WO2014178055A1 - Itinéraire d'optimisation de procédé de prise de décision en temps réel et moteur d'établissement de prix pour un transport de marchandises (fret) - Google Patents

Itinéraire d'optimisation de procédé de prise de décision en temps réel et moteur d'établissement de prix pour un transport de marchandises (fret) Download PDF

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Publication number
WO2014178055A1
WO2014178055A1 PCT/IL2014/050395 IL2014050395W WO2014178055A1 WO 2014178055 A1 WO2014178055 A1 WO 2014178055A1 IL 2014050395 W IL2014050395 W IL 2014050395W WO 2014178055 A1 WO2014178055 A1 WO 2014178055A1
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WIPO (PCT)
Prior art keywords
user
route
freight
data
optimization
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PCT/IL2014/050395
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English (en)
Inventor
Joel SELLAM
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G-Ils Transportation Ltd
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Publication of WO2014178055A1 publication Critical patent/WO2014178055A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem

Definitions

  • 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.
  • 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.
  • 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 C0 2 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
  • 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 C0 2 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
  • 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.
  • 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
  • FIGS. 8A and 8B present examples for the system's resulted outputs.
  • 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.
  • 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 “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.
  • Freight On Board 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.
  • 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.
  • 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.
  • 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 C0 2 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
  • 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 C0 2 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, IttraTM, TraxsonTM,
  • nodes represent locations and the lines (edges) represent rout segments.
  • 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.
  • 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.
  • system (100) is configured to extend the user business margin.
  • 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.
  • system (100) is configured to communicate with the user via a smart phone application.
  • decision engine (170) is configured with learning algorithms for learning the user's shipment history.
  • 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.
  • Fig. 1 presents an illustrated block diagram for a certain item shipment process.
  • the item to be transported first needs to leave its origin, for example the factory, and be transported to the departure port.
  • the documents of the transported item are being examined by the exporting customs.
  • 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.
  • 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.
  • 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.
  • 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.
  • Fig. 3 further illustrates the integrated-input-data collected by the integrated- data module (140), such as: carrier types, C0 2 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.
  • the integrated-input-data collected by the integrated- data module (140) such as: carrier types, C0 2 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.
  • FIG. 4 presents an illustrated diagram for the system's (100) computer system (110) and modules:
  • a freight-data collection module 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 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 C0 2 emission, minimize fuel consumption, packaging preference and any combination thereof;
  • an integrated-data module 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, IttraTM, TraxsonTM, opportunity network identifier (ONI), export customs, import customs, C0 2 emission, fuel consumption, spot pricing offers, accessorial charges, delivery charges, and any combination thereof;
  • a route optimization module 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 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.
  • Fig. 6 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.
  • Fig. 7 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.
  • Figs. 8 A and 8B 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.
  • 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.

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Abstract

L'invention concerne un moteur et un système de prise de décision en temps réel (100), pour un transport de marchandises, conçus pour une optimisation de temps, d'itinéraire et de coût. Le système comprend, entre autres choses, un moteur de décision (170) basé sur un module d'optimisation d'itinéraire (150) et un module de sélection de fournisseur (160), mis en œuvre par des algorithmes de la théorie des graphes, conçus pour trouver le chemin le plus court entre les nœuds source et cible. La somme du poids de ses bords constitutifs est réduite au minimum de telle sorte que ledit transport de marchandises est optimisé selon ladite préférence d'utilisateur, et les meilleurs résultats sont présentés. Le moteur d'optimisation de prise de décision peut être appliqué à de très grandes quantités de données, même plus grandes que celles qui sont communément connues comme étant de « grosses » données dans toute la chaîne de fret.
PCT/IL2014/050395 2013-05-01 2014-05-01 Itinéraire d'optimisation de procédé de prise de décision en temps réel et moteur d'établissement de prix pour un transport de marchandises (fret) WO2014178055A1 (fr)

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CN104376387A (zh) * 2014-12-05 2015-02-25 四川大学 高拱坝工程建设中的混凝土运输排队网络优化决策方法
CN108449268A (zh) * 2018-02-08 2018-08-24 四川速宝网络科技有限公司 对等网络中点到点最短路径计算系统
CN108564211A (zh) * 2018-04-09 2018-09-21 无锡太湖学院 物流运输路径规划方法及系统
US10332032B2 (en) 2016-11-01 2019-06-25 International Business Machines Corporation Training a machine to automate spot pricing of logistics services in a large-scale network
US10692039B2 (en) 2016-09-20 2020-06-23 International Business Machines Corporation Cargo logistics dispatch service with integrated pricing and scheduling
US10902356B2 (en) 2017-09-07 2021-01-26 International Business Machines Corporation Real-time cognitive supply chain optimization
CN112418749A (zh) * 2020-09-30 2021-02-26 南京力通达电气技术有限公司 一种大件电力设备运输效率综合评价方法
US10936992B1 (en) * 2019-11-12 2021-03-02 Airspace Technologies, Inc. Logistical management system
CN113793106A (zh) * 2021-09-28 2021-12-14 广东省电子口岸管理有限公司 外贸物流处理系统及处理方法
US11429801B1 (en) 2021-09-21 2022-08-30 Pitt Ohio System and method for carrier identification
US11468755B2 (en) 2018-06-01 2022-10-11 Stress Engineering Services, Inc. Systems and methods for monitoring, tracking and tracing logistics
US11773626B2 (en) 2022-02-15 2023-10-03 Stress Engineering Services, Inc. Systems and methods for facilitating logistics
US11783280B2 (en) 2021-10-14 2023-10-10 Pitt Ohio System and method for carrier selection
US11853956B2 (en) 2021-10-19 2023-12-26 Hammel Companies Inc. System and method for assembling a transport
CN117455346A (zh) * 2023-12-21 2024-01-26 广东鑫港湾供应链管理有限公司 一种用于药品仓储中心的包装追踪方法及系统

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Cited By (24)

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Publication number Priority date Publication date Assignee Title
CN104376387A (zh) * 2014-12-05 2015-02-25 四川大学 高拱坝工程建设中的混凝土运输排队网络优化决策方法
US10692039B2 (en) 2016-09-20 2020-06-23 International Business Machines Corporation Cargo logistics dispatch service with integrated pricing and scheduling
US10332032B2 (en) 2016-11-01 2019-06-25 International Business Machines Corporation Training a machine to automate spot pricing of logistics services in a large-scale network
US11176492B2 (en) 2016-11-01 2021-11-16 International Business Machines Corporation Training a machine to automate spot pricing of logistics services in a large-scale network
US10902356B2 (en) 2017-09-07 2021-01-26 International Business Machines Corporation Real-time cognitive supply chain optimization
CN108449268A (zh) * 2018-02-08 2018-08-24 四川速宝网络科技有限公司 对等网络中点到点最短路径计算系统
CN108449268B (zh) * 2018-02-08 2020-09-01 四川速宝网络科技有限公司 对等网络中点到点最短路径计算系统
CN108564211B (zh) * 2018-04-09 2020-05-26 无锡太湖学院 物流运输路径规划方法及系统
CN108564211A (zh) * 2018-04-09 2018-09-21 无锡太湖学院 物流运输路径规划方法及系统
US11468755B2 (en) 2018-06-01 2022-10-11 Stress Engineering Services, Inc. Systems and methods for monitoring, tracking and tracing logistics
US10936992B1 (en) * 2019-11-12 2021-03-02 Airspace Technologies, Inc. Logistical management system
US11068839B2 (en) 2019-11-12 2021-07-20 Airspace Technologies, Inc. Logistical management system
US11443271B2 (en) 2019-11-12 2022-09-13 Airspace Technologies, Inc. Logistical management system
CN112418749A (zh) * 2020-09-30 2021-02-26 南京力通达电气技术有限公司 一种大件电力设备运输效率综合评价方法
CN112418749B (zh) * 2020-09-30 2024-01-05 南京力通达电气技术有限公司 一种大件电力设备运输效率综合评价方法
US11995503B2 (en) 2021-09-21 2024-05-28 Pitt Ohio System and method for carrier identification
US11429801B1 (en) 2021-09-21 2022-08-30 Pitt Ohio System and method for carrier identification
CN113793106A (zh) * 2021-09-28 2021-12-14 广东省电子口岸管理有限公司 外贸物流处理系统及处理方法
CN113793106B (zh) * 2021-09-28 2022-06-21 广东省电子口岸管理有限公司 外贸物流处理系统及处理方法
US11783280B2 (en) 2021-10-14 2023-10-10 Pitt Ohio System and method for carrier selection
US11853956B2 (en) 2021-10-19 2023-12-26 Hammel Companies Inc. System and method for assembling a transport
US11773626B2 (en) 2022-02-15 2023-10-03 Stress Engineering Services, Inc. Systems and methods for facilitating logistics
CN117455346A (zh) * 2023-12-21 2024-01-26 广东鑫港湾供应链管理有限公司 一种用于药品仓储中心的包装追踪方法及系统
CN117455346B (zh) * 2023-12-21 2024-04-09 广东鑫港湾供应链管理有限公司 一种用于药品仓储中心的包装追踪方法及系统

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