ADAPTIVE SHIP MAINTENANCE

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ADAPTIVE SHIP MAINTENANCE RESCHEDULING
PATHIAH ABDUL SAMAT (UPM) ALICIA TANG Y. C.
(UNITEN) -- Presenter HAJAR MAT JANI
(UNITEN) NORASHIKIN ALI (UNITEN)
24 - 25 October, 2001 RESIDENCE
HOTEL UNITEN KAJANG
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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)
variables

• 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.

domains
constraints
result
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PROBLEM DEFINITION (4)
Go There

• Objectives Proposals
• to develop Adaptive Algorithms
• to decide (select) which activity to reschedule
• to develop Hopfield Neural N.
• to reschedule

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, etc

E.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)
WHAT HAD BEEN DONE?

• Improve G.A. by improving chromosome
  representation
• (increase ship availability)
• Achieved by ? search space
• (such as minimising overlapping of maintenance
  activity)

Table 1
Refer to articles 1 3, references section.
overlapping
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OUR APPROACH (1)

• USE GA
• To optimise
• USE CBR
• To find near optimum schedule that maximises
  availability

Hybrid Vs just 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
Item7
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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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R. O. F R A M E W O R K
N
N
N
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SOFTWARE

• PLATFORM
• Unix, Windows NT/ME/2000/9x
• PROPOSED 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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G.A CBR
G.A
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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)
Our solutions

• CBR G.A - to produce near optimum solution.
• Enhancement to be made to CBR.
• Hopfield Neural Network - to reschedule selected
  activities.

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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
less
higher
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Schedule Overlapping
Overlapping!!
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CBR Vs. Hybrid
Comparison between the CBR and the hybrid
approaches Approaches Objective function
(minimising no. of overlapping
activities) CBR alone 950.76 CBRGA 0.98 CBR
alone 1540.20 CBRGA 0.82
Class A
Class B
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Pattern activities and start-time
Refer to figure 2, full paper
An allele
Combination of no. of activities duration of
operation
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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