A REAL TIME DECISION MAKING METHOD OPTIMIZATION ROUTE AND PRICING ENGINE FOR FREIGHT TRANSPORTATION (CARGO)
RELATED APPLICATION
This application claims the benefit of priority of US provisional patent application No. 61/817,927 filed on May 1st 2013.
BACKGROUND OF THE INVENTION
Present invention relates to a system and method for real-time decision-making for freight transportation from a source location to a destination location.
Cargo or freight is goods or produce transportation, generally for commercial gain, by ship, aircraft, train, van, truck and any combination thereof. In modern times, containers are used in most long-haul cargo transport.
When choosing a carrier and/or a provider for a freight to be sent from the origin location to a destination location there are endless options and choices for whole shipment process, including: variety packaging methods, variety domestic transport providers, variety cargo terminals, variety of local customs, variety of optional carriers by aircraft and/or by ships, variety of optional target customs, variety cargo terminals, variety of insurance providers and multiple of inland carriers.
The term "best way to deliver" generally implies that the shipper will choose the carrier who offers the lowest rate (to the shipper) for the shipment. In some cases, however, other factors, such as better insurance or faster transit time will cause the shipper to choose an option other than the lowest bidder.
Therefore there is an unmet need for innovative method and system for fright management to support freight forwarding, transport, storage and carriers; to enable efficient and productive delivery processes with unique value to the individual user or company. SUMMARY OF THE INVENTION
An object of the present invention is a real-time decision-making system (100), for freight transportation, adapted for time-, route- and cost- optimization, comprising: a) a user computer system (110), including: memory, processor, user input device, and a display device; b) a freight-data collection module (120), configured to collect at least one freight-related- data selected from a group consisting of: source-location, target-location, weight, volume, schedule limitations, transportation limitations, packaging criteria and any combination thereof; c) a user-data collection module (130), configured to collect at least one user's optimization criterion selected from a group consisting of: time-schedule, cost, transportation method (air/ocean/sea/land, track/train), number of stops limitation, preferred route, avoid location or route, preferred provider, storage limitation, minimize C02 emission, minimize fuel consumption packaging preference and any combination thereof; d) an integrated-data module (140), configured to collect at least one integrated-data- input from a source selected from a group consisting of: said user's ERP database, said user's shipping history, IncoTerms, freight's FOB, Ittra™, Traxson™, opportunity network identifier (ONI), export customs, import customs, C02 emission, fuel consumption, spot pricing offers, accessorial charges, delivery charges, and any combination thereof; e) a route optimization module (150), configured for the selection of a route between the origin and the target destinations, via routes selected from a group consisting of: air, ocean/sea, rail, road and any combination thereof, while taking in account said freight- related-data, said integrated-data-input and said user's optimization criterion; f) a providers selection module (160), configured for the selection of at least one provider selected from a group consisting of: airline carriers, shipping carriers, road carriers, rail carriers transportation companies, freight forwarders, docking companies, storage companies, insurance companies and any combination thereof, while taking in account said freight-related-data, said integrated-data-input and said user's optimization criterion; wherein said system further comprises a decision engine (170) based on said a route optimization module (150) and said providers selection module (160) and implemented by graph-theory algorithms, adapted to find the shortest path between the source- and the target- nodes, with the sum of the weight of its constituent edges minimized; such that said freight transportation is optimized according to said user preference and best results are presented.
Another object of the present invention is the decision optimization engine and system (100) wherein said route optimization module (150) and/or said providers selection module (160) comprises at least one optimization procedure for a feature selected from a group consisting of: opportunities, activities, bidding, ONI and any combination thereof.
Another object of the present invention is the decision optimization engine and system (100) wherein said nodes represent locations and said lines (edges) represent rout segments.
Another object of the present invention is the decision optimization engine and system wherein said graph theory algorithms are at least one algorithm selected from a group consisting of: Dijkstra's algorithm, A* search algorithm, Bellman-Ford algorithm and any combination thereof.
Another object of the present invention is the decision optimization engine and system wherein said optimization engine and system (100) is configured to extend said user business margin.
Another object of the present invention is the decision optimization engine and system wherein said user-data collection module (130), is configured to collect additional data concerning said user's transportation preferences retracted from said user's social web account, such as: Facebook, Twitter, Linkedln and any other online profile. Another object of the present invention is the decision optimization engine and system wherein said optimization engine and system (100), is configured to communicate with said user via a smart phone application.
Another object of the present invention is the decision optimization engine and system wherein said decision engine (170) is configured with learning algorithms for learning said user's shipment history.
Another object of the present invention is the decision optimization engine and system wherein said route optimization module (150) is configured to limit the search area, of said selection of said route, to a predetermined diameter around the said origin and destination location.
Another object of the present invention is the disclosure of a real-time decision-making method, for freight and cargo transportation, along the whole cargo chain adapted for time-, route- and cost- optimization, comprising steps of: a) providing a user computer system (100), including: memory, processor, user input device, and a display device; b) collecting at least one freight-related-data selected from a group consisting of: weight, volume, schedule limitations, transportation limitations, packaging criteria and any combination thereof; c) collecting at least one user's optimization criterion selected from a group consisting of: time-schedule, cost, transportation method (air/ocean/sea/land, track/train), number of stops limitation, preferred route, avoid location or route, preferred provider, storage limitation, minimize C02 emission, minimize fuel consumption, packaging preference and any combination thereof; d) collecting at least one integrated-data-input coming from at least one source selected from a group consisting of: said user's ERP database, said user's shipping history, IncoTerms, freight's FOB, Ittra™, Traxson™, opportunity network identifier (ONI), export customs, import customs, C02 emission, fuel consumption, spot pricing offers, accessorial charges, delivery charges, and any combination thereof; g) optimizing a route between the origin and the target destinations, via routes selected from a group consisting of: air, ocean/sea, rail, road and any combination thereof, while taking in account said freight-related-data, said integrated-data-input and said user's optimization criterion; h) selecting at least one provider from a group consisting of: airline carriers, shipping carriers, road carriers, rail carriers transportation companies, freight forwarders, docking companies, storage companies, insurance companies and any combination thereof, while taking in account said freight-related-data, said integrated-data- input and said user's optimization criterion; wherein said method further comprising a step of decision making based on said step of optimizing a route and said step of selecting at least one provider and implementing graph- theory algorithms, adapted to find the shortest path between the source- and the target- nodes, with the sum of the weight of its constituent edges minimized; thereby said freight transportation is optimized according to said user preference.
Another object of the present invention is the disclosure of a decision optimization engine, system and method wherein said route optimization module (150) and/or said providers selection module (160) comprises at least one optimization procedure for a feature selected from a group consisting of: opportunities, activities, bidding, ONI and any combination thereof.
Another object of the present invention is the disclosure of a decision optimization engine, system and method wherein said nodes represent locations and said lines (edges) represent rout segments.
Another object of the present invention is the disclosure of a decision optimization method wherein said graph theory algorithms are at least one algorithm selected from a group consisting of: Dijkstra's algorithm, A* search algorithm, Bellman-Ford algorithm and any combination thereof.
Another object of the present invention is the disclosure of a decision optimization method wherein said system (100) is configured to extend said user business margin.
Another object of the present invention is the disclosure of a decision optimization method, wherein said user-data collection module (130), is configured to collect additional data concerning said user's transportation preferences retracted from said user's social web account, such as: Facebook, Twitter, Linkedln and any other online profile.
Another object of the present invention is the disclosure of a decision optimization method wherein said system (100), is configured to communicate with said user via a smart phone application.
Another object of the present invention is the disclosure of a decision optimization method wherein said decision engine (170) is configured with learning algorithms for learning said user's shipment history.
Another object of the present invention is the disclosure of a decision optimization engine, system and method wherein said route optimization module (150) is configured to limit the search area, of said selection of said route, to a predetermined diameter around the said origin and destination location.
BRIEF DESCRIPTION OF THE DRAWINGS
In order to understand the invention and to see how it may be implemented in practice, a plurality of embodiments will now be described, by way of non-limiting example only, with reference to the accompanying drawings, in which:
FIG. 1 , presents an illustrated diagram for the system's inputs;
FIG. 2, presents an illustrated worlds map with optional routes for a shipment;
FIG. 3, presents an illustrated diagram for the optional decisions variety
FIG. 4, presents an illustrated diagram for the system's modules; FIG. 5, is a prior art demonstration for the nodes and edges of the graph- theory algorithm;
FIG. 6, presents the various optimization criterions;
FIG. 7, present an example for the system's utilization; and
FIGS. 8A and 8B, present examples for the system's resulted outputs.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
The following description is provided, alongside all chapters of the present invention, so as to enable any person skilled in the art to make use of the invention and sets forth the best modes contemplated by the inventor of carrying out this invention. Various modifications, however, are adapted to remain apparent to those skilled in the art, since the generic principles of the present invention have been defined specifically to provide a novel and useful system and method for realtime decision-making for freight transportation from a source location to a destination location.
The decision making optimization engine described herein is applicable to huge amounts of data, even larger than that which is commonly known as "big" data throughout the entire cargo chain.
The term "cargo chain " refers to the entire decision tree from the manufacturer to the customer.
The term "Graph theory" refers hereinafter as mathematics and computer science to the study of graphs, which are mathematical structures used to model pair- wise relations between objects. A graph in this context is made up of vertices or nodes and lines called edges that connect them. A graph may be undirected, meaning that there is no distinction between the two vertices associated with each edge, or its edges may be directed from one vertex to another.
The term "Dijkstra's algorithm", refers hereinafter to a graph search algorithm that solves the single-source shortest path problem for a graph with non-negative edge path costs, producing a shortest path tree. This algorithm is often used in routing as a subroutine in other graph algorithms, or in GPS Technology. The term "IncoTerm" refers hereinafter to a type of agreement for the purchase and shipping of goods internationally. The Incoterm's rules are intended primarily to clearly communicate the tasks, costs, and risks associated with the transportation and delivery of goods. The Incoterm's rules are accepted by governments, legal authorities, and practitioners worldwide for the interpretation of most commonly used terms in international trade. They are intended to reduce or remove altogether uncertainties arising from different interpretation of the rules in different countries. As such they are regularly incorporated into sales contracts worldwide. For example Freight On Board (FOB): specifies which party (buyer or seller) pays for which shipment and loading costs, and/or where responsibility for the goods is transferred. The last distinction is important for determining liability or risk of loss for goods lost or damaged in transit from the seller to the buyer.
The term "activities" refers hereinafter to managing the costumers' sales process by predetermined task and activities, thereby improving the customers' sales process by showing activity and general sales scoring with unique learning path system that transforms into success.
The term "proposals" refers hereinafter to the offering of pricing which cover the freight accessorial charges, delivery charges for external customers, internal pricing requirements as well as bid pricing requirements.
The present invention is a new method and system for transportation methods. In particular, the invention is specially suited for the purposes of optimizing the transportation routes and providers selection according to a user selected preferences.
The present invention provides a real-time decision-making system (100), for freight transportation, adapted for time-, route- and cost- optimization, comprising: a) a user computer system (110), including: memory, processor, user input device, and a display device; b) a freight-data collection module (120), configured to collect at least one freight-related- data selected from a group consisting of: source-location, target-location, weight, volume, schedule limitations, transportation limitations, packaging criteria and any combination thereof; c) a user-data collection module (130), configured to collect at least one user's optimization criterion selected from a group consisting of: time-schedule, cost, transportation method (air/ocean/sea/land, track/train), number of stops limitation, preferred route, avoid location or route, preferred provider, storage limitation, minimize C02 emission, minimize fuel consumption, packaging preference and any combination thereof; d) an integrated-data module (140), configured to collect at least one integrated-data- input coming from at least one source selected from a group consisting of: the user's ERP database, the user's shipping history, IncoTerms, freight's FOB, Ittra™, Traxson™, opportunity network identifier (ONI), export customs, import customs, C02 emission fuel consimption, spot pricing offers, accessorial charges, delivery charges, and any combination thereof; e) a route optimization module (150), configured for the selection of a route between the origin and the target destinations, via routes selected from a group consisting of: air, ocean/sea, rail, road and any combination thereof, while taking in account the freight- related-data, the integrated-data-input and the user's optimization criterion; f) a providers selection module (160), configured for the selection of at least one provider selected from a group consisting of: airline carriers, shipping carriers, road carriers, rail carriers transportation companies, freight forwarders, docking companies, storage companies, insurance companies and any combination thereof, while taking in account the freight-related-data, the integrated-data-input and the user's optimization criterion; wherein the system further comprises a decision engine (170) based on a route optimization module (150) and the providers selection module (160) and implemented by graph-theory algorithms, adapted to find the shortest path between the source- and the target- nodes, with the sum of the weight of its constituent edges minimized; such that the freight transportation is optimized according to the user preference. The present invention further provides a real-time decision-making method, for freight transportation, adapted for time-, route- and cost- optimization, comprising steps of: a) providing a user computer system (110), including: memory, processor, user input device, and a display device; b) collecting at least one freight-related-data selected from a group consisting of: weight, volume, schedule limitations, transportation limitations, packaging criteria and any combination thereof; c) collecting at least one user's optimization criterion selected from a group consisting of: time-schedule, cost, transportation method (air/ocean/sea/land, track/train), number of stops limitation, preferred route, avoid location or route, preferred provider, storage limitation, minimize C02 emission, minimize fuel consumption, packaging preference and any combination thereof; d) collecting at least one integrated-data-input coming from at least one source selected from a group consisting of: the user's ERP database, the user's shipping history, IncoTerms, freight's FOB, Ittra™, Traxson™, opportunity network identifier (ONI), export customs, import customs, C02 emission, fuel consumption, spot pricing offers, accessorial charges, delivery charges, and any combination thereof; g) optimizing a route between the origin and the target destinations, via routes selected from a group consisting of: air, ocean/sea, rail, road and any combination thereof, while taking in account the freight-related-data, the integrated-data-input and the user's optimization criterion; h) selecting at least one provider from a group consisting of: airline carriers, shipping carriers, road carriers, rail carriers transportation companies, freight forwarders, docking companies, storage companies, insurance companies and any combination thereof, while taking in account the freight-related-data, the integrated-data-input and the user's optimization criterion; wherein the system method further comprising a step of decision making based on the step of optimizing a route and the step of selecting at least one provider and implementing graph-theory algorithms, adapted to find the shortest path between the source- and the target- nodes, with the sum of the weight of its constituent edges minimized; thereby the freight transportation is optimized according to the user preference.
In one embodiment the nodes represent locations and the lines (edges) represent rout segments.
In another embodiment the graph theory algorithms are at least one algorithm selected from a group consisting of: Dijkstra's algorithm, A* search algorithm, Bellman-Ford algorithm and any combination thereof.
In another embodiment the route optimization module (150) and/or the providers selection module ( 160) comprises at least one optimization procedure for a feature selected from a group consisting of: opportunities, activities, bidding, ONI and any combination thereof.
In another embodiment the system (100) is configured to extend the user business margin.
In another embodiment the user-data collection module (130), us configured to collect additional data concerning the user's transportation preferences retracted from the user's social web account, such as: Facebook, Twitter, Linkedln and any other online profile.
In another embodiment the system (100), is configured to communicate with the user via a smart phone application.
In another embodiment the decision engine (170) is configured with learning algorithms for learning the user's shipment history.
In another embodiment the route optimization module (150) is configured to limit the search area, of the selection of the route, to a predetermined diameter around the the origin and destination location. Reference is now made to Fig. 1 , which presents an illustrated block diagram for a certain item shipment process. As present in the figure the item to be transported first needs to leave its origin, for example the factory, and be transported to the departure port. In the next step the documents of the transported item are being examined by the exporting customs. In the following step the item is being handled by the port and docked at the main carrier such as an aircraft or a ship. When the item has reached to the arrival port, it is docked and handled by at the arrival port. In the following step documents are being examined by the import customs and then the item is being transported to its destination, which is a buyer in this example. The optional carriers are further demonstrated in Fig.l such as: Trucking companies, airlines, shipping lines, train and 3 party logistics.
Reference is now made Fig. 2, presents an illustrated world map with optional routes for a certain item's shipment, suggesting a variety of air or ocean/sea routes combines with land routes.
Reference is now made Fig. 3, presents an illustrated diagram for the optional decisions and their multiples, showing the optional packaging criteria, multiplied by the optional domestic transport providers, multiplied by the optional cargo terminals, multiplied by the optional number of local customs, multiplied by the number of optional carriers by aircraft and by ships, multiplied by the number of optional target customs, multiplied by the number of optional cargo terminals.
As shown, Fig. 3 further illustrates the integrated-input-data collected by the integrated- data module (140), such as: carrier types, C02 emission, delivery charges, IncoTerms, shipment history, transit time, customer preferences, optional routs, FOB changes, shipment type, volume and actual weight, optional carriers, spot pricing, accessorial charges and customer's history.
An example of the user's preferences:
• Chose an optimization criterion (time, cost, distance);
• Limit or open the number of the transportation stops;
• Limit the transportation method to air, ocean/sea, land (track, train); • Goods limitations: time-schedule, over-weight, extended -volume or specific dimensions; and
• Limit the map: e.g. do not leave the continental.
Reference is now made Fig. 4, presents an illustrated diagram for the system's (100) computer system (110) and modules:
• a freight-data collection module (120), configured to collect at least one freight-related- data selected from a group consisting of: source-location, target-location, weight, volume, schedule limitations, transportation limitations, packaging criteria and any combination thereof;
• a user-data collection module (130), configured to collect at least one user's optimization criterion selected from a group consisting of: time-schedule, cost, transportation method (air/ocean/sea/land, track/train), number of stops limitation, preferred route, avoid location or route, preferred provider, storage limitation, minimize C02 emission, minimize fuel consumption, packaging preference and any combination thereof;
• an integrated-data module (140), configured to collect at least one integrated-data- input coming from at least one source selected from a group consisting of: the user's ERP database, the user's shipping history, IncoTerms, freight's FOB, Ittra™, Traxson™, opportunity network identifier (ONI), export customs, import customs, C02 emission, fuel consumption, spot pricing offers, accessorial charges, delivery charges, and any combination thereof;
• a route optimization module (150), configured for the selection of a route between the origin and the target destinations, via routes selected from a group consisting of: air, ocean/sea, rail, road and any combination thereof, while taking in account the freight- related-data, the integrated-data-input and the user's optimization criterion;
• a providers selection module (160), configured for the selection of at least one provider selected from a group consisting of: airline carriers, shipping carriers, road carriers, rail carriers transportation companies, freight forwarders, docking companies, storage companies, insurance companies and any combination thereof, while taking in account the freight-related-data, the integrated-data-input and the user's optimization criterion; the system (100) further comprises a decision engine (170) based on the a route optimization module (150) and the providers selection module (160).
The decision engine (170) is implemented by graph-theory algorithms, adapted to find the shortest path between the source- and the target- nodes, with the sum of the weight of its constituent edges minimized; the nodes represent locations and the lines (edges) represent rout segments. Fig. 5, is a prior art demonstration for the nodes and edges of the graph-theory algorithm.
Reference is now made to Fig. 6, which presents the various optimization criterions used by the route optimization module (150) and/or by the providers selection module (160). The optimization criterions include: opportunities, activities, bidding, ONI and any combination thereof. Fig. 6 further demonstrates the customer profile enrichment, the self learning models for the activities and the integration of proposals for social community buildup.
Reference is now made to Fig. 7, which present the system's utilization according to the LOT top utilization during 2012. It is shown in the figure that utilization weights are divided between: total net sell, customers, profiles, opportunities, gross weight (average/total), chargeable weight (average/total). Fig. 7 further demonstrates the growing system's proof of value (POV) gain.
Reference is now made to Figs. 8 A and 8B, which present an example for the system's output. Fig. 8A demonstrates an example of a fright delivery from Lyon, France to Phoenix Arizona by air. Out of 197,211 optional routes, the system (100) extracted five best optimal routes and their costs, including alternate combinations for airports air-carriers and land routes, optimized according to the user's preferences and the integrated-input-data. Fig. 8B demonstrates the same example of a fright delivery from Lyon, France to Phoenix Arizona by ocean. Out of 71,937 optional routes, the system (100) extracted five best optimal routes and their costs, including alternate combinations for ocean/sea-ports ship-carriers and land routes, optimized according to the user's preferences and the integrated-input-data. It will be appreciated by a person skilled in the art that the present invention is not limited by what has been particularly shown and described hereinabove. Rather the scope of the present invention includes both combinations and sub-combinations of the features described hereinabove as well as modifications and variations thereof which would occur to a person of skill in the art upon reading the foregoing description and which are not in the prior art.