WO2024257883A1 - Information processing device, information processing method, and program - Google Patents

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

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

　Technology for managing inventory held in warehouses that serve as logistics bases has existed for some time. For example, Patent Document 1 describes a logistics management system that allocates stocked products to product orders received by retailers from customers and transmits the allocation results and delivery dates for the orders to the retailers.

Japanese Patent Application Publication No. 9-136704

However, in recent years, there has been a demand to reduce the CO2 emissions generated during logistics activities (delivery, storage, work, etc.) on the transportation 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 the transport company (e.g., logistics company) based on information unilaterally requested by the shipper, and such transportation 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; however, with conventional logistics management systems, including the technology described in Patent Document 1, no information was disclosed to the shipper side.

The present invention was made in light of these circumstances, and aims to realize the creation of transportation plans that efficiently reduce CO2 emissions by making the amount of CO2 emissions reduced visible not only for the logistics side but also for the shipper side.

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 that manages transportation of the moving body, the information including at least a transportation cost required to transport the product from the transportation origin to the transportation destination and a moving body characteristic amount that indicates a characteristic of the moving body;
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 a carbon dioxide cost per unit amount based on the carbon dioxide emission amount per unit amount and the transportation cost per unit amount;
Equipped with.
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.

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

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. FIG. 2 is a diagram showing an example of a method for calculating CO2 emissions applied to the present service of FIG. 1. 1 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. FIG. 4 is a block diagram showing a hardware configuration of the server in FIG. 3 . FIG. 5 is a functional block diagram showing an example of a functional configuration of the server in FIG. 4 . FIG. 13 is a diagram showing an example of a screen for registering delivery destination, registering cargo, and selecting a vehicle on which a shipper enters information when searching for a transportation plan. FIG. 13 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. 5 is a diagram showing an example of a calculation of a transportation plan executed in the planning unit of FIG. 4, which is a functional configuration of the server of FIG. 3; FIG. FIG. 1 is a diagram showing an example of factors that affect fuel efficiency and are required to predict CO2 emissions. FIG. 1 is a diagram showing an example of a CO2 emission prediction model as an AI model. FIG. 13 is a diagram showing a specific example to explain a general vehicle determination decision process in terms of transportation planning. FIG. 12 is a diagram illustrating 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). This figure shows the results of considering logistics based on the unit price of vehicles (trucks) and the minimum unit price of goods (case/weight). This figure shows that there are cases where the effect of logistics cannot be achieved simply by adopting unit case prices to improve loading efficiency. FIG. 13 is a diagram showing a specific example of the case unit price when switching from a 4-ton vehicle to a 10-ton vehicle. FIG. 15 is a diagram showing preconditions for calculating CO 2 emissions for the cases shown in FIGS. 11 to 14 . 17 is a diagram showing the results of calculating CO2 emissions by applying the method of calculating CO2 emissions in FIG. 2 based on the preconditions in FIG. 16 . FIG. This figure explains an example in which, for the cases shown in Figures 11 to 14, another embodiment of an information processing device to which the present invention is applied creates a transportation plan that takes into account CO2 emissions based on the calculation results of CO2 emissions in Figures 16 and 17. FIG. 11 is a diagram showing a specific example of CO2 emissions per case when switching from a 4-ton vehicle to a 10-ton vehicle.

Below, an embodiment of the present invention will be explained with reference to the drawings.

FIG. 1 is an image 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.

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."

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

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 will arrive 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 (arrival information)".

In addition, this service manages various types of information that logistics company L possesses for business purposes. Specifically, information (hereafter referred to as "vehicle information") related to cargo transport vehicles (hereafter referred to as "trucks") used to transport goods, such as the vehicle number, load capacity, vehicle class, and driver information (for example, name, age, working hours, overtime hours, etc.) for each cargo transport vehicle, is managed.

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)".

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 that logistics company L previously managed (procurement information (arrival information), vehicle information, and sales information (shipment information)) and the information that shipper S previously managed (order information and ordering information). In other words, it makes it possible to create an effective transportation plan based on information from logistics company L and information from shipper S. As a result, efficient transportation without waste can be realized.

In recent years, there has been a demand for reduction in CO2 emissions generated in the logistics activities (delivery, storage, operations, etc.) of the logistics company L.
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 shipper 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 shipper 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 shipper S, and the ordering information, which is a decision made between the shipper S and the supplier V.
Therefore, there is a limit to how much CO2 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 ordering information, which is a decision-making process between shipper S and supplier V. That is, this service creates a transportation plan that takes CO2 emissions into consideration as a transportation plan for procuring (receiving) and selling (shipping) goods, and the amount of CO2 emissions is presented to and shared with shipper S and logistics company L.
In other words, at least some of the shippers S and logistics companies 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.

Below is an overview of the transportation plan that takes CO2 emissions into consideration and that will be applied to this service.

FIG. 2 is a diagram showing an example of a method for calculating the amount of CO2 emissions applied to the present service of FIG.
As shown in FIG. 2, the amount of CO2 emissions (tCO2) per truck is expressed by the following formula.
CO2 emissions = fuel consumption (kl) x 2.58 (tCO2/kl)

Here, 2.58 (tCO2/Kl) is the CO2 emission coefficient per kl of diesel, and is calculated by the following formula.
2.58 (tCO2/kl) = unit calorific value (GJ/Kl) x emission coefficient (tC/GJ) x 22/12 (tCO2/tC)
According to Notification No. 66 of the Ministry of Economy, Trade and Industry (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 and the Ministry of Economy, Trade and Industry.

On the other hand, the fuel consumption amount (kl) is calculated by 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).

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, the worse the loading efficiency, the higher the CO2 emissions will be, not per truck, but per unit of cargo carried by one truck (per unit, assuming one case or pallet is one unit). 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).

Furthermore, for ease of comparison, a CO2 cost is calculated based on the amount of 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 the cost per unit of cargo transported by one truck (per unit, where one unit is a case or a pallet).
Then, the sum of the CO2 cost and transportation cost per unit of cargo transported by one truck (per unit, where one unit is a case or pallet) is calculated, and a transportation plan is created based on this sum.

Next, a configuration of an information processing system including the 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.

The information processing system shown in FIG. 3 is composed of a server 1, logistics company terminals 2-1 through 2-n (n is an integer value of 1 or more), and shipper terminals 3-1 through 3-m (m is an integer value of 1 or more), all connected to one another via a specified network N such as the Internet.

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."

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 shipper 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."

Next, a 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.

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.

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

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 memory unit 18, a communication unit 19, and a drive 20 are connected to the input/output interface 15.

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.

The drive 20 is provided as necessary. Removable media 30, which may be 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 necessary. The removable media 30 can also store various data stored in the storage unit 18 in the same way as the storage unit 18.

Although not shown, the logistics company terminal 2 and the shipper terminal 3 also have the hardware configuration shown in FIG. 4.

By cooperation of the various hardware and software constituting the information processing system of FIG. 3, including the server 1 of FIG. 4, various processes for providing the present service of FIG. 1 can be executed.

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

As shown in FIG. 5, in the CPU 11 of the server 1, a shipper information acquisition unit 101, a logistics information acquisition unit 102, a CO2 emission amount prediction unit 103, a planning unit 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.

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

The logistics side information acquisition unit 102 acquires, as logistics company side information, procurement information (arrival information), vehicle information, and sales information (shipment information) that were previously managed by logistics company L, as well as information on each base station for determining routes.
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 transport source to the transport destination.
Furthermore, the logistics side information acquisition unit 102 acquires, as logistics side information, information required to determine whether or not the delivery deadline 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 .

The CO2 emission prediction unit 103 sets predetermined preconditions including the travel route from the source to the destination based on at least a portion of the shipper information and the logistics information, and predicts the CO2 emission amount per unit amount for weight or volume when, for example, a truck moves under those preconditions.
Specifically, the CO2 emission prediction unit 103 calculates the amount of CO2 emissions per truck based on the distance traveled and fuel efficiency when, for example, a truck moves under the above-mentioned prerequisites, and then calculates the amount of CO2 emissions per unit of weight or volume based on the amount of CO2 emissions per truck and the weight or volume of the cargo, and uses this as a predicted value of the CO2 emissions.
In addition, the CO2 emission prediction unit 103 may extract the values of each input parameter from the shipper information and the logistics information, and substitute these extracted values into the CO2 emission prediction model 403 to output the result as the prediction result of the CO2 emission amount per cargo volume.

Here, the CO2 emission prediction model 403 is a model that predicts and outputs the amount of CO2 emission per cargo amount (pallet/case) when 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 allows the distance of each to be specified), and luggage weight are input as input parameters.

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 charge per unit (transportation cost) plus the CO2 cost, ie, a freight charge 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 a pallet), and creates a transportation plan based on that sum.
The transportation plan will be described later with reference to FIG.

The presentation unit 105 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 presents the amount of CO2 emissions (forecast result) 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 amount of CO2 emissions and the freight charge per unit + CO2 cost are presented and shared not only with the logistics company L but also with the shipper S. As a result, not only the logistics company L but also the shipper S can participate in activities to reduce CO2 generated in logistics activities.

To summarize the above, in the CPU 11 of the server 1, a shipper side information acquisition unit 101, a logistics side information acquisition unit 102, a CO2 emission prediction unit 103, a planning unit 104, and a presentation unit 105 function to create a transportation plan, which is a plan for transporting the shipper S's goods from the source to the destination, for example by truck.
The shipper side information acquisition unit 101 acquires information on the shipper S side, which includes at least the weight or volume of the luggage (cargo, goods), as well as the shipping origin (e.g., Tokyo) and shipping destination (e.g., Osaka).
The logistics side information acquisition unit 102 acquires, as logistics side information, information from the logistics company L that manages truck transportation, including at least the transportation costs required to transport luggage (cargo, goods) from the source (e.g., Tokyo) to the 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 preconditions including the travel route from the origin (e.g., Tokyo) to the destination (e.g., Osaka) based on at least a portion of the shipper information and logistics information, and predicts the CO2 emission amount per unit amount for weight or volume when the truck travels under those preconditions.
The planning unit 104 plans one or more transportation plans based on a CO2 cost per unit amount based on the amount of CO2 emissions per unit amount, and a freight charge (transportation cost) per unit amount.
This makes it possible to develop transportation plans that efficiently reduce CO2 emissions.

In addition, in the CPU 11 of the server 1, the presentation unit 105 presents one or more proposed transportation plans, as well as their respective CO2 costs per unit amount and freight rates (transportation costs) per unit amount, to at least the shipper side terminal 3 on the shipper S side.
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 enabling the development of transportation plans that efficiently reduce CO2 emissions.

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

When the shipper S searches for a transportation plan, the screen (display) of the shipper terminal 3 operated by the shipper S displays an input section (reference numbers omitted) for performing delivery destination registration KG1 regarding the registration of the delivery destination. Specifically, as shown in the figure, input fields (reference numbers omitted) such as "departure point" (corresponding to the above-mentioned transportation origin), "arrival point" (corresponding to the above-mentioned transportation destination), "delivery date", "time designation", "travel time", "rest/break time", "estimated work time", "estimated total delivery time", and "comments" are displayed (entry is not necessarily required, and may be automatically entered).
The screen also displays input fields (reference numbers omitted) for performing cargo registration KG2 related to cargo registration. Specifically, as shown in the figure, input fields (reference numbers omitted) for the number of items, number of pallets, type, conditions, weight, etc. are displayed.
The screen also displays an input field (reference numerals omitted) for selecting a vehicle KG3. Specifically, the screen displays "recommended vehicle", "mixed cargo", and "not selected" in the form of check boxes as shown in the figure.

When the shipper S has completed the above input and clicks on 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.
7 is a diagram showing an example of a display of search results obtained by a shipper searching for a transportation plan, for comparison with the results of a conventional search. Note that the above conventional search results are provided by the applicant of the present application for comparison.

As a conventional search result, for example, transportation plans No. 1 to 3 are displayed. For each transportation plan, a checkbox-type "selection," the transportation plan's "No.", "truck (maximum load capacity),""freight," and "delivery date" are displayed.
Of the transportation plans No. 1 to 3, the shipper S usually selects the transportation plan No. 1, which has the lowest freight rate.

In contrast to this, future search results, i.e. in this embodiment, will display a transportation plan with the additional items shown in Figure 7: "CO2 emission amount,""unit price per kg of cargo,""CO2 cost per kg," and "freight + CO2 cost per kg."
Therefore, in the past, the No. 1 transportation plan with the lowest freight rate was selected, but in the future, the No. 3 transportation plan with the lowest "freight rate + CO2 cost per kg" shown in Figure 7 will be selected.
According to this embodiment, it becomes possible to realize the creation of a transportation plan that efficiently reduces CO2 emissions.

In addition, the "unit price per kg of cargo" shown in Figure 7 refers to the unit price per 1 kg of cargo (freight, transportation cost). Also, the "CO2 cost per kg" refers to the CO2 cost per 1 kg of cargo. Also, the "freight + CO2 cost per kg" refers to the sum of the unit price per 1 kg of cargo (freight, transportation cost) and the CO2 cost per 1 kg of cargo.
In FIG. 7 (and also in FIG. 8 described later), it is stated that the cost is "per 1 kg of cargo," but it is also possible to use the cost per unit of cargo carried by one truck (per unit, where one unit is a case or pallet).

FIG. 8 is a diagram showing an example of calculation of a transportation plan executed in the planning unit in FIG. 4 which is the functional configuration of the server in FIG.
In addition, the transportation plans No. 1 to 3 shown in Fig. 8 are obtained by adding preconditions for the shipper S to make a decision to the transportation plans No. 1 to 3 shown in Fig. 7.

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 unit price per 1 kg of cargo (freight, transportation cost) and CO2 cost per 1 kg of cargo) shown in FIG. 8 are calculated in the planning unit 104.

Specifically, "CO2 emissions per kg of cargo" (CO2 emissions per 1 kg of cargo) is calculated by dividing "CO2 emissions" by "cargo weight."
In addition, the "unit price per kilogram of cargo" (unit price per 1 kg of cargo (freight, transportation cost)) is calculated by dividing the "freight" by the "cargo weight."
In addition, the "CO2 cost per kg" (CO2 cost per kg of cargo) is calculated by multiplying the above "CO2 emissions per kg of cargo" (CO2 emissions per kg of cargo) by "CO2 per kg" (cost per kg of CO2).
In addition, "Freight charge + CO2 cost per kg" (the sum of the unit price per kg of cargo (freight charge, transportation cost) and the CO2 cost per kg of cargo) is calculated by adding the above "unit price per kg of cargo" (unit price per kg of cargo (freight charge, transportation cost)) and the above "CO2 cost per kg" (CO2 cost per kg of cargo).

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

For example, in the above example, the transportation plan was targeted at trucks, but it is not limited to this and can be used for any moving object that emits CO2 during movement, such as passenger cars, motorcycles, airplanes, ships, etc.

For example, in the above example, the weight of the luggage is acquired and the amount of CO2 emissions per 1 kg of the luggage is calculated, but this is not intended to be limiting. In other words, the weight or volume of the luggage may be acquired and the amount of CO2 emissions per any unit amount converted from the weight or volume may be calculated. In addition, the amount of CO2 emissions per pallet and case converted from the weight may be calculated.

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

Specifically, for example, in the prediction method shown in Fig. 2, the CO2 emissions per truck are calculated based on fuel efficiency, and the factors that affect the fuel efficiency are only general roads and expressways. In other words, the only input parameters for varying the fuel efficiency are general roads and expressways.
However, as shown in FIG. 9, various kinds of elements can be adopted as input parameters (elements) for varying the fuel efficiency.
FIG. 9 shows an example of factors that affect fuel efficiency and are required 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.

Furthermore, for example, the CO2 emission prediction model 403 may be a model that uses AI to convert the dispatcher's empirical rules, as shown in FIG. 10 .
FIG. 10 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, and accumulates planned and actual CO2 emissions (fuel consumption) as well as error-corrected data.
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 optimal transportation plans.
In this way, it will be possible to convert the dispatcher's experience into AI and propose optimization (optimal transportation plans).

Here, we will explain another embodiment, using a specific example, in which a transportation plan is created that efficiently reduces CO2 emissions.

First, in terms of transportation planning, a general vehicle selection process will be described.
FIG. 11 shows a specific example to explain a general vehicle determination decision process in terms of transportation planning.
The specific example in Figure 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 the goods of the shipper S is 2,900 kg and the travel distance is 100 km.

As shown in Figure 11, a typical vehicle selection decision process in terms of transportation planning involves the following five steps:

The first step is a step of determining whether the vehicle is overloaded.
In the first step, the load weight of one vehicle (truck) of distributor 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 and therefore unable to deliver, so the result is NG (marked with an x).

The second step is a quality judgment step.
In this case, the quality conditions are that the goods are refrigerated, so the vehicle is a refrigerated vehicle. Of the four vehicles, Company D has a room temperature vehicle, so the result is NG (marked with an x).

The third step is a step of determining the delivery date.
In this example, the determination is whether the vehicle can be operated on the delivery date and time of the shipper.
Company E is unable to operate on 5/7 for some reason, so the verdict is NG (marked with an x).

The fourth step is a cost determination step.
In this case, the freight rate agreed upon between shipper S and consumer C is 35,000 yen, so the judgement for companies D and F, which exceed this freight rate, is NG (marked with an x).

The fifth step is a comprehensive judgment step, that is, a step of comprehensively evaluating the judgments in the first step to the fourth step.
In particular, the first step is an item stipulated by law, and therefore is an item (absolute condition) that must be considered when making a judgment. That is, since the first step is stipulated by law, the judgment criteria must be observed, but if the consumer C or the shipper S does not require the criteria of 2. Quality and 3. Delivery date, they may skip the judgments of steps 2 and 3 and proceed to the next process (judgment of steps S4 and 5).
In this example, since company C satisfies all of the judgment criteria in steps 1 to 4, it was decided to use company C's vehicle for delivery.

It should be noted that the example in FIG. 11 is merely a simple example for facilitating understanding of the present invention, and in practice, a determination process may be adopted that further takes into consideration the following factors.
That is, the shipper S may adopt a judgment process that considers the quality of the goods, whether the specified temperature was maintained, the exterior of the goods, dirt, damage, and incorrect quantity. The logistics company L may adopt a judgment process that considers the inspection and daily maintenance to ensure safety of the vehicle, which is the equipment, the driving conditions such as sudden stops and sudden starts for the driver, and whether the conditions such as manners and reception of consumers were observed because the driver contacts the consumer C on behalf of the shipper S. In addition, a judgment process may be adopted that considers whether the transportation quality is an air suspension vehicle for goods that are sensitive to vibration, and further, the shock level when driving by attaching a vibration device. Recently, it is necessary to include a quality evaluation of safe operation in the quality judgment of step S2, and for this purpose, it is important to adopt a judgment process that evaluates the safety quality taking into account the operating conditions of the logistics company L such as whether the working hours are in accordance with the law, rest, breaks, health conditions, blood pressure, etc., and the health conditions of the individuals, and to visualize the safety quality.

As described above, the logistics evaluation adopted in the general vehicle selection process shown in Fig. 11, i.e., the conventional logistics evaluation based on a distance-based logistics contract, is a logistics (freight) evaluation based on whether the unit price of the vehicle (truck) is low or high, as shown in Fig. 12. That is, conventionally, logistics evaluation was based on the unit price of the vehicle (truck).
FIG. 12 is a diagram for explaining a conventional logistics evaluation based on the unit price of a vehicle (truck), which is adopted in the general vehicle selection process shown in FIG.

In Figure 12, the delivery areas and delivery dates and times for Consumer C's orders vary. Shipper S makes delivery requests for each order from Consumer C, so vehicle loading efficiency suffers. Even if loading efficiency suffers, shipper S does not encounter any problems under a distance-based logistics contract. A distance-based logistics contract uses the difference between the contracted freight rate with Consumer C and the contracted freight rate with logistics company L as the evaluation standard. This is a logistics system in which shipper S will not incur a loss if there is a difference in freight rates or unit price of vehicles.

As shown in Figure 12, when distance-based freight rates were adopted, the unit price per vehicle (per truck) was traditionally the standard. Therefore, if the delivery and delivery conditions are the same, there is a tendency for a logistics company with a lower unit price to be selected, as shown in the example in Figure 11.
Logistics company L's efforts to improve quality, such as safety measures and human resource development, by spending money and time on them, are not rewarded. Even if fuel prices rise and labor costs rise due to the battle for talent, shipper S's logistics evaluation is based on the "unit price of the vehicle," so for logistics company L, this contract type makes it difficult to accept requests for price increases even if the business environment becomes tougher. Therefore, even in the current inflationary environment, the company is still in the midst of price competition.
For this reason, the inventor came up with the following problem: does the freight rate, which has a low unit price for a vehicle, actually contribute to the management of shipper S? Also, is this evaluation axis acceptable as it is?

In order to solve this problem, the inventor considered logistics from the viewpoint of what is a coexistent hourly speed logistics evaluation, as shown in FIG. 13, from the unit price of a vehicle (truck) to the minimum unit price of an item (case/weight).
FIG. 13 is a diagram showing the results of considering physical distribution based on the unit price of a vehicle (truck) and the minimum unit price of an item (case/weight).

As shown in FIG. 13, if the evaluation criteria are changed from the unit price of the vehicle to the minimum unit price of the item, a completely different evaluation axis is created.
Here, the minimum unit price of the goods means "the cost per unit of cargo transported by one truck (one unit when one unit is a case or a pallet)" in the above embodiment. In other words, the above embodiment is an embodiment in which the minimum unit price of the goods is used instead of the unit price of the vehicle.
Specifically, for example, in terms of the unit price of the vehicle, Company A's freight charge of 33,000 yen is the cheapest unit price for shipper S, and if the logistics quality does not change, Company A will be selected for vehicle allocation.
On the other hand, if we evaluate the loading efficiency of goods, Company C's delivery cost per kilogram will be 6.6 yen. Note that the prerequisite is that they receive an order for 2,177 cases. In contrast, Company A, which had a lower vehicle unit cost, will pay 12.2 yen. Therefore, if Company C takes action to improve its loading efficiency, it will be evaluated highly.
However, since Company C's unit price per kg is based on a loading efficiency of 100%, it needs to devise a new transportation system to consolidate Consumer C's orders.
However, unlike when the unit price of vehicles was used, using the unit price of items as the evaluation axis is likely to create an opportunity to proactively make improvements.
In addition, since this study is a case study focused on evaluating logistics, the discussion of how to redesign the system has been omitted.

The results of the consideration of FIG. 13 can be summarized as follows.
In other words, the observations in Figure 13 show that the picture changes when the evaluation framework is changed, such as the unit price of a vehicle (truck) versus the minimum unit price of goods (case/weight).
Specifically, if shipper S evaluates logistics based on the unit price of the vehicle, logistics costs will naturally be incurred if the goods are transported.
In the first place, distance-based freight contracts are determined by the unit price of each truck vehicle used for delivery.
Here, the premise is that once the maximum load capacity of a 4-ton vehicle is exceeded, a 10-ton vehicle will be adopted.
In other words, the rule is that you can load anything as long as it does not exceed the load capacity, and you don't have to load anything at all, so the shipper S is simply adopting a distance-based freight rate. In this case, the logistics department of the shipper S cooperates with activities to reduce the vehicle unit price, and the sales department only cooperates with the rule on overloading. In other words, it seems that the shipper S has no opportunity to think about how to improve its logistics.
However, the service shown in Figure 1 according to the embodiment described above is proposed from the viewpoint that it would be a good idea to minimize logistics costs (price per case, cost per weight, etc.) and to disclose the logistics costs as a percentage of the sales price, thereby disclosing the business activities of shipper S as a whole.

However, an important point to note here is that, assuming distance-based freight rates, if the quantity of cargo collected (total case volume) is increased in order to improve loading efficiency, the minimum unit price of goods (case/weight) will generally decrease, but there are cases where this does not necessarily happen.
Therefore, based on the results of the considerations shown in FIG. 13, the inventors have devised a new method of comparing the distance-based freight rate with the minimum unit price of goods (case/weight) and changing the method of transportation.
This new approach will now be described with reference to Figures 14 to 18.

FIG. 14 is a diagram showing cases where the effect of logistics cannot be achieved simply by adopting the unit case price to increase loading efficiency.
In the right diagram of Fig. 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 of goods. The bar graph shows the freight cost by shipping quantity unit (distance freight cost).
The problem with distance-based fares is that the cost per case increases when changing vehicles. This is because, in order to comply with regulations, it is necessary to change to a larger vehicle when the vehicle's maximum load capacity is exceeded.
FIG. 15 is a diagram showing a specific example of the case unit price when switching from a 4-ton vehicle to a 10-ton vehicle.
As shown in Figure 15, when an order is placed for 500 cases using a 4-ton truck, the cost per case is 80 yen. In contrast, as soon as a switch is made 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 FIG. 14, for a vehicle with the same maximum loading capacity, costs will basically decrease as the number of orders increases and loading efficiency improves. Therefore, it is preferable to evaluate logistics at the minimum unit price (case price in the example of FIG. 15) taking into account the circumstances of logistics company L.
However, the cost per case does not decrease monotonically with order volume (shipment volume). When the order volume (shipment volume) reaches a level that requires a change in vehicle type, the cost per case jumps up and the cost per case becomes expensive until a certain amount is reached.
For example, in the relationship between consumer C and shipper S, if shipper S wishes to order more than 500 cases, then a contract can be made with distributor L that specifies that orders must be placed for 900 cases or more, thereby avoiding the payment of unnecessary freight charges.

Incidentally, in order to apply the method newly devised by the present inventor, that is, 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.

The method for calculating the amount of CO2 emissions has already been described with reference to FIG. 2. The method for calculating the amount of CO2 emissions can be used as is in the embodiment of the information processing device of the present invention to which the method newly devised by the inventors is applied.

FIG. 16 shows preconditions for calculating the amount of CO2 emissions for the cases shown in FIGS.
FIG. 17 shows the results of calculating the amount of CO2 emissions by applying the method of calculating the amount of CO2 emissions in FIG. 2 based on the preconditions in FIG.
Figure 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 into account CO2 emissions based on the calculation results of CO2 emissions in Figures 16 and 17 for the cases shown in Figures 11 to 14.

In the right diagram of Fig. 18, the solid line forming the line graph indicates the CO2 emission per case (CO2 emission per truck divided by the number of cases). In other words, the CO2 emission per case is an example of the "CO2 emission per unit of weight or volume" described in the above embodiment. The bar graph is the same as that in Fig. 14, and indicates the freight rate (distance-based freight rate) by shipping quantity unit.
The problem with distance-based fares is that the amount of CO2 emissions per case increases when vehicles are switched over. This is because, in order to comply with legal requirements, it is necessary to switch to a larger vehicle when the vehicle's maximum load capacity is exceeded.
FIG. 19 is a diagram showing a specific example of CO2 emissions per case when switching from a 4-ton vehicle to a 10-ton vehicle.
As shown in FIG. 19, as soon as the vehicle was switched to a 10-ton truck, the amount of CO2 emissions per case exceeded 0.15 g-CO2/l (l: liter) in the 600 case, 700 case, and 800 case.
In other words, as shown in Figure 18, for a vehicle with the same maximum loading capacity, as the number of orders increases and loading efficiency improves, the "amount of CO2 emissions per unit of weight or volume" will basically decrease, so it is preferable to evaluate logistics using the "amount of CO2 emissions per unit of weight or volume" (the amount of CO2 emissions per case in the example of Figure 18).
However, the "CO2 emissions per unit amount of weight or volume" does not monotonically decrease as the order quantity (shipment quantity) increases. Rather, when the order quantity (shipment quantity) reaches a level that results in a change in vehicle, the "CO2 emissions per unit amount of weight or volume" jumps up, and thereafter exceeds a certain amount 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 more than 500 cases, a rule can be made with distributor L that orders must be placed for 900 cases or more, making it possible to keep CO2 emissions below an appropriate level.

To summarize the above, another embodiment of the information processing device of the present invention which applies the method newly devised by the inventor, i.e., comparing the distance-based freight rate with the minimum unit price (case/weight) of the goods and changing the method of transportation, has the following functional configuration in addition to the functional configuration of Figure 5.
That is, the CO2 emission prediction unit 103 in FIG. 5 predicts the amount of carbon dioxide emission per unit amount by adopting predetermined preconditions including the condition that a distance-based fare is adopted as the fare for the vehicle and that the size of the vehicle is determined according to the load weight or load volume.
If the predicted carbon dioxide emission per unit amount exceeds a maximum carbon dioxide emission (e.g., 0.15 g-CO2/l (l: liter) in the example of Figure 19) specified in advance for the size of the vehicle (e.g., a 10-ton vehicle in the examples of Figures 18 and 19) determined based on the weight or volume of the product, the planning unit 104 prohibits the planning of a transportation plan and proposes a weight or volume of the vehicle that is less than or equal to the maximum CO2 emission for the size of the vehicle.

Furthermore, each hardware configuration shown in FIG. 4 is merely an example for achieving the objectives of the present invention and is not particularly limited.

Furthermore, the functional block diagram shown in FIG. 5 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 that can execute 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.

In addition, the locations of the functional blocks are not limited to those shown in Fig. 5 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.

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.

Recording media containing such programs are not only configured as removable media that are distributed separately from the device itself in order to provide each user with the program, but also as recording media etc. that are provided to each user in a state where they are already installed in the device itself.

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.

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. 3)
In an information processing device that creates a transportation plan (e.g., the transportation plan in FIG. 7) for transporting merchandise of a shipper (shipper S in FIG. 1) from a transportation origin (e.g., a departure point in FIG. 6) to a transportation destination (e.g., an arrival point in FIG. 6) by a mobile object,
A shipper information acquisition unit (e.g., shipper information acquisition unit 101 in FIG. 5) 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. 5 ) that acquires, as logistics side information, information on a logistics side that manages transportation of the moving object, the information including at least a transportation cost (e.g., freight charge in FIG. 7 ) required to transport the goods from the transportation origin to the transportation destination, and a moving object characteristic amount (e.g., vehicle information in FIG. 1 ) that indicates a characteristic of the moving object;
a carbon dioxide emission prediction means (e.g., the CO2 emission prediction unit 103 in FIG. 5 ) for predicting a carbon dioxide emission amount per unit amount for weight or volume (e.g., “CO2 emission amount per kg of cargo” (CO2 emission amount per 1 kg of cargo) in FIG. 8 ) when the moving object moves under a predetermined precondition (e.g., the precondition shown in FIG. 8 ) 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 for planning one or more of the transportation plans based on a carbon dioxide cost per unit amount based on the carbon dioxide emission amount per unit amount (for example, "CO2 cost per 1 kg" (CO2 cost per 1 kg of cargo) in FIG. 8) and the transportation cost per unit amount;
Equipped with.
This makes it possible to develop transportation plans that efficiently reduce CO2 emissions.

In addition, an information processing device to which the present invention is applied (for example, the server 1 in FIG. 3)
The system further includes a presentation means (e.g., the presentation unit 105 in FIG. 5) that presents the one or more transportation plans that have been prepared, and the carbon dioxide cost and the transportation cost per unit amount, respectively, to at least the shipper's terminal (e.g., the 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 side, thereby enabling the development of transportation plans that efficiently reduce CO2 emissions.

In addition, 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
Calculating a carbon dioxide emission amount per moving body based on a travel distance and a fuel consumption when the moving body travels under the precondition;
The amount of carbon dioxide emission per unit amount is calculated based on the amount of carbon dioxide emission per moving object and the weight or volume of the product.
This will make it possible to more reliably develop transportation plans that efficiently reduce CO2 emissions.

In addition, 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
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. 5 that has been converted into AI as shown in FIG. 10) is obtained,
The model is used to calculate the amount of carbon dioxide emitted per unit amount.
This will make it possible to more reliably develop transportation plans that efficiently reduce CO2 emissions.

In addition, an information processing device to which the present invention is applied (for example, the server 1 in FIG. 3)
The shipper side information acquisition means further acquires the shipper side information including the delivery date desired by the shipper (for example, the delivery date shown 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,
The planning means further plans one or more transportation plans based on information indicating whether the delivery deadline will be met and the quality.
This will make it possible to more reliably develop transportation plans that efficiently reduce CO2 emissions.

In addition, 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 (e.g., the 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 a fare for the moving body (e.g., a vehicle such as a truck) and the size of the moving body (e.g., a 4-ton vehicle or a 10-ton vehicle) is determined according to a loading weight or a loading volume,
When the predicted carbon dioxide emission per unit amount exceeds a maximum carbon dioxide emission amount (e.g., 0.15 g-CO2/l (l: liter) in the example of FIG. 19) that is specified in advance for the size of the moving body (e.g., a 10-ton vehicle in the examples of FIGS. 18 and 19) determined according to the weight or volume of the product, the planning means (e.g., the planning unit 104 in FIG. 5) prohibits the planning of the transportation plan and proposes a weight or volume of the vehicle that is equal to or less than the maximum carbon dioxide emission amount for the size of the moving body.
It is possible.

1: Server, 2: Logistics company terminal, 3: Shipper terminal, 11: CPU, 12: ROM, 13: RAM, 14: Bus, 15: Input/output interface, 16: Input section, 17: Output section, 18: Storage section, 19: Communication section, 20: Drive, 30: Removable media, 101: Shipper information acquisition section, 102: Logistics information acquisition section, 103: CO2 emissions forecast section, 104: Planning section, 105: Presentation section, 401: Shipper DB, 402: Logistics DB, 403: CO2 emissions forecast model, C: Consumer, S: Shipper, V: Supplier, L: Logistics company

Claims (8)

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 that manages transportation of the moving body, the information including at least a transportation cost required to transport the product from the transportation origin to the transportation destination and a moving body characteristic amount that indicates a characteristic of the moving body;
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 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 device comprising: The 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. The carbon dioxide emission prediction means
calculating a carbon dioxide emission amount per moving body based on a travel distance and a fuel consumption when the moving body travels under the precondition;
Calculating 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;
The information processing device according to claim 1 . The carbon dioxide emission prediction means
A model obtained as a result of machine learning based on actual carbon dioxide emissions 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 emissions per unit amount is obtained,
Using the model, calculate the amount of carbon dioxide emission per unit amount.
The information processing device according to claim 1 . The shipper side information acquisition means further acquires the shipper side information including a delivery date desired by the shipper,
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,
The planning means further plans one or more of the transportation plans based on the information indicating whether the delivery date will be met and the quality.
The information processing device according to claim 1 . The carbon dioxide emission prediction means 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 moving body and a size of the moving body is determined according to a loading weight or a loading volume,
When the predicted carbon dioxide emission per unit amount exceeds a maximum carbon dioxide emission amount designated in advance for the size of the moving body determined according to the weight or the volume of the product, the planning means prohibits the planning of the transportation plan, and proposes a weight or volume of the moving body that is equal to or less than the maximum carbon dioxide emission amount for the size of the vehicle.
The information processing device according to claim 1 . 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 information acquisition step of 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 step of acquiring, as logistics side information, information on a logistics side that manages transportation of the moving body, the information including at least a transportation cost required to transport the goods from the transportation origin to the transportation destination and a moving body characteristic amount that indicates a characteristic of the moving body;
a carbon dioxide emission prediction step of 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 for weight or volume when the moving object moves under the precondition;
A planning step of planning one or more of the 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 comprising: 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 information acquisition step of 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 step of acquiring, as logistics side information, information on a logistics side that manages transportation of the moving body, the information including at least a transportation cost required to transport the goods from the transportation origin to the transportation destination and a moving body characteristic amount that indicates a characteristic of the moving body;
a carbon dioxide emission prediction step of 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 for weight or volume when the moving object moves under the precondition;
A planning step of planning one or more of the 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;
A program that executes control processing including:

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