Method to imporove the performance of a distrubuted scheduler

• US 20090005895A1
• Filed: 06/24/2008
• Published: 01/01/2009
• Est. Priority Date: 06/28/2007
• Status: Abandoned Application

• Current Assignee: Siemens AG
• Original Assignee: Siemens AG
• Inventors: Ruggieri, Vito Massimo, Giebels, Mark Mathieu Theodorus
• Application Number:
  US12/214,961
• Publication Number:
  US 20090005895A1
• Time in Patent Office: Days
• US Class Current: 700/101
• CPC Class Codes: G06Q 10/08 Logistics, e.g. warehousing, loading or distribution; Inventory or stock management

Abstract

To improve the performance of a distributed scheduler for a manufacturing process comprising activities including a production resource management, a production definition management, and a detailed production scheduling, a method provides a decision and prediction function over the activities which carries out an analysis of a variability of the manufacturing process. Further, the method takes results of the analysis into account while making an actual detailed schedule in the production scheduling.

Claims

1. A method of improving a performance of a distributed scheduler for a manufacturing process comprising activities including a production resource management, a production definition management, and a detailed production scheduling, the method comprising:

providing a decision and prediction function over the activities which carries out an analysis of a variability of the manufacturing process; and

taking results of said analysis into account while making an actual detailed schedule in the production scheduling.

2. The method of claim 1, wherein the activities and the decision and prediction function are in level 3 according to the standard ISA 95.00.03.

3. The method of claim 1, further comprising:

modeling variability on historical data;

setting priorities; and

transmitting the priorities to the detailed production scheduling.

4. The method of claim 1, further comprising

detecting a conflict of done reservations by the decision and prediction function;

anticipating a reservation by the decision and prediction function; and

transmitting it via a message to the detailed production scheduling.

5. The method of claim 1, further comprising:

analyzing the influence of variability through orders path by the decision and prediction function;

setting scheduling rules for a production modeler agent based detailed production scheduling; and

allocating a work order by the production modeler agent based detailed production scheduling for a more reliable assembly station.