SYSTEMS, METHODS AND APPARATUS FOR JUST-IN TIME SCHEDULING AND PLANNING

• US 20110173042A1
• Filed: 01/13/2010
• Published: 07/14/2011
• Est. Priority Date: 01/13/2010
• Status: Active Grant

• Current Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Original Assignee: Lockheed Martin Corporation (Martin Marietta Corporation)
• Inventors: RIEPSHOFF, Robert D., KLINEFELTER, Phillip
• Granted Patent: US 8,655,705 B2
• Time in Patent Office: Days
• US Class Current: 705/7.26
• CPC Class Codes: G06Q 10/06316 Sequencing of tasks or work
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders

Abstract

The disclosure relates generally to methods and apparatus to optimize a supply plan through a hybrid meta-heuristic approach based on genetic algorithms to optimize inventory and generate a supply plan. The apparatuses include a supply chain planner that interacts with the processes of a supply chain network. To provide a complete optimization for the type of platform being deployed in theater a heuristic algorithm is devised to decompose the supply plan problem into a production center schedule and an asset routing problem, which will be tackled one after the other. The decomposed supply plan problem is solved with different heuristic algorithms. Namely, genetic algorithms are used to optimize the supply plans based on ever changing set of operational demands from in theater and the priority of those demands to the assigned depots, while efficient constructive heuristics are used to deal with footprint and timing constraints.

Claims

1. A computer-accessible medium having executable instructions to optimize inventory and to generate a supply plan for managing the routing of at least one item through a supply chain network having a number of geographically distributed storage and production centers, the executable instructions capable of directing a processor to:

accumulate performance data relating to a plurality of processes associated with the supply chain network;

receive operational demands for the routing of the at least one item through the supply chain network, wherein each received operational demand includes a priority level;

model a supply planning problem for the supply chain network based on the received operational demands;

decompose the supply planning problem into a production center schedule problem and an asset routing problem;

optimize the decomposed supply planning problem through heuristic processing; and

generate a supply plan that incorporates the optimized decomposed supply planning problem.

2. The computer-accessible medium of claim 1, wherein heuristic processing comprises at least one of genetic algorithm processing, constructive heuristic processing, and heuristic stochastic processing.

3. The computer-accessible medium of claim 2, wherein optimization is at least one of maximization of operational availability, minimization of logistic footprint, maximization of mission success, and minimization of cost.

4. The computer-accessible medium of claim 3, wherein genetic algorithm processing maximizes operational availability.

5. The computer-accessible medium of claim 3, wherein constructive heuristic processing minimizes logistic footprint.

6. The computer-accessible medium of claim 2, wherein the performance data comprises data from one of virtual data sensors, automated information sources, and event exception input devices.

7. The computer-accessible medium of claim 2, wherein a received demand is at least one of mission demand, supply demand, maintenance demand, event exception demand, user defined demand.

8. A method to optimize inventory and to generate a supply plan for managing the flow of at least one item through a supply chain network having a number of geographically distributed storage and production centers, comprising:

accumulating performance data relating to a plurality of processes associated with the supply chain network;

receiving operational demands for the flow of the at least one item through the supply chain network, wherein each received operational demand includes a priority level;

modeling a supply planning problem for the supply chain network based on the received operational demands;

decomposing the supply planning problem into a production center schedule problem and an asset routing problem;

optimizing the decomposed supply planning problem through heuristic processing; and

generating a supply plan that incorporates the optimized decomposed supply planning problem.

9. The method of claim 8, wherein heuristic processing comprises at least one of genetic algorithm processing, constructive heuristic processing, and heuristic stochastic processing.

10. The method of claim 9, wherein optimization is at least one of maximization of operational availability, minimization of logistic footprint, maximization of mission success, and minimization of cost.

11. The method of claim 10, wherein genetic algorithm processing maximizes operational availability.

12. The method of claim 10, wherein constructive heuristic processing minimizes logistic footprint.

13. The method of claim 9, wherein the performance data comprises data from one of virtual data sensors, automated information sources, and event exception input devices.

14. The method of claim 9, wherein a received demand is at least one of mission demand, supply demand, maintenance demand, event exception demand, user defined demand.

15. An apparatus to optimize inventory and to generate an optimize a supply plan for managing the flow of one or more items through a supply chain network having a number of geographically distributed storage and production centers, comprising:

a memory to store supply plan optimizing instructions; and

a processor to execute the supply plan optimizing instructions to cause the generation of an optimized supply plan by;

accumulating performance data relating to a plurality of processes associated with the supply chain network;

receiving operational demands for the flow of the at least one item through the supply chain network, wherein each received operational demand includes a priority level;

modeling a supply planning problem for the supply chain network based on the received operational demands;

decomposing the supply planning problem into a production center schedule problem and an asset routing problem;

optimizing the decomposed supply planning problem through heuristic processing; and

generating a supply plan that incorporates the optimized decomposed supply planning problem.

16. The apparatus of claim 15, wherein heuristic processing comprises at least one of genetic algorithm processing, constructive heuristic processing, and heuristic stochastic processing.

17. The apparatus of claim 16, wherein optimization is at least one of maximization of operational availability, minimization of logistic footprint, maximization of mission success, and minimization of cost.

18. The apparatus of claim 17, wherein genetic algorithm processing maximizes operational availability.

19. The apparatus of claim 17, wherein constructive heuristic processing minimizes logistic footprint.

20. The apparatus of claim 19, wherein the performance data comprises data from one of virtual data sensors, automated information sources, and event exception input devices;

and wherein a received demand is at least one of mission demand, supply demand, maintenance demand, event exception demand, user defined.