COST AND LATENCY REDUCTIONS THROUGH DYNAMIC UPDATES OF ORDER MOVEMENT THROUGH A TRANSPORTATION NETWORK

• US 20140180952A1
• Filed: 12/16/2013
• Published: 06/26/2014
• Est. Priority Date: 12/20/2012
• Status: Active Grant

• Current Assignee: Oracle International Corporation (Oracle Corporation)
• Original Assignee: Oracle International Corporation (Oracle Corporation)
• Inventors: ARUNAPURAM, Sundararajan
• Granted Patent: US 10,043,150 B2
• Time in Patent Office: Days
• US Class Current: 705/330
• CPC Class Codes: G06Q 10/047 Optimisation of routes or paths, e.g. travelling salesman problem
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
• CPC Class Codes: G06Q 10/08355 Routing methods

Abstract

A method, system, and computer program product for shipping management. The computer implemented method commences upon identifying a set of orders to be shipped from a source region to a destination region using a transportation network, and determining candidate options for performing stops over possible routes between the source region and the destination region. A clustering analysis process is performed over the candidate options such that the clustering analysis considers many order consolidation possibilities while observing timing constraints. Low-cost options from among the candidate options are considered to identify one or more low-cost options, and a multi-stop route plan is generated to correspond to a selected low-cost option. The orders are shipped in accordance with the multi-stop route plan, and in accordance with the corresponding consolidation of the set of orders.

Claims

1. A method comprising:

using a computing system having at least one processor to perform a process, the process comprising;

identifying a set of orders to be shipped from a source region to a destination region using a transportation network;

identifying candidate options for performing stops over possible routes between the source region and the destination region;

performing a clustering analysis upon the candidate options, wherein the clustering analysis considers a plurality of order consolidation possibilities while observing timing constraints;

identifying a lowest cost option from among the candidate options to form an identified lowest cost option; and

generating a multi-stop route plan based at least in part upon the identified lowest cost option.

2. The method of claim 1, wherein at least one of the source region and the destination region comprises at least one overland leg.

3. The method of claim 1, wherein the possible routes comprise a starting point of a leg, at least one throughpoint, and an ending point of a leg.

4. The method of claim 1, wherein the candidate options for performing stops over possible routes comprise a multi-leg itinerary.

5. The method of claim 1, wherein the clustering analysis comprises analysis of at least some costs of the candidate options.

6. The method of claim 1, wherein the clustering analysis comprises analysis of at least one of, a pickup timing constraint, and a delivery timing constraint.

7. The method of claim 1, wherein the clustering analysis uses a network solver to observe least one of, a pickup timing constraint, and a delivery timing constraint.

8. The method of claim 1, wherein the multi-stop route plan observes a crossdock arrival constraint.

9. The method of claim 1, wherein the multi-stop route plan observes a crossdock departure constraint.

10. A computer program product embodied in a non-transitory computer readable medium, the computer readable medium having stored thereon a sequence of instructions which, when executed by a processor causes the processor to execute a process, the process comprising:

identifying a set of orders to be shipped from a source region to a destination region using a transportation network;

identifying candidate options for performing stops over possible routes between the source region and the destination region;

performing a clustering analysis upon the candidate options, wherein the clustering analysis considers a plurality of order consolidation possibilities while observing timing constraints;

identifying a lowest cost option from among the candidate options to form an identified lowest cost option; and

generating a multi-stop route plan based at least in part upon the identified lowest cost option.

11. The computer program product of claim 10, wherein at least one of the source region and the destination region comprises at least one overland leg.

12. The computer program product of claim 10, wherein the possible routes comprise a starting point of a leg, at least one throughpoint, and an ending point of a leg.

13. The computer program product of claim 10, wherein the candidate options for performing stops over possible routes comprise a multi-leg itinerary.

14. The computer program product of claim 10, wherein the clustering analysis comprises analysis of at least some costs of the candidate options.

15. The computer program product of claim 10, wherein the clustering analysis comprises analysis of at least one of, a pickup timing constraint, and a delivery timing constraint.

16. The computer program product of claim 10, wherein the clustering analysis uses a network solver to observe least one of, a pickup timing constraint, and a delivery timing constraint.

17. The computer program product of claim 10, wherein the multi-stop route plan observes a crossdock arrival constraint.

18. The computer program product of claim 10, wherein the multi-stop route plan observes a crossdock departure constraint.

19. A computer system comprising:

a first storage area to store a set of orders to be shipped from a source region to a destination region using a transportation network;

a second storage area to store candidate options for performing stops over possible routes between the source region and the destination region;

a dynamic clustering engine to perform clustering analysis upon the candidate options, wherein the clustering analysis considers a plurality of order consolidation possibilities while observing timing constraints; and

a computer processor to identify at least one lower cost option from among the candidate options used to form a multi-stop route plan based at least in part upon the identified at least one lower cost option.

20. The computer system of claim 19, further comprising a memory element to hold at least one overland leg formed from the source region and the destination region.