redecs 20011 adaptive ship maintenance rescheduling 24 - 25 october, 2001 residence hotel uniten...
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REDECS 2001 1
ADAPTIVE SHIP MAINTENANCE RESCHEDULING
24 - 25 October, 2001RESIDENCE HOTELUNITENKAJANG
PATHIAH ABDUL SAMAT (UPM)ALICIA TANG Y. C. (UNITEN) -- Presenter
HAJAR MAT JANI (UNITEN)NOR’ASHIKIN ALI (UNITEN)
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AGENDA (1)
PROBLEM DEFINITION– WHAT IS THE PROBLEM?– OBJECTIVES
BACKGROUND INFORMATION– WHAT HAD BEEN DONE?
OUR APPROACH– CBR + GA– HOPFIELD Neural Network– Operational Research Framework
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AGENDA (2)
SOFTWARE CONCLUSION FUTURE WORKS
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PROBLEM DEFINITION (1)
Ships - assets in naval defence Ships - expensive They should be fully utilised High rate of availability is anticipated AVAILABILITY
– depends on effectiveness of Preventive Maintenance Schedule (PMS)
Unable to avoid rescheduling!!
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PROBLEM DEFINITION (2) If (uncertainty) breakdowns occur
–availability of ship is Low availability and high
maintenance costs are problems in ship maintenance management
This problem can jeopardise the defence system of the country
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PROBLEM DEFINITION (3)
SHIP MAINTENANCE (RE)SCHEDULING– is a process of deciding start-times of
maintenance activities that satisfy all precedence and resource constraints & optimize the ship availability.
variables
domains
constraintsresult
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Objectives: Proposals–to develop Adaptive Algorithms
• to decide (select) which activity to reschedule
–to develop Hopfield Neural N.• to reschedule
PROBLEM DEFINITION (4)Go There
Click Me
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MAINTENANCE SCHEDULE FOR A SHIP
Factors– Running hours of the ships– Operational requirement– Status of parts availability– Status of operational defects– Dockyard availability
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BACKGROUND INFORMATION (1)
Scheduling / time-tabling problem – Neural Network
– Constraints Logic Programming
– Graph Coloring
– Heuristics, etcE.g. ILOG, CHIP
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BACKGROUND INFORMATION (2)
CONSTRAINT SOLVING– Reduce search domain/space
– therefore faster & save storage
– how? It minimizes backtracking
• Solve problems: ‘design’, ‘diagnosis’ & ‘planning’• Build schedule that satisfies ‘temporal’ and ‘resource’ constraints
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BACKGROUND INFORMATION(4)
Improve G.A. by improving chromosome representation
(increase ship availability) Achieved by search space (such as minimising overlapping of
maintenance activity)
WHAT HAD BEEN DONE?
Table 1
overlappingRefer to articles 1 & 3, references section.
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OUR APPROACH (1)
USE GA– To “optimise”
USE CBR– To find near optimum
schedule that maximises availability
Hybrid Vsjust CBR
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OUR APPROACH (2)
TO RE-SCHEDULE:–USE HOPFIELD NN
• CONSTRAINTS
• NEURON
–BASED ON CBR-GA DERIVED DATA
2 LAYERS
Soumen and Badrul (1996) - rescheduling of power system
Item#7
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THE HYBRID G.A. ALGORITHM
Step 1: code the start times and pattern of activity
Step 2: create initial population Step 3: determine start times and pattern of
activity by the GA Step 4: build feasible schedule using CBR Step 5: evaluate the schedule.
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Proposal
Analyse data and research question
Changes,i.eUncertaintybreakdown Process constraint
Time andResourceConstraint
Analyse the part of CBR-GA where adaptive enhancement can be made
Testing the adaptive algorithm
Develop adaptive algorithm
Adaptive?
Analyse existing performance measure
Reconstructperformancemeasure
Analyse rescheduling algorithm
Develop rescheduling algorithm
Testing rescheduling algorithm
Reschedule?optimiseReportresult
Develop newperformancemeasure
R. O.
FRAMEWORK
N
N
N
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SOFTWARE
PLATFORM–Unix, Windows
NT/ME/2000/9xPROPOSED LANGUAGE
–C++Used in previous works
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Proposed Software Components
Scheduling program Ship program (Solver) Constraints program G.A Maintenance program Many header files Adaptive scheduler Rescheduling using Hopfield Neural Net
Keeps repeating until “fit” enough
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Procedure GA-CBR
BeginCreate constraint variable S1
Read constraints C(S1)Post constraints C(S1) to variable S1
Post constraint C(Si) to variable S1
t 0initialise P(t)evaluate P(t)while (not termination condition) do
Begint t + 1select P(t) from P(t – 1)alter P(t)copy allele values from P(t) to value v of constraint variable S1
BeginPartitioning values v, into pattern of activity, c and pattern of activity, stuses pattern of activity c to create pattern of activity C1 for each ship activitiescopy st into first maintenance activities, stset type of resource, sumPbpost constraint from file, smscstPROCESS-CONSTRAINTS( Si, v)Total objective-function, obj, for each domain,Get total-objective, objTot for whole activities, mtnReturn objTot
EndEndReturn legal values v to genome in population P(t)Evaluate P(t)End While
End GA-CBR
G.ACBR
G.A
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g
g
f
f
f
x1
x2
xn
g
c1
c2
c3
b1
b2
b3
Constraints layer Neurone layer
Constraints Also constraints New Schedule
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CONCLUSION (1) Re-design of existing algorithms is
necessary. Therefore, new algorithms need to
be developed. Reschedule of activities based on
the temporal and resource constraints is required so as to adapt to the changes that may occur.
Rescheduling Algorithms
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CONCLUSION (2)
•CBR + G.A - to produce near optimum solution.
•Enhancement to be made to CBR.
•Hopfield Neural Network - to reschedule selected activities.
Our solutions:
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FUTURE WORKS
Fuzzy Logic - to address “over constraints” of the selection of activities and the rescheduling process.
Application in other areas: School time-tabling, Financial control and planning, Classification & Prediction.
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THE END
Thank You.
Questions?
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Improve Chromosome Representation
ChromosomeFitness function(minimising number of overlapping)
With pattern activities Ship of class A 0.84 Ship of class B 0.75
Without pattern activities Ship of class A 0.98 Ship of class B 0.82
less
higher
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Schedule Overlapping
Ship 1
Ship 2
Ship 3
Ship 4Weeks
Overlapping!!
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CBR Vs. Hybrid
Comparison between the CBR and the hybrid approaches:
Approaches Objective function (minimisingno. of overlapping activities)
CBR alone 950.76CBR+GA 0.98
CBR alone 1540.20CBR+GA 0.82
Class A
Class B
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Pattern activities and start-time
Start time Pattern-1 Pattern-2 Pattern-3 Pattern-4 Pattern-5 Pattern-61234::16
1 2 3 ……….. 67 8 ……………..13 ……….:::91 ……………………… 96
An allele
Combination of no. of activities + duration of operation
Refer to figure 2, full paper
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Values of GA parameters for Ship Class A No. of population = 45 No. of generation = 60 Probability of mutation = 0.01 Type of crossover = single-point Type of GA = steady state Size of chromosome = 4 Size of allele = 96 Fitness function = maximise availability Scaling = Linear scaling