U.S.C. 154(b) by 188 days. 2008/0215408 A1* 9/2008 Pachon et a1. .. 705/8 2009/0070158 Al* 3/2009 Virine et a1. 705/7 (21) APPLNQ; 13/046,346 2009/0113442 A1* 4/2009 Deidda et a1. .. 718/105
2009/0228129 A1 * 9/2009 Moyne et a1. .... .. 700/l02
. _ 2009/0234482 A1 * 9/2009 Ide ... .. 700/l00
(22) Flied- Ma“ 11’ 2011 2010/0114353 A1 * 5/2010 Domrose 700/100
_ _ _ 20ll/0ll2676 Al * 5/20ll IZumi ... .. 700/l0l
(65) Prior Publication Data 2011/0166688 A1* 7/2011 Moyne et a1. ... .. 700/104
Reiated U-s- Application Data Primary Examiner * Ramesh Patel
(60) Provisional application No. 61/313,210, ?led on Mar. (74) A110" "6% A8611!) 01’ Firm *Poiomac Patent Group
12, 2010. PLLC
(51) Int. Cl. (57) ABSTRACT
G06F 19/00 (201101) Computerized scheduling methods and computerized sched
G06Q 10/00 (2012.01) ul1ng systems accord1ng to exemplary embod1ments. A . . . com
(52) US. Cl. puter1Zed schedul1ng method may be stored 1n a memory and - - -
USPC ... .. 700/100; 700/95; 700/96; 700/99; executed on one or more processors. The method may include
700/102; 700/104; 705/7'12; 705/7'13 de?ning a main multi-machine scheduling problem as a plu (58) Fieid 0f Classi?cation Search rality of single machine scheduling problems; independently
USPC ~~~~~~ ~~ 700/ 99*1 04; 705/ 712*713, 722*728; solving the plurality of single machine scheduling problems _ _ 7i 8/1 0&1 04 thereby calculating a plurality of near optimal single machine See aPPiiCaiiOii iiie for Coiiipiate Search history scheduling problem solutions; integrating the plurality of _ near optimal single machine scheduling problem solutions (56) References Cited into a main multi-machine scheduling problem solution; and Us PATENT DOCUMENTS putputting the main multi-machine scheduling problem solu
1on.
4,858,147 A * 8/1989 ConWell ... .. 706/19
5,155,679 A * 10/1992 Jain et a1. ... .. 700/106 27 Claims, 34 Drawing Sheets
BE§§1EEW$BL3Q§ETSY°3FEZ1U11L£E A‘
MACHlNE SCHEDULING PROBLEMS
FOR axon SMSP, muss DATESAND DUE 55a
1 11120111111511211111151250 ’
RELEASETIMES lNDEPENDENTLV SOLVE THE PLURAUTY OF /5D6
$111015 mums SCHEDULING PROBLEMS
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FIG.
2
PARAMETER DEFINITION
SCHEDULING REPORT
PLANNED/REAL SCHEDULING COMPARISON
JOBS TARDINESS COMPARISON JOBS WEIGHT
COMPARISON COMMUNICATION
GUI WITH THE SYSTEM
VISUALIZATION OF THE RESULTS
(EXTENDS)
SCHEDULING GANTT CHART
204
AGENTJOB AGENTRESOURCE
OPERATIONS’
DISTRIBUTION BY THEIR AGENTSRESOURCE
SOLUTIONS COMMUNICATION TO
AGENTUI
FIG. 4
DEFINE MAIN JOB SHOP SCHEDULING PROBLEM AS A PLURALITY OF SINGLE MACHINE SCHEDULING PROBLEMS
/504
V
INDEPENDENTLY SOLVE THE PLURALITY OF SINGLE MACHINE SCHEDULING PROBLEMS
/506
V
INTEGRATE THE PLURALITY OF SINGLE MACHINE SCHEDULING PROBLEMS
/508
V
OUTPUT SINGLE MAIN JOB SHOP SCHEDULING PROBLEM SOLUTION
END 512
V
FOR EACH SMSP, RELEASE DATES AND DUE
DATES DETERNINED BASED ON INPUTTED /558 OPERATION DUE DATES AND INPUTTED
RELEASETINES
"1
EACH SMSP SOLvED INDEPENDENTLY USING /560 TABU SEARCH ORA GENETIC ALGORITHM
V
INTEGRATE SOLUTIONS TO FIND SOLUTION TO /562 EJSSP
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lnrtral lndrvrduai
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| /
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-0peratlons Scheduling Time
scheduimgbyMachine 10007 10008 10009 10010 10011 10012 10013 10014 10015 10016 10017
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lobstWeignt
Jobs’ Tardiness Textlllode
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M1
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‘ Pause | Stop Play |
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Scheduling Plan
% Time mCommunication If Output |
10000 10005 10010 10015 10020 10025 10030 10035
003 I I Plan
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012 10013 10014 10015 10016 10017
g 02
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M1
E102 H03 1104 B01
S1mu1ation Speed 0 2x @ 10x 0 100x
‘ 1 Pause Stop Play 1
/ LastAction: Processing completed viewHistorxn
/ I
101
