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

REDECS 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

REDECS AGENDA (2)  SOFTWARE  CONCLUSION  FUTURE WORKS

REDECS 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!!

REDECS 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

REDECS 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

REDECS  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

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

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

REDECS 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

REDECS 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.

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

REDECS 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

REDECS 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.

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

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

REDECS 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

REDECS G.A CBR G.A

REDECS Constraints Also constraintsNew Schedule

REDECS 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

REDECS 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:

REDECS 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.

REDECS THE END Thank You. Questions?

REDECS Improve Chromosome Representation less higher

REDECS Schedule Overlapping Overlapping!!

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

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

REDECS 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