Simulation Study of Scheduling Heuristics for Parallel Machines with Sequence-Independent Setups

Abstract

In this paper we examine a dynamic job scheduling problem on identical, parallel machines. Jobs that belong to different families arrive to the system at random time intervals. Stochastic processing times of jobs are considered and each family has different due date allowances. The manufacturing system consists of a single input queue and several machines. When switching from one job type to another on some machine, a sequence-independent setup is incurred. The scheduling of jobs is performed by some priority rule and nine alternative rules are considered in this study. The discrete-event simulation model of the system is developed and the performance of the scheduling heuristics is assessed in terms of the metrics of mean cycle time, mean earliness and mean tardiness. A total of thirteen simulation cases is considered, where the differentiating factors of the cases are the average job interarrival times, the due date and the setup parameters of jobs.