LOGISTICAL SERVICE FOR PROCESSING MODULAR DELIVERY REQUESTS
1. A method of scheduling a vehicle transporting freight on a mesh network using a machine learning process comprising:
- receiving transportation requests over a network from a plurality of customer portals that include loading times, loading locations, destination locations, delivery times, and freight requirements;
mining a plurality of large data sets from remote sites that reflect distances between the loading locations and the destination locations and corresponding freight rates associated with distances through the machine learning process;
predicting a plurality of shipping schedules that include predicted departure time and a predicted arrival time associated with the plurality of shipping schedules; and
matching the transportation requests with the plurality of shipping schedules in real time based on a plurality of shipping preferences, carrier availabilities, and projected probabilities that a plurality of carriers will accept loads.
A system and method (referred to as a system) schedules vehicles transporting freight on a mesh network using a machine learning process. The system receives transportation requests over a network from a plurality of customer portals that include loading times, loading locations, destination locations, delivery times, and freight requirements. The system mines large data sets from remote sites that reflect distances between the loading locations and the destination locations and corresponding freight rates associated with distances through the machine learning process. The system predicts shipping schedules that include predicted departure time and a predicted arrival time associated with the plurality of shipping schedules. The system matches the transportation requests with the plurality of shipping schedules in real time based on a plurality of shipping preferences, carrier availabilities, and projected probabilities that a plurality of carriers will accept loads.
- 1. A method of scheduling a vehicle transporting freight on a mesh network using a machine learning process comprising:
receiving transportation requests over a network from a plurality of customer portals that include loading times, loading locations, destination locations, delivery times, and freight requirements; mining a plurality of large data sets from remote sites that reflect distances between the loading locations and the destination locations and corresponding freight rates associated with distances through the machine learning process; predicting a plurality of shipping schedules that include predicted departure time and a predicted arrival time associated with the plurality of shipping schedules; and matching the transportation requests with the plurality of shipping schedules in real time based on a plurality of shipping preferences, carrier availabilities, and projected probabilities that a plurality of carriers will accept loads.
- View Dependent Claims (2, 3, 4, 5, 6, 7)
- 8. A non-transitory machine-readable medium encoded with machine-executable instructions for scheduling a vehicle transporting freight on a mesh network using a machine learning process, where execution of the machine-executable instructions is for:
- View Dependent Claims (9, 10, 11, 12, 13, 14, 15, 16)
- 17. A system that schedules a vehicle transporting freight comprising:
a customer portal for receiving transportation requests over a network from a plurality of customer portals that include loading times, loading locations, destination locations, delivery times, and freight requirements; a mileage miner for mining a plurality of large data sets from remote sites that reflect distances between the loading locations and the destination locations through a machine learning process; a mileage miner for mining a second plurality of large data and corresponding freight rates associated with distances through the machine learning process; a routing module programmed to predict a plurality of shipping schedules that include predicted departure time and a predicted arrival time associated with the plurality of shipping schedules; and a matching engine that matches the transportation requests with the plurality of shipping schedules in real time based on a plurality of shipping preferences, carrier availabilities, and projected probabilities that a plurality of carriers will accept loads.
- View Dependent Claims (18, 19, 20)
This application claims priority to provisional application of Ser. No. 62/753,257 filed on Oct. 31, 2018, titled “System, Method and Architecture for Processing Modular Requests for Delivery,” which is herein incorporated by reference in its entirety.
This disclosure relates to transport, and specifically to the logistics of moving freight.
Over the road transport is challenging. Shippers, freight brokers, and carriers move freight by linking carriers to shippers, and carriers and shippers must stay in touch. This means that agreements must be reached before loads are picked up, and confirmations must verify that the loads are picked up and delivered on time.
Freight movement is typically judged by supply and demand. A transport system is effective if it meets the need at an expected cost. That cost is then passed on to consumers. Current systems fail to consider transport efficiency or respond to changes in transport environments. The systems fail to provide sufficient notice to shippers, freight brokers, and carriers that is usually needed to deliver freight on-time.
The disclosure is better understood with reference to the following drawings and description. The elements in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the disclosure. Moreover, in the figures, like-referenced numerals designate corresponding parts throughout the different views.
The disclosed systems and methods (referred to as transport systems) efficiently automate the logistics that move freight. The transport systems coordinate complex delivery routes that may include intermodal transport that moves freight over the road, over rails, across the water, and/or through the air (e.g., planes, trains, trucks, and boats). The transport systems optimize deliveries across one or more modes of transport and construct one or more transport options through one or multiple delivery legs. The development of the logistics behind the transport system is based on productivity and efficiency that ensures safe and cost-effective deliveries in a cost efficient and a time-sensitive schedule. The effectiveness and performance of the transport systems impact the productivity and competitiveness of businesses, as well as the prices customers pay for goods and services.
The transport systems generate an environment that provides access to programs, files, and options via innovative interfaces. Through icons, menus, and dialog boxes, shippers, freight brokers, carriers, and other users can select automated shipping options or accept automated recommendations (e.g., highlighted on a price-finder interface). Multiple elements may be included in the innovative interface (referred to as an interface), such as a market awareness panel, for example, that functions the same way in all software applications, because the element provides the same functionality to the end users via software routines that reside above the system software and automatically adapt to changes in computing environments without user involvement. Different software applications call these elements (e.g., software routines) with specific parameters rather than reproducing the software code from scratch allowing the computer systems that delivers these systems to operate more efficiently. Further, the interfaces and elements are device independent, meaning that as new input data is received, different users access the interfaces, and/or computer architectures changes, the interfaces and elements adapt and provide consistent functionality without user involvement. The transport systems and integrated applications run, without modification, on any device and are extensible. Further, in some systems, the disclosed transport systems are device and operating system agnostic, which increases the accessibility of the system to local and remote distributed users.
The innovative interfaces include a price finder user interface that can reflect pricing rendered via a price finder matrix across different modes of transportation (a full truck load (FTL), less than truck load (LTL), Intermodal, etc.). The price finder user interface can also generate forecasts that render time and/or price tradeoffs, provide information regarding special-circumstance pricing (e.g., such as backhauls, contract-based pricing for some lanes, container repositioning, etc.), and provide appointment price tradeoffs, including working around the hours of service restrictions, for example.
In the disclosed system, a broker agent module 102 automatically assists shippers with freight ready to or predicted to haul by finding carriers who are qualified to haul their loads. The broker agent module 102 makes it easier to broker agreements between shippers and carriers that facilitate the movement of the shippers'"'"' freight. In some extensions, the broker agent module 102 through a freight visibility module 248 maintains real time communication with the carrier agent module 104 to track and update the motor carrier'"'"'s status, direction of travel, and progress of the shipper'"'"'s load to its delivery destination that may be rendered on geographic real time maps. Real time operations are those in which the machine'"'"'s activities (e.g., the freight visibility module 248, for example, tracking of the motor carrier'"'"'s movement) exceed the user'"'"'s perception of time or those in which the computer processes input data within milliseconds (preferably less than about 100 milliseconds) or at a faster rate so that its output is available virtually immediately as feedback.
With reference to
The master data management modules 214 and 216 support data management by removing duplicates data entries, standardizing and normalizing data (mass maintaining), and executing rules that eliminate or prevent incorrect or corrupt data from entering the transport system. The master data management modules 214 and 216 render authoritative sources of master data in the transport system. The master data management modules 214 and 216 access processes that collect, aggregate, match, consolidate, quality-assure, persist, and distribute data improving the integrity and accuracy of the data. This functionality is enhanced by a common reference data repository 218, a metadata management module 220, a data quality management module 222, a data integration management module 224, and workflow engines 226. The workflow engines 226 manage and monitor the state of activities of the transport workflow, by linking of multiple modes of transport in some applications, and determining which transport mode to transition to according to pre-defined transport-flows, some being established by the carrier profiles), that can be downloaded and updated as the transport network changes.
For example, the rate miner 230 may render a weighted average price in different transport regions. The price may be adjusted or filtered to render a forecasted price based on one or more filter factors (also referred to as the freight filters and freight filter factors) by the price algorithm. The filter factors may include one or any combination of adjustment factors that reflect: a predicted statistical confidence of the transport system to procure transportation, an expected equipment type to be used, a measure of supply and demand, adjustments for seasonal variations (e.g., the use of transport during harvest seasons), weekly variations (some prices vary by the day of the week), a desired profit margin and/or subjective criteria, such as a desire to grow or expand transport in a geographic area, transport facility attributes (some facilities load and off-load vehicles quickly and others are frequently delayed), pickup and delivery times during the day, urgency (e.g., must be picked up within twenty four hours), a team drive (e.g., the use of two or more drivers to deliver the freight), dimensions and weight of the shipment, number of stops in multi-pick/multi-drop shipments (described below), etc. Other system pricing is practiced in alternate system in the absence of (i.e., at the explicit exclusion of) any one or combination of the filter factors above and any price adjustment factors which are not specifically disclosed herein.
The freight router 208 calculates the distance between the points of origin and the destination, forecasts the average time it will take to travel that distance, and estimates the cost of delivery per mile. That data is transmitted to the broker-agent module 102 that calculates the total cost of the deliveries through different transport modes and time frames that is rendered on a price-finder interface such as the exemplary interface shown in
Transport transactions are processed in two stages through two independent and separate modules: the broker agent module 102 and the carrier agent module 104, which are linked together through an order stack 108 residing in the order management module 212 and a carrier contract profile 232 residing in the carrier agent module 104. In
Carrier assignments may be based on a number of criteria. For example, consider a use case in which a shipper does not have a time requirement for delivery but is price sensitive and another shipper that is time sensitive to delivery but is not price sensitive. Orders from shippers are stored in the order stack 108 in the order management module 212 and matched by the freight router module 208 and the cargo planner module 210. In another use case, carrier'"'"'s cargo and route parameters are processed by the freight router module 208 and cargo planner module 210 when assigning potential carrier assignments to orders retrieved from the order stack. Route parameters may include loading times, loading locations, destination locations, delivery times, and freight requirements, for example. An external load board 110 may be used in alternate systems. External load boards 110 match carriers and shippers when a shipment is not otherwise assigned to a carrier.
In some alternate systems, a freight matching engine 802 (shown in
As orders are placed by the broker agent module 102, the order module 244 stores the orders, shipping documents, transport documents, and cargo documents via a structured query language developer data module (SDM) 240 in a database schema. The broker agent module 102 and carrier agent module 104 may access and monitor open orders and receive confirmations on closed orders. In
Some disclosed systems can provide a shipper with an instant price commitment (e.g., a guarantee) before a carrier assignment is made. The commitment is based on a weighted function of at least four parameters. In other use cases, more or fewer parameters are used. The parameters may include some or all of a vehicle/transport selection, its point of origination, the date of departure, the time of departure, a destination selection, the date of arrival, the time of arrival and/or the need for extra services. In a use case, the transport system provides a price commitment based on three parameters: a truck/vehicle selection, the point of origination selection, and the destination. The price commitment is devoid of any other parameters or factors. With these parameters, the transport system can provide an instant price commitment, and in some applications, automatically provide a counter offer (e.g., a subsequent proposal). In some examples, the system can transmit a counter or a subsequent price, automatically, in response to a rejection.
A full truck load (FTL) is a type of shipping mode in which a truck transports one dedicated shipment. It may be associated with a spot price meaning it is a dynamic rate that the transport system offers on the spot to move a load from an origin to a destination. Because it is based on market conditions, spot prices are shown to change over the course of a day via the price finder interface. FTL shipments occupy some or all of the entire weight or space capacity of the vehicle. Thus, FTL shipments are less encumbered by size and weight restrictions, often reach destinations sooner as there are less pickups and drop-offs (e.g., faster transit times because they are sent directly to destinations), and cargo is less susceptible to damage because it is subject to less freight handling as there are fewer transfers amongst vehicles in mid-transit. Base rates are generated from multiple data sources and are subject to the freight filters described here.
Less than truck load (LTL) combines the freight handling of many shipments in a common vehicle. It may also be associated with a spot price. LTL is used for transportation of smaller freight that does not require the use of an entire trailer or container. In the price finder matrix shown in the price-finder interface in
Within a window of time, there may also be some shipment orders that are either LTL or partial loads. The transport system'"'"'s algorithms automatically consolidate such orders into a single FTL load to reduce cost and improve reliability in some applications. When consolidation occurs, a multistop FTL is created (via a multi-pick, multi-drop, or both). The same freight tracking/visibility and other freight execution automation technologies described herein are applied to such multistop loads. Further, the number of stops may also be reflected in one of the freight factors that adjust carrier pricing.
In intermodal transport, two or more modes of freight delivery are used to ship freight to its destination, with each mode of transport having its own carrier and its own agreement. In some use cases, railway, airfreight, and/or water transport systems are used, which may consume less fuel than if road transport were used exclusively. In the price finder matrix, the pricing algorithm is priced off of the railway, airfreight, and/or water transport schedules and also reflects spot prices. The algorithms factor in distances to terminals, drayage service, the expense of tracking two or more modes of transport, and the type of cargo such as whether it is perishables and/or hazardous, for example, that may be subject to restrictions or exclusions. When a customer'"'"'s order (e.g., the shipper) qualifies for intermodal transport, meaning it meets the transport requirements in terms of shipping time, location, and types of cargo, the price-finder interface presents intermodal transport options and prices automatically when the interactive dynamic control associated with it is enabled. In
Besides providing spot price processes, the transport system also offers contract and spot rates for users who have agreements for shipping lane. A contract price is the price that the transport system agrees to move a shipper'"'"'s freight in a set of shipping lanes over a set period of time. A contract price also means that the transport system will assure the same freight type will reach its destination every time under some agreements. In the transport system, the contract price is rendered via another price-finder interface, which reports the amount of volume moved under the contract and the commitment yet to be met. Even when a commitment has not been met, the freight router 208, in some transport systems, may recommend a spot price when prices under the contract diverge from spot rates. In these use cases, the transport system monitors the customer'"'"'s usage and forecasts the customer'"'"'s future use. When projected volumes exceed the contract volume by a predetermined threshold and the spot rates fall below the contract price by a predetermined threshold, some router and cargo planner modules 208 and 210 recommend rates outside of the contract, which may result in a discount to the contract rate. This allows shippers to reduce spot market exposure by entering into contract prices, while taking advantage of spot market discounts when the spot market rates fall below contract prices.
Under the special circumstance mode layer, a freight router and cargo planner module 208 and 210 may recommend better pricing options that are not available on the open market. A better price opportunity may occur when the transport system services a contract price for a shipping lane. When a second, a third, or a fourth, or any number of customers enter the same origin and destination or an origin and destination within a threshold radius of its contract locations, the router and cargo planner modules 208 and 210 supplement the spot rates offered on the price finder matrix with other rates in some systems such as the same contract rate promised under the contact for the contract customer or that rate adjusted with a margin.
Under the special circumstance mode layer, the transport systems also reposition empty freight containers (referred to as containers). In this flow, an inbound container finds an outbound load within a predetermined mileage radius, once it has been unloaded through the transport system. Repositioning begins immediately after a container has been unloaded. Because containers arriving at a destination must eventually leave, either empty or full, and the longer the containers are delayed the higher the cost, the transport systems and more specifically, the router module and cargo planner modules 208 and 210 exploit a price arbitrage automatically to generate and recommend one-time transport rates in the price matrix below the spot market that results in a balanced flow. The backhaul movement transporting freight back over some or all of the same shipping lane that the container took to reach its current location generates revenue and reduces the cost of storing empty containers. Those costs include the costs of idle trucks sitting in congested terminals due to the aggregation of empty containers, the expense in returning empty containers, and the revenue lost by an imbalanced flow. The ability to balance flow through a unitary and fully automated logistical automated service is uncommon.
Similarly, the special circumstance mode layer transporting freight back over all or part of the same route it took to get to its current location irrespective of containers. In an effort to save both time and money, the transport systems may offer reduced freight rates in the price matrix that is also below the spot market. Because the transport system serves both public and private fleets, special circumstance backhauls allow carriers to reposition their vehicles, shippers to receive discounted freight rates, and shippers to receive freight delivery without delay without relying on brokers and carriers to seek out opportunities that are often held as secrets. The opportunities are automatically rendered and made accessible via the price finder matrix.
The four layers of pricing that are part of the price finder matrix rendered in the price-finder interface may be compared against market indicator element shown as a sidebar graphic element automatically in
The exemplary pricing algorithm removes outliers by comparing monthly averages and comparing absolute values by filtering at 610. In a use case, rates should fall within about three standard deviations of the monthly average and preferably lie within ten percent of one another. In
When the urgent option is selected at 616, the calculated rate is calculated from the forecasted week ahead which is added to a predetermined premium margin. In some exemplary use cases, the premium margin ranges from about forty to sixty percent. When the team drive option is selected at 618, a premium factor is added to the computed rate of the urgent option. In the exemplary use case, a one percent margin is added to the urgent rate.
In some exemplary pricing algorithms, adjustments are applied to the base rate ahead with urgent and team drive options at 620. Each adjustment rule can be applied as an absolute vales or percent, and in some applications, encompass the filter factors described herein. Generally, the rates are applied in a first created, first applied sequence. Further, a margin is applied to base rate at 622 before an urgent or team drive option is selected.
In some transport systems, the freight visibility module 248 maintains real time communication with the carrier agent module 104 or directly with the motor carrier associated with the carrier agent module 104 to track and update the motor carrier'"'"'s status, direction of travel, and progress of the shipper'"'"'s load to its delivery destination. The freight visibility module 248 reports are based on direct communication with the motor carrier. The communication is used to track and update the motor carrier'"'"'s transport status in real time via real time geographical maps that are rendered on a display. In some systems, the real time geographical maps show satellite imagery, aerial photography, street maps, and/or three-hundred-and-sixty-degree panoramic views of the streets the motor carrier is traveling through or parked at, weather along the route, real time traffic conditions in the shipping lanes, and the projected shipping lane congestion, and direction the motor carrier is traveling at.
In some systems communication occurs through modal interfaces that make use of truck-to-air-to-transport system transceivers to communicate directly with the visibility module 248 of the transport systems. In other alternate systems, truck-to-truck, truck-to-rail-to-truck, rail-to-sea, and truck-to-sea transceivers are used to track motor carrier transport directly to provide faster than real time communication used to track motor carrier directions, their movement, and provide notifications between them. Through the use of a parallel processing architecture and various code templates reflecting the output characteristics of mobile devices stored in a database, the transceivers may process data through a modular level adaptive transmission controllers (not shown) that deliver faster than real time encoding and multiple adaptive bit rate outputs, that deliver multiple resolutions and deliver output formats in real time or near real time to many (e.g., two or more) devices and many display form factors such the thousands of form factors and displays used in mobile devices in use today. The adaptive bit rate outputs meet compliance requirements of the output devices and are adaptable to developing requirements such as the output requirement of mobile devices and the various operating platforms, OS versions, and proliferation of features that operate on top of them.
In the transport systems and processes described above, deep and machine learning algorithms train and evaluate the fitness of the neural networks used to integrate data and make transport recommendations, such as the highlighted $1,683 recommended rate shown in
The training process begins with the receipt of the machine-learning models to be trained. An extraction and translation process transform a portion of the training data into training vectors and the other or remaining portion of the supplemented or conditioned training data into evaluation vectors used to validate the trained models (e.g., usually a seventy-five to twenty-five percent split is used). Applying an iterative learning process, such as a stochastic gradient descent learning, some or all of the weights, layer sequences, and/or layer parameters of the neural network models are adjusted to minimize a loss function. Training may be limited to a fixed number of training cycles, periods of time, and/or until the neural network exceeds a fitness threshold.
The trained neural network is evaluated at a training cluster server by processing the evaluation vectors that are separate from and different from the training vectors. Based on the trained neural network'"'"'s performance, the training cluster calculates a fitness value by executing the evaluation vectors. In some applications, a user or transport application defines the acceptable fitness level. When the fitness level is reached, the respective machine learning algorithms are trained and enter service in the systems and processes during a transport system session.
A broker agent module 102 automatically assists shippers with freight ready to or predicted to haul by finding carriers who are qualified to haul their loads. Orders from shippers are stored in the order stack 108 in the order management module 212 and matched in the router module 106. In
The memory 804 and 806 and/or storage disclosed may retain an ordered listing of executable instructions for implementing the functions described above in a non-transitory computer code. The machine-readable medium may selectively be, but is not limited to, an electronic, a magnetic, an optical, an electromagnetic, an infrared, or a semiconductor medium. A non-exhaustive list of examples of a machine-readable medium includes: a portable magnetic or optical disk, a volatile memory, such as a Random-Access Memory (RAM), a Read-Only Memory (ROM), an Erasable Programmable Read-Only Memory (EPROM or Flash memory), or a database management system. The memory 804 and 806 may comprise a single device or multiple devices that may be disposed on one or more dedicated memory devices or disposed on a processor or other similar device. The engines may comprise a processor or a portion of a program that executes or supports recognition system or processes. When functions, steps, etc. are said to be “responsive to” or occur “in response to” another function or step, etc., the functions or steps necessarily occur as a result of another function or step, etc. It is not sufficient that a function or act merely follow or occur subsequent to another. Further, the term “engine” generally refers to a device, processor, and/or program executed by a hardware processor that manages and manipulates data as programmed to execute the functionality associated with the device. Computer-mediated technology enables human communication that occurs through two or more electronic devices. The devices may provide input from various sources including, but not limited to, audio, text, images, video, etc. A session is the time during which a program accepts input and processes information about a particular species. For transport, it may be the time during which the user transacts a freight shipment rather than the entire time the user is accessing resources on a transport site. The term “about” encompasses variances between one and five percent or an exact percent in that range that excludes other percentages in that range that may be associated with the particular variable.
While each of the systems and methods shown and described herein operate automatically and operate independently, they also may be encompassed within other systems and methods including any number (N) of iterations of some or all of the process used to recognize input, render recognized results, and/or render an output such as a business classification, for example. Alternate transport systems may include any combination of structure and functions described or shown in one or more of the FIGS. These systems are formed from any combination of structures and functions described. The structures and functions may process the same, additional, or different input and may include the data integrations from multiple distributed sources as shown in
The functions, acts or tasks illustrated in the FIGS. or described herein may be executed in response to one or more sets of logic or instructions stored in or on non-transitory computer readable media as well. The functions, acts, or tasks are independent of the particular type of instructions set, storage media, processor or processing strategy, and may be performed by software, hardware, integrated circuits, firmware, micro code and the like, operating alone or in combination.
The disclosed systems efficiently automate the logistics of moving freight. The transport systems coordinate complex delivery routes that may include FTL, LTL, multistop/consolidation and intermodal transport. The transport systems optimize deliveries across one or more modes of transport and construct one or more transport options through one or multiple delivery legs. The development of the logistics behind the transport system is based on productivity and efficiency that ensures safe and cost-effective deliveries in a cost efficient and a time-sensitive schedule.
The disclosed systems include innovative interfaces, such as the price finder user interface, that reflect pricing rendered via a price finder matrix across different modes of transportation. The modes of transportation include FTL, LTL, multistop/consolidation, and Intermodal, for example. The price finder interface also generates forecasts that render time and/or price tradeoffs, provides special-circumstance pricing choices (such as backhauls, contract-based pricing for some lanes, container repositioning, etc., for example), and provides appointment price tradeoffs including working around the hours of service restrictions, for example.
Other systems, methods, features and advantages will be, or will become, apparent to one with skill in the art upon examination of the figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the disclosure, and be protected by the following claims.