deliverer information storage unit 202 and the map information storage unit 204 are realized by the HDD 504 shown in FIG.

[0042] The status update unit 201 updates the deliverer information stored in the deliverer information storage unit 202 based on the status information received from the terminal device 40. The deliverer information includes the current location of the deliverer, the active status of the deliverer, and the upper limit weight that the deliverer can deliver in one go. The active status of the deliverer is information indicating whether the deliverer is currently available for delivery or not.

[0043] The deliverer information is stored in the deliverer information storage unit 202. The deliverer information is created when a deliverer is registered in the delivery planning system 1000, and is updated by the status update unit 201 as needed.

[0044] The information acquisition unit 203 acquires delivery information related to the item to be delivered and deliverer information related to the deliverer. The information acquisition unit 203 acquires the delivery information by extracting the delivery information from the delivery instruction received from the order receiving device 10. The information acquisition unit 203 acquires the deliverer information by reading out the deliverer information from the deliverer information storage unit 202.

[0045] The map information storage unit 204 stores map information showing a map of a target area for which a delivery plan is to be created. In this embodiment, the target area is an area within a predetermined range from the delivery origin of the delivery item, and is an area to which the delivery item can be delivered from the delivery origin. Therefore, the delivery origin of the delivery item, the delivery destination, and the current location of the deliverer are all assumed to be within the target area.

[0046] The distance calculation unit 205 calculates a distance matrix based on the map information read from the map information storage unit 204. The distance matrix is ​​a matrix that indicates the distance between the delivery origin and each delivery destination.

[0047] The function generation unit 206 generates an objective function based on the delivery information and deliverer information acquired by the information acquisition unit 203 and the distance matrix calculated by the distance calculation unit 205. The objective function is an objective function for minimizing the maximum waiting time, the total delivery distance, and the number of deliverers. The maximum waiting time is the maximum waiting time until the item to be delivered arrives at the consumer. The maximum waiting time can also be said to be the maximum time from when an order for the item to be delivered is accepted until the item is delivered to the delivery destination. The total delivery distance is the sum of the distances that the deliverers deliver the items to be delivered. The number of deliverers is the number of active deliverers who actually deliver the items to be delivered.

[0048] The optimization unit 207 optimizes the delivery plan for each deliverer based on the objective function generated by the function generation unit 206. The optimization unit 207 generates an Ising model based on the objective function, and transmits an optimization request including the Ising model to the annealing machine 30. The optimization unit 207 receives the optimization result from the annealing machine 30, and generates a delivery route for each deliverer based on the optimization result.

[0049] The plan output unit 208 outputs a delivery plan to each deliverer. The plan output unit 208 creates a delivery plan for each deliverer and transmits the delivery plan to the terminal device 40 corresponding to the deliverer. The delivery plan includes the delivery route generated by the optimization unit 207 and information on the items to be delivered along the delivery route.

[0050] Terminal Device As shown in FIG. 3, the terminal device 40 in this embodiment includes a status notification unit 401 and a schedule display unit 402.

[0051] The status notification unit 401 and the plan display unit 402 are realized by processing that is executed by the CPU 501 according to a program loaded onto the RAM 503 from the HDD 504 shown in FIG.

[0052] The status notification unit 401 transmits status information indicating the status of the deliverer operating the terminal device 40 to the delivery planning device 20. The status information includes the current location of the terminal device 40 and the active status of the deliverer. The status information may also include the upper limit weight that the deliverer can deliver. Since the deliverer holds the terminal device 40 at all times, the current location of the terminal device 40 can be considered the same as the current location of the deliverer. The active status or the upper limit weight is input into the terminal device 40 by the deliverer.

[0053] The plan display unit 402 outputs the delivery plan received from the delivery plan device 20 to the delivery person. The plan display unit 402 may display the delivery plan on the display device 506. The plan display unit 402 may synthesize an audio signal explaining the delivery plan and output it from a speaker connected to the external I / F 508.

[0054] <Objective function> The objective function in this embodiment will be described in detail below. The objective function in this embodiment uses Hamiltonian H in formula (1) in order to realize simultaneous optimization taking into account a plurality of factors.

[0055]

number

[0056] Here, H waittime is a term for prioritizing delivery destinations with long waiting times (hereafter also called the "time term"). H distance is the term for minimizing the total delivery distance (hereafter also called the “distance term”). H worker is a term for minimizing the number of deliverers (hereinafter referred to as the "number of people term").

[0057] H weight is the weight constraint. H weight is a constraint to ensure that the delivery weight of each shipper is within the upper limit of the shipper’s weight. route is a constraint on the path. H route is a constraint to exclude routes that are not realistic.

[0058] λ1,λ2,λ3,λ const are weights for the time term, distance term, number of people term, and constraint term, respectively, and are used to adjust the priority of each term. const Since is a weight for the constraint term, it is advisable to give it a value larger than the other weights λ1, λ2, and λ3 (for example, 100 to 1000 times).

[0059] time term H waittime is expressed by equation (2).

[0060]

number

[0061] However, N node is the number of delivery destinations, and N worker is the number of active shippers, and q n ij is an Ising variable that indicates whether the shipper n passes through the destination i for the jth time, and w i is the waiting time for destination i.

[0062] distance term H distance is expressed by equation (3).

[0063]

number

[0064] However, d ik is the distance between destination (or origin) i and destination (or origin) k, and d' n is the distance between the current location of the deliverer n and the delivery origin.

[0065] Number of people term H worker is expressed by equation (4).

[0066]

number

[0067] Weight constraint term H weight is expressed by equation (5).

[0068]

number

[0069] constraint term H route is the constraint term H route1 ~H route3 It consists of:

[0070]

number

[0071] constraint term H route1 is the constraint for making a single delivery to each destination i. route2 is a constraint that prevents a sender n from simultaneously sending to multiple destinations. H route3 is the domain wall constraint for turning.

[0072] As described above, the Hamiltonian H in this embodiment is a time term H waittime and the distance term H distance and the number of people term H to minimize the number of delivery people worker The objective function H includes a time term H waittime , distance term H distance and number of people H worker are weighted and added using weights λ1, λ2, and λ3.

[0073] Therefore, according to Hamiltonian H, it is possible to realize simultaneous optimization taking into account the maximum waiting time, the total delivery distance, and the number of deliverers. At this time, the priority order for the maximum waiting time, the total delivery distance, and the number of deliverers can be arbitrarily determined, and a delivery plan that can flexibly meet the needs of users can be created.

[0074] <Processing Procedure> A delivery planning method executed by the delivery planning system in this embodiment will be described with reference to Fig. 4. Fig. 4 is a flowchart showing an example of a processing procedure of the delivery planning method in this embodiment.

[0075] In step S101, the status notification unit 401 of the terminal device 40 generates status information of the deliverer. Next, the status notification unit 401 transmits the generated status information to the delivery planning device 20.

[0076] The status information includes the current location of the terminal device 40 and the active status of the deliverer. The current location may be acquired, for example, using a Global Positioning System (GPS) or the like. The active status may be input, for example, to the terminal device 40 by the operation of the deliverer.

[0077] The status information may include the upper limit weight of the deliverer. The upper limit weight may be input, for example, by the delivery person's operation into the terminal device 40. The upper limit weight may be input, for example, by the delivery person selecting a means of transportation. The means of transportation may include, for example, a car, a bicycle, a motorcycle, walking, etc.

[0078] In step S102, the status update unit 201 of the delivery planning device 20 receives the status information from the terminal device 40. Next, the status update unit 201 updates the deliverer information stored in the deliverer information storage unit 202 based on the received status information.

[0079] Fig. 5 is a diagram showing an example of deliverer information in this embodiment. As shown in Fig. 5, the deliverer information in this embodiment has, as data items, information indicating the deliverer (e.g., the deliverer name, the deliverer ID, etc.), the deliverer's current location, the deliverer's upper limit weight, the deliverer's active status, etc. Each piece of information included in the deliverer information is updated to the latest data every time the status update unit 201 receives status information.

[0080] The process from step S101 to step S102 is repeatedly executed at a predetermined time interval for all terminal devices 40 included in the delivery planning system 1000. As a result, the latest deliverer information is always stored in the deliverer information storage unit 202.

[0081] Furthermore, the process from step S103 onwards is repeatedly executed in response to a predetermined trigger, independent of the process from step S101 to step S102. The predetermined trigger may be each time a predetermined time interval elapses, when a predetermined number of delivery instructions are accumulated, or when delivery according to the previously created delivery plan is completed.

[0082] Returning to FIG. 4, in step S103, the information acquisition unit 203 of the delivery planning device 20 acquires delivery information related to the item to be delivered and deliverer information related to the deliverer. Specifically, the information acquisition unit 203 first receives a delivery instruction from the order receiving device 10. The delivery instruction includes delivery information related to the item to be delivered. Next, the information acquisition unit 203 acquires deliverer information whose active status is active from the deliverer information storage unit 202. Then, the information acquisition unit 203 sends the delivery information and deliverer information to the distance calculation unit 205 and the function generation unit 206.

[0083] Fig. 6 is a diagram showing an example of delivery information in this embodiment. As shown in Fig. 6, the delivery information in this embodiment has, as data items, information indicating the delivery destination (for example, the name of the consumer who placed the order, the consumer ID, etc.), the date and time when the order was accepted (order date and time), information indicating the location of the delivery destination (for example, the address of the delivery destination, etc.), information indicating the type of product, and the weight of the item to be delivered.

[0084] Returning to FIG. 4, in step S104, the distance calculation unit 205 of the delivery planning device 20 receives the delivery information and the deliverer information from the information acquisition unit 203. Next, the distance calculation unit 205 reads out map information of the target area from the map information storage unit 204. Next, the distance calculation unit 205 calculates a distance matrix indicating the distance between each of the delivery origin and delivery destination based on the read map information. Then, the distance calculation unit 205 sends the distance matrix to the function generation unit 206.

[0085] FIG. 7 is a diagram showing an example of map information in this embodiment. As shown in FIG. 7, map information M1 in this embodiment is information showing a map of a target area including a delivery origin S. The map information M1 shows the layout of roads, facilities, houses, etc. that exist in the target area. The map information M1 includes the positions of the delivery origin S, delivery destinations A to E, and deliverers α and β. The position of the delivery origin S is registered in advance in the delivery planning system 1000. The positions of the delivery destinations A to E are shown in the delivery information. The positions of deliverers α and β are shown in the deliverer information.

[0086] 8 is a diagram showing an example of a distance matrix in this embodiment. As shown in FIG. 8, the distance matrix in this embodiment is a distance d ik This is matrix data showing (i,k=S,A,B,C,D,E).

[0087] 4, in step S105, the function generating unit 206 of the delivery planning device 20 receives the distance matrix from the distance calculating unit 205. In addition, the function generating unit 206 receives the delivery information and the deliverer information from the information acquiring unit 203.

[0088] Next, the function generating unit 206 generates coefficient information to be set in the objective function based on the delivery information and the deliverer information. The coefficient information is the number N of active deliverers. worker , waiting time w for delivery destination i (i=A,B,C,D,E) i , shipping weight W to destination i i, the distance d' between the location of the shipper n (n=α,β) and the shipper S n , the upper limit weight c of shipper n n weight Includes the number of deliverers N worker , distance d' n , and upper weight limit c n weight can be obtained from the delivery information. Waiting time w i and shipping weight W i can be obtained from the delivery information.

[0089] Next, the function generating unit 206 calculates the Ising variable q n ij Define the Ising variable q n ij indicates whether or not shipper n passes through destination i as the jth shipper.

[0090] FIG. 9 is a diagram illustrating an example of an Ising variable in this embodiment. As shown in FIG. 9, the Ising variable q n ij is a matrix data indicating the jth (j=1,2,3,4,5th) destination i (i=A,B,C,D,E) that each shipper n (n=α,β) passes through. An Ising variable is a variable that can take +1 or -1. For example, in the optimized delivery plan, when shipper α passes through destination B second, the Ising variable q α B2 takes +1, and the Ising variable q α A2 ,q α C2 ,q α D2 ,q α E2 will take -1.

[0091] Then, the function generating unit 206 generates a Hamiltonian H based on the distance matrix, the coefficient information, and the Ising variables. The function generating unit 206 sends the generated Hamiltonian H to the optimization unit 207.

[0092] Returning to Fig. 4, the description will be given. In step S106, the optimization unit 207 of the delivery planning device 20 receives the Hamiltonian H from the function generation unit 206. Next, the optimization unit 207 generates an Ising model based on the Hamiltonian H. Then, the optimization unit 207 transmits an optimization request to the annealing machine 30. The optimization request includes the Ising model.

[0093] The annealing machine 30 receives an optimization request from the delivery planning device 20. Next, the annealing machine 30 acquires an Ising model from the received optimization request. Then, the annealing machine 30 performs annealing to search for the ground state of the acquired Ising model. Then, the annealing machine 30 transmits the optimization result to the delivery planning device 20. The optimization result includes the Ising model in the ground state.

[0094] In the delivery planning device 20, the optimization unit 207 receives the optimization result from the annealing machine 30. Next, the optimization unit 207 acquires a ground-state Ising model from the received optimization result. Next, the optimization unit 207 generates a delivery route for each deliverer based on the ground-state Ising model. The delivery route is calculated by dividing the Ising variable q n ij The optimization unit 207 then sends the delivery route for each deliverer n to the plan output unit 208.

[0095] In step S107, the plan output unit 208 of the delivery planning device 20 receives a delivery route for each delivery person n from the optimization unit 207. Next, the plan output unit 208 creates a delivery plan for each delivery person n. The delivery plan includes the delivery route generated in step S106 and information indicating the items to be delivered to the delivery destination i passing through the delivery route. The information indicating the items to be delivered can be generated based on the delivery information acquired in step S103. Then, the plan output unit 208 transmits the delivery plan for each delivery person n to the terminal device 40-n operated by that delivery person n.

[0096] In step S108, the plan display unit 402 of the terminal device 40 receives the delivery plan from the delivery planning device 20. Next, the plan display unit 402 outputs the received delivery plan to the deliverer.

[0097] Fig. 10 is a diagram showing an example of a delivery plan in this embodiment. As shown in Fig. 10, the delivery plan in this embodiment shows, for each deliverer n (n = α, β), the delivery destination i to which the deliveries will be made and the order in which the deliveries will be made to those destinations. In the example shown in Fig. 10, it is shown that deliverer α loads the items to be delivered at delivery origin S, and delivers them from delivery destination A to delivery destination B. It is also shown that deliverer β loads the items to be delivered at delivery origin S, and delivers them from delivery destination E to delivery destination C via delivery destination D.

[0098] <Evaluation Results> The evaluation results of the delivery planning system 1000 in this embodiment will be described with reference to Fig. 11 to Fig. 14. The evaluation of the delivery planning system 1000 was performed from two perspectives: the processing time required to create a delivery plan, and the waiting time until delivery to the delivery destination.

[0099] <Processing time required to create a delivery plan> The evaluation results of the processing time required to create a delivery plan will be described with reference to Fig. 11 and Fig. 12. Fig. 11 is a diagram showing an example of a delivery plan according to the conventional technology. Fig. 12 is a diagram showing an example of a delivery plan according to this embodiment.

[0100] In this evaluation, a delivery plan was created for 10 delivery personnel to deliver to 36 locations. The prior art used a delivery optimization algorithm using sequence processing disclosed in Patent Document 1. In Figs. 11 and 12, the origin (0,0) is the location of the delivery origin, and the other plots are each delivery destination. The x and y axes are distance measures according to the coordinate system of the target area. The broken lines R1 to R10 connecting the delivery origin to each delivery destination represent the delivery route for each delivery personnel. The plots C1 and C2, which are not passed by the delivery routes R1 to R10, are delivery destinations that are not delivered to in the created delivery plan.

[0101] As shown in Fig. 11, in the conventional technology, ten delivery routes R1 to R10 were created. That is, a delivery plan was created in which ten delivery agents would make deliveries. However, two delivery destinations C1 and C2 were not deliverable in the delivery plan. The processing time required to create the delivery plan was 206 seconds.

[0102] On the other hand, as shown in FIG. 12, in this embodiment, eight delivery routes R1 to R8 were created. That is, a delivery plan in which eight delivery persons carry out delivery was created. In this embodiment, a delivery plan that allows delivery to all delivery destinations was created. The processing time required to create the delivery plan was 28 seconds.

[0103] 11 and 12 show that the delivery planning device 20 of this embodiment can create a delivery route that allows delivery to all delivery destinations with a small number of deliverers in a processing time that is significantly shorter than in the past. Therefore, according to this embodiment, a delivery plan can be created in a short time, making it possible to realize a local delivery network with high speed delivery. In addition, according to this embodiment, a delivery plan can be created that reduces labor costs for deliverers.

[0104] <Waiting time until delivery to destination> The evaluation results of the waiting time until delivery to the delivery destination will be described with reference to Fig. 13 and Fig. 14. Fig. 13 is a diagram showing an example of the waiting time distribution according to the conventional technology. Fig. 14 is a diagram showing an example of the waiting time distribution according to the present embodiment.

[0105] In the evaluation of waiting time, a simulation was performed under the following conditions, and the waiting time for each order was measured. The waiting time was defined as the time from when a consumer at the delivery destination places an order until the ordered product is delivered to the delivery destination. The simulation conditions were determined based on the rough estimation results of actual data collected from an existing delivery service.

[0106] The target area was set to a 6km diameter range from a real city. The number of consumers was set to 4,819 within the target area, with 86 of them placing orders per hour. 768 delivery personnel were placed within the target area, with 24 of them being active. Two delivery sources were placed within the target area. The time period was set to four hours, from 11:00 am to 3:00 pm, simulating the busy hours. Consumers randomly placed orders from 15 predetermined products, with an average of three orders.

[0107] In the simulation using the conventional technology, the minimum waiting time was 10 minutes, and the maximum waiting time was 1 hour and 42 minutes. On the other hand, in the simulation using this embodiment, the minimum waiting time was 13 minutes 13 seconds, and the maximum waiting time was 56 minutes. Although the minimum waiting time was shorter with the conventional technology, it cannot be said that the delay in this embodiment is large compared to the service target. The maximum waiting time in this embodiment is reduced by 45% compared to the conventional technology.

[0108] FIG. 13 shows the distribution of waiting times for all orders in a simulation using the conventional technology. FIG. 14 shows the distribution of waiting times for all orders in a simulation using the present embodiment. Comparing FIG. 13 with FIG. 14, the peaks of waiting times in both cases occur in time periods where waiting times are relatively short, around 25 to 30 minutes. However, it can be seen that the peaks of waiting times are higher in the present embodiment, and there is less distribution of time periods where waiting times are relatively long. FIG. 13 and FIG. 14 show that the delivery planning device 20 in the present embodiment can reduce the overall waiting time compared to the conventional technology.

[0109] <Effects> The delivery planning device 20 in this embodiment optimizes a delivery route for each delivery person based on an objective function for minimizing the maximum waiting time until the delivery item is delivered to the delivery destination, the total delivery distance that the delivery person delivers the delivery item, and the number of delivery people who deliver the delivery item. The delivery planning device 20 performs simultaneous optimization taking into account multiple conditions, so that it is possible to optimize a delivery plan with less calculations than the conventional technology that requires sequential processing. In one aspect, according to this embodiment, it is possible to optimize a delivery plan with less calculations, and it is possible to realize a local delivery network with reduced delivery costs.

[0110] The delivery planning device 20 in this embodiment may cause an annealing machine to calculate the ground state of the Ising model based on the objective function. The annealing machine may be a quantum annealing device. The annealing machine is a computer specialized in solving combinatorial optimization problems, and the quantum annealing device can solve combinatorial optimization problems at high speed using quantum devices. Therefore, according to this embodiment, the delivery plan can be optimized at high speed.

[0111] The objective function in this embodiment may include a time term for minimizing the maximum waiting time, a distance term for minimizing the total delivery distance, and a number of people term for minimizing the number of delivery personnel. The objective function may be a weighted sum of the time term, the distance term, and the number of people term. Therefore, according to this embodiment, a delivery plan that can flexibly meet the needs of the user can be created.

[0112] The objective function in this embodiment may include a constraint term for restricting the weight of the item to be delivered by the deliverer to the weight that the deliverer can deliver or less. The objective function may include a constraint term for restricting delivery to one delivery destination only once. Therefore, according to this embodiment, a realistic delivery plan with a high feasibility can be created.

[0113] In this embodiment, the delivery destination and the deliverer may be located within a predetermined range based on the delivery origin from which the item is to be delivered. Therefore, according to this embodiment, the delivery plan of the local delivery network can be optimized with a small amount of calculation.

[0114] [Second embodiment] In the first embodiment, a configuration has been described in which all items to be delivered are delivered from one delivery origin to a delivery destination within a target area. The number of delivery origins is not limited to one, and items to be delivered may be delivered from multiple delivery origins. In the second embodiment, a configuration will be described in which items are delivered from multiple delivery origins to a delivery destination within a target area.

[0115] The multiple delivery origins may be operated by one business operator or different business operators. For example, the first delivery origin may be operated by a first business operator that delivers fresh food, and the second delivery origin may be operated by a second business operator that delivers daily necessities. In this case, an order receiving device 10 corresponding to each business operator may exist, and the delivery planning device 20 may acquire delivery information from each order receiving device 10.

[0116] The delivery planning system in the second embodiment will be described below, focusing on the differences from the first embodiment.

[0117] <Objective function> In comparison with the objective function in the first embodiment, the objective function in this embodiment has a distance term H distance , and the constraint term H route is different.

[0118] The distance term H in this embodiment distance is expressed by equation (9).

[0119]

number

[0120] However, N shop is the number of shipping origins, and d n Siis the distance between the current location of the shipper n and the shipper Si, and d Sij is the distance between the source Si and the destination j, and q n Si is an Ising variable that indicates whether or not the deliverer n loads the item to be delivered at the delivery origin Si.

[0121] The constraint term H in this embodiment route is the constraint term H route1 ~H route3 In addition, the constraint term H route4 Further includes:

[0122]

number

[0123] constraint term H route4 is the constraint for each deliverer n to load items at any delivery origin Si.

[0124] As described above, the Hamiltonian H in this embodiment is the distance d n Si and the distance d from the delivery source to the delivery destination Sij A distance term H with a term indicating distance Therefore, according to the Hamiltonian H in this embodiment, when there are multiple delivery sources Si, it is possible to realize simultaneous optimization taking into account the maximum waiting time, the total delivery distance, and the number of deliverers.

[0125] <Processing Procedure> The delivery planning method executed by the delivery planning system in this embodiment is different from the delivery planning method in the first embodiment in the processes of steps S103 to S105.

[0126] In step S103, the information acquisition unit 203 of the delivery planning device 20 acquires delivery information related to the item to be delivered and deliverer information related to the deliverer. Fig. 15 is a diagram showing an example of delivery information in this embodiment. As shown in Fig. 15, the delivery information in this embodiment further includes information indicating the location of the delivery origin (for example, the address of the delivery origin) in addition to each item included in the delivery information in the first embodiment.

[0127] In step S104, the distance calculation unit 205 of the delivery planning device 20 calculates a plurality of distance matrices based on the map information of the target area read from the map information storage unit 204. In this embodiment, the distance calculation unit 205 calculates a first distance matrix indicating the distance between each of the delivery destinations, a second distance matrix indicating the distance between the current location of the deliverer and the delivery origin, and a third distance matrix indicating the distance between the delivery origin and the delivery destination.

[0128] Fig. 16 is a diagram showing an example of map information in this embodiment. As shown in Fig. 16, map information M2 in this embodiment is information showing a map of a target area including multiple delivery origins S1 to S3. The positions of the delivery origins S1 to S3 are shown in the delivery information.

[0129] 17 is a diagram showing an example of the first distance matrix in this embodiment. As shown in FIG. 17, the first distance matrix is ​​a distance d ik This is matrix data showing (i,k=A,B,C,D,E).

[0130] 18 is a diagram showing an example of the second distance matrix in this embodiment. As shown in FIG. 18, the second distance matrix is ​​a distance d n Si (n=α, β, Si=S1, S2, S3).

[0131] 19 is a diagram showing an example of the third distance matrix in this embodiment. As shown in FIG. 19, the third distance matrix is ​​a distance d Sij This is matrix data showing (Si=S1,S2,S3,j=A,B,C,D,E).

[0132] In step S105, the function generating unit 206 generates coefficient information to be set in the objective function based on the delivery information and the deliverer information. The coefficient information in this embodiment is the number N of active deliverers. worker , waiting time w for delivery destination i (i=A,B,C,D,E) i , shipping weight W to destination i i , the upper limit weight c of shipper n n weight In addition, the number of shipping origins N shop The number of shipping origins N shop can be obtained from the delivery information.

[0133] The function generating unit 206 generates the Ising variable q n ij In addition to the Ising variable q n Si FIG. 20 is a diagram showing an example of an Ising variable in this embodiment. As shown in FIG. 20, the Ising variable q n Si is a matrix data indicating the first (0th) passing delivery origin Si (Si = S1, S2, S3) for each delivery agent n (n = α, β). For example, in the optimized delivery plan, when delivery agent α passes delivery origin S1 for the 0th time, the Ising variable q α S1 takes +1, and the Ising variable q α S2 ,q α S3 will take -1.

[0134] Fig. 21 is a diagram showing an example of a delivery plan in this embodiment. As shown in Fig. 21, the delivery plan in this embodiment shows, for each deliverer n (n = α, β), the delivery origin Si (Si = S1, S2, S3) where the delivery items are loaded, the delivery destinations i (i = A, B, C, D, E) where the delivery items are delivered, and the order of delivery. In the example shown in Fig. 19, it is shown that deliverer α loads the delivery items at delivery origin S1 and delivers them from delivery destination A to delivery destination B. It is also shown that deliverer β loads the delivery items at delivery origin S2 and delivers them from delivery destination E to delivery destination C via delivery destination D.

[0135] <Effects> The distance term included in the objective function in this embodiment includes a term whose coefficient is the distance between each of the deliverers and each of the multiple delivery origins, and a term whose coefficient is the distance between each of the multiple delivery origins and each of the delivery destinations. Therefore, according to this embodiment, when there are multiple delivery origins, it is possible to realize simultaneous optimization that takes into account the maximum waiting time, the total delivery distance, and the number of deliverers.

[0136] [supplement] Each function of the above-described embodiments can be realized by one or more processing circuits. Here, the term "processing circuit" in this specification includes a processor programmed to execute each function by software, such as a CPU (Central Processing Unit) or GPU (Graphics Processing Unit) implemented by an electronic circuit, and an ASIC (Application Specific Integrated Circuit), DSP (Digital Signal Processor), FPGA (Field Programmable Gate Array), conventional circuit module, and other devices designed to execute each function described above.

[0137] Although the embodiments of the present invention have been described in detail above, the present invention is not limited to these embodiments, and various modifications and changes are possible within the scope of the gist of the present invention described in the claims. [Explanation of symbols]

[0138] 1000 Delivery Planning System 10 Order receiving device 20 Delivery planning device 201 Status Update Section 202 Deliverer information storage unit 203 Information Acquisition Department 204 Map information storage unit 205 Distance calculation section 206 Function Generator 207 Optimization Department 208 Planning Output Section 30 Annealing Machine 40 Terminal Equipment 401 Status notification section 402 Plan display section

Claims

1. An information acquisition unit configured to acquire delivery information regarding an item to be delivered to a delivery destination and deliverer information regarding a deliverer capable of delivering the item; a function generating unit configured to generate an objective function for minimizing a maximum waiting time until the item is delivered to the delivery destination, a total delivery distance for the item to be delivered by the deliverer, and a number of the deliverers who deliver the item, based on the delivery information and the deliverer information; an optimization unit configured to optimize a delivery route for each of the deliverers based on the objective function; a plan output unit configured to output a delivery plan including information on the delivery route and the items to be delivered along the delivery route to the deliverer; A delivery planning device comprising:

2. The delivery planning device according to claim 1, The optimization unit is configured to cause an annealing machine to calculate a ground state of an Ising model based on the objective function. Delivery planning device.

3. The delivery planning device according to claim 2, The annealing machine is a quantum annealing machine. Delivery planning device.

4. A delivery planning device according to any one of claims 1 to 3, The objective function includes a time term for minimizing the maximum waiting time, a distance term for minimizing the total delivery distance, and a number of people term for minimizing the number of delivery personnel. Delivery planning device.

5. The delivery planning device according to claim 4, The objective function is a weighted sum of the time term, the distance term, and the number of people term. Delivery planning device.

6. The delivery planning device according to claim 4, The time term is a coefficient of the waiting time of each of the delivery destinations. Delivery planning device.

7. The delivery planning device according to claim 4, The distance term has a coefficient representing the distance between the delivery origin from which the item is delivered and each of the delivery destinations. Delivery planning device.

8. The delivery planning device according to claim 7, The distance term includes a term having a coefficient representing the distance between each of the deliverers and each of the plurality of delivery origins, and a term having a coefficient representing the distance between each of the plurality of delivery origins and each of the delivery destinations, Delivery planning device.

9. The delivery planning device according to claim 4, The objective function includes a constraint term for making the weight of the item delivered by the deliverer equal to or less than the weight that the deliverer can deliver. Delivery planning device.

10. The delivery planning device according to claim 4, The objective function includes a constraint term for restricting delivery to one delivery destination only once. Delivery planning device.

11. A delivery planning device according to any one of claims 1 to 3, The delivery destination and the delivery person are within a predetermined range based on the delivery origin from which the item to be delivered is sent. Delivery planning device.

12. A delivery planning device according to any one of claims 1 to 3, The plan output unit outputs the delivery plan by displaying points indicating a delivery origin position and a plurality of delivery destination positions, and lines connecting the delivery origin position and the plurality of delivery destination positions, on a diagram having a distance scale corresponding to a coordinate system of a delivery area as an x-axis and a y-axis. Delivery planning device.

13. A delivery planning system in which a terminal device held by a deliverer and a delivery planning device can communicate with each other via a network, The delivery planning device includes: An information acquisition unit configured to acquire delivery information regarding an item to be delivered to a delivery destination and deliverer information regarding the deliverer capable of delivering the item; a function generating unit configured to generate an objective function for minimizing a maximum waiting time until the item is delivered to the delivery destination, a total delivery distance for the item to be delivered by the deliverer, and a number of the deliverers who deliver the item, based on the delivery information and the deliverer information; an optimization unit configured to optimize a delivery route for each of the deliverers based on the objective function; a plan output unit configured to output to the deliverer a delivery plan including information on the delivery route and the items to be delivered along the delivery route; Equipped with The terminal device a status notification unit configured to transmit status information indicating a status of the deliverer to the delivery planning device; A plan display unit configured to display information about the delivery route and the items to be delivered; A delivery planning system comprising:

14. The computer A step of acquiring delivery information regarding an item to be delivered to a delivery destination and deliverer information regarding a deliverer capable of delivering the item; A step of generating an objective function for minimizing a maximum waiting time until the item is delivered to the delivery destination, a total delivery distance for the item to be delivered by the deliverer, and the number of the deliverers who deliver the item, based on the delivery information and the deliverer information; optimizing a delivery route for each of the deliverers based on the objective function; a step of outputting to the deliverer a delivery plan including information on the delivery route and the items to be delivered along the delivery route; A delivery planning method to execute.

15. On the computer, A step of acquiring delivery information regarding an item to be delivered to a delivery destination and deliverer information regarding a deliverer capable of delivering the item; A step of generating an objective function for minimizing a maximum waiting time until the item is delivered to the delivery destination, a total delivery distance for the item to be delivered by the deliverer, and the number of the deliverers who deliver the item, based on the delivery information and the deliverer information; optimizing a delivery route for each of the deliverers based on the objective function; a step of outputting to the deliverer a delivery plan including information on the delivery route and the items to be delivered along the delivery route; A program for executing the above.

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