Robust schedules and disruption management for job shops

Abstract

This dissertation documents the results of research evaluating policies to schedule for unanticipated disruptions in job shops. The disruptions studied in this research are of two types - machine failure and job release-time. The study was conducted using modified classical job shop problems with the minimize maximum completion time (Cmax) objective. Best random non-delay schedules (BRS) provided job sequences for each machine. Different slack policies based on frequency, duration and location of slacks in the schedule were used to strategically insert slack in the BRS schedule. The resulting robust schedules proactively managed machine or job releasetime disruptions. The change in Cmax quantified schedule robustness. Simulation was used to generate and modify the BRS schedules with and without slacks and disruptions, simulate disruption events and evaluate schedule performance. It was observed that policies that equally distribute slack to all tasks on heavily utilized machines and those that equally distribute slack to all tasks on all machines performed best. When the number of jobs to process was more than the number of machines and "big jobs" with long processing times on heavily utilized machines were present, the policy distributing slack tasks equally on heavily utilized machines exhibited superior performance. For systems with less variability both policies performed equally well. By comparing the average, minimum and maximum schedule deviations across all policies, the study concluded that strategically distributing slack to tasks on heavily utilized machines results in good robust schedules that can absorb the effects of disruptions.