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