Information processing device, information processing method, and program

Abstract

The present invention realizes making of a transportation plan for efficiently reducing the amount of CO2 emission, by visualizing the amount of reduction in the amount of CO2 emission not only on the physical distributor side but also on the consignor side. A consignor-side information acquisition unit 101 acquires consignor-side information. A physical distributor-side information acquisition unit 102 acquires physical distributor-side information. A CO2 emission amount prediction unit 103 sets a prescribed prerequisite including a movement route from a transportation source to a transportation destination on the basis of at least a portion of the consignor-side information and the physical distributor-side information, and predicts the amount of carbon dioxide emission per unit amount in terms of weight or volume obtained when a mobile body moves under the prerequisite. A plan-making unit 104 makes at least one transportation plan on the basis of: the cost of carbon dioxide unit amount based on the amount of carbon dioxide emission per said unit amount; and the cost of transportation per said unit amount.

Description

Information processing device, information processing method, and program

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

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

[0003] Japanese Patent Application Publication No. 9-136704

[0004] However, in recent years, there has been a demand for reducing 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 reducing CO2 emissions. This is because transport (e.g., logistics companies) create transportation plans based on information unilaterally requested by shippers, and such transportation plans are not efficient for reducing CO2 emissions. In other words, by making the amount of CO2 emission reduction visible not only to the transport side but also to the shippers, it is possible to create transportation plans that efficiently reduce CO2 emissions, but conventional logistics management systems, including the technology described in Patent Document 1, do not disclose any information to the shippers.

[0005] The present invention was made in consideration of this situation, and aims to realize the development of transportation plans that efficiently reduce CO2 emissions by making the amount of CO2 emissions reduced visible not only to the logistics side but also to the shipper side.

[0006] In order to achieve the above-mentioned object, one aspect of the present invention is an information processing device that prepares a transportation plan for transporting a shipper's goods by a mobile object from a transportation origin to a destination, and that comprises: a shipper information acquisition means that acquires, as shipper information, information from the shipper side that includes at least the weight or volume of the goods, and the transportation origin and the destination; a logistics side information acquisition means that acquires, as logistics side information, information from the logistics side that manages the transportation of the mobile object that includes at least the transportation cost required to transport the goods from the transportation origin to the destination and mobile object characteristics that indicate the characteristics of the mobile object; a carbon dioxide emission prediction means that sets, based on at least a part of the shipper side information and the logistics side information, predetermined preconditions including a travel route from the transportation origin to the destination, and predicts the carbon dioxide emission amount per unit amount for weight or volume when the mobile object travels under those preconditions; and a planning means that prepares one or more transportation plans based on a carbon dioxide cost per unit amount based on the carbon dioxide emission amount per unit amount and the transportation cost per unit amount. An information processing method and a program according to one aspect of the present invention are a method and a program corresponding to an information processing device according to one aspect of the present invention.

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

[0008] 1 is an image 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. It is a diagram showing an example of a CO2 emission calculation method applied to the present service of FIG. 1. It is a diagram showing the configuration of an information processing system including a server according to an embodiment of the information processing device of the present invention. It is a block diagram showing the hardware configuration of the server of FIG. 3. It is a functional block diagram showing an example of the functional configuration of the server of FIG. 4. It is a diagram showing an example of a screen for delivery destination registration, cargo registration, and vehicle selection, on which a shipper enters information when searching for a transportation plan. It is a diagram showing an example of a display in which search results obtained by a shipper searching for a transportation plan are compared with conventional search results. It is a diagram showing an example of a calculation of a transportation plan executed in the planning unit of FIG. 4, which is the functional configuration of the server of FIG. 3. It is a diagram showing an example of factors that change fuel efficiency required to predict CO2 emissions. It is a diagram showing an example of a CO2 emission prediction model as an AI model. It is a diagram showing a specific example for explaining a general vehicle determination judgment process in terms of transportation planning. It is a diagram explaining a conventional logistics evaluation adopted in the general vehicle determination judgment process shown in FIG. 11, which is a logistics evaluation based on the unit price of a vehicle (truck). 16 is a diagram showing the results of considering logistics based on the minimum item (case / weight) unit price from the unit price of the vehicle (truck). This diagram shows a case where simply increasing loading efficiency by adopting a case unit price may not be effective in logistics. This diagram shows a specific example of the case unit price when switching from a 4-ton vehicle to a 10-ton vehicle. This diagram shows the assumptions for calculating CO2 emissions for the cases shown in FIGS. 11 to 14. This diagram shows the results of calculating CO2 emissions by applying the CO2 emission calculation method of FIG. 2 based on the assumptions of FIG. 16. This diagram explains an example in which another embodiment of an information processing device to which the present invention is applied creates a transportation plan that takes CO2 emissions into consideration, based on the CO2 emission calculation results of FIGS. 16 and 17 for the cases shown in FIGS. 11 to 14. This diagram shows a specific example of CO2 emissions per case when switching from a 4-ton vehicle to a 10-ton vehicle.

[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 the information processing device of the present invention.

[0011] As shown in Figure 1, in this service, consumers C, which may be companies, retail wholesalers, individuals (end users), etc., place orders for products with shippers S. In this service, when consumers C place an order with shippers S, the contents of the order are managed as "order information."

[0012] The shipper S may be a manufacturer, an individual, a company, etc., 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 is made up of manufacturers, manufacturers, etc., and supplies goods to distributor L based on order information. From distributor L's perspective, goods are received from supplier V. From the shipper S's perspective, this is equivalent to requesting distributor L to procure goods based on order information. Therefore, in this service, when shipper S requests distributor L to procure goods (when goods arrive at distributor L from supplier V), the details are managed as "procurement information (arrival information)."

[0014] This service also manages various types of information held by logistics company L 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, load capacity, vehicle class, and driver information (such as name, age, working hours, overtime hours, etc.) for each cargo transport vehicle, is managed.

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

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

[0017] In recent years, there has been a demand for distributor L to reduce CO2 emissions generated during its logistics activities (delivery, storage, operations, etc.). Therefore, distributor L itself has been engaged in various activities to reduce CO2 emissions. However, as described above, the procurement (arrival) of goods, which is the basis of logistics activities, is determined by an order from shipper S to supplier V. Furthermore, the sale (shipment) of goods, which is the basis of logistics activities, is determined by an order from consumer C to shipper S. In other words, distributor L's logistics activities are determined by order information, which is a decision made between consumer C and shipper S, and order information, which is a decision made between shipper S and supplier V. Therefore, there are limits to how much CO2 emissions distributor L alone can reduce. Therefore, this service enables distributor L to take action to reduce CO2 emissions from the generation stage of order information, which is a decision made between consumer C and shipper S, and order information, which is a decision made between shipper S and supplier V. That is, with this service, transportation plans are created that take CO2 emissions into consideration as transportation plans for product procurement (receiving) and sales (shipping), and the CO2 emissions are presented to and shared with the shipper S and the logistics company L. In other words, at least some of the shipper S, logistics company L, etc. will be able to create transportation plans that are optimal for themselves while visually checking whether the presented CO2 emissions will reach the reduction target. The remaining few will be able to visually view the created transportation plans along with their CO2 emissions.

[0018] Below, we will explain the outline of the transportation plan that takes CO2 emissions into consideration and that will be applied to this service.

[0019] Figure 2 shows an example of a method for calculating CO2 emissions applied to the service shown in Figure 1. As shown in Figure 2, the CO2 emissions (tCO2) per truck are calculated using the following formula: 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, 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. According to the Ministry of the Environment's Global Environment Bureau's Guidelines for Calculating Greenhouse Gas Emissions from Businesses (Draft Ver. 1.6), the CO2 emission calculation formula based on the Fuel Method, and the Ministry of Economy, Trade and Industry / Japan Logistics Systems Association's "2003 Environmentally Friendly Logistics Promotion Manual," the emission coefficient is 0.0187.

[0021] Meanwhile, fuel consumption (kl) is calculated using the following formula: Fuel consumption (kl) = Distance traveled (km) / Fuel efficiency (km / kl) 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 cargo (number of cases or pallets).

[0022] In other words, if traveling the same route, the fuel efficiency of a fully loaded truck and an empty truck is the same, and as a result, the CO2 emissions per truck are also the same. In other words, the CO2 emissions per truck do not depend on the amount of cargo carried by the truck. In other words, the CO2 emissions per unit of cargo carried by a truck (per case or pallet, if one unit is used) increase as the loading efficiency decreases. For example, if the CO2 emissions per truck are 100, and there are 100 units of cargo, the CO2 emissions per unit of cargo are 1. On the other hand, if there are 10 units of cargo, the CO2 emissions per unit of cargo are 10, which is 10 times higher. Therefore, this service creates transportation plans based on the CO2 emissions per unit of cargo carried by a truck (per case or pallet, if one unit is used).

[0023] Furthermore, to facilitate comparison, a CO2 cost is calculated based on the CO2 emissions per unit of cargo transported by one truck (per unit, where one unit is a case or pallet).The transportation cost is also converted into a cost per unit of cargo transported by one truck (per unit, where one unit is a case or pallet).The total CO2 cost and transportation cost per unit of cargo transported by one truck (per unit, where one unit is a case or pallet) are then calculated, and a transportation plan is created based on this total.

[0024] Next, a configuration of an information processing system including a server 1 that executes various processes for providing this service will be described. Fig. 3 is a diagram showing the configuration of an information processing system including a server according to an embodiment of the information processing device of the present invention.

[0025] The information processing system shown in FIG. 3 is configured by interconnecting a server 1, distributor terminals 2-1 to 2-n (n is an integer value of 1 or greater), and shipper terminals 3-1 to 3-m (m is an integer value of 1 or greater) via a predetermined network N such as the Internet.

[0026] In this embodiment, each of the distributor terminals 2-1 to 2-n is configured as a personal computer, a smartphone, a tablet terminal, etc. Each of the distributor terminals 2-1 to 2-n is operated by a respective person in charge of n distributors L. Hereinafter, when there is no need to distinguish between each of the distributor terminals 2-1 to 2-n, they will be collectively referred to as the "distributor terminal 2."

[0027] In this embodiment, each of the shipper side terminals 3-1 to 3-m 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 respective person in charge of the mth shipper S. In the following, when there is no need to individually distinguish between the shipper side terminals 3-1 to 3-m, these will be collectively referred to as the "shipper side terminal 3."

[0028] Next, the hardware configuration of the server 1 that executes various processes for providing this service will be described. Fig. 4 is a block diagram showing the hardware configuration of the server shown in Fig. 3.

[0029] 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.

[0030] The CPU 11 executes various processes according to programs recorded in the ROM 12 or programs 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.

[0031] The CPU 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output interface 15 is also connected to this bus 14. An input unit 16, an output unit 17, a storage unit 18, a communication unit 19, and a drive 20 are connected to the input / output interface 15.

[0032] The input unit 16 is composed of various hardware leads and the like, and inputs various information. The output unit 17 is composed of various liquid crystal displays and the like, and outputs various information. The memory unit 18 is composed of DRAM (Dynamic Random Access Memory) and the like, and stores various data. The communication unit 19 controls communication with other devices (for example, the logistics company terminals 2-1 to 2-n and the shipper terminals 3-1 to 3-m in FIG. 4) via a network N including the Internet.

[0033] The drive 20 is provided as needed. Removable media 30, such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is appropriately attached to the drive 20. Programs read from the removable media 30 by the drive 20 are installed in the storage unit 18 as needed. The removable media 30 can also store various data stored in the storage unit 18 in the same way as the storage unit 18.

[0034] Although not shown, the distributor terminal 2 and the shipper terminal 3 also have the hardware configuration shown in FIG.

[0035] The various hardware and software components constituting the information processing system of FIG. 3, including the server 1 of FIG. 4, work together to execute various processes for providing the present service of FIG.

[0036] Next, the functions of the server 1 having the hardware configuration as shown in Fig. 4 will be described with reference to Fig. 5. Fig. 5 is a functional block diagram showing an example of the functional configuration of the server 1 of Fig. 4.

[0037] 5, a shipper information acquisition unit 101, a logistics information acquisition unit 102, a CO2 emission prediction unit 103, a planning unit 104, and a presentation unit 105 function in the CPU 11 of the server 1. A shipper DB 401, a logistics DB 402, and a CO2 emission prediction model 403 are provided in one area of ​​the storage unit 18.

[0038] The shipper information acquisition unit 101 acquires, as shipper information, the order information and purchase order information that the shipper S has previously managed. The shipper information acquisition unit 101 also acquires, as shipper information, information including the weight or volume of the cargo (cargo, product) and the shipping origin and destination. Furthermore, the shipper information acquisition unit 101 also acquires, as shipper information, the delivery date desired by the shipper S. The shipper information acquired by the shipper information acquisition unit 101 is stored and managed in the shipper DB 401.

[0039] The logistics side information acquisition unit 102 acquires, as logistics company side information, procurement information (arrival information), vehicle information, sales information (shipment information), and information on each base station for determining routes, which have traditionally been managed by logistics company L. The logistics side information acquisition unit 102 also acquires, as logistics side information, information including the transportation costs required to transport luggage (cargo, goods) from the source to the destination. Furthermore, the logistics side information acquisition unit 102 also acquires, as logistics side information, information required to determine whether the delivery date will be met and information regarding quality. The logistics side information acquired by the logistics side information acquisition unit 102 is stored and managed in the logistics side DB 402.

[0040] The CO2 emission prediction unit 103 sets predetermined assumptions, including a travel route from the shipping origin to the shipping destination, based on at least a portion of the shipper information and the logistics information, and predicts the CO2 emissions per unit weight or volume when, for example, a truck travels under these assumptions. Specifically, the CO2 emission prediction unit 103 calculates the CO2 emissions per truck based on the travel distance and fuel efficiency when, for example, a truck travels under these assumptions, and then calculates the CO2 emissions per unit weight or volume based on the CO2 emissions per truck and the weight or volume of the cargo, and uses this as a predicted CO2 emission value. Note that the CO2 emission prediction unit 103 may also extract values ​​of each input parameter from the shipper information and the logistics information, and output the result obtained by substituting these extracted values ​​into the CO2 emission prediction model 403 as a predicted CO2 emission amount per unit cargo volume.

[0041] Here, the CO2 emission prediction model 403 is a model that predicts and outputs CO2 emissions per cargo amount (pallet / case) when values ​​of one or more input parameters are input. The CO2 emission prediction model 403 is individually provided depending on the CO2 emission prediction method. For example, in the case of a model corresponding to the prediction method shown in FIG. 2 described above, which is a model that assumes that fuel efficiency differs between expressways and ordinary roads, values ​​of the vehicle class (whether it is a 4-ton vehicle or a 10-ton vehicle), route information (route information that divides the route into expressways and ordinary roads and allows the distance of each to be specified), and cargo weight are input as input parameters.

[0042] The planning unit 104 plans one or more transportation plans based on a CO2 cost per unit amount based on the CO2 emissions per unit amount for the weight or volume of the cargo, and a freight cost per unit (transportation cost) calculated by adding the CO2 cost to the freight cost per unit (transportation cost), i.e., a freight cost per unit + CO2 cost. Specifically, the planning unit 104 calculates the sum of the CO2 cost and transportation cost per amount of cargo transported by one truck (per unit, where one unit is a case or pallet), and plans a transportation plan based on this sum. Transportation plans will be described later with reference to FIG. 7.

[0043] The presentation unit 105 presents the prepared transportation plan to the distributor L via the distributor terminal 2, and also to the shipper S via the shipper terminal 3. Here, the transportation plan also presents the CO2 emissions (predicted results) and the freight charge per unit + CO2 cost (CO2 cost per unit amount plus freight charge per unit (transportation cost)). In this way, information on the CO2 emissions and the freight charge per unit + CO2 cost are presented and shared not only with the distributor L but also with the shipper S. As a result, not only the distributor L but also the shipper S can participate in activities to reduce CO2 generated in logistics activities.

[0044] In summary, the CPU 11 of the server 1 functions to create a transportation plan for transporting goods of a shipper S from a shipping origin to a shipping destination, for example, by truck, by using a shipper information acquisition unit 101, a logistics information acquisition unit 102, a CO2 emission estimation unit 103, a planning unit 104, and a presentation unit 105. The shipper information acquisition unit 101 acquires, as shipper information, information from the shipper S, including at least the weight or volume of the goods (cargo, goods), the shipping origin (e.g., Tokyo), and the shipping destination (e.g., Osaka). The logistics information acquisition unit 102 acquires, as logistics information, information from the logistics company L that manages truck transportation, including at least the shipping cost required to transport the goods (cargo, goods) from a shipping origin (e.g., Tokyo) to a shipping destination (e.g., Osaka) and truck characteristics (e.g., vehicle information) that indicate the characteristics of the truck. The CO2 emission prediction unit 103 sets predetermined assumptions, including a travel route from the transportation origin (e.g., Tokyo) to the transportation destination (e.g., Osaka), based on at least a portion of the shipper information and the logistics information, and predicts the CO2 emissions per unit amount for weight or volume when the truck travels under these assumptions. The planning unit 104 creates one or more transportation plans based on the CO2 cost per unit amount based on the CO2 emissions per unit amount and the freight rate (transportation cost) per unit amount. This makes it possible to create transportation plans that efficiently reduce CO2 emissions.

[0045] Furthermore, in the CPU 11 of the server 1, the presentation unit 105 presents the one or more transportation plans that have been prepared, as well as the CO2 cost per unit amount and the freight rate per unit amount (transportation cost) for each, to at least the shipper terminal 3 of the shipper S. This makes it possible to visualize the amount of CO2 emission reduction not only for the logistics company L (logistics side) but also for the shipper S side, thereby realizing the preparation of transportation plans that efficiently reduce CO2 emissions.

[0046] FIG. 6 shows an example of a screen for registering delivery destinations, registering cargo, and selecting vehicles, on which the shipper enters information when searching for a transportation plan.

[0047] When the shipper S searches for a transportation plan, the screen (display unit) of the shipper terminal 3 operated by the shipper S displays an input section (reference numerals omitted) for registering a delivery destination KG1. Specifically, as shown in the figure, input fields (reference numerals omitted) for "Departure Point" (corresponding to the above-mentioned transportation origin), "Destination Point" (corresponding to the above-mentioned transportation destination), "Delivery Date," "Time Specification," "Travel Time," "Rest / Rest Time," "Estimated Work Time," "Estimated Total Delivery Time," and "Comments" are displayed (although these fields are not necessarily required and may be entered automatically). The screen also displays input fields (reference numerals omitted) for registering cargo KG2. Specifically, as shown in the figure, input fields (reference numerals omitted) for the quantity, number of pallets, type, conditions, weight, and so on are displayed. The screen also displays an input field (reference numerals omitted) for selecting a vehicle KG3. Specifically, as shown in the figure, check boxes for "Recommended Vehicle," "Mixed Load," and "Not Selected" are displayed.

[0048] When the shipper S has completed the above input and clicks a search button (not shown) with, for example, a mouse, a plurality of transportation plans as shown in Fig. 7 are displayed to the shipper S as search results. Fig. 7 is a diagram showing an example of a display in which the search results obtained by the shipper searching for transportation plans are compared with conventional search results. Note that the conventional search results are provided by the applicant of the present application for comparison.

[0049] As a conventional search result, for example, transportation plans No. 1 to No. 3 are displayed. Each transportation plan displays a checkbox for "Selection," the transportation plan's "No.", "Truck (Maximum Load Capacity)," "Freight," and "Delivery Date." Of transportation plans No. 1 to No. 3, the shipper S usually selects transportation plan No. 1, which has the lowest freight rate.

[0050] In contrast, future search results, i.e., in this embodiment, will display transportation plans that have the additional items of "CO2 emissions," "unit price per kg of cargo," "CO2 cost per kg," and "shipping cost + CO2 cost per kg" shown in Figure 7. Therefore, while in the past, transportation plan No. 1 with the lowest shipping cost would be selected, in the future, transportation plan No. 3 with the lowest "shipping cost + CO2 cost per kg" shown in Figure 7 will be selected. According to this embodiment, it becomes possible to create transportation plans that efficiently reduce CO2 emissions.

[0051] Note that the "unit price per kg of cargo" shown in Figure 7 refers to the unit price per kg of cargo (freight, transportation cost). Also, the "CO2 cost per kg" refers to the CO2 cost per kg of cargo. Also, the "freight + CO2 cost per kg" refers to the sum of the unit price per kg of cargo (freight, transportation cost) and the CO2 cost per kg of cargo. Although Figure 7 (and also Figure 8, which will be described later) uses the term "per kg of cargo," it is also possible to use the amount of cargo transported by one truck (per unit, where one unit is a case or pallet).

[0052] Fig. 8 is a diagram showing an example of calculation of a transportation plan executed in the planning unit of Fig. 4, which is the functional configuration of the server of Fig. 3. Note that the transportation plans No. 1 to No. 3 shown in Fig. 8 are obtained by adding prerequisites for decision-making by the shipper S to the transportation plans No. 1 to No. 3 shown in Fig. 7.

[0053] The "CO2 emissions per kg of cargo" (CO2 emissions per 1 kg of cargo), "unit price per kg of cargo" (unit price per 1 kg of cargo (freight, transportation cost)), "CO2 cost per kg" (CO2 cost per 1 kg of cargo), and "freight + CO2 cost per kg" (total of unit price per 1 kg of cargo (freight, transportation cost) and CO2 cost per kg of cargo) shown in Figure 8 are calculated in the planning unit 104.

[0054] Specifically, "CO2 emissions per kg of cargo" (CO2 emissions per 1 kg of cargo) is calculated by dividing "CO2 emissions" by "cargo weight." Also, "unit cost per kg of cargo" (unit cost per 1 kg of cargo (freight, transportation cost)) is calculated by dividing "freight" by "cargo weight." Also, "CO2 cost per kg" (CO2 cost per 1 kg of cargo) is calculated by multiplying the above-mentioned "CO2 emissions per kg of cargo" (CO2 emissions per 1 kg of cargo) by "CO2 per kg" (cost per 1 kg of CO2). Also, "freight + CO2 cost per kg" (the sum of unit cost per kg of cargo (freight, transportation cost) and CO2 cost per kg of cargo) is calculated by adding the above-mentioned "unit cost per kg of cargo" (unit cost per kg of cargo (freight, transportation cost)) and the above-mentioned "CO2 cost per kg" (CO2 cost per kg of cargo).

[0055] 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 of achieving the object of the present invention are included in the present invention.

[0056] For example, in the above example, the target of the transportation plan was trucks, but this is not limited to this, and any mobile vehicle that emits CO2 during movement, such as passenger cars, motorcycles, airplanes, ships, etc., can be used.

[0057] For example, in the above example, the weight of the package is acquired and the CO2 emissions per 1 kg of the package are calculated, but this is not intended to be limiting. In other words, the weight or volume of the package may be acquired and the CO2 emissions per any unit amount may be calculated by converting the weight or volume. Alternatively, the CO2 emissions per pallet or case may be calculated by converting the weight.

[0058] 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.

[0059] Specifically, for example, in the prediction method shown in FIG. 2 , 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, only general roads or expressways are used as input parameters for varying fuel efficiency. However, as shown in FIG. 9 , various factors can be used as input parameters (factors) for varying fuel efficiency. FIG. 9 shows an example of factors that affect fuel efficiency and are required for predicting CO2 emissions. For example, seasonal factors such as Obon / New Year's holiday, time of day, driver habits, weather, etc. may be used as input parameters (factors) for varying fuel efficiency.

[0060] 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. 10 . FIG. 10 is a diagram illustrating an example of a CO2 emission prediction model as an AI model. That is, although not illustrated, the server 1 or another information processing device may also be equipped with a learning unit. When the dispatcher decides on a route and actually drives the truck, the learning unit collects route decision-making and fuel usage records, as well as actual CO2 emission data. Here, the data collection tools for route decision-making, fuel usage records, and CO2 emissions are not particularly limited, and various tools can be used, such as existing dispatch systems (transportation plans), digital tachographs (operation status), drive recorders (image data, etc.), fuel tank gauges (fuel consumption data), and terminals carried by the dispatcher. By repeating the above process, the learning unit accumulates CO2 emission data, and accumulates planned and actual CO2 emissions (fuel usage) data, as well as error-corrected data. The learning unit uses the data accumulated in this way as learning data and performs machine learning to generate or update an AI model that can propose optimization for reducing CO2 emissions as the CO2 emission prediction model 403. The CO2 emission prediction unit 103 and the planning unit 104 can use such an AI model to plan an optimal transportation plan. In this way, it becomes possible to convert the dispatcher's empirical rules into AI and propose optimization (optimal transportation plans).

[0061] Here, as another embodiment, a specific example will be used to explain another embodiment in which a transportation plan is created that efficiently reduces CO2 emissions.

[0062] First, a general process for determining which vehicle to use will be described in terms of transportation planning. FIG. 11 shows a specific example for explaining a general process for determining which vehicle to use in terms of transportation planning. The specific example in FIG. 11 is an example of a pattern based on a logistics contract with a distance-based freight rate. In this example, the prerequisites are that the total weight of goods of shipper S is 2,900 kg and the travel distance is 100 km.

[0063] As shown in FIG. 11, a general vehicle determination process in terms of transportation planning includes the following first to fifth steps.

[0064] The first step is the step of determining whether or not there is an overload. In this step, the load weight of one vehicle (truck) of logistics company L is compared with the total weight of goods of two shippers, 2900 kg, to evaluate whether or not there is an overload. In this example, Company A and Company B are overloaded, so they cannot deliver, and the determination is NG (marked with an x).

[0065] The second step is the quality judgment step. In this case, the quality conditions are that the goods are refrigerated, so the vehicle is a refrigerated vehicle. There are four vehicles, and Company D's is a room temperature vehicle, so the judgment is NG (marked with an x).

[0066] The third step is the step of determining the delivery date. In this example, it is determined whether the vehicle can be operated on the delivery date and time of the shipper. Since Company E cannot operate on May 7th for some reason, the determination is NG (marked with an X).

[0067] The fourth step is the cost assessment step. In this example, the freight rate agreed between the shipper S and the consumer C is 35,000 yen, so the assessment of the companies D and F, whose freight rates exceed this rate, is NG (marked with an X).

[0068] The fifth step is a comprehensive assessment step, i.e., a step in which the assessments of steps 1 to 4 are comprehensively evaluated. In particular, step 1 is an item stipulated by law, and therefore is an item (an absolute condition) that must be absolutely considered when making a judgment. In other words, since step 1 is stipulated by law, the assessment criteria must be complied with. However, if consumer C or shipper S does not require the criteria of 2. Quality and 3. Delivery date, they may proceed to the next process (the assessment of steps S4 and S5) without making the assessments of steps 2 and 3. In this case, since company C met all of the assessment criteria of steps 1 to 4, it was decided to use company C's vehicle for delivery.

[0069] Note that the example in Figure 11 is merely a simplified example for ease of understanding the present invention, and in practice, a judgment process may be adopted that takes into account the following factors as well. For example, a shipper (S) may adopt a judgment process that takes into account the quality of the goods, whether the specified temperature was maintained, the goods' packaging, soiling, damage, incorrect quantities, etc. For a logistics company (L), a judgment process may be adopted that takes into account the safety inspections and daily maintenance of the vehicles, the driving conditions of the driver (e.g., sudden stops and starts), and whether the conditions were met, such as manners and customer reception, since the driver interacts with the consumer (C) on behalf of the shipper (S). Furthermore, a judgment process may be adopted that takes into account the transport quality, such as whether the vehicle is an air-suspension vehicle for goods that are sensitive to vibration, or even the impact caused by driving with a vibration device. Recently, the quality assessment in step S2 must also include a quality evaluation of safe operation. To this end, it is important to adopt a judgment process that evaluates and visualizes safety quality, taking into account whether working hours are in accordance with laws and regulations, rest periods, breaks, health status, blood pressure, and other aspects of the logistics company (L)'s operational status and the individual's health status.

[0070] As explained above, the logistics evaluation adopted in the general vehicle selection process shown in Figure 11, i.e., the conventional logistics evaluation based on a distance-based freight logistics contract, is a logistics (freight) evaluation based on whether the unit price of the vehicle (truck) is low or high, as shown in Figure 12. In other words, conventionally, logistics evaluation was based on the unit price of the vehicle (truck). Figure 12 is a diagram explaining the conventional logistics evaluation adopted in the general vehicle selection process shown in Figure 11, and is a logistics evaluation based on the unit price of the vehicle (truck).

[0071] In Figure 12, the delivery areas and delivery dates and times for orders from Consumer C vary. Shipper S makes delivery requests for each order from Consumer C, which results in poor vehicle loading efficiency. Even if loading efficiency deteriorates, shipper S will not face any problems under a distance-based logistics contract. In a distance-based logistics contract, the evaluation standard is the difference between the contracted freight rate with Consumer C and the contracted freight rate with logistics company L. This is a logistics system in which shipper S will not incur any losses if there is a difference in freight rates or unit prices for vehicles.

[0072] As shown in Figure 12, when distance-based freight rates are adopted, the unit price per vehicle (per truck) has traditionally been used as the standard. Therefore, given the same delivery and delivery conditions, logistics companies with lower unit prices tend to be selected, as shown in the example in Figure 11. Logistics company L's efforts to improve quality, such as safety measures and personnel training, are not rewarded, even if they spend time and money. Even with rising fuel prices and labor costs due to the competition for talent, shipper S's logistics evaluation is based on the "unit price of the vehicle." Therefore, even as the business environment becomes more difficult, logistics company L finds it difficult to accept price increases under this contract structure. Therefore, even in the current inflationary environment, the company is still caught in the midst of price competition. This led the inventor to consider the following questions: Does a low unit price per vehicle actually contribute to shipper S's business? Furthermore, is this evaluation basis appropriate?

[0073] Therefore, in order to solve this problem, the inventors have considered logistics from the viewpoint of what kind of logistics evaluation is possible at a coexistent hourly rate, by considering the unit price of the vehicle (truck) and the minimum unit price of the goods (case / weight), as shown in Figure 13. Figure 13 is a diagram showing the results of considering logistics from the unit price of the vehicle (truck) and the minimum unit price of the goods (case / weight).

[0074] As shown in Figure 13, changing the evaluation criteria from the unit price of vehicles to the minimum unit price of goods results in a completely different evaluation axis. Here, the minimum unit price of goods refers to the "cost per unit of cargo transported by one truck (where one unit is a case or pallet)" as used in the above-mentioned embodiment. In other words, the above-mentioned embodiment uses the minimum unit price of goods instead of the unit price of vehicles. Specifically, for example, in terms of unit price of vehicles, Company A's freight rate of 33,000 yen is the cheapest unit price for shipper S, and if the logistics quality remains the same, Company A will be selected for vehicle allocation. On the other hand, when evaluating the loading efficiency of goods, Company C's delivery cost per kilogram is 6.6 yen. Note that this assumes that an order is placed for 2,177 cases. In contrast, Company A, which had the lowest unit price of vehicles, would be 12.2 yen. Therefore, if Company C takes steps to improve loading efficiency, it will be evaluated highly. However, Company C's unit price per kg is based on the assumption of 100% loading efficiency, so they need to come up with a new transportation system to consolidate Consumer C's orders. However, unlike when using the unit price of the vehicle, by using the unit price of the item as the evaluation axis, it is likely that an opportunity will be created to proactively make improvements. Note that this study is an example from the perspective of evaluating logistics, so how to redesign the system will be omitted.

[0075] The results of the analysis in Figure 13 can be summarized as follows. The results of the analysis in Figure 13 show that the picture changes when the evaluation framework is changed, such as the minimum unit price of goods (case / weight) versus the unit price of the vehicle (truck). Specifically, if shipper S evaluates logistics based on the unit price of the vehicle, logistics costs naturally occur when transportation is carried out. Distance-based freight contracts are primarily determined by the unit price of each truck used for delivery. Here, there is a prerequisite: if the maximum load capacity of a 4-ton truck is exceeded, a 10-ton truck is used. In other words, shipper S simply adopts distance-based freight because the rule is that anything can be loaded within the load capacity, and it is not necessary to load anything at all. In this case, shipper S's logistics department cooperates in efforts to reduce the unit price of the vehicle, while the sales department merely cooperates with the overloading rule. In other words, shipper S feels that there is no opportunity to consider how to improve logistics. However, the service shown in Figure 1 according to the above embodiment is proposed from the viewpoint that it would be better to minimize logistics costs (price per case, cost per weight, etc.) and disclose the logistics costs as a percentage of the sales price, thereby disclosing shipper S's business activities to the whole.

[0076] However, the important point here is that, assuming a distance-based freight rate, increasing the number of collected packages (total case volume) in order to improve loading efficiency will, in principle, lower the minimum unit price (case / weight) of goods, but this principle may not always work. Therefore, based on the results of the considerations in Figure 13, the inventor has devised a new method of comparing the distance-based freight rate with the minimum unit price (case / weight) of goods and changing the transportation method. This new method will be explained with reference to Figures 14 to 18.

[0077] Figure 14 illustrates how simply increasing loading efficiency by adopting a case unit price may not be effective in achieving logistics benefits. In the right diagram of Figure 14, the solid line forming the line graph shows the freight cost per case (the freight cost divided by the number of cases), which is an example of the minimum unit price for goods. The bar graph shows freight rates by shipping quantity unit (distance-based freight). The problem with distance-based freight is that the case unit price increases when switching vehicles. This is because, from a legal compliance perspective, exceeding the vehicle's maximum load capacity requires switching to a larger vehicle. Figure 15 illustrates a specific example of the case unit price when switching from a 4-ton truck to a 10-ton truck. As shown in Figure 15, for an order of 500 cases using a 4-ton truck, the cost per case is 80 yen. In contrast, as soon as the order is switched to a 10-ton truck, the logistics cost per case exceeds 80 yen for orders of 600, 700, and 800 cases. In other words, as shown in Figure 14, for vehicles with the same maximum load capacity, costs generally decrease as orders increase and loading efficiency improves. Therefore, it is preferable to evaluate logistics at the minimum unit price (case price in the example of Figure 15) taking into account the circumstances of logistics company L. However, the case price does not monotonically decrease with order volume (shipment volume). When the order volume (shipment volume) reaches a level that requires a change in vehicle, the case price jumps and becomes expensive until a certain amount is reached. Therefore, for example, in the relationship between consumer C and shipper S, if shipper S wishes to order 500 cases or more, by entering into a contract with distributor L that requires orders to be placed for 900 cases or more, unnecessary freight charges can be avoided.

[0078] Incidentally, in order to apply the newly devised method by the inventor, namely, the method of comparing the distance-based freight rate with the minimum unit price (case / weight) of the goods and changing the method of transportation, as one embodiment of the information processing device of the present invention, it is necessary to take into account CO2 emissions when the information processing device of the one embodiment creates a transportation plan.

[0079] The method for calculating CO2 emissions has already been described with reference to Fig. 2. In the embodiment of the information processing device of the present invention to which the method newly devised by the inventors is applied, the method for calculating CO2 emissions can be adopted as is.

[0080] Fig. 16 shows preconditions for calculating CO2 emissions for the examples shown in Fig. 11 to Fig. 14. Fig. 17 shows the results of calculating CO2 emissions by applying the CO2 emission calculation method of Fig. 2 based on the preconditions of Fig. 16. Fig. 18 is a diagram illustrating an example in which another embodiment of an information processing device to which the present invention is applied creates a transportation plan that takes CO2 emissions into consideration, based on the calculation results of CO2 emissions in Fig. 16 and Fig. 17 for the examples shown in Fig. 11 to Fig. 14.

[0081] In the right diagram of Figure 18, the solid line forming the line graph indicates CO2 emissions per case (CO2 emissions per truck divided by the number of cases). In other words, CO2 emissions per case are an example of the "CO2 emissions per unit of weight or volume" described in the above-mentioned embodiment. The bar graph is the same as Figure 14 and shows freight rates (distance-based freight rates) by shipping quantity unit. The problem with distance-based freight rates is that CO2 emissions per case increase when switching vehicles. This is because, from a legal compliance perspective, exceeding the vehicle's maximum load capacity requires switching to a larger vehicle. Figure 19 shows a specific example of CO2 emissions per case when switching from a 4-ton truck to a 10-ton truck. As shown in Figure 19, as soon as the truck is switched to a 10-ton truck, CO2 emissions per case exceed 0.15 g-CO2 / L (L: liter) for 600, 700, and 800 cases. In other words, as shown in Figure 18, for vehicles with the same maximum load capacity, increasing orders and improving loading efficiency generally reduces "CO2 emissions per unit of weight or volume." Therefore, it is preferable to evaluate logistics using "CO2 emissions per unit of weight or volume" (CO2 emissions per case in the example of Figure 18). However, "CO2 emissions per unit of weight or volume" do not monotonically decrease as order volume (shipment volume) increases. When the order volume (shipment volume) reaches a level that requires a vehicle switch, "CO2 emissions per unit of weight or volume" jumps and continues to exceed a certain level until a certain amount is reached. Therefore, for example, depending on the relationship between consumer C and shipper S, if shipper S wishes to order 500 cases or more, a contract with distributor L that requires orders of 900 cases or more can keep CO2 emissions below an appropriate level.

[0082] In summary, another embodiment of the information processing device of the present invention, which employs a newly devised method by the inventors, i.e., a method of comparing distance-based freight rates with the minimum unit price (case / weight) of goods to change the transportation method, has the following additional functional configuration in addition to the functional configuration of FIG. 5 . Specifically, the CO2 emission prediction unit 103 in FIG. 5 predicts the carbon dioxide emissions per unit amount based on predetermined assumptions, including the condition that a distance-based freight rate is used as the vehicle fare and that the size of the vehicle is determined based on the load weight or volume. If the predicted carbon dioxide emissions per unit amount exceed the maximum carbon dioxide emissions (e.g., 0.15 g-CO2 / L (L: liter) in the example of FIG. 19 ) specified for the vehicle size (e.g., a 10-ton vehicle in the examples of FIGS. 18 and 19 ) determined based on the weight or volume of the goods, the planning unit 104 prohibits the creation of a transportation plan and proposes a weight or volume of the vehicle that is equal to or less than the maximum CO2 emissions for the vehicle size.

[0083] Furthermore, the hardware configurations shown in FIG. 4 are merely examples for achieving the object of the present invention, and are not particularly limited.

[0084] 5 is merely an example and is not particularly limited. That is, it is sufficient for the information processing system to have the function of executing the above-described series of processes as a whole, and the type of functional block used to realize this function is not particularly limited to the example of FIG.

[0085] Furthermore, the locations of the functional blocks are not limited to those shown in Fig. 5 and may be arbitrary. For example, at least some of the functional blocks on the server 1 side may be provided in the distributor terminal 2, the shipper terminal 3, or an information processing device (not shown), or vice versa. Furthermore, one functional block may be configured as a hardware unit or in combination with software units.

[0086] When the processing of each functional block is performed by software, the programs constituting the software are installed onto a computer or the like from a network or a recording medium. The computer may be a computer incorporated into dedicated hardware. Alternatively, the computer may be a computer capable of performing various functions by installing various programs thereon, such as a server, a general-purpose smartphone, or a personal computer.

[0087] The recording medium containing such a program may not only be composed of removable media that is distributed separately from the device itself in order to provide the program to each user, but may also be composed of recording media that are provided to each user in a state where they are pre-installed in the device itself.

[0088] In this specification, the steps describing the program recorded on the recording medium include not only processes that are performed in chronological order, but also processes that are not necessarily performed in chronological order but are performed in parallel or individually. Also, in this specification, the term "system" means an overall device composed of multiple devices or multiple means, etc.

[0089] In summary, the information processing apparatus to which the present invention is applied is sufficient if it has the following configuration, and can take on a variety of different embodiments. That is, an information processing device (e.g., server 1 in FIG. 3) to which the present invention is applied is an information processing device that formulates a transportation plan (e.g., transportation plan in FIG. 7) for transporting merchandise of a shipper (shipper S in FIG. 1) from a transportation origin (e.g., departure point in FIG. 6) to a transportation destination (e.g., arrival point in FIG. 6) by a mobile object, and includes: shipper side information acquisition means (e.g., shipper side information acquisition unit 101 in FIG. 5) that acquires, as shipper side information (e.g., shipper side information in FIG. 1), information on the shipper side, which includes at least the weight or volume of the merchandise, and the transportation origin and the transportation destination; logistics side information acquisition means (e.g., logistics side information acquisition unit 102 in FIG. 5) that acquires, as logistics side information, information on the logistics side that manages the transportation of the mobile object, which includes at least the transportation cost (e.g., freight in FIG. 7) required to transport the merchandise from the transportation origin to the transportation destination, and mobile object feature quantities (e.g., vehicle information in FIG. 1) that indicate the features of the mobile object; The system includes: a carbon dioxide emission prediction means (e.g., the CO2 emission prediction unit 103 in FIG. 5) that sets predetermined preconditions (e.g., the preconditions shown in FIG. 8) 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 predicts the amount of carbon dioxide emission per unit amount for weight or volume (e.g., the "CO2 emission per kg of cargo" (CO2 emission per 1 kg of cargo) in FIG. 8) when the moving object moves under the preconditions; and a planning means that prepares one or more transportation plans based on the carbon dioxide cost per unit amount (e.g., the "CO2 cost per 1 kg" (CO2 cost per 1 kg of cargo) in FIG. 8) based on the carbon dioxide emission per unit amount, and the transportation cost per unit amount. This makes it possible to realize the preparation of transportation plans that efficiently reduce CO2 emissions.

[0090] Furthermore, the information processing device (e.g., server 1 in FIG. 3) to which the present invention is applied further includes a presentation means (e.g., presentation unit 105 in FIG. 5) that presents the one or more prepared transportation plans and the carbon dioxide cost and transportation cost per unit amount to at least the shipper's terminal (e.g., shipper's terminal 3 in FIG. 3). This makes it possible to visualize the amount of CO2 emission reduction not only for the logistics company L (logistics side) but also for the shipper S, thereby realizing the preparation of transportation plans that efficiently reduce CO2 emissions.

[0091] Furthermore, in an information processing device to which the present invention is applied (for example, the server 1 in FIG. 3), the carbon dioxide emission prediction means calculates the amount of carbon dioxide emission per moving object based on the travel distance and fuel efficiency when the moving object travels under the preconditions, and calculates the amount of carbon dioxide emission per unit amount based on the amount of carbon dioxide emission per moving object and the weight or volume of the product. This makes it possible to more reliably create a transportation plan that efficiently reduces CO2 emissions.

[0092] Furthermore, in an information processing device to which the present invention is applied (for example, server 1 in FIG. 3), the carbon dioxide emission prediction means obtains a model obtained as a result of machine learning based on the actual carbon dioxide emission when the mobile object moves under the actual preconditions, and when at least a portion of the shipper information and the logistics information is input, the carbon dioxide emission prediction means outputs the amount of carbon dioxide emission per unit amount (for example, CO2 emission prediction model 403 in FIG. 5 that has been implemented using AI as in FIG. 10), and calculates the amount of carbon dioxide emission per unit amount using the model. This makes it possible to more reliably create transportation plans that efficiently reduce CO2 emissions.

[0093] Furthermore, in an information processing device (e.g., server 1 in FIG. 3 ) to which the present invention is applied, the shipper information acquisition means further acquires the shipper information including a delivery date desired by the shipper (e.g., the delivery date in FIG. 6 ), the logistics information acquisition means further acquires the shipper information including information necessary to determine whether the delivery date will be met and information regarding quality, and the planning means further plans one or more transportation plans based on the information indicating whether the delivery date will be met and the quality. This makes it possible to more reliably plan transportation plans that efficiently reduce CO2 emissions.

[0094] Furthermore, in an information processing device (for example, server 1 in FIG. 3) to which the present invention is applied, the carbon dioxide emission prediction means (for example, CO2 emission prediction unit 103 in FIG. 5) predicts the carbon dioxide emission per unit amount by adopting the predetermined preconditions including a condition that a distance-based fare is adopted as the fare for the mobile body (for example, a vehicle such as a truck) and that the size of the mobile body (for example, a 4-ton truck or a 10-ton truck) is determined according to the load weight or the load volume, and the planning means (for example, planning unit 104 in FIG. 5) can prohibit the planning of the transportation plan if the predicted carbon dioxide emission per unit amount exceeds a maximum carbon dioxide emission (for example, 0.15 g-CO2 / l (l: liter) in the example of FIG. 19) specified in advance for the size of the mobile body (for example, a 10-ton truck in the examples of FIGS. 18 and 19) determined according to the weight or the volume of the product, and propose a weight or volume of the vehicle that is equal to or less than the maximum carbon dioxide emission for the size of the mobile body.

[0095] DESCRIPTION OF SYMBOLS 1: Server, 2: Logistics company terminal, 3: Shipper terminal, 11: CPU, 12: ROM, 13: RAM, 14: Bus, 15: Input / output interface, 16: Input unit, 17: Output unit, 18: Memory unit, 19: Communication unit, 20: Drive, 30: Removable media, 101: Shipper information acquisition unit, 102: Logistics company information acquisition unit, 103: CO2 emission prediction unit, 104: Planning unit, 105: Presentation unit, 401: Shipper DB, 402: Logistics company DB, 403: CO2 emission prediction model, C: Consumer, S: Shipper, V: Supplier, L: Logistics company

Claims

1. An information processing device that prepares a transportation plan as a plan for transporting a shipper's goods by a mobile object from a transportation origin to a transportation destination, the information processing device comprising: a shipper side information acquisition means that acquires, as shipper side information, information on the shipper side, which includes at least the weight or volume of the goods, and the transportation origin and the transportation destination; a logistics side information acquisition means that acquires, as logistics side information, information on the logistics side that manages the transportation of the mobile object, which includes at least the transportation cost required to transport the goods from the transportation origin to the destination and mobile object characteristics that indicate the characteristics of the mobile object; a carbon dioxide emission prediction means that sets predetermined preconditions including a movement route from the transportation origin to the destination based on at least a part of the shipper side information and the logistics side information, and predicts the carbon dioxide emission amount per unit amount for weight or volume when the mobile object moves under the preconditions; and a planning means that prepares one or more transportation plans based on the carbon dioxide emission amount per unit amount based on the carbon dioxide emission amount per unit amount and the transportation cost per unit amount.

2. An information processing device according to claim 1, further comprising a presentation means for presenting, at least on a terminal of the shipper, one or more of the transportation plans that have been prepared, and the carbon dioxide cost and the transportation cost per unit amount of each of the transportation plans.

3. The information processing device according to claim 1, wherein the carbon dioxide emission prediction means calculates the amount of carbon dioxide emission per moving body based on the travel distance and fuel efficiency when the moving body travels under the prerequisite conditions, and calculates 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.

4. The information processing device according to claim 1, wherein the carbon dioxide emission prediction means is a model obtained as a result of machine learning based on the actual amount of carbon dioxide emission when the mobile object moves under the actual preconditions, and when at least a portion of the shipper side information and the logistics side information are input, a model is obtained that outputs the amount of carbon dioxide emission per unit amount, and the carbon dioxide emission per unit amount is calculated using the model.

5. The information processing device of claim 1, wherein the shipper side information acquisition means further acquires the shipper side information including a delivery date desired by the shipper, the logistics side information acquisition means further acquires the shipper side information including information necessary to determine whether the delivery date will be met and information regarding quality, and the planning means further plans one or more transportation plans based on the information indicating whether the delivery date will be met and the quality.

6. The information processing device of claim 1, wherein the carbon dioxide emission prediction means predicts the carbon dioxide emission per unit amount by adopting the predetermined preconditions including the condition that a distance-based fare is adopted as the fare for the mobile body and the size of the mobile body is determined according to the load weight or load volume, and the planning means prohibits the planning of the transportation plan if the predicted carbon dioxide emission per unit amount exceeds a maximum carbon dioxide emission specified in advance for the size of the mobile body determined according to the weight or volume of the goods, and proposes a weight or volume of the mobile body that is less than or equal to the maximum carbon dioxide emission for the size of the vehicle.

7. An information processing method executed by an information processing device that formulates a transportation plan for transporting a shipper's goods from a shipping origin to a shipping destination by a mobile object, the information processing method including: a shipper side information acquisition step of acquiring, as shipper side information, information on the shipper side that includes at least the weight or volume of the goods, and the shipping origin and the shipping destination; a logistics side information acquisition step of acquiring, as logistics side information, information on the logistics side that manages the transportation of the mobile object, that includes at least the transportation cost required to transport the goods from the shipping origin to the shipping destination and mobile object characteristics that indicate the characteristics of the mobile object; a carbon dioxide emission prediction step of setting predetermined preconditions including a movement route from the shipping origin to the shipping destination based on at least a part of the shipper side information and the logistics side information, and predicting the carbon dioxide emission amount per unit amount for weight or volume when the mobile object moves under the preconditions; and a planning step of formulating one or more of the transportation plans based on the carbon dioxide emission amount per unit amount and the transportation cost per unit amount.

8. A program for causing a computer that prepares a transportation plan for transporting a shipper's goods from a shipping origin to a destination by a mobile object to execute control processing including: a shipper side information acquisition step of acquiring, as shipper side information, information on the shipper side that includes at least the weight or volume of the goods, and the shipping origin and the shipping destination; a logistics side information acquisition step of acquiring, as logistics side information, information on the logistics side that manages the transportation of the mobile object, that includes at least the transportation cost required to transport the goods from the shipping origin to the destination and mobile object characteristics that indicate the characteristics of the mobile object; a carbon dioxide emission prediction step of setting predetermined preconditions including a movement route from the shipping origin to the destination based on at least a part of the shipper side information and the logistics side information, and predicting the carbon dioxide emission amount per unit amount for weight or volume when the mobile object moves under the preconditions; and a planning step of preparing one or more transportation plans based on the carbon dioxide cost per unit amount based on the carbon dioxide emission amount per unit amount and the transportation cost per unit amount.

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