Logistics efficiency and optimization evaluation system

Abstract

To provide a logistics streamlining and optimization evaluation system for eliminating wasteful logistics by visualizing (achieving visualization of) a palletization operation, a truck loading operation, and a service schedule creation operation, which were conventionally analog (left to the experience and intuition or the like of a field operator or the like), by digital technology.SOLUTION: A logistics streamlining and optimization evaluation system 1 includes: advance load quantity data storage means 3; palletization image creation means 4 for creating a picture image from data stored in the advance load quantity data storage means 3; vehicle loading image creation means 5 for creating a picture image from the data stored in the advance load quantity data storage means 3; service schedule creation means 6; and delivered load collapse prevention calculation means 10 including streamlining / optimization verification means 7 for verifying and optimizing results obtained from the palletization image creation means 4, the vehicle loading image creation means 5, and the service schedule creation means 6.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] This invention relates to a logistics efficiency and optimization evaluation system that uses digital technology to visualize palletizing work, truck loading work, and operation schedule creation work, which have traditionally been analog (relying on the experience and intuition of field workers, etc.), and performs efficiency and optimization verification (including through machine learning), thereby not only preventing the generation of wasteful logistics, but also making logistics just like a "routed bus," and enabling "real-time understanding" of the loading status (of trucks). [Background technology]

[0002] Recent efforts to reform work styles in the transportation industry, predictions for post-COVID economic recovery, and SDGs (Sustainable Development Goals) ) Considering the current situation, such as the movement to reduce CO2 emissions, it is predicted that in the future, about 30% of the current total volume of freight transported will become difficult to transport. In light of this current situation, it is likely that not only transport companies but also customers who request deliveries from transport companies will be forced to think about how to deliver goods as efficiently as possible. As a measure to achieve this, there is a demand to build an efficient and optimal logistics system by managing data in advance, rather than the current performance management (which relies on intuition and experience). In other words, it is necessary to collect digital data generated in all logistics processes, visualize it to promote the efficiency of the logistics system, and analyze it to promote the optimization of the logistics system.

[0003] Furthermore, the trend towards DX (digital transformation: the concept that the penetration of IT will change people's lives in every aspect for the better) has also reached the logistics industry, and new solutions that have never been seen before will be required to efficiently carry out logistics processes.In other words, it is hoped that the efficiency and optimization of logistics will be achieved by controlling the entire logistics system using an IT platform (not only the use of digital data, but also the use of AI such as machine learning and deep learning) for all logistics processes.

[0004] Patent Document 1 discloses a method for optimizing logistics via relay points (Patent Document 1: Title of the invention) that aims to provide a method for optimizing logistics while taking into account storage constraints at relay points when transporting products from a production factory to a user, and creates all possible combinations of transportation routes for the product for each product transport volume for each user over a certain period of time, taking into account the factories that can produce the product and the storage capacities at relay points, stock points and deposit points, which depend on the physical properties of the product, and calculates from among these combinations the transportation route that minimizes the total logistics cost, i.e., the sum of the user's (transfer costs + loading and unloading costs + storage costs + delivery costs) (Patent Document 1: Summary). [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2002-356232 A Summary of the Invention [Problem to be solved by the invention]

[0006] The method for optimizing logistics via relay points according to Patent Document 1 (Patent Document 1: Title of the invention) requires not only transportation costs but also loading and storage costs in order to transfer products from a factory to a warehouse or temporary storage and deliver them to each user. Based on the fact that the positional relationship between the factory, the warehouse or temporary storage, and the user is the most important factor for reducing logistics costs, the method creates combinations of all transportation routes from the factory of each product to the user via the relay points, corresponding to the storage constraints of the factory and relay points that can produce the product, based on the transportation volume of each product transported to each user, and calculates the transportation route that minimizes the total logistics costs required for transportation from the factory to the user from among those combinations. However, from the perspective of collecting digital data generated in all logistics processes, visualizing and analyzing it, and promoting the use of information to improve the efficiency and optimization of the logistics system, it is important to establish an efficiency and optimization evaluation scheme for the logistics system that pursues efficiency by making the most of the learning model generated by machine learning and analyzing the efficiency and optimization.

[0007] The object of the present invention is to provide a logistics efficiency and optimization evaluation system that uses digital technology to visualize palletizing work, truck loading work, and schedule creation work, which have traditionally been analog (relying on the experience and intuition of field workers, etc.), and performs efficiency and optimization verification (including through machine learning), thereby not only preventing the generation of wasteful logistics, but also making logistics just like a "routed bus," and enabling "real-time understanding" of the loading status (of trucks). [Means for solving the problem]

[0008] The invention described in claim 1 includes a load aggregation and management platform for inputting and transmitting load data of a plurality of customers, a pre-load data storage means for storing the load data inputted and transmitted to the load aggregation and management platform, a palletizing image creation means for creating an image from the load data stored in the pre-load data storage means to instruct a shipping worker to load cargo onto a pallet, an on-board image creation means for creating an image from the load data stored in the pre-load data storage means to instruct a driver to load the pallet onto a truck, a traffic diagram creation means for creating a timetable and a stowage table from the load data stored in the pre-load data storage means, and an efficient and optimal output from the palletizing image creation means, the on-board image creation means, and the traffic diagram creation means. the efficiency and optimization verification means verifies whether the efficiency and optimization verification is appropriate, an on-board data storage means for storing palletizing image data and on-board image data after the efficiency and optimization verification by the efficiency and optimization verification means, an operation data storage means for storing schedule and stowage data after the efficiency and optimization verification by the efficiency and optimization verification means, and an optimal inventory calculation means for presenting an optimal inventory quantity to a customer from the palletizing image data and on-board image data stored in the on-board data storage means after the efficiency and optimization verification by the efficiency and optimization verification means, the schedule and stowage data stored in the operation data storage means after the efficiency and optimization verification by the efficiency and optimization verification means, and the cargo data stored in the advance cargo data storage means, and the efficiency and optimization verification means verifies delivery confirmation data that is confirmed by two days before the delivery date, and presents an optimal inventory quantity to a customer from the palletizing image data and on-board image data stored in the on-board data storage means after the efficiency and optimization verification by the efficiency and optimization verification means, the schedule and stowage data stored in the operation data storage means after the efficiency and optimization verification by the efficiency and optimization verification means, and the cargo data stored in the advance cargo data storage means, As a result of the above verification, if there is any cargo that cannot be loaded onto the vehicle, we will make a proposal to improve efficiency and optimization by either loading it onto a later vehicle or having a vehicle on another route stop by and collect it. Furthermore, the system is characterized by being a logistics efficiency and optimization evaluation system equipped with a calculation means for preventing cargo from shifting during delivery, using a method that uses machine learning (generating a trained model by learning "loading form that prevents cargo from shifting" and "delivery route" as labels (answers) for "individual shapes of cargo to be delivered" and "delivery location including delivery date and time").

[0009] The invention described in claim 2 is the invention described in claim 1, characterized in that the cargo volume data stored in the advance cargo volume data storage means is a logistics efficiency and optimization evaluation system that includes the dimensions of the packing boxes forming the luggage, the number of packing boxes (if there are multiple packing boxes, the number of packing boxes for each dimension), the weight of the packing boxes + contents (if there are multiple packing boxes, the weight of the packing boxes + contents for each packing box), and the delivery location (including delivery date and time).

[0012] The invention described in claim 3 is the invention described in claim 1 or claim 2, The cargo collapse prevention calculation means is characterized as a logistics efficiency and optimization evaluation system in which a large amount of data is given as answers (labels) in the form of an optimal image of the palletized state (data is quantified), an image of the truck on board (data is quantified), a timetable, and a stowage table for the dimensions of the packing boxes forming the cargo, the number of packing boxes (if there are multiple packing boxes, the number of packing boxes for each dimension), the weight of the packing boxes + contents (if there are multiple packing boxes, the weight of each packing box + contents), and the delivery location (including delivery date and time), and a so-called supervised learned model is used to calculate an optimal image of the palletized state, an image of the truck on board, a timetable, and a stowage table for the unknown advance cargo volume data.

[0013] The invention described in claim 4 is the invention described in claims 1 to 3, characterized in that it is a logistics efficiency and optimization evaluation system equipped with a transport route disclosure means for disclosing details of vehicle passing times and vehicle loading conditions at specified points on a specified driving route by displaying them in detail as a map on the Internet based on the timetable and loading table created by the operation timetable creation means.

[0014] The invention described in claim 5 is the invention described in claim 4, characterized in that it is a logistics efficiency and optimization evaluation system equipped with an optimal route search means for improving the efficiency of the specified driving route published by the transportation route publishing means. Effect of the Invention

[0015] The present invention relates to a system that obtains digital logistics data (cargo volume data) from customers who request delivery, processes and uses the digital logistics data (cargo volume data) to streamline (including automate) palletizing work, truck loading work, and operation schedule creation work, and evaluates the efficiency of these operations, thereby exploring the efficiency and optimization (including through machine learning) of logistics through the above-mentioned series of steps.

[0016] The system is equipped with a cargo aggregation and management platform for inputting and transmitting cargo volume data of multiple customers, a pre-load data storage means for storing the cargo volume data inputted to and transmitted from the cargo aggregation and management platform, a palletizing image creation means for creating an image from the data stored in the pre-load data storage means to instruct a shipping worker to load cargo onto a pallet, an on-board image creation means for creating an image from the data stored in the pre-load data storage means to instruct a driver to load the pallet into a truck, and a traffic schedule creation means for creating an appropriate timetable and an appropriate stowage table from the data stored in the pre-load data storage means.

[0017] The advance cargo volume data storage means stores data (cargo volume data) regarding cargo to be delivered sent from multiple customers, and allows data regarding the cargo to be delivered from the customer requesting delivery (conditions such as cargo type, delivery date and time, and delivery location: what type of cargo, where and by when it will be collected, and where and by when it will be delivered, etc.) to be processed into digital data in an appropriate format and stored.

[0018] The palletizing image creating means creates an image of instructions for a shipping worker to load goods onto a pallet from the advance load data, allowing the shipping worker to visually recognize the state of the goods loaded onto the pallet through an image.The vehicle image creating means creates an image of instructions for a driver to load a pallet onto a truck from the advance load data, allowing the shipping worker to visually recognize the state of the pallet loaded onto the truck bed through an image.Therefore, the quality level does not decrease even if the shipping worker or driver changes, and quality can be maintained.

[0019] The train schedule creation means creates an appropriate schedule table and an appropriate loading table based on default train schedule data (data for each route and each flight) from advance loading data, which eliminates the need for operators to create train schedules based on their intuition and experience. This means that the quality level does not decrease even if the operator changes, and quality can be maintained.

[0020] The advance load data is processed and stored in the advance load data storage means by going through the load aggregation and management platform, so that various data formats obtained from customers, such as CSV format, can be converted (including processing) into a format suitable for database storage. It is also possible to consider unifying the data format in the future. Furthermore, the advance load data includes the dimensions of the packing boxes that form the package, the number of packing boxes (if there are multiple packing boxes, the number of packing boxes for each dimension), the weight of the packing boxes + contents (if there are multiple packing boxes, the weight of the packing boxes + contents for each packing box), and the (respective) delivery location (including the delivery date and time). It goes without saying that other necessary data can be added at any time as necessary.

[0021] Furthermore, by using the efficiency / optimization verification means for verifying and optimizing the results obtained from the palletizing image creation means, the on-board image creation means, and the operation schedule creation means, it is now possible to verify the results of comparing the delivery plan data with the delivery performance data, and make efficiency / optimization proposals to customers after the fact. At the same time, it has become possible to make improvements in efficiency / optimization within the company. Furthermore, it is now possible to verify delivery confirmation data prior to the delivery date, and make efficiency proposals to customers by the day before the delivery date.

[0022] As an effect of the load shift prevention calculation method (one example of a method for improving efficiency and optimization using machine learning), by acquiring the optimal loading form taking into account the delivery destination (delivery order) by machine learning using a large amount of data, anyone can reproduce the know-how of loading to prevent "load shifting due to inertia" that occurs when the cargo (including palletized state) loaded on the truck during delivery by the truck suddenly brakes, for example. Therefore, even non-expert workers can perform high-level loading work. To summarize the above, the benefits of the logistics efficiency and optimization evaluation scheme for our company are that we can reduce labor costs by reducing the number of people we put in and deal with labor shortages. The quality level does not decrease even if the shipping worker, driver, or operator is changed, so the work quality can be maintained. By designing logistics in advance, unnecessary truck driving can be reduced, and CO2 emissions can be reduced. Furthermore, as benefits for the customer, we can reduce logistics costs and curb special services (transportation of cargo that could not be accommodated in regular transport), so we can reduce the number of vehicles put in and reduce transportation design costs.

[0023] The optimal inventory calculation means presents the optimal inventory quantity to the customer based on the palletizing image data and the on-board image data stored in the on-board data storage means after the efficiency and optimization verification by the efficiency and optimization verification means, the schedule and stowage data stored in the operation data storage means after the efficiency and optimization verification by the efficiency and optimization verification means, and the cargo data stored in the advance cargo data storage means, thereby reducing excess inventory (on the customer's side) and contributing to production management (on the customer's side). (Note that the optimal inventory calculation means can also use the data on the in- and out-of-stock items managed by the so-called "existing in- and out-of-stock management system" before the logistics efficiency and optimization evaluation system according to the present invention is operated.) The transportation company can also save on storage space for inventory and allocate that storage space for other operations. In short, waste due to "overproduction" can be reduced. Furthermore, this contributes to reducing CO2 emissions during production (at the customer's factory) and CO2 emissions during transportation (on the transportation company's side).

[0024] The transportation route disclosure means, which displays and publishes the vehicle passing times and vehicle loading status at specified points on a specified driving route in detail as a map on the Internet based on the timetable and loading table created by the operation timetable creation means, provides the customer with useful information (transportation route, passing times at each point) when considering the transportation method, etc., i.e., by disclosing the transportation company's operation status, it has become possible to shorten the time required for the customer to consider the transportation method, etc. (the time to arrange vehicles for regular services and special services, etc.).For the transportation company side, by disclosing the transportation route, it has become possible to increase the probability that the customer will select the company as a transportation company and to reduce the time to arrange vehicles when a special service occurs, etc.

[0025] By showing an efficient "driving route" by the optimum route search means for making the predetermined driving route published by the transportation route publishing means more efficient, the driving of the vehicle (truck) can be optimized and made more efficient, and waste during driving can be reduced. In other words, not only does it contribute to reducing CO2 emissions during transportation, but it also leads to reducing the variation in driving time due to the difference between so-called veteran drivers and new drivers. In other words, it has become possible to standardize the quality of drivers. [Brief description of the drawings]

[0026] [Figure 1] FIG. 1 is a diagram for explaining an overview of a logistics efficiency and optimization evaluation system according to the present invention. [Diagram 2] FIG. 13 is a palletizing image diagram created by a palletizing instruction means. [Diagram 3] FIG. 11 is a truck in-vehicle image diagram created by the in-vehicle instruction means. [Figure 4] This is a diagram to explain Method A of the efficiency / optimization verification means (post-facto efficiency improvement by comparing planned data with actual data). [Diagram 5] This is a diagram to explain Method B of the efficiency / optimization verification means (proposing efficiency improvements by the day before the delivery date for delivery confirmed data before the delivery date). [Figure 6] This is a diagram to explain examples of proposals made to customers based on the results obtained from efficiency and optimization verification means. [Figure 7] FIG. 13 is a diagram for explaining a cargo collapse prevention calculation means. [Figure 8] FIG. 13 is a diagram for explaining a trained model of the cargo collapse prevention calculation means. [Figure 9] FIG. 13 is a diagram for explaining an example of operation by a transportation route disclosure means. [Figure 10] FIG. 1 is a diagram for explaining a comprehensive logistics system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0027] Hereinafter, an embodiment of the system 1 for evaluating efficiency and optimization of logistics according to the present invention will be described in detail with reference to Figs. 1 to 10. Before describing the details of the system 1 for evaluating efficiency and optimization of logistics, a conventional logistics system and problems of the conventional logistics system will be described. A transportation company basically goes to a supplier (often a delivery requester: a customer) to collect parcels to be delivered (purchasing (collection) work), stores them temporarily in a temporary storage location such as a logistics center, and delivers them to the delivery destination (delivery work). Delivery forms include regular delivery (delivery of parcels to a fixed place and at a fixed date and time according to a monthly plan) and special delivery (delivery of parcels that could not be loaded onto a regular delivery by a special delivery, etc.).

[0028] In the current logistics system, in many cases, operators create operation schedules based on existing operation schedules, taking into consideration the cargo volume data written on order requests from customers (often paper orders, not digital data). Specifically, operators manually create operation schedules using spreadsheet software, etc., based on cargo volume data from customers (dimensions of each cargo, weight of each cargo, supplier (purchase date and time), delivery destination (delivery date and time), etc. (Note: in this specification, "purchase" and "delivery" are collectively defined as "delivery"). The palletizing work of loading cargo onto pallets is performed by shipping workers based on their intuition and experience, and the loading layout of the palletized pallets onto the truck bed is also considered by drivers based on their intuition and experience. In other words, the palletizing work and the loading work onto the truck bed are performed according to the loading image that the shipping workers and drivers themselves have in their minds.

[0029] Due to this reality (the problem was that the package appearance was not visual, or in short, not visible), the shipping workers could not do the palletizing work properly, or the drivers could not load the vehicles onto the truck bed properly. In other words, the palletizing work and the loading work onto the truck bed at the shipping site could not be done smoothly. This not only significantly extends the shipping work time due to redoing the loading work, but also leads to some of the packages that could have been loaded in one go being left behind. In other words, if the packages overflow, a special service (extra service) is required, which results in unnecessary costs. Furthermore, when it comes to organizing the operation schedule, there should be an efficient organization skill that cannot be achieved by the operator's intuition and experience alone, that is, an organization skill that avoids the occurrence of special services (extra service) as much as possible.

[0030] In order to prevent the occurrence of special services (temporary services) in advance, it should be necessary to stop creating operation schedules based on the intuition and experience of operators, and palletizing and truck loading work based on the intuition and experience of shipping workers and drivers, and to make it possible to create appropriate operation schedules even if operators are changed, and to ensure that the quality level of palletizing work does not decrease and quality can be maintained even if shipping workers are changed, and to ensure that the quality level of truck loading work does not decrease and quality can be maintained even if drivers are changed.In the future, taking into account the interests of society as a whole, cargo volume data should be collected as shareable data, and this should benefit the logistics industry as a whole.

[0031] <Outline of logistics efficiency and optimization evaluation system> FIG. 1 is a diagram for explaining an outline of the system 1 for evaluating efficiency and optimization of logistics according to the present invention. As shown in FIG. 1, the system 1 for evaluating efficiency and optimization of logistics includes a load aggregation and management platform 2 for inputting and transmitting load data of a plurality of customers, a pre-load data storage means 3 for storing the load data inputted and transmitted to the load aggregation and management platform 2, a palletizing image creation means 4 for creating an image for instructing a shipping worker to load cargo onto a pallet from the data stored in the pre-load data storage means 3, a vehicle-mounted image creation means 5 for creating an image for instructing a driver to load the pallet onto a truck from the data stored in the pre-load data storage means 3, and a traffic diagram creation means 6 for creating an appropriate traffic diagram table and an appropriate stowage table based on default traffic diagram data (data for each route and each flight) from the data stored in the pre-load data storage means 3. The pre-load data may be stored in the pre-load data storage means 3 in a state that is easily processed by passing through the load aggregation and management platform 2.

[0032] The system is provided with an efficiency / optimization verification means 7 for verifying whether the outputs from the palletizing image creation means 4, the on-board image creation means 5, and the operation schedule creation means 6 are efficient and optimal.

[0033] The system is provided with an on-board data storage means 8 that stores palletizing image data and on-board image data after efficiency improvement and optimization verification by the efficiency improvement and optimization verification means 7, and an operation data storage means 9 that stores timetable and stowage data after efficiency improvement and optimization verification by the efficiency improvement and optimization verification means 7. The data stored in the on-board data storage means 8 and the operation data storage means 9 is updated every time the efficiency improvement and optimization verification means 7 performs a verification (of efficiency and optimization).

[0034] The system includes an optimal inventory calculation means 11 that calculates optimal inventory quantities based on the palletizing image data and on-board image data stored in the on-board data storage means 8 after efficiency and optimization verification by the efficiency and optimization verification means 7, the schedule and loading data stored in the operation data storage means 9 after efficiency and optimization verification by the efficiency and optimization verification means 7, and the load data stored in the advance load data storage means 3, and presents the calculated optimal inventory quantities to the customer. The system also includes a transport route disclosure means 12 that displays (specifically, on the Internet) on a map (specifically, publicizes on the Internet) what time and minute the transportation company's vehicle (truck with its load status also disclosed) will pass through at what point on what route (including directions), and an optimal route search means 13 that calculates and presents which road the transportation company's vehicle (truck with its load status also disclosed) should travel on to achieve the most efficient transport. The optimal inventory calculation means 11 and the optimal route search means 13 can be improved in accuracy by machine learning. The more data is accumulated, the more accurate machine learning can be. The details of the calculation method 10 for preventing cargo from shifting during delivery using machine learning techniques (positioned as a method for improving efficiency and optimizing using machine learning techniques) will be described later.

[0035] The flow of the logistics efficiency and optimization evaluation system 1 is that, based on the advance load data stored in the advance load data storage means 3 for storing the load data input and transmitted to the load aggregation and management platform 2, the palletizing image creation means 4, the on-board image creation means 5, and the operation schedule creation means 6 produce outputs. Then, the efficiency and optimization verification means 7 verifies whether the outputs from the palletizing image creation means 4, the on-board image creation means 5, and the operation schedule creation means 6 are efficient and optimal. Then, the (optimized) palletizing image data and the on-board image data after the efficiency and optimization calculations by the efficiency and optimization verification means 7 are stored in the on-board data storage means 8, which stores them. At the same time, the (optimized) schedule and stowage data after the efficiency and optimization calculations (by the efficiency and optimization verification means 7) taking into account constraints such as time and place in delivery are stored in the operation data storage means 9, which stores the (optimized) schedule and stowage data.

[0036] Even after the palletizing image creation means 4, on-board image creation means 5, and operation schedule creation means 6 have output based on the advance load data stored in the advance load data storage means 3 (if it is before the delivery date), if the data stored in the on-board data storage means 8 that stores the palletizing image data and on-board image data, and / or the data stored in the operation data storage means 9 are updated, the palletizing image data, on-board image data, and schedule / stowage data calculated (verified) using the updated data are often optimal. Therefore, recalculation is performed by the efficiency / optimization verification means 7 based on the updated data, and the palletizing image creation means 4, on-board image creation means 5, and operation schedule creation means 6 are again output (recalculation: see Figure 1).

[0037] The optimal inventory quantity calculated by program calculations (including machine learning) from the palletizing image data and on-board image data stored in the on-board data storage means 8 (after efficiency and optimization verification), the timetable and stowage data stored in the operation data storage means 9, and the cargo volume data stored in the advance cargo volume data storage means 3 is presented to the customer (optimal inventory calculation means 11: see Figure 1).

[0038] Furthermore, based on the timetable and loading data accumulated in the operation data storage means 9 (after efficiency and optimization verification), the transportation routes of the transportation companies are made public on the Internet (transportation route publishing means 12 (see Fig. 1)), and (taking into account data such as daily traffic conditions) "which roads" the vehicles (trucks) of the transportation companies should travel on to achieve the most efficient transportation are calculated and presented, and searched for by the optimal route search means 13 (which may be linked to a global positioning system: GPS) (see Fig. 1).

[0039] The load aggregation and management platform 2 is accessible by a customer who is a delivery requester and can input advance load data, etc. Security measures against intruders are taken so that only those who have been granted access rights, such as customers, can access the platform. For example, it is preferable that security measures such as one-time passwords are taken. The advance load data storage means 3, the in-vehicle data storage means 8, and the operation data storage means 9 are storage media capable of storing (saving) digital data, and may be used as long as they can be used as a so-called database, and a memory (RAM, etc.) built into a personal computer may be used. Furthermore, they may be composed of non-volatile memory such as flash memory, auxiliary storage devices and media such as hard disks, DVD discs, Blu-ray (registered trademark) discs, cloud storage with security measures, etc.

[0040] FIG. 2 is a palletizing image diagram created by the palletizing image creation means 4. The shipping worker can perform palletizing work while looking at the screen (or the state printed out on paper) using the palletizing image creation means 4 (see FIG. 1). The screen can be displayed in color, so by color-coding, the shipping worker can more clearly recognize (the state when the palletizing work is completed as a picture image). FIG. 3 is a truck-mounted image diagram created by the on-board image creation means 5. The driver can perform on-board work in the truck while looking at the screen (or the state printed out on paper) using the on-board image creation means 5 (see FIG. 1). The screen can be displayed in color, so by color-coding, the driver can more clearly recognize (the state when the on-board work in the truck is completed as a picture image).

[0041] The operation schedule creation means 6 is a system that quickly calculates delivery routes, and specifically creates a delivery plan, such as "when (what time and minute), which truck will visit which delivery destination (supplier, delivery destination), and in what order," while taking into consideration complex conditions and constraints such as special circumstances at the delivery site, and designs an efficient delivery route. It creates a schedule table (a diagram showing what time to stop at which base) and a loading table (a table showing which delivery destination cargo is loaded on each truck). The operation schedule creation means 6 can automatically create an operation schedule (by executing a program), so it can be created without an operator. The operation schedule creation means 6 may be a cloud-based system.

[0042] <Efficiency and optimization verification methods> Fig. 4 is a diagram for explaining method A (ex-post efficiency improvement by comparing planned data with actual data) of the efficiency / optimization verification means 7. Fig. 5 is a diagram for explaining method B (efficiency proposal by the day before the delivery date for delivery confirmation data before the delivery date) of the efficiency / optimization verification means 7.

[0043] Let us now turn to Figure 4. A delivery plan for a given month (month N) is (usually) created in month N-1. Normally, delivery plan data (or freight volume data) is received from the customer by the 4th week of month N-1, and the freight volume aggregation and management platform 2 processes the delivery plan data (or freight volume data) from the customer as necessary, and then stores it in the advance freight volume data storage means 3, thereby finalizing the delivery plan. At this point, it is expected that all deliveries based on the delivery plan created from the delivery plan data (or freight volume data) from the customer will be completed for regular routes only.

[0044] However, in reality, in the actual results for month N, there are cases where parcels that overflow from regular deliveries are delivered via additional special deliveries (extra deliveries). Figure 4 shows that three special deliveries (extra deliveries) were made. (In other words, when comparing the plan with the actual results, a discrepancy equivalent to three special deliveries has occurred.) When the actual data for month N is compiled in the first week of month N+1, a discrepancy between the plan and the actual results (equivalent to three special deliveries) is seen. In method A of the efficiency and optimization verification means (post-facto efficiency improvement by comparing planned data with actual data), advice is given to the customer regarding orders (efficiency proposals to the customer: see Figure 4) based on verification by comparing planned data with actual data (by efficiency and optimization verification means 7).

[0045] Specifically, to avoid the occurrence of special services, multiple improvement plans can be presented to the customer, such as loading the cargo loaded on the special service (extra service: extra costs will be incurred) onto an earlier service (regular service), onto a later service (regular service), onto a service on a different route (existing route), or by rearranging or combining with other routes, and if orders are placed based on these improvement plans, the special services can be reduced, i.e., a proposal can be made to review the ordering plan. At this time, a concrete figure can be proposed, such as the number of vehicles used could have been reduced by "XX units."

[0046] FIG. 5 will be explained. N day is the delivery date. By N-2 day, the delivery confirmation data to be delivered on N day is verified by the efficiency and optimization verification means 7 based on past data. (The reason why the load data for N day, which is the delivery date, is verified on N-2 day is because the load data provided by the customer one month before the delivery date often fluctuates, and practically speaking, the day when the load data from the customer is confirmed is often 2 to 3 days before.) If it is found that a special delivery will be issued as a result, the customer is notified of this and a proposal to change the delivery plan is presented. Specifically, a proposal is made to avoid the occurrence of a special delivery by combining with a previous or subsequent delivery, or a proposal is made to avoid the occurrence of a special delivery by combining with a delivery on a different route. If the verification by the efficiency and optimization verification means 7 shows that not only is it possible to avoid the occurrence of a special delivery (with great effect) but also to reduce regular delivery, a proposal is made to reduce the number of vehicles used for regular delivery. In addition, in order to avoid any disruption to delivery operations, it is desirable to receive final approval from the customer by N-1 day. Promptly thereafter, by the N-1th day, the operation schedule creating means 6 creates a schedule table and a stowage table, and by the Nth day (the day), the vehicle loading image creating means 5 is able to instruct the driver on the optimal vehicle loading state. Needless to say, the palletizing image creating means 4 also instructs the shipping worker on the palletizing work.

[0047] FIG. 6 is a diagram for explaining examples of proposals made to customers based on the results obtained from the efficiency / optimization verification means 7. As shown in FIG. 6, specifically, the following efficiency proposals can be proposed to customers: (1) Loading the cargo that cannot be loaded onto the vehicle (overflow goods) onto the next vehicle (10 ton truck) to suppress the occurrence of special delivery (optimum candidate A pattern); (2) Loading the cargo that cannot be loaded onto the vehicle onto a special delivery (4 ton truck (note: lower cost than the 10 ton truck)) (optimum candidate B pattern); (3) Stopping and collecting the cargo that cannot be loaded onto the vehicle on another route vehicle to suppress the occurrence of special delivery (optimum candidate C pattern). That is, the reason why multiple optimal delivery patterns are presented and the customer can select one from among them according to their convenience is because the customer's interests are also taken into consideration. The present invention is characterized in that it has an effect of reducing costs not only for the company (the entity that uses the efficiency improvement and optimization evaluation scheme of the logistics system according to the present invention) but also for other companies (customers). Furthermore, it goes without saying that in the efficiency improvement / optimization verification means 7, by accumulating a large amount of data and using that large amount of data for machine learning (supervised learning, unsupervised learning, reinforcement learning, etc.), it is possible to improve (upgrade) the calculation accuracy of the palletizing image creation means 4, the on-board image creation means 5, and the bus schedule creation means 6.

[0048] <Method to prevent cargo from shifting during delivery using machine learning> The present invention is characterized in that it does not simply improve the efficiency and optimize the loading work (taking into consideration the delivery date and time, delivery location (delivery order)) including the palletizing work of the cargo onto the truck and evaluates the degree of completion of the efficiency and optimization, but improves the degree of completion of the efficiency and optimization while learning the optimal loading form by machine learning using a large amount of data to prevent the cargo from collapsing due to inertia, which occurs when the cargo loaded onto the truck suddenly brakes (which may be unavoidable) during delivery. This will be described below. The cargo collapse prevention calculation means 10 is an example of the efficiency and optimization evaluation of logistics by machine learning.

[0049] FIG. 7 is a diagram for explaining the load collapse prevention calculation means 10. First, the loads are stacked on a flat pallet from the bottom, and then loaded (palletized) on the pallet and then loaded onto the truck bed. When stacking the loads on the pallet, the heavier loads are placed on the bottom, and the weights gradually become lighter as they move up. The loads are loaded onto the truck bed by pallet, and there is a basic rule that the loads are loaded from the rear end of the truck bed in the order of the load to be delivered first, the load to be delivered second, the load to be delivered third, and so on (see FIG. 7(a)). This is because if the load (pallet) to be unloaded first is not the rear end, a space will be created between the loads (pallets) (a state where the middle part is missing), and the load will collapse when the brakes are applied during delivery.

[0050] Naturally, the cargo (pallets) is loaded onto the truck when it is stopped (in a very stable state). However, once the truck starts moving, the cargo loaded onto the truck is constantly subjected to external forces such as vibration and shock. When the truck starts and stops, centrifugal forces are applied back and forth relative to the direction of travel, up and down when passing over a step, and left and right when turning. The more sudden the action and the greater the speed, the greater the impact. For example, if the cargo loaded onto the truck is in a state such as that shown in Figure 7(b), the cargo will collapse due to inertial forces when the truck brakes suddenly. This is logically understandable, but the current situation is that the know-how to deal with it is left to the experience and intuition of each individual driver.

[0051] It is difficult to perfectly predict "when, where, at what timing, and how much force will be generated" during driving for delivery. However, by accumulating data on loading configurations (including the relationship with the individual shapes of the packages to be delivered, the delivery location, and the delivery order) based on the experience and intuition of each driver, and further by accumulating a large amount of data on loading configurations (including the individual shapes of the packages to be delivered, the delivery location, and the delivery order) over time, it is believed that a certain correlation will be found between "the individual shapes of the packages to be delivered, the delivery location, and the delivery order" and "loading configurations that prevent load shifting," that is, the direction of measures to prevent load shifting will become clear.

[0052] FIG. 8 is a diagram for explaining the trained model of the load shift prevention calculation means 10. A "trained model" is generated by analyzing a large amount of data using a machine learning technique. Specifically, the trained model is generated by learning "loading form that prevents load shifting (palletizing image, vehicle image: see FIG. 8) and delivery route ("descending order" obtained from the timetable and stowage table: see FIG. 8)" as labels (answers) for "individual forms (dimensions, weight) of the cargo to be delivered and delivery location including delivery date and time (cargo volume data: see FIG. 8)".

[0053] By learning from a large amount of data, it becomes possible to predict (actually calculate) that "for a specific cargo type (cargo dimensions, cargo weight, etc.) and a specific delivery location (which may include delivery time), this type of packing (loading form) and delivery route are optimal (outputting the packing form in the form of an image, and outputting a timetable and stowage table)." In other words, by inputting new (never before known) "individual cargo types to be delivered and delivery location (which may include delivery time)," the corresponding "optimal loading form (to prevent cargo from falling over), timetable, and stowage table" will be output. In other words, the optimal loading form to prevent cargo from falling over, and the optimal delivery route will be output.

[0054] The trained model is generated by providing a large amount of data with answers (labels) such as images of the optimal palletized state (data is quantified), images of the truck on board (data is quantified), timetables, and stowage tables for the dimensions of the boxes that make up the package (many have set standards, such as 30cm x 30cm x 30cm), the quantity of each box, the weight of each box + contents, and the delivery location (delivery time can also be included), and allowing the model to learn under supervision. The accuracy of machine learning increases with the amount of data, so this process will be carried out continuously into the future. Each time, the trained model is improved, resulting in a trained model with higher accuracy.

[0055] As a result of machine learning, when the unknown "dimensions of the boxes that make up the package (most of them have fixed standards, such as 30cm x 30cm x 30cm), the number of each box, the weight of each box + contents, and the delivery location (can also include delivery time)" are input, it will not only be possible to achieve efficiency and optimization of the work of loading the packages onto the truck (including palletizing work), but it will also be possible to calculate the optimal loading form, taking into consideration the prevention of "load collapse due to inertia," which occurs when the package being loaded onto the truck suddenly brakes during delivery. Eventually, even if an inexperienced person joins the company, the AI ​​(artificial intelligence) will be able to show a loading form that is equivalent to that of a skilled worker that does not cause load collapse, which will be a very desirable state.

[0056] As a loading technique to prevent cargo from shifting, there is know-how of stacking cargo in different patterns (alternate row stacking, brick stacking, split stacking, pinwheel stacking, etc.) for each layer, rather than stacking cargo in the same arrangement. When the cargo is to be stacked high, there is also know-how that laying interleaf paper in the middle layer makes it difficult for the cargo to slip sideways, which leads to preventing cargo from shifting. In addition, there are known cargo-shift prevention wraps, cargo-shift prevention belts, and cargo-shift prevention nets to prevent cargo from shifting. It is true that if cargo loaded on a pallet is wrapped around with cargo-shift prevention wrap, secured with cargo-shift prevention belts, and covered with cargo-shift prevention nets, it seems that the shifting of cargo during delivery can be significantly reduced. However, it is clear that this will result in excessive loading costs and will not be profitable.

[0057] Experienced shipping workers and drivers often know from past experience the optimal loading form to prevent "load collapse due to inertial forces" that is tailored to the delivery route of each package they deliver (they have an image of what they think is the optimal palletizing state and loading state on the truck), so if this know-how is digitized and input in advance, the time it takes to generate a trained model can be shortened. Even after the trained model is completed, it will continue to be improved by adding data on a daily basis.

[0058] When the trained model is improved, the palletizing image data, on-board image data, and schedule / stowage data calculated (estimated) by the improved trained model are optimal, so recalculation is performed based on the improved trained model, and output is again performed by the palletizing image creation means 4, on-board image creation means 5, and schedule creation means 6 (recalculation: see FIG. 1). Note that, although the present specification has mainly described a supervised learning method as a machine learning method, it goes without saying that a reinforcement learning method or an unsupervised learning method other than supervised learning can also be adopted as long as they can obtain the same effect as the present invention.

[0059] Fig. 9 is a diagram for explaining an example of operation by the transport route disclosure means 12. The transport route disclosure system 12 discloses the transport routes of transport company vehicles (trucks with their cargo loading status also disclosed), the passing times at each point on the transport route, etc. on a special site on the Internet (see Fig. 9: Disclosed Map Information (Real-time Transport Information, Transport Route Information)). Member companies (shipper customers or other transport companies) who have the authority to access the special site can make a transport plan for products while checking the existing operating routes of the transport companies. After placing an order, customers can check the status of their ordered products (where their products are currently located (on a map), etc.) on their computers (according to the transport plan they have made) (see Fig. 9).

[0060] Furthermore, the transportation route disclosure means 12 contributes greatly to on-site coordination during on-site meetings with customers. That is, when conditions such as transport items and collection / delivery times presented by the customer are input (on-site) into a tablet terminal or the like during on-site meetings with the customer, the artificial intelligence (AI) operates on a system including the transportation route disclosure means 12 (the most efficient "travel route" may be selected by the optimal route search system 13), the operation schedule creation means 6, the efficiency / optimization verification means 7, etc., so that an optimal operation schedule (provisional operation schedule) can be automatically created instantly and displayed on the tablet terminal or the like (see FIG. 9). In addition, an "estimate" can also be displayed (at the same time) on the tablet terminal.

[0061] FIG. 10 is a diagram for explaining an integrated logistics system (ILS). The integrated logistics system (ILS) is one of the goals of the logistics efficiency and optimization evaluation system according to the present invention. As shown in FIG. 10, the integrated logistics system (ILS) is a mechanism in which two or more transport companies (two transport companies, A and B, are shown in FIG. 10, but it goes without saying that there may be more than that number of transport companies) work together.

[0062] Transport company A and transport company B each manage data such as cargo volume data, stopover platform time, etc., in a system for managing incoming and outgoing goods (incoming and outgoing goods / inventory data management system), a system for managing logistics data (stopover condition data management system), etc. (See Figure 10). As shown in Figure 10, transport company A and transport company B can share their data (collect into one data group) through the integrated logistics system (ILS), and obtain information such as optimal timetables and loading tables (timetables and loading data) for joint transport (with transport company A and transport company B as one transport company community), optimal loading layouts (palletizing image data, and vehicle image data), etc., which are compiled from the cargo volume data, stopover platform time, etc., managed in the systems for managing incoming and outgoing goods and the systems for managing logistics data, etc.

[0063] The timetable / stowage data, palletizing image data, and on-board image data are data that have been made efficient and optimized by the efficiency / optimization verification means 7 and then stored by the operation data storage means 9 (where the timetable / stowage data is stored) and the on-board data storage means 8 (where the palletizing image data and on-board image data are stored).Moreover, the data may be ones whose accuracy has been improved by machine learning.

[0064] Member companies (companies that make up the transportation company consortium) can not only view information related to their own transportation, but also, subject to certain conditions, view information such as optimal timetables and stowage tables (timetables and stowage data) for joint transportation, and optimal loading layouts (palletizing image data and on-board image data).If they determine that it would be more efficient to entrust a certain transportation project to another company rather than to undertake it themselves, they can request transportation from each other (see Figure 10).

[0065] Furthermore, during meetings with customers (on-site), a "special website" linked to the Integrated Logistics System (ILS) can be referred to (on-site) on a tablet terminal or the like, and necessary data can be entered to check information such as the optimal timetable and loading table (timetable and loading data) for joint transport (with Transport Company A and Transport Company B as one transport company consortium), and the optimal loading layout (palletizing image data and vehicle image data) (however, information from other companies may be subject to conditions such as permission). In other words, when data such as quantities related to transportation is entered into a tablet terminal or the like (on-site), artificial intelligence (AI) is activated, and a timetable and loading table can be automatically created instantly and displayed on the tablet terminal or the like (see Figure 10). In addition, a "quote" can also be displayed on the tablet terminal (at the same time).

[0066] <Improvement of logistics efficiency and the effect of optimization evaluation system> The logistics efficiency and optimization evaluation system 1 of the present invention obtains cargo volume data in advance from the customer who requests the delivery, and uses the cargo volume data (while making full use of AI technology) to make palletizing work, truck loading work, and operation schedule creation work more efficient, and through the above-mentioned series of steps of evaluating the efficiency of these, it aims to improve the efficiency and optimization of logistics, presents the optimal inventory quantity to the customer (contributing to the customer's production management), makes the transportation routes of transportation companies public on the Internet, and allows searches to be made (possibly in conjunction with GPS) to determine which roads the transportation company's trucks (vehicles whose cargo loading status is also disclosed) should travel on to achieve the most efficient transportation, and further achieves the efficiency of logistics by building a system that allows multiple transportation companies to work together.

[0067] A major feature of this system is that it has a cost-reduction effect not only on the transport companies that implement the logistics efficiency and optimization evaluation system 1, but also on the customers who request the delivery. By expanding the scope of logistics efficiency and optimization evaluation system 1 to the entire logistics industry, it will be possible to take advantage of the recent efforts in work style reform in the transport industry, the post-COVID economic recovery (prediction), and the SDGs (Sustainable Development Goals).) This is an attempt to respond to the movement to reduce CO2 emissions.

[0068] Before discussing the efficiency of logistics and the effects of the optimization evaluation system 1, let us first discuss the problems that existed when creating a conventional operation schedule. When creating or revising an operation schedule, the daily cargo volume was calculated from the monthly cargo volume (planned base), and the number of trucks to be used was set. At this time, trucks were divided into high and low loads based on the full load standard setting value per truck (load volume ÷ loadable volume: set arbitrarily).

[0069] If the full load standard is set to prevent special (extra) deliveries in anticipation of last-minute additions of freight from customers (called fluctuating freight volume), the freight will be low-loaded, meaning a large amount of air will be transported, resulting in high costs. On the other hand, if the full load standard is raised to allow for higher loads, the frequency of special (extra) deliveries will increase if there is an additional delivery request, such as an increase in daily production volume, from the customer. For this reason, when creating a schedule, the person in charge must check the freight volume on-site and confirm the situation with the customer and the driver, which means that adjustment work takes a lot of time, and this needed to be improved.

[0070] According to the present invention, by disclosing confirmed daily order data several days before the delivery date, the evaluation scheme (Method B of the efficiency and optimization verification means 6 (proposing efficiency improvements by the day before the delivery date for delivery confirmed data before the delivery date)) by the efficiency and optimization verification means 6 can be run, and optimal vehicle loading and delivery can be considered, which makes it possible to prevent the occurrence of special (unscheduled) deliveries.

[0071] As a result, the time required for schedule adjustments has been significantly reduced. Clients who request truck-based transport (a freight charge is incurred for each truck) can reduce the number of vehicles used, which should lead to reduced logistics costs and CO2 emissions. Clients who request individual-unit transport (a freight charge is incurred for each package) can reduce the outgoing costs on the part of the recipient (excessive vehicle input costs, labor costs, etc.) and CO2 emissions. Furthermore, there was also the issue of frequent changes to the amount of cargo three days or the day before, but now it is possible to make proposals to clients based on clear data, such as "We could have reduced the number of special (extra) flights on this flight."

[0072] The benefits for the transport company are that it can increase the loading rate, improve profit margins (individual transport), reduce CO2 emissions, and reduce the number of special flights (temporary flights).Furthermore, it has become possible to reduce the time required for arrangements and the costs incurred for special flights (temporary flights) at the company's own expense.

[0073] It is now possible to calculate how to perform palletizing efficiently, which reduces sorting time (improves work efficiency), thereby reducing labor costs, improving profit margins, and eliminating sorting errors. By giving optimal palletizing instructions, waste is eliminated, and quality does not decline even when workers are replaced. It is now possible to calculate how to load items onto vehicles, which reduces on-board time, thereby reducing labor costs, improving profit margins, and eliminating on-board errors. And by giving optimal on-board instructions, left-behind items and overflows are eliminated, quality does not decline even when drivers are replaced, and detailed transportation plans can be made, which makes it possible to achieve high efficiency from the planning stage. Furthermore, it is now possible to reduce the time required to make transportation plans, which makes it possible to reduce labor costs.

[0074] The benefits for customers are that they can increase the loading rate and reduce the number of trips, and they can also reduce special (extra) trips as much as possible, which has led to reduced transportation costs. They can also simulate how to palletize efficiently, which has reduced sorting time, and they can also simulate how to load items efficiently, which has reduced loading time, which has led to reduced transportation costs.

[0075] It is possible to reduce the total cost by outsourcing the logistics operations, and since it is possible to create detailed transportation plans, it has become possible to achieve high efficiency at the planning stage. Furthermore, it has become possible to reduce labor costs by shortening the time required to create transportation plans.

[0076] The transportation route publishing means 12 (which may be used together with the optimum route search means 13) displays the vehicle passing time at a specified point on a specified driving route and the vehicle loading status in detail as a map on the Internet, making truck transportation an "omnibus" system, and making it possible for anyone to see the loading, operation status, and operation plan (like a "routed bus" + real-time understanding of the loading status), thereby making truck transportation an infrastructure (available to anyone, open to the public), and thereby making it possible to make the transportation itself more efficient.

[0077] Specifically, it becomes possible to efficiently select the optimal road (from a number of alternative roads) connecting each point that a truck stops at during transportation. The effects of the transportation route disclosure means 12 and the optimal route search means 13 are great, and not only do they contribute to the reduction of CO2 emissions during transportation in the trucking industry as a whole, but they also lead to the reduction of variations in driving time due to differences between veterans and newcomers, and they enable the standardization of driver quality, which will benefit the entire trucking industry.

[0078] Considering the future of the logistics industry, it can be said that the future is not bright if the current situation continues. In order to overcome this crisis, the logistics industry should work together toward the goal. For example, it was previously unthinkable to deliver heavy goods (beer, etc.) and light goods (snacks, etc.) mixed together on a truck, but from the perspective of logistics efficiency, it should be mixed and transported to reduce costs. Such efforts are beginning to be seen. However, it must be said that there are still high hurdles to overcome in order to make it a reality. By visualizing logistics (visualizing the vehicle loading status, delivery route, etc.) with this invention, it is expected that different industries will come together (including disclosing delivery information to each other) and become a major trend in the logistics industry. In other words, the effect of breaking down the barriers between industries will lead to further development of the logistics industry.

[0079] <Examples of changes to the logistics efficiency and optimization evaluation system> The logistics efficiency and optimization evaluation system according to the present invention is not limited to the above-mentioned embodiments, and the configurations of the cargo amount aggregation and management platform, advance cargo amount data storage means, palletizing image creation means, on-board image creation means, operation schedule creation means, efficiency and optimization verification means, on-board data storage means, operation data storage means, cargo collapse prevention calculation means, optimal inventory calculation means, transportation route publication means, optimal route search means, etc. can be appropriately changed as necessary within the scope of the present invention. For example, the palletizing instruction means and on-board instruction means can be displayed in a more visual form in 3D. [Industrial Applicability]

[0080] Since the logistics efficiency and optimization evaluation system of the present invention has the excellent effects as described above, it can be suitably used as a system for improving the efficiency of logistics by collecting digital data generated in all logistics processes and visualizing the data. [Explanation of symbols]

[0081] 1. Logistics efficiency and optimization evaluation system 2. Load aggregation and management platform 3. Pre-load data storage means 4. Palletizing image creation method 5. Vehicle image creation method 6. How to create a train schedule 7. Efficiency and optimization verification methods 8. In-vehicle data storage means 9. Means of storing operation data 10. Calculation method for preventing cargo from shifting (an example of a method for improving efficiency and optimizing using machine learning) 11. Optimal inventory calculation method 12. Public transport route means 13. Optimal route search method

Claims

1. A cargo aggregation and management platform for inputting and transmitting cargo data from multiple customers; A pre-load data storage means for storing the load data input and transmitted to the load aggregation and management platform; a palletizing image creating means for creating an image for a shipping worker to instruct how to load the cargo onto a pallet from the cargo weight data stored in the advance cargo weight data storage means; an on-board image creating means for creating an image based on the load data stored in the advance load data storage means to instruct a driver to load the pallet onto a truck; a train schedule creating means for creating a train schedule table and a stowage table from the load data stored in the advance load data storage means; an efficiency / optimization verification means for verifying whether outputs from the palletizing image creation means, the on-board image creation means, and the bus schedule creation means are efficient and optimal; an on-vehicle data storage means for storing palletizing image data and on-vehicle image data after the efficiency improvement and optimization verification means has verified the efficiency improvement and optimization; an operation data storage means for storing schedule and loading data after the efficiency and optimization verification by the efficiency and optimization verification means; an optimal inventory calculation means for presenting an optimal inventory quantity to a customer based on the palletizing image data and the on-board image data stored in the on-board data storage means after the efficiency / optimization verification by the efficiency / optimization verification means, the schedule / loading data stored in the operation data storage means after the efficiency / optimization verification by the efficiency / optimization verification means, and the cargo amount data stored in the advance cargo amount data storage means, The efficiency / optimization verification means verifies delivery confirmation data that is finalized up to two days before the delivery date, and if there are any packages that cannot be loaded onto the vehicle as a result of the verification up to the day before the delivery date, makes an efficiency / optimization proposal to either load the packages onto a later vehicle or have a vehicle with another route stop by and collect the packages, Furthermore, the system is characterized by being equipped with a calculation means for preventing cargo from shifting during delivery, using a method that uses machine learning (a trained model is generated by learning "loading form that prevents cargo from shifting" and "delivery route" as labels (answers) for "individual shapes of cargo to be delivered" and "delivery location including delivery date and time").

2. The logistics efficiency and optimization evaluation system according to claim 1, characterized in that the cargo volume data stored in the advance cargo volume data storage means is the dimensions of the packing boxes forming the luggage, the number of packing boxes (if there are multiple packing boxes, the number of packing boxes for each dimension), the weight of the packing boxes + contents (if there are multiple packing boxes, the weight of the packing boxes + contents for each packing box), and the delivery location (including delivery date and time).

3. The logistics efficiency and optimization evaluation system according to claim 1 or 2, characterized in that the cargo collapse prevention calculation means is provided with a large amount of data in which the answers (labels) are an image of the optimal palletized state (the data is quantified), an image of the truck on board (the data is quantified), a timetable, and a stowage table for the dimensions of the packing boxes forming the cargo, the number of packing boxes (if there are multiple packing boxes, the number of packing boxes for each dimension), the weight of the packing boxes + contents (if there are multiple packing boxes, the weight of the packing boxes + contents for each packing box), and the delivery location (including delivery date and time), and calculates the optimal image of the palletized state, the image of the truck on board, the timetable, and the stowage table for the unknown advance cargo volume data using a learned model that has been subjected to so-called supervised learning.

4. The logistics efficiency and optimization evaluation system according to any one of claims 1 to 3, characterized in that it further comprises a transport route publishing means for publishing details of vehicle passing times and vehicle loading conditions at specified points on a specified travel route by displaying them in detail as a map on the Internet based on the timetable and loading table created by the operation timetable creating means.

5. 5. The system for evaluating efficiency and optimization of physical distribution according to claim 4, further comprising an optimum route search means for improving the efficiency of the predetermined travel route published by the transportation route publishing means.

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