1. REDECS 20011 ADAPTIVE SHIP MAINTENANCE RESCHEDULING 24 - 25 October, 2001 RESIDENCE HOTEL UNITEN KAJANG PATHIAH ABDUL SAMAT (UPM) ALICIA TANG Y. C. (UNITEN) -- Presenter HAJAR MAT JANI (UNITEN) NOR’ASHIKIN ALI (UNITEN)

2. REDECS 20012 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

3. REDECS 20013 AGENDA (2)  SOFTWARE  CONCLUSION  FUTURE WORKS

4. REDECS 20014 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!!

5. REDECS 20015 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

6. REDECS 20016 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 constraints result

7. REDECS 20017  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

8. REDECS 20018 MAINTENANCE SCHEDULE FOR A SHIP  Factors –Running hours of the ships –Operational requirement –Status of parts availability –Status of operational defects –Dockyard availability

9. REDECS 20019 BACKGROUND INFORMATION (1)  Scheduling / time-tabling problem –Neural Network –Constraints Logic Programming –Graph Coloring –Heuristics, etc E.g. ILOG, CHIP

10. REDECS 200110 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

11. REDECS 200111 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 overlapping Refer to articles 1 & 3, references section.

12. REDECS 200112 OUR APPROACH (1)  USE GA – To “optimise”  USE CBR – To find near optimum schedule that maximises availability Hybrid Vs just CBR

13. REDECS 200113 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

14. REDECS 200114 THE HYBRID G.A. ALGORITHM  Step 1: code the start times and pattern of activitystart 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.

15. REDECS 200115 R. O. F R A M E W O R K N N N

16. REDECS 200116 SOFTWARE  PLATFORM –Unix, Windows NT/ME/2000/9x  PROPOSED LANGUAGE –C++ Used in previous works

17. REDECS 200117 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

18. REDECS 200118 G.A CBR G.A

19. REDECS 200119 Constraints Also constraintsNew Schedule

20. REDECS 200120 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

21. REDECS 200121 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:

22. REDECS 200122 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.

23. REDECS 200123 THE END Thank You. Questions?

24. REDECS 200124 Improve Chromosome Representation less higher

25. REDECS 200125 Schedule Overlapping Overlapping!!

26. REDECS 200126 CBR Vs. Hybrid Comparison between the CBR and the hybrid approaches: ApproachesObjective function (minimising no. of overlapping activities) CBR alone950.76 CBR+GA0.98 CBR alone1540.20 CBR+GA0.82 Class A Class B

27. REDECS 200127 Pattern activities and start-time An allele Combination of no. of activities + duration of operation Refer to figure 2, full paper

28. REDECS 200128 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