Information processing device, information processing method and program

Abstract

To create a transportation plan which efficiently reduces CO2 emissions by visualizing a reduction amount of the CO2 emissions to a cargo owner side as well as to a physical distribution side.SOLUTION: A cargo owner-side information acquisition part 101 acquires cargo owner-side information. A physical distribution-side information acquisition part 102 acquires physical distribution-side information. A CO2 emission prediction part 103 sets prescribed preconditions including a moving route from a shipping source to a shipping destination based on at least part of the cargo owner-side information and the physical distribution-side information, and predicts carbon dioxide emissions per unit volume in weight or volume when the moving object moves under the preconditions. A planning part 104 creates one or more transportation plans based on each carbon dioxide emission predicted when the preconditions are changed.SELECTED DRAWING: Figure 6

Description

[Technical field]

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

[0002] There have been technologies for managing inventory held in warehouses that serve as logistics bases. For example, Patent Document 1 describes a logistics management system that allocates products in stock to product orders from customers received by retailers and transmits the allocation results and delivery dates for the orders to the retailers. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 9-136704 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in recent years, there has been a demand to reduce the CO2 emissions generated during logistics activities (delivery, storage, work, etc.) on the transport side, but conventional logistics management systems, including the technology described in Patent Document 1, have limitations in terms of reducing CO2 emissions. This is because transportation plans are drawn up by transport companies (e.g. logistics companies) based on information unilaterally requested by shippers, and such plans are not efficient in reducing CO2 emissions. In other words, by making the amount of CO2 emission reduction visible not only to the logistics side but also to the shipper side, it becomes possible to develop transportation plans that efficiently reduce CO2 emissions. Conventional logistics management systems, including the technology described in Patent Document 1, did not disclose any information to the shipper side.

[0005] The present invention has been made in consideration of the above-mentioned circumstances, and aims to realize the development of transportation plans that efficiently reduce CO2 emissions by making the amount of CO2 emission reduction visible not only for the logistics side but also for the shipper side. [Means for solving the problem]

[0006] In order to achieve the above object, an information processing device according to one aspect of the present invention comprises: In an information processing device that creates a transportation plan for transporting goods of a shipper from a transportation origin to a transportation destination by a mobile vehicle, A shipper information acquisition means for acquiring, as the shipper information, information on the shipper side, including at least the weight or volume of the commodity, and the shipping origin and the shipping destination; a logistics side information acquisition means for acquiring, as logistics side information, information on a logistics side managing transportation of the moving object, the information including at least a moving object characteristic amount indicating a characteristic of the moving object; a carbon dioxide emission prediction means for setting a predetermined precondition including a moving route from the transportation origin to the transportation destination based on at least a part of the shipper side information and the logistics side information, and predicting a carbon dioxide emission amount per unit amount in weight or volume when the moving object moves under the precondition; A planning means for planning one or more of the transportation plans based on each of the carbon dioxide emission amounts predicted when the preconditions are changed; Equipped with. Effect of the Invention

[0007] According to the present invention, by making the reduction in CO2 emissions visible not only to the logistics side but also to the shipper side, it is possible to realize the creation of transportation plans that efficiently reduce CO2 emissions. [Brief description of the drawings]

[0008] [Figure 1] FIG. 2 is a conceptual diagram showing an overview of the present service that can be realized by various processes executed by a server according to an embodiment of the information processing device of the present invention. [Diagram 2] FIG. 2 is a diagram showing an example of a method for calculating CO2 emissions applied to the present service of FIG. 1. [Diagram 3] An example of the change in delivery cost per parcel volume is shown. [Figure 4] 2 is a diagram showing a configuration of an information processing system including the server of FIG. 1. [Diagram 5] FIG. 3 is a block diagram showing a hardware configuration of the server in FIG. 2. [Figure 6] 4 is a functional block diagram showing an example of a functional configuration of the server in FIG. 3. [Figure 7] FIG. 3 is a diagram showing an example of CO2 emission per truck on a predetermined route in the case where the method for predicting CO2 emission in the example of FIG. 2 is adopted. [Figure 8] 8 is an example of a predicted result of CO2 emission per cargo amount (one pallet and one case) in the case where the predicted result of CO2 emission per truck in FIG. 7 is adopted. [Figure 9] FIG. 9 is a diagram showing preconditions for an example of a transportation plan created by the present service, which is an example different from the examples of FIGS. 7 and 8. [Figure 10] 9 is an example of a transportation plan drawn up by this service, which is an example of a predicted result of CO2 emissions per cargo amount (one pallet and one case) in an example different from the examples of FIG. 7 and FIG. 8. [Figure 11] FIG. 11 is a diagram showing an example of a transportation plan drawn up by the present service, which is an example different from the examples of FIGS. 9 and 10, and illustrates prerequisites that utilize a transportation company found by a matching site. [Figure 12] 11 is an example of a transportation plan drawn up by this service, which is an example of a predicted result of CO2 emissions per cargo amount (one pallet and one case) in an example different from the examples of FIGS. 9 and 10. FIG. [Figure 13] FIG. 1 is a diagram showing an example of factors that affect fuel efficiency and are required to predict CO2 emissions. [Figure 14] FIG. 1 is a diagram showing an example of a CO2 emission prediction model as an AI model. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0010] FIG. 1 is a conceptual diagram showing an overview of this service that can be realized by various processes executed by a server according to an embodiment of an information processing device of the present invention.

[0011] As shown in FIG. 1, in this service, consumers C include companies, retail wholesalers, individuals (end users), etc., and place orders for goods with shippers S. In this service, when a consumer C places an order with a shipper S, the contents of the order are managed as "order information."

[0012] The shipper S may be a manufacturer, an individual, or a company, and places an order for the supply of goods with the supplier V based on the order information. In this service, when the shipper S places an order with the supplier V, the contents of the order are managed as "order information."

[0013] Supplier V consists of manufacturers, manufacturers, etc., and supplies goods to logistics company L based on order information. From the perspective of logistics company L, goods are received from supplier V. Also, from the perspective of shipper S, this is equivalent to requesting logistics company L to procure goods based on order information. Therefore, in this service, when shipper S requests logistics company L to procure goods (when logistics company L receives goods from supplier V), the details are managed as "procurement information (receiving information)".

[0014] This service also manages various information that logistics company L possesses for business purposes. Specifically, information (hereinafter referred to as "vehicle information") related to cargo transport vehicles (hereinafter referred to as "trucks") used to transport goods, such as the vehicle number, loading capacity, vehicle class, and information related to the driver (such as name, age, working hours, overtime hours, etc.) of each cargo transport vehicle, is managed.

[0015] In this service, logistics company L ships the goods received (procured) from supplier V and delivers them to consumer C based on the order information. From the viewpoint of shipper S, this is equivalent to selling the goods to consumer C based on the order information. Therefore, in this service, when shipper S requests logistics company L to sell the goods (when logistics company L ships the goods), the details are managed as "sales information (shipping information)".

[0016] With this service, the above-mentioned order information, ordering information, procurement information (arrival information), vehicle information, and sales information (shipment information) are managed all in one place, and flexible transportation plans are created that take this information into consideration. That is, this service makes it possible to create an effective transportation plan based on the information previously managed by logistics company L (procurement information (arrival information), vehicle information, and sales information (shipment information)) and the information previously managed by shipper S (order information and ordering information). In other words, it makes it possible to create an effective transportation plan based on information from the logistics company and information from the shipper. As a result, efficient transportation without waste can be achieved.

[0017] In recent years, there has been a demand for logistics company L to reduce the amount of CO2 generated in its logistics activities (delivery, storage, operations, etc.). Therefore, logistics company L itself has been carrying out various activities to reduce CO2 emissions. However, as described above, the procurement (arrival) of goods that are the basis of logistics activities is determined in response to an order from the consignor S to the supplier V. Also, the sale (shipment) of goods that are the basis of logistics activities is determined in response to an order from the consumer C to the consignor S. In other words, the logistics activities of the distributor L are determined by the order information, which is a decision made between the consumer C and the consignor S, and the ordering information, which is a decision made between the consignor S and the supplier V. Therefore, there is a limit to how much CO2 emissions can be reduced by logistics company L alone. Therefore, this service is designed to enable activities to reduce CO2 emissions from the generation stage of order information, which is a decision-making process between consumer C and shipper S, and purchase order information, which is a decision-making process between shipper S and supplier V. In other words, this service creates a transportation plan that takes CO2 emissions into consideration as a transportation plan for product procurement (receiving) and sales (shipping), and the CO2 emissions are presented to and shared with shipper S and logistics company L. In other words, at least some of the shippers S and carriers L will be able to create optimal transportation plans for themselves while visually checking whether the presented CO2 emissions will reach the reduction target. The remaining part will be able to visually check the created transportation plans together with their CO2 emissions.

[0018] Below, we will provide an overview of the transportation plan that takes CO2 emissions into consideration and is applied to this service.

[0019] FIG. 2 is a diagram showing an example of a method for calculating CO2 emissions applied to the present service of FIG. As shown in Figure 2, the CO2 emissions (tCO2) per truck are calculated as follows: CO2 emissions = Fuel consumption (kl) x 2.58 (tCO2 / Kl)

[0020] Here, 2.58 (tCO2 / Kl) is the CO2 emission coefficient per kl of diesel, and is calculated using the following formula. 2.58(tCO2 / kl) = unit calorific value (GJ / Kl) x emission coefficient (tC / GJ) x 22 / 12(tCO2 / tC) According to Ministry of Economy, Trade and Industry Notification No. 66 (March 29, 2006), the unit calorific value is 37.7. The emission coefficient is 0.0187 according to the CO2 emission calculation formula using the fuel method in the Guidelines for Calculating Greenhouse Gas Emissions from Businesses (Draft ver.1.6) set forth by the Global Environment Bureau of the Ministry of the Environment, and the "2003 Environmentally Friendly Logistics Promotion Manual" set forth by the Japan Logistics System Association, Ministry of Economy, Trade and Industry.

[0021] On the other hand, fuel consumption (kl) is calculated using the following formula: Fuel consumption (kl) = distance traveled (km) ÷ fuel efficiency (km / kl) Here, it is known that fuel efficiency differs depending on the route, such as an ordinary road or an expressway, but does not vary significantly depending on the truck's load (weight and volume), i.e., the amount of luggage (amount of cases or pallets).

[0022] In other words, if a truck is traveling the same route, the fuel efficiency is the same whether it is fully loaded or empty, and as a result, the amount of CO2 emissions per truck is the same. In other words, the amount of CO2 emissions per truck does not depend on the amount of cargo carried by the truck. In other words, in terms of CO2 emissions not per truck, but per unit of cargo carried by a truck (per unit, where one case or pallet is considered a unit), the worse the loading efficiency, the greater the amount. For example, if the CO2 emissions per truck are 100, and there are 100 units of cargo, then the CO2 emissions per unit of cargo is 1. On the other hand, if there are 10 units of cargo, then the CO2 emissions per unit of cargo is 10, which is 10 times higher. Therefore, with this service, transportation plans are drawn up based on the amount of CO2 emissions per unit of cargo transported by a single truck (per unit, where one unit is a case or pallet). In other words, this service develops transportation plans that increase transportation efficiency in order to reduce CO2 emissions per unit of cargo transported by a single truck (per unit, assuming that one unit is a case or pallet).

[0023] Now, let us refer to Figure 3 and consider this from the perspective of transportation efficiency and logistics costs. Figure 3 shows an example of the trend in delivery cost per parcel volume. In FIG. 3, the freight rates in the basic information are the freight rates (logistics costs) for chartering 4-ton and 10-ton trucks, and are calculated in ton x kilometer units. A ton is a unit price determined by the size of the truck (4 ton or 10 ton). In principle, the goods (products) that can actually be transported are within the maximum load capacity of each truck, and only light goods are allowed within the volume of the truck box. In other words, as long as the goods (products) are within the maximum load capacity of the vehicle, the freight fee is the same no matter how many tons are loaded. Kilometers are the distance traveled by trucks. However, rather than being based on the exact distance (from Kofu City, Yamanashi Prefecture to Mitaka City, Tokyo), a flat rate is adopted for each prefecture based on the approximate distance (100km from Yamanashi Prefecture to Tokyo). For example, when cargo is transported from Yamanashi to Tokyo in a 10-ton truck, the shipping fee is a fixed amount, say 50,000 yen, regardless of the amount of cargo. For example, a 10-ton truck can transport up to 1,920 cases of goods on a maximum of 16 pallets, in which case the transportation efficiency (loading rate in Figure 3) is 100%. The cost per case at 100% transportation efficiency is 26 yen. On the other hand, a 4-ton truck can transport 480 cases on four pallets, so 600 cases on five pallets would be the minimum transportation efficiency, at 31%. The cost per case at a transportation efficiency of 31% is 83 yen, more than three times more expensive than at 100% transportation efficiency.

[0024] In this way, in terms of logistics costs, it is appropriate to use the unit price per unit of cargo transported by one truck (per unit, assuming that one unit is a case or pallet), and the higher the transportation efficiency, the lower the unit price. In summary, this service enables transportation plans to be drawn up from the perspective of transportation efficiency, making it possible to reduce both CO2 emissions and logistics costs.

[0025] Next, a configuration of an information processing system including the server 1 that executes various processes for providing this service will be described. FIG. 4 is a diagram showing a configuration of an information processing system including a server according to an embodiment of the information processing device of the present invention.

[0026] The information processing system shown in FIG. 4 is configured by connecting a server 1, logistics company terminals 2-1 to 2-n (n is an integer value of 1 or more), and shipper terminals 3-1 to 3-m (m is an integer value of 1 or more) to each other via a predetermined network N such as the Internet.

[0027] In this embodiment, each of the distributor terminals 2-1 to 2-n is configured with a personal computer, a smartphone, a tablet terminal, etc. Each of the distributor terminals 2-1 to 2-n is operated by a person in charge of each of the n distributors L. In the following description, when there is no need to distinguish between the distributor side terminals 2-1 to 2-n, they will be collectively referred to as the "distributor side terminal 2."

[0028] Each of the shipper side terminals 3-1 to 3-m in this embodiment is configured with a personal computer, a smartphone, a tablet terminal, etc. Each of the shipper side terminals 3-1 to 3-m is operated by a person in charge of each of the shippers S of m. In the following description, when there is no need to distinguish between the shipper side terminals 3-1 to 3-m, they will be collectively referred to as the "shipper side terminal 3."

[0029] Next, a hardware configuration of the server 1 that executes various processes for providing this service will be described. FIG. 5 is a block diagram showing a hardware configuration of the server in FIG.

[0030] The server 1 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an input unit 16, an output unit 17, a memory unit 18, a communication unit 19, and a drive 20.

[0031] The CPU 11 executes various processes according to a program recorded in the ROM 12 or a program loaded from the storage unit 18 into the RAM 13 . The RAM 13 also stores data and the like necessary for the CPU 11 to execute various processes.

[0032] The CPU 11, ROM 12, and RAM 13 are connected to one another via a bus 14. An input / output interface 15 is also connected to this bus 14. An output unit 16, an input unit 17, a storage unit 18, a communication unit 19, and a drive 20 are connected to the input / output interface 15.

[0033] The input unit 16 is composed of various hardware leads and the like, and inputs various types of information. The output unit 17 is composed of various liquid crystal displays and the like, and outputs various information. The storage unit 18 is configured with a dynamic random access memory (DRAM) or the like, and stores various data. The communication unit 19 controls communications with other devices (for example, the distributor side terminals 2-1 to 2-n and the shipper side terminals 3-1 to 3-m in FIG. 4) via a network N including the Internet.

[0034] The drive 20 is provided as necessary. Removable media 30, which may be a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is appropriately mounted in the drive 20. The program read from the removable media 30 by the drive 20 is installed in the storage unit 18 as necessary. The removable media 30 can also store various data stored in the storage unit 18 in the same manner as the storage unit 18.

[0035] Although not shown, the distributor terminal 2 and the shipper terminal 3 also have the hardware configuration shown in FIG.

[0036] Next, the function of the server 1 having such a hardware configuration will be described with reference to FIG. FIG. 6 is a functional block diagram showing an example of a functional configuration of the server 1 of FIG.

[0037] As shown in FIG. 6, in the CPU 11 of the server 1, a shipper information acquisition unit 101, a logistics information acquisition unit 102, a CO2 emission prediction unit 103, a planner 104, and a presentation unit 105 function. In one area of ​​the storage unit 18, a shipper DB 401, a logistics DB 402, and a CO2 emission prediction model 403 are provided.

[0038] The shipper information acquisition unit 101 acquires shipper information including order information and ordering information. The shipper side information acquired by the shipper side information acquisition unit 101 is stored and managed in the shipper side DB 401.

[0039] The logistics side information acquisition unit 102 acquires information required for planning transportation plans, such as vehicle information, from the information managed by logistics company L (e.g., vehicle information, information on each base station for determining routes, etc.) as logistics company side information. The logistics side information acquired by the logistics side information acquisition unit 102 is stored and managed in the logistics side DB 402 .

[0040] Here, the CO2 emission prediction model 403 is a model that predicts and outputs the CO2 emission amount per cargo amount (pallet / case) when the values ​​of one or more input parameters are input. The CO2 emission prediction model 403 is provided individually according to the prediction method of the CO2 emission. For example, in the case of a model corresponding to the prediction method shown in Fig. 2 described above, which is based on the premise that fuel efficiency differs between expressways and general roads, the vehicle size (whether it is a 4-ton vehicle or a 10-ton vehicle), route information (route information that is divided into expressways and general roads and the distance of each can be specified), and baggage weight are input as input parameters. A specific example of the CO2 emission prediction model 403 will be described later with reference to FIG. 7 and subsequent figures.

[0041] The CO2 emission prediction unit 103 extracts the values ​​of each input parameter from the shipper information and the logistics information, substitutes them into the CO2 emission prediction model 403, and outputs the result as a prediction result of the CO2 emission per cargo volume.

[0042] The planning unit 104 plans transportation plans for incoming and outgoing shipments based on the shipper information, logistics information, and the predicted results of the CO2 emissions per cargo volume, so that the CO2 emissions are kept below a predetermined amount. For example, in the case where the CO2 emission prediction method in the example of Figure 2 described above is applied, the planning unit 104 creates a transportation plan so as to reduce CO2 emissions by improving loading efficiency (and at the same time reduce the logistics cost per unit of cargo). A specific example of creating a transportation plan will be described later with reference to FIGS.

[0043] The presentation unit 104 presents the proposed transportation plan to the distributor L via the distributor's terminal 2 and to the shipper S via the shipper's terminal 3 . Here, the transportation plan also shows the amount of CO2 emissions (forecast results). In this way, information on CO2 emissions is shown and shared not only with logistics company L but also with shipper S. As a result, not only logistics company L but also shipper S can participate in activities to reduce CO2 emissions generated in logistics activities.

[0044] To summarize, in the CPU 101 of the server 1, the shipper side information acquisition unit 101, the logistics side information acquisition unit 102, the CO2 emission prediction unit 103, the planning unit 104, and the presentation unit 105 function to create a transportation plan for transporting the shipper S's goods by truck from the source to the destination. The shipper information acquisition unit 101 acquires, as shipper information, information on the shipper S side, including at least the weight or volume of the product, and the shipping origin and destination. The logistics side information acquisition unit 102 acquires, as logistics side information, information on the logistics side that manages truck transportation and that includes at least truck characteristic quantities that indicate the characteristics of the truck, such as vehicle class. The CO2 emission prediction unit 103 sets predetermined preconditions including the travel route from the origin to the destination based on at least a portion of the shipper information and the logistics information, and predicts the CO2 emission per unit amount (case or pallet) in terms of weight or volume when the moving object moves under those preconditions. The planning unit 104 plans one or more transportation plans based on the respective CO2 emissions predicted when the preconditions are changed. The presentation unit 105 presents the one or more transportation plans that have been created and the predicted values ​​of the CO2 emissions per unit amount for each of them to the distributor terminal 2 and the shipper terminal 3, respectively.

[0045] Next, a specific example of a transportation plan created by this service will be described with reference to FIGS.

[0046] FIG. 7 is a diagram showing an example of CO2 emission per truck on a predetermined route in the case where the method for predicting CO2 emission in the example of FIG. 2 is adopted. In the example of Figure 7, a 10-ton vehicle (a truck with a maximum load capacity of 12,000 kg) travels from Hokuto City, Yamanashi Prefecture to Suita City, Osaka Prefecture. Specifically, the route adopted in the example of Figure 7 consists of 12 km of general roads from Suita City, Yamanashi Prefecture to Kobuchizawa IC, 373 km of expressway from Kobuchizawa IC to Suita IC, and 15 km of general roads from Suita IC to Suita City, Osaka Prefecture. Such travel routes, that is, the travel distances for each of the general roads and expressways in the table of FIG. 7, can be acquired from the order information or purchase order information of the shipper side information and the base information, etc. of the logistics side information. Here, the fuel efficiency is the fixed value in the table at the bottom of FIG. 7, and since it is assumed that the vehicle is carrying luggage (goods), the actual vehicle fuel efficiency is used. In addition, the fact that the truck on this travel route is a 10-ton vehicle can be obtained from the vehicle information in the logistics information. That is, the CO2 emission prediction unit 103 extracts from the shipper information and the logistics information that the truck is a 10-ton truck, as well as the travel distances on general roads and expressways, as the respective values ​​of the input parameters, and substitutes these values ​​into the CO2 emission prediction model 403. Then, the CO2 emission prediction model 403 outputs the results of the table in Fig. 7. That is, the CO2 emission prediction unit 103 outputs the table in Fig. 7 as the prediction results of the CO2 emission per truck (10-ton truck).

[0047] FIG. 8 shows an example of the predicted results of CO2 emissions per cargo amount (one pallet and one case) when the predicted results of CO2 emissions per truck in FIG. 7 are adopted.

[0048] In FIG. 8, 1) basic information is based on the preconditions of the example in FIG. 7, and the freight rate indicates the freight rate for chartering a 10-ton truck described above with reference to FIG. 2) The table showing the amount of CO2 emissions when delivering based on basic information is the same as the table showing the predicted results in Figure 7.

[0049] 3) Regarding the difference in CO2 emissions per pallet and case depending on the volume of goods transported, the table on the left shows the logistics cost (unit price) per amount of cargo (1 pallet and 1 case), and the table on the right shows the amount of CO2 emissions per amount of cargo (1 pallet and 1 case). Here, the maximum load capacity of a 10-ton truck is 12,000 kg, but the goods (products) that can actually be loaded are loaded in pallet units, so the total is 11,920 kg, or 16 pallets.

[0050] After obtaining the results of the table in Fig. 7 (table 2) in Fig. 8), the CO2 emission prediction unit 103 further extracts the cargo volume from the shipper information, etc. as an input parameter value and substitutes it into the CO2 emission prediction model 403. Then, the CO2 emission prediction model 403 outputs the CO2 emission per pallet and per case as prediction results according to the table on the right side of 3) in Fig. 8. For example, in the case of 16 pallets, the CO2 emission per pallet is 22.23 t-CO2 and the CO2 emission per case is 0.19 t-CO2. That is, the CO2 emission prediction unit 103 outputs the prediction result of the CO2 emission amount per cargo amount (one pallet and one case).

[0051] Here, the reason why it is stated that the cargo volume as the input parameter value is extracted from the shipper's information, etc., is because, in principle, it is obtained from order information and purchase order information, but logistics information may also be used. That is, when making a flexible transportation plan, such as transporting the cargo mixed with other cargo, transporting the cargo by distributing it among two or more trucks, or loading products in stock at the base of carrier L as cargo, logistics information is also required. For example, suppose the cargo volume obtained from the shipper's information is 5 pallets. In this case, the CO2 emissions per pallet are 71.12 t-CO2, and the CO2 emissions per case are 0.59 t-CO2. In other words, the loading efficiency is poor, so the CO2 emissions are high. In this case, if it is possible to obtain from logistics information that a truck is traveling from Yamanashi to Osaka and is scheduled to transport 11 pallets of other goods, then if the input parameter value is set to 16 pallets, consisting of the 11 pallets of those other goods and the 5 pallets obtained from the shipper information, then the predicted CO2 emissions per pallet are 22.23t-CO2 and the predicted CO2 emissions per case are 0.19t-CO2. In other words, this is more efficient than transporting with 5 pallets, and less CO2 emissions will be required (greater reduction). In order to have the CO2 emission prediction unit 103 make predictions under such various conditions, not only the shipper information but also the logistics information is referred to comprehensively.

[0052] That is, the CO2 emission prediction unit 103 uses shipper information and logistics information to calculate the predicted results of CO2 emissions per cargo volume under various conditions (such as changing the route, changing the truck, mixing or distributing cargo, etc.). The planning unit 104 can create an optimal transportation plan for incoming or outgoing shipments based on the respective prediction results of the CO2 emissions per cargo amount for each of these various conditions.

[0053] FIG. 9 is a diagram showing preconditions for an example of a transportation plan created by this service, which is different from the examples of FIG. 7 and FIG. In this example, the method for predicting CO2 emissions shown in FIG. 2 is also used. Company A (an example of shipper S) receives an order from customer B (an example of consumer C) to deliver item D (720 cases) to destination Y in Osaka by February 10, 2022. In other words, the order information in the shipper's information is as follows. Here, it is assumed that the base information in the logistics information indicates that the base from which item D is shipped (sold) is Koto-ku, Tokyo. Based on the information shown in FIG. 9, the CO2 emission prediction unit 103 predicted the amount of CO2 emission per case, as shown in FIG.

[0054] FIG. 10 shows an example of a transportation plan drawn up by this service, which is an example of a predicted result of CO2 emissions per cargo amount (one pallet and one case) in an example different from the examples in FIGS.

[0055] In FIG. 10, 1) basic information is based on the preconditions of the example in FIG. 9, and the freight rate indicates the freight rate for chartering a 10-ton truck described above with reference to FIG. 2) The table regarding CO2 emissions when delivering based on basic information shows the predicted CO2 emissions per truck (10 ton vehicle). That is, as shown in Fig. 9, the CO2 emission prediction unit 103 recognizes that the weight of item D is 4,470 kg and therefore needs to be transported by a 10-ton truck. Then, from the information in Fig. 9, the CO2 emission prediction unit 103 extracts that it is a 10-ton truck and the travel distances on general roads and expressways as the respective values ​​of the input parameters, and substitutes these into the CO2 emission prediction model 403. Then, the CO2 emission prediction model 403 outputs the results of table 2) in Fig. 10. That is, the CO2 emission prediction unit 103 outputs the table 2) in Fig. 10 as the prediction result of the CO2 emission per truck (10-ton truck).

[0056] In Figure 10, regarding 3) the difference in CO2 emissions per pallet and per case depending on the logistics volume of goods, the table on the left shows the logistics cost (unit price) per amount of cargo (per pallet and per case), and the table on the right shows the CO2 emissions per amount of cargo (per pallet and per case). Here, the maximum load capacity of a 10-ton truck is 12,000 kg, but the goods (products) that can actually be loaded are loaded in pallet units, so the total is 11,920 kg, or 16 pallets.

[0057] After obtaining the results of the table in 2) of Fig. 10, the CO2 emission prediction unit 103 further extracts the cargo volume from the shipper information, etc. as an input parameter value, and substitutes it into the CO2 emission prediction model 403. Then, the CO2 emission prediction model 403 outputs the CO2 emission per pallet and per case as prediction results according to the table on the right side of 3) of Fig. 8. That is, the CO2 emission prediction unit 103 outputs the prediction result of the CO2 emission amount per cargo amount (one pallet and one case). Specifically, in the example in Figure 9, the weight of item D is 4,470 kg, which means 6 pallets, or 720 cases, so the CO2 emissions per case are 0.61 tCO2, according to the table on the right of 3) in Figure 10. Therefore, the planning unit 104 presented the transportation plan as shown in FIG. 9 and the predicted CO2 emission amount of 0.61 tCO2 to Company A (an example of shipper S) via the shipper terminal 3.

[0058] Company A's annual policy was to keep CO2 emissions per case between Tokyo and Osaka to below 0.33 tCO2. However, the transportation plan it presented predicted CO2 emissions of 0.61 tCO2, which fell short of the target, making it clear that this was a delivery plan that placed a significant burden on the environment. Therefore, since company A has three days including the lead time for logistics, the company A requested the planning department 104 to create a transportation plan using a different transportation company, in order to search for a transportation company that can reduce CO2 emissions more than the usual transportation company T.

[0059] As a result, the planning unit 104 presented the newly planned transportation plan shown in Figure 11 (route from black circle 2 to black circle 4) and the predicted CO2 emissions shown in Figure 12 (predicted result for the route from black circle 2 to black circle 4) to Company A (an example of shipper S) via the shipper terminal 3.

[0060] FIG. 11 is an example of a transportation plan drawn up by this service, and shows prerequisites for an example different from the examples of FIGS. 9 and 10, in which a transportation company found by a matching site is utilized. That is, the planning unit 104 utilized a matching site (operated by Company C) for searching for transportation companies, and searched for a 10-ton truck (maximum load capacity 11,300 kg) from Transportation Company O that could transport 5,200 kg of item F from delivery destination X in Shizuoka to delivery destination W in Osaka. The planning unit 104 recognized that the maximum load capacity of the O transport company's truck is 11,300 kg, so that the remaining 6,100 kg of cargo can be loaded, and that the delivery destination Y of the D item of the A company is located earlier than the W delivery destination. Therefore, the planner 104 sets the route of black circle 2 → route of black circle 3 → route of black circle 4 as shown in FIG. 11 as a precondition. The route marked with black circle 2 is the route from the base in Koto-ku, Tokyo to the delivery destination of X in Fuji City, Shizuoka Prefecture, and is the route where only goods D are transported by the 10-ton truck of the original transport company T. The distance traveled on ordinary roads on the route marked with black circle 2 is 10 km, and the distance traveled on expressways is 138 km. The route marked with black circle 3 is from delivery destination X in Fuji City, Shizuoka Prefecture to delivery destination Y in Osaka City, Osaka Prefecture, and is a route in which a 10-ton truck from transport company O transports a mixture of goods F and goods D. The distance traveled on ordinary roads for the route marked with black circle 3 is 3 km, and the distance traveled on expressways is 361 km. The route marked with black circle 4 is from delivery destination Y in Osaka City, Osaka Prefecture to delivery destination W in Osaka City, Osaka Prefecture, and is a route in which a 10-ton truck from transportation company O transports only F goods. The route marked with black circle 4 is a distance of 27 km on public roads.

[0061] The CO2 emission prediction unit 103 predicted the CO2 emission per 10-ton truck for each of the routes indicated by black circles 2 to 4, and predicted the CO2 emission per case based on the prediction results. The prediction results are shown in FIG. FIG. 12 shows an example of a transportation plan drawn up by this service, which is an example of a predicted result of CO2 emissions per cargo amount (one pallet and one case) in an example different from the examples in FIGS. 9 and 10. In the table in Figure 12, from the left side, there are items for CO2 emissions (per 10-ton truck), CO2 emissions per case, freight (for a 10-ton truck), and freight per case, and the results (numerical values) of each of the routes in black circles 1 to 4 are substituted into these. Here, the route marked with a black circle 1 is shown for reference and indicates the example route in FIG. 9 (the route in the original transportation plan). Comparing the results for the route in black circle 1 (the initial transport plans in the examples of Figures 9 and 10) with the results for the total routes in black circles 2 to 4 (the second transport plan in the example of Figure 11), the CO2 emissions per case in the second transport plan did not reach the annual policy target of 0.33 tCO2, but an improvement of 0.22 tCO2 was observed within the limited vehicle information (search results from a matching site). In addition, shipping costs (logistics costs) also improved, with the unit price per case decreasing from 250 yen to 179 yen. Therefore, Company A decided to adopt the second transportation plan (the example transportation plans in Figures 11 and 12). In this way, Company A can compare the CO2 emissions and unit prices per case, and therefore can select an appropriate transportation plan from multiple transportation plans (if an appropriate plan does not exist, a further transportation plan can be created).

[0062] Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and modifications, improvements, etc. within the scope that can achieve the object of the present invention are included in the present invention.

[0063] For example, in the above example, the target of the transportation plan was trucks, but this is not limited to this, and any moving body that emits CO2 during movement, such as passenger cars, motorcycles, airplanes, ships, etc., can be used.

[0064] For example, in the above example, the weight of the package is acquired, and the amount of CO2 emissions per pallet and per case is calculated based on the weight, but the present invention is not limited to this. That is, the weight or volume of the luggage may be acquired, and the amount of CO2 emissions per any unit amount may be calculated based on the weight or volume.

[0065] For example, in the above embodiment, the CO2 emission prediction model 403 employs a model corresponding to the prediction method shown in FIG. 2, but is not limited to this.

[0066] Specifically, for example, in the prediction method shown in Figure 2, the CO2 emissions per truck are calculated based on fuel efficiency, and the only factors that affect fuel efficiency are general roads and expressways. In other words, the only input parameters for varying fuel efficiency are general roads and expressways. However, as shown in FIG. 13, various elements can be adopted as input parameters (elements) for varying fuel efficiency. FIG. 13 shows an example of factors that affect fuel efficiency and are necessary to predict CO2 emissions. For example, seasonal factors such as Obon / New Year's holiday, time of day factors, driver's habits, weather, etc. may be adopted as input parameters (factors) for varying fuel efficiency.

[0067] Furthermore, for example, the CO2 emission prediction model 403 may be an AI model based on the dispatcher's empirical rules, as shown in FIG. FIG. 14 is a diagram showing an example of a CO2 emission prediction model as an AI model. That is, although not shown, the server 1 or other information processing device may also include a learning unit. When a dispatcher actually drives a truck after making his or her own route decisions, the learning department collects information on the route decisions and fuel usage, as well as data on actual CO2 emissions. Here, the data collection tools for route decision-making, fuel usage history, and CO2 emissions are not particularly limited, and a variety of tools can be used, such as existing vehicle dispatch systems (transportation plans), digital tachographs (operation status), drive recorders (image data, etc.), fuel tank gauges (fuel consumption data), and terminals carried by vehicle dispatchers. By repeating the above process, the learning unit accumulates data on CO2 emissions, as well as planned and actual CO2 emissions (fuel consumption) and data with error correction. The learning unit uses the data accumulated in this manner as learning data and performs machine learning to generate or update an AI model that can propose optimization for reducing CO2 emissions as a CO2 emission prediction model 403. The CO2 emission prediction unit 103 and the planning unit 104 can use such an AI model to create an optimal transportation plan. In this way, it will be possible to convert the dispatcher's experience into AI and propose optimization (optimal transportation plans).

[0068] Moreover, the hardware configurations shown in FIG. 5 are merely examples for achieving the object of the present invention, and are not particularly limited.

[0069] In addition, the functional block diagram shown in Fig. 6 is merely an example and is not particularly limited. In other words, it is sufficient that the information processing system is provided with a function capable of executing the above-mentioned series of processes as a whole, and the type of functional block used to realize this function is not particularly limited to the example in Fig. 6.

[0070] In addition, the locations of the functional blocks are not limited to those shown in Fig. 6 and may be arbitrary. For example, at least a part of the functional blocks on the server 1 side may be provided in the distributor's terminal 2, the shipper's terminal 3, or an information processing device (not shown), or vice versa. A single functional block may be configured as a single piece of hardware, or may be configured in combination with a single piece of software.

[0071] When the processing of each functional block is executed by software, the program constituting the software is installed into a computer or the like from a network or a recording medium. The computer may be a computer built into dedicated hardware, or may be a computer capable of executing various functions by installing various programs, such as a server, a general-purpose smartphone, or a personal computer.

[0072] A recording medium containing such a program is not only composed of removable media that is distributed separately from the device body in order to provide each user with the program, but also composed of a recording medium that is provided to each user in a state in which it is pre-installed in the device body.

[0073] In this specification, the steps of describing a program to be recorded on a recording medium include not only processes that are performed chronologically according to the order, but also processes that are not necessarily performed chronologically but are executed in parallel or individually. In addition, in this specification, the term "system" refers to an overall device that is composed of a plurality of devices, a plurality of means, etc.

[0074] In summary, the information processing apparatus to which the present invention is applied is sufficient if it has the following configuration, and can take various different embodiments. That is, an information processing device to which the present invention is applied (for example, the server 1 in FIG. 4) In an information processing device that creates a transportation plan for transporting goods of a shipper (shipper S in FIG. 1) from a transportation origin to a transportation destination by a mobile vehicle, A shipper information acquisition unit (e.g., shipper information acquisition unit 101 in FIG. 6) that acquires shipper information (e.g., shipper information in FIG. 1) including at least the weight or volume of the product, and the shipping origin and the shipping destination; a logistics side information acquisition unit (e.g., logistics side information acquisition unit 102 in FIG. 6) that acquires, as logistics side information (e.g., logistics side information in FIG. 1), information on a logistics side that manages transportation of the moving object, the information including at least a moving object feature quantity (e.g., vehicle information in FIG. 1) that indicates a feature of the moving object; a carbon dioxide emission prediction means (e.g., the CO2 emission prediction unit 103 in FIG. 6) for predicting a carbon dioxide emission amount per unit amount for weight or volume (e.g., the CO2 emission amount per pallet and per case shown in FIG. 8) when the moving object moves under a predetermined precondition (e.g., the precondition shown in FIG. 7) including a moving route from the transportation origin to the transportation destination based on at least a part of the shipper side information and the logistics side information; A planning means (e.g., the planning unit 104 in FIG. 6) that plans one or more of the transportation plans based on each of the carbon dioxide emission amounts predicted when the preconditions are changed; Equipped with.

[0075] A presentation means (e.g., the presentation unit 105 in FIG. 6) for presenting the one or more transportation plans and the predicted values ​​of the carbon dioxide emission per unit amount to the terminals of the shipper and the logistics side, respectively. The sensor may further include:

[0076] The carbon dioxide emission prediction means Calculating the amount of carbon dioxide emissions per moving body based on the travel distance and fuel efficiency when the moving body travels under the preconditions (for example, calculating as shown in 2) of FIG. 2, FIG. 7, or FIG. 8); Calculate the amount of carbon dioxide emission per unit amount based on the amount of carbon dioxide emission per moving body and the weight or volume of the product (for example, calculate as shown in 3) of FIG. 8). It is possible.

[0077] When there are three or more bases, with the source and destination as bases (for example, as shown in FIG. 11), The carbon dioxide emission prediction means sets the preconditions for each of two or more travel routes between base stations (for example, as shown in FIG. 11 ), For each of the two or more travel routes, the carbon dioxide emission amount per unit amount is predicted (for example, as shown in FIG. 12). It is possible.

[0078] The carbon dioxide emission prediction means A model obtained as a result of machine learning based on the actual carbon dioxide emission amount when the moving body moves under the actual preconditions, and when at least a part of the shipper side information and the logistics side information is input, a model that outputs the carbon dioxide emission amount per unit amount (for example, the CO2 emission prediction model 403 in FIG. 6 which is implemented as an AI as shown in FIG. 14) is obtained, Using the model, calculate the amount of carbon dioxide emission per unit amount. It is possible. [Explanation of symbols]

[0079] 1: server, 2, 2-1, 2-n: logistics company side terminal, 3, 3-1, 3-m: shipper side terminal, 11: CPU, 12: ROM, 13: RAM, 14: bus, 15: input / output interface, 16: output section, 17: input section, 18: memory section, 19: communication section, 20: drive, 30: removable media, 101: shipper side information acquisition section, 102: logistics side information acquisition section, 103: CO2 emission prediction section, 104: planning section, 105: presentation section, 401: shipper side DB, 402: logistics side DB, 403: CO2 emission prediction model, C: consumer, S: shipper, V: supplier, L: logistics company

Claims

1. An information processing device that creates a transportation plan for transporting goods of a shipper from a transportation origin to a transportation destination by a mobile vehicle, a shipper information acquisition means for acquiring, as the shipper information, information on the shipper side, including at least the weight or volume of the commodity, the shipping origin, and the shipping destination; a logistics-side information acquisition means for acquiring, as logistics-side information, information on a logistics side that manages transportation of the moving object, the information including at least a moving object characteristic quantity that indicates a characteristic of the moving object; a carbon dioxide emission prediction means for setting predetermined preconditions including a movement route from the transportation origin to the transportation destination based on at least a part of the shipper information and the logistics information, and predicting, as a first evaluation index, the amount of carbon dioxide emission per unit amount in terms of weight or volume when the moving object moves under the preconditions; a second evaluation index acquisition means for acquiring a second evaluation index related to an element other than the carbon dioxide emission amount based on the precondition; a planning means for planning one or more transportation plans based on the first evaluation index and the second evaluation index predicted when the precondition is changed; An information processing device comprising:

2. The second evaluation index is the freight rate in the transportation plan or the freight rate per unit amount, The information processing device according to claim 1 .

3. The shipper information includes information regarding the delivery date of the product, The second evaluation index is the time required for transportation from the transportation source to the transportation destination. The information processing device according to claim 1 .

4. A presentation means for presenting the one or more transportation plans that have been prepared, as well as the first evaluation index and the second evaluation index, on the terminals of the shipper and the logistics side, respectively. The information processing device according to claim 1 , further comprising:

5. An information processing method executed by an information processing device that creates a transportation plan for transporting goods of a shipper from a transportation origin to a transportation destination by a mobile vehicle, comprising: a shipper-side information acquisition step of acquiring, as the shipper-side information, information including at least the weight or volume of the product, the shipping origin, and the shipping destination; a logistics-side information acquisition step of acquiring, as logistics-side information, information on a logistics side that manages transportation of the moving object, the information including at least a moving object feature quantity that indicates a feature of the moving object; a carbon dioxide emission prediction step of setting predetermined preconditions including a movement route from the transportation origin to the transportation destination based on at least a part of the shipper side information and the logistics side information, and predicting, as a first evaluation index, the carbon dioxide emission amount per unit amount in terms of weight or volume when the moving object moves under the preconditions; a second evaluation index acquisition step of acquiring a second evaluation index related to an element other than the carbon dioxide emission amount based on the precondition; a planning step of planning one or more transportation plans based on the first evaluation index and the second evaluation index predicted when the precondition is changed; An information processing method including:

6. A computer that creates a transportation plan for transporting goods of a shipper from a transportation origin to a transportation destination by a mobile vehicle, a shipper-side information acquisition step of acquiring, as the shipper-side information, information including at least the weight or volume of the product, the shipping origin, and the shipping destination; a logistics-side information acquisition step of acquiring, as logistics-side information, information on a logistics side that manages transportation of the moving object, the information including at least a moving object feature quantity that indicates a feature of the moving object; a carbon dioxide emission prediction step of setting predetermined preconditions including a movement route from the transportation origin to the transportation destination based on at least a part of the shipper side information and the logistics side information, and predicting, as a first evaluation index, the carbon dioxide emission amount per unit amount in terms of weight or volume when the moving object moves under the preconditions; a second evaluation index acquisition step of acquiring a second evaluation index related to an element other than the carbon dioxide emission amount based on the precondition; a planning step of planning one or more transportation plans based on the first evaluation index and the second evaluation index predicted when the precondition is changed; A program that executes control processing including:

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