Transportation planning with multi-level pooling model

• US 20060241990A1
• Filed: 04/25/2005
• Published: 10/26/2006
• Est. Priority Date: 04/25/2005
• Status: Abandoned Application

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: Yadappanavar, Vinay Muralidhara, Sun, Rongming, Srinivasan, Mukundan
• Application Number:
  US11/113,514
• Publication Number:
  US 20060241990A1
• Time in Patent Office: Days
• US Class Current: 705/8
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06316 Sequencing of tasks or work
• CPC Class Codes: G06Q 10/06375 Prediction of business process outcome or impact based on a proposed change
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management

Abstract

Systems, methodologies, media, and other embodiments associated with multi-level pooling in transportation planning are described. One exemplary method embodiment includes, co-operatively and in parallel choosing pooling points and order-legs to consolidate. The example method may also include constructing a cost-optimal trip based, at least in part, on the contemporaneously chosen pooling points and orders-legs.

Claims

1. A computer-implemented method for transportation planning using a network flow based multi-level pooling model, the method comprising:

accessing a set of orders;

accessing a transportation planning model comprising;

data concerning a transportation network that includes a set of potential pooling points; and

a compatibility data concerning compatibilities between orders and pooling points;

decomposing an order in the set of orders into one or more order-leg flows;

selecting from the set of potential pooling points one or more eligible pooling points through which an order-leg may pass;

producing a network flow graph that includes the order-leg flows, where the order-leg flows may be characterized by one or more flow variables;

selectively assigning a value to the one or more flow variables;

evaluating a compatibility between an order-leg flow and a potential pooling point based, at least in part, on the compatibility data;

selectively aggregating one or more order-leg flows into one or more arc flows; and

selecting one or more included pooling points from the one or more eligible pooling points and selecting one or more order-legs to consolidate into one or more consolidated order-legs based, at least in part, on minimizing a transportation cost associated with transporting the set of orders.

2. The method of claim 1, where an order includes data concerning one or more of, a commodity to be shipped, an order time window, an earliest pickup time, an earliest delivery time, a latest pickup time, a latest delivery time, a source, and a destination.

3. The method of claim 1, where the transportation planning model includes data concerning one or more of, a carriage mode, a carrier, a carriage rate, and a facility.

4. The method of claim 1, where a potential pooling point may be one or more of, a consolidation point, a deconsolidation point, and a cross-docking point and where one or more pooling point selection criteria parameters are user configurable.

5. The method of claim 1, where the compatibility data includes one or more of, an item to be carried, a pooling point owner, a carrier that can use a pooling point, an item that can use a pooling point, and an equipment that can use a pooling point.

6. The method of claim 1, including:

estimating the number of vehicles needed to carry an item included in the arc-flows;

evaluating a cost of transporting the item; and

posting the cost as an objective value associated with a mixed integer network flow model.

7. The method of claim 2, including translating an order time window into one or more flexible time windows for an order leg-flow.

8. The method of claim 7, where selectively aggregating one or more order-leg flows into one or more arc flows includes identifying one or more order-leg flows having intersecting flexible time windows.

9. The method of claim 1, where producing a network flow graph includes identifying one or more potential paths through the transportation network, the potential paths including at least the paths associated with the order-leg flows.

10. The method of claim 9, including modeling as a node capacity one or more of, a loading capacity of a potential pooling point, an unloading capacity of a potential pooling point, and a dock capacity of a potential pooling point.

11. The method of claim 1, including pre-selecting one or more included pooling points.

12. The method of claim 1, where the one or more flow variables are configured to store data concerning one or more of, an order-leg flow capacity, an order-leg flow cost, an order-leg flow availability, and an order-leg flow priority.

13. The method of claim 1, including producing an actionable plan of loads using a bin-packing heuristic to construct one or more loads based, at least in part, on the included pooling points and the consolidated order-legs.

14. A computer-readable medium storing processor executable instructions operable to perform the method of claim 1.

15. A computer-implemented method for transportation planning using a network flow based multi-level pooling model, the method comprising:

accessing a set of orders, where an order includes data concerning one or more of, a commodity to be shipped, an order time window, an earliest pickup time, an earliest delivery time, a latest pickup time, a latest delivery time, a source, and a destination;

accessing a transportation planning model comprising;

data concerning a transportation network that includes a set of potential pooling points, where a potential pooling point may be one or more of, a consolidation point, a deconsolidation point, and a cross-docking point;

a compatibility data concerning compatibilities between orders and pooling points, where the compatibility data includes one or more of, an item to be carried, a pooling point owner, a carrier that can use a pooling point, an item that can use a pooling point, and an equipment that can use a pooling point; and

data concerning one or more of, a carriage mode, a carrier, a carriage rate, and a facility;

decomposing an order in the set of orders into one or more order-leg flows;

selecting from the set of potential pooling points one or more eligible pooling points through which an order-leg may pass;

producing a network flow graph that includes the order-leg flows, where the order-leg flows may be characterized by one or more flow variables, where producing the network flow graph includes identifying potential paths through the transportation network, the potential paths including at least all the paths associated with the order-leg flows, and where the one or more flow variables are configured to store data concerning one or more of, an order-leg flow capacity, an order-leg flow cost, an order-leg flow availability, and an order-leg flow priority;

selectively assigning a value to the one or more flow variables;

evaluating a compatibility between an order-leg flow and a potential pooling point based, at least in part, on the compatibility data;

translating an order time window into one or more flexible time windows for an order-leg flow;

selectively aggregating one or more order-leg flows into one or more arc flows including identifying one or more order-leg flows having intersecting flexible time windows;

modeling as a node capacity one or more of, a loading capacity of a potential pooling point, an unloading capacity of a potential pooling point, and a dock capacity of a potential pooling point;

estimating the number of vehicles needed to carry an item included in the arc-flows;

evaluating a cost of transporting the item;

posting the cost as an objective value associated with a mixed integer network flow model;

selecting one or more included pooling points from the eligible pooling points and selecting one or more order-legs to consolidate into one or more consolidated order-legs based, at least in part, on minimizing a transportation cost associated with transporting the set of orders; and

producing an actionable plan of loads using a bin-packing heuristic to construct one or more loads based, at least in part, on the included pooling points and the consolidated order-legs.

16. A computer-implemented network flow based multi-level pooling transportation planning method, comprising:

substantially in parallel, co-operatively choosing one or more pooling points and one or more order-legs to consolidate based, at least in part, on a mixed integer network flow model;

providing a first data concerning a set of order-leg divisions associated with the one or more pooling points; and

providing a second data concerning the one or more order-legs to consolidate.

17. The method of claim 16, where choosing a pooling point includes:

accessing a network model;

accessing a data concerning pooling points to consider;

accessing a set of orders;

evaluating a compatibility between an order and a pooling point to identify one or more candidate pooling points; and

computing a transportation cost for routing one or more consolidated order-legs through the one or more candidate pooling points.

18. The method of claim 17, where choosing an order-leg to consolidate includes:

decomposing an order in the set of orders into one or more order-leg flows;

producing a network flow graph that includes the one or more order-leg flows;

selectively aggregating one or more order-leg flows into one or more arc flows; and

selecting one or more order-legs to consolidate into consolidated order-legs based, at least in part, on minimizing a transportation cost associated with transporting the consolidated order-legs through the candidate pooling points.

19. The method of claim 18, including modeling one or more non-linear advantages of the consolidated order-legs, where the consolidated order-legs are shipped by one or more of, an air mode, a parcel mode, a less-than-truckload mode, and a truckload mode.

20. A network flow based multi-level pooling transportation planning system, comprising:

a data store configured to store a set of orders, a transportation model, and a compatibility data;

a first logic operably connected to the data store, the first logic being configured to identify a pooling point;

a second logic operably connected to the data store and the first logic, the second logic being configured to identify one or more order-legs to consolidate into a consolidated order-leg; and

a third logic operably connected to the first logic and the second logic, the third logic being configured to provide an actionable plan of loads that selectively routes consolidated order-legs through the pooling point;

the first logic and the second logic being configured to operate substantially in parallel with each other and to co-operate with each other in identifying the pooling point and the one or more order-legs to consolidate.

21. The system of claim 20, the first logic being configured to identify a pooling point by:

evaluating a compatibility between one or more orders and one or more pooling points to identify one or more candidate pooling points; and

computing a cost for shipping the one or more orders through the one or more candidate pooling points.

22. The system of claim 21, the second logic being configured to identify order-legs to consolidate by:

decomposing one or more orders into one or more order-leg flows;

producing a network flow graph that includes the nodes associated with one or more order-leg flows;

selectively aggregating two or more order-leg flows into one or more arc flows; and

selecting one or more order-legs to consolidate based, at least in part, on minimizing a cost for shipping the consolidated order-leg.

23. The system of claim 22, the third logic being configured to produce the actionable plan of loads using a bin-packing heuristic.

24. The system of claim 23, including an execution logic configured to interact with one or more carriers.

25. A system, comprising:

means for automatically choosing a pooling point;

means for automatically, contemporaneously, and co-operatively choosing one or more order-legs to consolidate; and

means for determining a cost-optimal trip based on the pooling point and the one or more order-legs.

26. A set of application programming interfaces embodied on a computer-readable medium for execution by a computer component in conjunction with multi-level pooling transportation planning based on a network flow model, comprising:

a first interface for communicating a pooling point data;

a second interface for communicating a consolidated order data; and

a third interface for communicating a cost-optimal trip data, where the cost-optimal trip is computed from the pooling point data and the order data.