On Pareto Optimal Solution for Production and Maintenance Jobs Scheduling Problem in a Job Shop and Flow Shop with an Immune Algorithm

Abstract

In the paper a job shop and flow shop scheduling problems with availability time constraint for maintenance are considered. Unavailability time due to maintenance is estimated basing on information about predicted Mean Time To Failure/To First Failure and Mean Time of Repair of a machine. Maintenance actions are introduced into a schedule to keep the machine available in a good operation condition. The efficiency of predictive schedules (PS) is evaluated using criteria: makespan, flow time, total tardiness, idle time. The efficiency of reactive schedules (RSs) is evaluated using criteria: solution and quality robustness. For basic schedule generation Multi Objective Immune Algorithm is applied. For predictive scheduling Minimal Impact of Disturbed Operation on the Schedule is applied. After doing computer simulations for the job shop scheduling problem following question arises: do dominated Pareto optimal basic schedules achieve better PSs? Although a single Pareto-optimal solution is achieved on Pareto-optimal frontier three different schedules have the same quality in the flow shop scheduling problem. The question is: which schedule is the most robust solution?