Complex scheduling method and device

• US 6,606,529 B1
• Filed: 06/09/2000
• Issued: 08/12/2003
• Est. Priority Date: 06/09/2000
• Status: Expired due to Term

• Current Assignee: Frontier Bio Technology Company Ltd.
• Original Assignee: Frontier Bio Technology Company Ltd.
• Inventors: Crowder, George E. Jr., Mehlberg, Jerry L.
• Primary Examiner(s): Follansbee, John
• Assistant Examiner(s): Hartman, Jr., Ronald D
• Application Number:
  US09/591,278
• Time in Patent Office: 1,159 Days
• Field of Search: 700/100, 700/99, 700/97, 705/8-9
• US Class Current: 700/100
• CPC Class Codes: G05B 2219/32335 Use of ann, neural network
• 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

Abstract

A device and method for the real time optimization of scheduling for manufacturing and information transfer systems and similar applications. The device is a computer readable medium whose contents cause a computer system to generate an optimal solution to a scheduling problem in accordance with a method encoded on the medium. The method employs a filtering algorithm to schedule minimally-conflicting events. The remaining unscheduled events are partitioned into non-interactive sub-sets. Following partitioning, artificial intelligence is used to select one of a plurality of algorithms which is employed to provide an optimal scheduling solution for each sub-set of scheduling requests. The purpose of artificial intelligence is to recognize certain characteristics in request data comprising each sub-set of event scheduling requests and select an algorithm which is optimal for scheduling each particular sub-set.

Claims

1. A computer readable medium whose contents include instructions that cause a computer system to perform a scheduling function upon receipt of scheduling input data and a scheduling command from a scheduler, by performing the steps of:

(a) receiving a set of scheduling input data comprising a plurality of requests for scheduling an event from the scheduler, wherein each scheduling request comprising the plurality of requests has a scheduling parameter connected therewith, and the set of scheduling input data includes at least one scheduling conflict;

(b) receiving a request command to perform a scheduling function from the scheduler;

(c) invoking the instructions contained on said computer readable medium to perform the scheduling function; and

(d) providing the scheduler with a schedule having a minimum number of conflicts,

wherein said instructions comprise a step of partitioning the set comprising a plurality of scheduling requests into a plurality of conflicting and non-conflicting subsets by invoking a low order conflict resolution filter.

2. The computer readable medium of claim 1 wherein said instructions further include a step of invoking a high order conflict partition filter to further partition said conflicting subsets into a second subset comprising mathematically independent conflicting subsets.

3. The computer readable medium of claim 2 wherein said instructions include the step of evaluating said parameters connected with each said mathematically independent conflicting subset of scheduling requests and selecting a preferred algorithm from a plurality of candidate algorithms, said preferred algorithm being operable for maximizing the number of said plurality of scheduling requests that are scheduled.

4. A method for scheduling a set consisting of a plurality of scheduling requests, wherein each scheduling request has at least one parameter connected therewith and wherein a plurality of the scheduling requests comprising the set are conflicting, the method comprising the steps of:

(a) partitioning the set comprising a plurality of scheduling requests into a plurality of conflicting and non-conflicting subsets by invoking a low order conflict resolution filter;

then

(b) invoking a high order conflict partition filter to further partition said conflicting subsets into a second subset comprising mathematically independent conflicting subsets;

then

(c) evaluating said parameters connected with each said mathematically independent conflicting subset of scheduling requests and selecting a preferred algorithm from a plurality of candidate algorithms, said preferred algorithm being operable for maximizing the number of said plurality of scheduling requests that are scheduled.