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 are known techniques for optimizing routes for delivering items to their destinations. For example, Patent Document 1 discloses a collection and delivery plan optimization device that divides multiple collection and delivery destination points into multiple groups, calculates the shortest route for visiting the multiple collection and delivery destination points included in each group using annealing, and repeatedly reduces the number of groups to which the items are assigned if the required time for the route is within the shift time, and increases the number of groups to which the items are assigned if the required time for the route is not within the shift time. [Prior art documents] [Patent documents]

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

[0004] However, conventional technologies minimize the number of delivery personnel through sequential processing, which poses a problem of large calculation volume. 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 who can deliver 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 that a deliverer will have to deliver the items to be delivered, and the number of deliverers who will deliver 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 that includes information regarding the delivery route and the items to be delivered along the delivery route. [Effects 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 explanation of the drawings]

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

[0009] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In this specification and drawings, components having substantially the same functional configurations are designated 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 multiple 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, causing delivery costs to continue to rise. Meanwhile, the electronic commerce (EC) market is expanding, and delivery demand is growing rapidly. Furthermore, demographic changes and the aging population are making it difficult for more people to do their daily shopping, and this is expected to lead to an increase in demand for all types of delivery.

[0012] 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 delivery personnel sequentially within a limited local area, thereby realizing short waiting times between ordering and receiving the product.

[0013] The value of "quick delivery" is particularly high for fresh food or pre-cooked food (boxed lunches or prepared meals, etc.) In fact, some companies that have incorporated existing local delivery networks have seen a significant increase in service usage due to the improved convenience.

[0014] However, conventional local delivery networks achieve "prompt delivery" 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 of high delivery costs and costs increasing in proportion to the expansion of the service scale. One possible solution to this problem is for delivery personnel to deliver to multiple locations in a single delivery, minimizing the number of delivery personnel.

[0015] Conventional delivery optimization algorithms for home delivery services, etc., perform optimization under the assumption 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 performed sequentially, which requires a large amount of calculation and processing time. Therefore, conventional delivery optimization algorithms are not suitable for sequentially optimizing local delivery networks while satisfying constraints on delivery time and deliverable weight.

[0016] In this embodiment, simultaneous optimization taking multiple conditions into consideration realizes 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 for consumers. In one aspect, this embodiment enables optimization of delivery plans with a small amount of calculation, thereby realizing a local delivery network with reduced delivery costs.

[0017] <Overall structure> 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, workstation, or server that receives orders involving the delivery of items to be delivered. The order receiving device 10 receives orders from consumers and transmits delivery instructions to the delivery planning device 20, specifying the ordered items as the items to be delivered. The order receiving device 10 may receive orders via a communication line such as the Internet, or via communication means such as telephone or fax. In this embodiment, the items to be delivered may be any object that can be transported. Examples of items to be delivered include fresh food, prepared food, daily necessities, books, furniture, and home appliances.

[0021] The delivery planning device 20 is an information processing device such as a personal computer, workstation, or 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 delivery instructions from the order receiving device 10 and acquires delivery information related to the items to be delivered. The delivery planning device 20 creates a delivery plan for delivering the items to be delivered to their destinations by performing simultaneous optimization taking into account multiple conditions using an annealing machine 30.

[0022] The annealing machine 30 is a computer specialized for 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 material 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, tablet terminal, or smartphone operated by the delivery person. The terminal device 40 may also be a wearable device such as a smart watch or smart glasses worn by the delivery person. The terminal device 40 transmits status information indicating the status of the delivery person to the delivery planning device 20. The terminal device 40 outputs the delivery plan received from the delivery planning device 20 to the delivery person.

[0024] The overall configuration of the delivery planning system 1000 shown in FIG. 1 is one 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, delivery planning device 20, annealing machine 30, and terminal device 40. For example, the order receiving device 10, delivery planning device 20, and annealing machine 30 may be realized by multiple computers, or may be realized as a cloud computing service. The classification of devices such as the order receiving device 10, delivery planning device 20, annealing machine 30, and terminal device 40 shown in FIG. 1 is one 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, delivery planning device 20, and 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 a computer in this embodiment.

[0027] 2, the computer 500 includes a CPU (Central Processing Unit) 501, a ROM (Read Only Memory) 502, a RAM (Random Access Memory) 503, a HDD (Hard Disk Drive) 504, an input device 505, a display device 506, a communication I / F (Interface) 507, and an external I / F 508. The CPU 501, the ROM 502, and the RAM 503 form a so-called computer. The hardware components of the computer 500 are 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 computing device that reads programs and data from a storage device such as the ROM 502 or the HDD 504 onto the RAM 503 and executes the processes, thereby realizing 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, data, etc. 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 starts up, as well as data such as OS (Operating System) settings and network settings.

[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 executed by the CPU 501.

[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 flash memory as a storage medium (e.g., an SSD (Solid State Drive)) instead of the HDD 504.

[0032] The input device 505 includes a touch panel, operation keys and buttons, a keyboard and mouse, a microphone for inputting sound data such as voice, and the like, 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 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 loading a 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 ROMs and flash memories. This allows the computer 500 to read from and / or write 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 on 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 via the communication I / F 507 from a network different from the communication network.

[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 that is 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, the upper limit weight that the deliverer can deliver in one delivery, etc. The active status of the deliverer is information that indicates whether the deliverer is currently available for delivery.

[0043] 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 the 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 item to be delivered, and is an area to which the item can be delivered from the delivery origin. Therefore, the delivery origin of the 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 source 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, total delivery distance, and 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 destination. The total delivery distance is the sum of the distances that 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 sends an optimization request including the Ising model to the annealing machine 30. The optimization unit 207 receives the optimization results from the annealing machine 30 and generates a delivery route for each deliverer based on the optimization results.

[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 that deliverer. The delivery plan includes the delivery route generated by the optimization unit 207 and information about the items to be delivered along that 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 plan display unit 402.

[0051] The status notification unit 401 and the plan display unit 402 are realized by the processing that the CPU 501 executes by the program loaded from the HDD 504 onto the RAM 503 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. Because the deliverer always carries the terminal device 40, 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 planning 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 equation (1) to realize simultaneous optimization taking multiple factors into consideration.

[0055]

number

[0056] where H waittime is a term for prioritizing delivery destinations with long waiting times (hereafter also referred to as the "time term"). distance is the term for minimizing the total delivery distance (hereinafter also referred to as the "distance term"). H worker is a term for minimizing the number of delivery persons (hereinafter also referred to as the "number of persons term").

[0057] H weight is the weight constraint. H weight is a constraint to ensure that the delivery weight for 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 deliverer n passes through 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 a constraint to deliver to each destination i only once. route2 is a constraint that prevents deliverer n from delivering to multiple destinations at the same time. 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 to minimize the total delivery distance. distance and the number of people term H to minimize the number of delivery people worker The objective function H includes the time term H waittime , distance term H distance and number of people term H worker are weighted and added using weights λ1, λ2, and λ3.

[0073] Therefore, Hamiltonian H can realize simultaneous optimization taking into account the maximum waiting time, total delivery distance, and number of deliverers. At this time, the priorities for the maximum waiting time, total delivery distance, and number of deliverers can be arbitrarily set, making it possible to create a delivery plan that can flexibly meet user needs.

[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 the 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 using, for example, a Global Positioning System (GPS). The active status may be input to the terminal device 40 by, for example, an operation by the deliverer.

[0077] The status information may include the maximum weight limit of the deliverer. The maximum weight limit may be input, for example, by the deliverer's operation into the terminal device 40. The maximum weight limit may be input, for example, by the deliverer 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 includes, 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 weight limit, and the deliverer's active status. Each piece of information included in the deliverer information is updated to the latest data each time the status update unit 201 receives status information.

[0080] The processing from step S101 to step S102 is repeatedly executed at predetermined time intervals 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 processing from step S103 onwards is repeatedly executed in response to a predetermined trigger, independently of the processing from step S101 to step S102. The predetermined trigger may be each time a predetermined time interval has elapsed, when a predetermined number of delivery instructions have been accumulated, or when delivery according to the previously created delivery plan has been 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 delivery information and deliverer information from the information acquisition unit 203. Next, the distance calculation unit 205 reads map information of the target area from the map information storage unit 204. Subsequently, 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 delivery origin S. Map information M1 shows the layout of roads, facilities, residences, etc. that exist within the target area. Map information M1 includes the locations of delivery origin S, delivery destinations A to E, and deliverers α and β. The location of delivery origin S is registered in advance in delivery planning system 1000. The locations of delivery destinations A to E are shown in delivery information. The locations of deliverers α and β are shown in 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 matrix of distances d between two points for all combinations of a delivery source S and delivery destinations A to E. 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. The function generating unit 206 also receives delivery information and deliverer information from the information acquiring unit 203.

[0088] Next, the function generation 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 , the waiting time for delivery destination i (i=A,B,C,D,E) w i , delivery weight W to destination i i, the distance d' between the location of the deliverer n (n=α,β) and the delivery source S n , 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 person information. i and shipping weight W i can be obtained from the shipping information.

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

[0090] 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 matrix data that indicates the jth (j=1,2,3,4,5) delivery destination i (i=A,B,C,D,E) that each delivery person n (n=α,β) passes through. Ising variables are variables that can take on +1 or -1. For example, in an optimized delivery plan, if delivery person α passes through delivery 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 takes -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 explanation 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. Subsequently, 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. Subsequently, 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 results from the annealing machine 30. Next, the optimization unit 207 acquires a ground-state Ising model from the received optimization results. Subsequently, 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 by the order j of delivery destinations to be passed through for each deliverer n. 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 the 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 information indicating the delivery route generated in step S106 and the items to be delivered to destination i that will pass through that 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 delivery will be made and the order in which delivery will be made to those destinations. In the example shown in Fig. 10, it is shown that deliverer α loads the item to be delivered at delivery origin S and delivers it from delivery destination A to delivery destination B. It is also shown that deliverer β loads the item to be delivered at delivery origin S and delivers it 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 Figures 11 to 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 prior art. 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 was the delivery optimization algorithm using sequence processing disclosed in Patent Document 1. In Figures 11 and 12, the origin (0,0) is the location of the delivery source, and the other plots represent each delivery destination. The x and y axes are distance scales according to the coordinate system of the target area. The broken lines R1 to R10 connecting the delivery source to each delivery destination represent the delivery route for each delivery personnel. Plots C1 and C2, which are not passed through by delivery routes R1 to R10, are delivery destinations that are not delivered to in the created delivery plan.

[0101] As shown in Figure 11, in the conventional technology, ten delivery routes R1 to R10 were created. In other words, 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 Figure 12, in this embodiment, eight delivery routes R1 to R8 were created. That is, a delivery plan was created in which eight delivery persons would make deliveries. In this embodiment, a delivery plan was created that allowed delivery to all delivery destinations. 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 destinations with a small number of deliverers in a processing time that is significantly shorter than conventional methods. Therefore, this embodiment can create a delivery plan in a short time, thereby realizing a local delivery network with high speed. Furthermore, this embodiment can create a delivery plan 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 this embodiment.

[0105] To evaluate 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 destination. The simulation conditions were determined based on the approximate results of actual data collected from an existing delivery service.

[0106] The target area was set to a 6km diameter 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 set within the target area, with 24 of them set to be active. Two delivery sources were set within the target area. The time period was set to four hours, from 11:00 AM to 3:00 PM, simulating busy hours. Consumers randomly ordered from 15 predetermined products, with an average of three orders.

[0107] In a 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 a simulation using this embodiment, the minimum waiting time was 13 minutes and 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 was large compared to the service target. The maximum waiting time in this embodiment was reduced by 45% compared to the conventional technology.

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

[0109] <Effects> The delivery planning device 20 in this embodiment optimizes delivery routes for each delivery person based on an objective function for minimizing the maximum waiting time until the delivery item is delivered to the destination, the total delivery distance the delivery person must deliver the delivery item, and the number of delivery people delivering the delivery item. The delivery planning device 20 performs simultaneous optimization taking multiple conditions into consideration, and therefore can optimize delivery plans with less computational effort than conventional techniques that require sequential processing. In one aspect, this embodiment enables the delivery plan to be optimized with less computational effort, thereby realizing a local delivery network with reduced delivery costs.

[0110] The delivery planning device 20 in this embodiment may use an annealing machine to calculate the ground state of an Ising model based on an objective function. The annealing machine may be a quantum annealing device. An annealing machine is a computer specialized for solving combinatorial optimization problems, and a quantum annealing device can solve combinatorial optimization problems at high speed using quantum devices. Therefore, according to this embodiment, delivery plans 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 also be a weighted sum of the time term, distance term, and number of people term. Therefore, according to this embodiment, a delivery plan that can flexibly meet user needs can be created.

[0112] The objective function in this embodiment may include a constraint term to limit the weight of the item to be delivered by the deliverer to the weight that the deliverer can deliver. The objective function may also include a constraint term to limit 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 delivery item is sent. Therefore, according to this embodiment, the delivery plan for the local delivery network can be optimized with a small amount of calculation.

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

[0115] The multiple delivery sources may be operated by one business operator or by different businesses. For example, the first delivery source may be operated by a first business operator that delivers fresh food, and the second delivery source may be operated by a second business operator that delivers daily necessities. In this case, an order receiving device 10 may exist corresponding to each business operator, 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 on the path 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 delivery origins, and d n Siis the distance between the current location of the deliverer n and the delivery source Si, and d Sij is the distance between the delivery source Si and the delivery destination j, and q n Si is an Ising variable that indicates whether or not the deliverer n loads the delivery item at the delivery origin Si.

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

[0122]

number

[0123] constraint term H route4 is a constraint for each deliverer n to load items to be delivered 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, simultaneous optimization can be achieved 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 differs from the delivery planning method in the first embodiment in the processing 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 source (for example, the address of the delivery source) in addition to the respective items 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 delivery destination, a second distance matrix indicating the distance between the delivery origin and the current location of the deliverer, 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 sources S1 to S3. The locations of delivery sources 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 matrix of distances 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 matrix of distances d between two points for all combinations of deliverers α and β and delivery sources S1 to S3. 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 matrix of distances d Sij (Si=S1,S2,S3, j=A,B,C,D,E)

[0132] In step S105, the function generation 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 , the waiting time for delivery destination i (i=A,B,C,D,E) w i , delivery weight W to destination i i , upper limit weight c of shipper n n weight In addition, the number of shipping origins N shop The number of delivery origins N shop can be obtained from the shipping information.

[0133] The function generator 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 matrix data that indicates the first (0th) passing delivery source Si (Si = S1, S2, S3) for each delivery person n (n = α, β). For example, in the optimized delivery plan, when delivery person α passes through delivery source S1 for the 0th time, the Ising variable q α S1 takes +1, and the Ising variable q α S2 ,q α S3 takes -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 indicates, for each deliverer n (n=α, β), the delivery origin Si (Si=S1, S2, S3) where the delivery items will be loaded, the delivery destination i (i=A, B, C, D, E) to which the delivery items will be delivered, and the order in which they will be loaded. In the example shown in FIG. 19, it is shown that deliverer α will load the delivery items at delivery origin S1 and deliver them from delivery destination A to delivery destination B. It is also shown that deliverer β will load the delivery items at delivery origin S2 and deliver them from delivery destination E to delivery destination D to delivery destination C.

[0135] <Effects> The distance terms included in the objective function in this embodiment include a term whose coefficient is the distance between each deliverer 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, simultaneous optimization can be achieved taking into account the maximum waiting time, total delivery distance, and 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 central processing unit (CPU) or a graphics processing unit (GPU) implemented by an electronic circuit, as well as devices such as an application specific integrated circuit (ASIC), a digital signal processor (DSP), a field programmable gate array (FPGA), and conventional circuit modules designed to execute each of the above-described functions.

[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 Unit 202 Deliverer information storage unit 203 Information Acquisition Department 204 Map information storage unit 205 Distance calculation section 206 Function Generation Unit 207 Optimization Department 208 Planning Output Section 30 Annealing Machine 40 Terminal Equipment 401 Status Notification Unit 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 simultaneously optimizing the maximum waiting time until the item is delivered to the destination, the total delivery distance that the deliverer will deliver the item, and the number of the deliverers who will deliver the item, based on the delivery information and the deliverer information; an optimization unit configured to optimize the delivery route for each of the deliverers by causing an annealing machine to calculate a ground state of an Ising model 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 information acquisition unit acquires the delivery information based on a delivery instruction specifying the product ordered by the consumer as the item to be delivered, and acquires the deliverer information based on status information received from a terminal device held by the deliverer, repeatedly executing the steps of acquiring the delivery information and the deliverer information, generating the objective function, optimizing the delivery route, and outputting the delivery plan; Delivery planning device.

2. A delivery planning device according to claim 1, When a predetermined number of the delivery instructions have been accumulated, generating the objective function, optimizing the delivery route, and outputting the delivery plan are executed. Delivery planning device.

3. The delivery planning device according to claim 1, The annealing machine is a quantum annealing machine. Delivery planning device.

4. 4. The delivery planning device according to claim 1, 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 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 has a coefficient of the waiting time for 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 delivery item is sent 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 delivery 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. 4. The delivery planning device according to claim 1, 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. 4. The delivery planning device according to claim 1, The plan output unit outputs the delivery plan by displaying points indicating the location of the delivery origin and the locations of multiple delivery destinations, and lines connecting the location of the delivery origin and the locations of the multiple delivery destinations, on a diagram with distance scales corresponding to the coordinate system of the delivery area as x and y axes. 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 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 simultaneously optimizing the maximum waiting time until the item is delivered to the destination, the total delivery distance that the deliverer will deliver the item, and the number of the deliverers who will deliver the item, based on the delivery information and the deliverer information; an optimization unit configured to optimize the delivery route for each of the deliverers by causing an annealing machine to calculate a ground state of an Ising model 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 the 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; Equipped with The information acquisition unit acquires the delivery information based on a delivery instruction specifying the product ordered by the consumer as the item to be delivered, and acquires the deliverer information based on status information received from the terminal device, repeatedly executing the steps of acquiring the delivery information and the deliverer information, generating the objective function, optimizing the delivery route, and outputting the delivery plan; Delivery planning system.

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 who can deliver the item; A step of generating an objective function for simultaneously optimizing the maximum waiting time until the item is delivered to the destination, the total delivery distance that the deliverer will deliver the item, and the number of the deliverers who will deliver the item, based on the delivery information and the deliverer information; optimizing a delivery route for each of the deliverers by calculating a ground state of an Ising model based on the objective function using an annealing machine; 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; Run The step of acquiring includes acquiring the delivery information based on a delivery instruction for the product ordered by the consumer as the delivery item, acquiring the deliverer information based on status information received from a terminal device held by the deliverer, repeatedly executing the steps of acquiring the delivery information and the deliverer information, generating the objective function, optimizing the delivery route, and outputting the delivery plan; Delivery planning methods.

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 who can deliver the item; A step of generating an objective function for simultaneously optimizing the maximum waiting time until the item is delivered to the destination, the total delivery distance that the deliverer will deliver the item, and the number of the deliverers who will deliver the item, based on the delivery information and the deliverer information; optimizing a delivery route for each of the deliverers by calculating a ground state of an Ising model based on the objective function using an annealing machine; 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; Execute The step of acquiring includes acquiring the delivery information based on a delivery instruction for the product ordered by the consumer as the delivery item, acquiring the deliverer information based on status information received from a terminal device held by the deliverer, repeatedly executing the steps of acquiring the delivery information and the deliverer information, generating the objective function, optimizing the delivery route, and outputting the delivery plan; program.

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