Predictable shop scheduling in the presence of machine breakdowns

Sanjay Vasantlal Mehta, Purdue University

Abstract

The predictive production schedule released to the shop floor has two important functions: allocating shop resources to the different jobs to optimize some measure of shop performance and serving as a basis for planning support activities such as material procurement, preventive maintenance and delivery of orders to external or internal customers. This predictive schedule is modified during execution on the occurrence of disruptions such as machine breakdowns or arrival of urgent orders. The schedule modification process may delay or render infeasible the execution of activities planned on the basis of the predictive schedule. Thus it is of interest to develop predictive schedules which can absorb disruptions without affecting planned support activities (schedule predictability), and also maintain high performance in terms of performance measures such as meeting customer due dates. We present a predictable scheduling approach where the predictive schedule is built with such objectives. The effects of disruptions on planned support activities are measured by deviations in completion times of jobs from the predictive schedule to the realized schedule. The specific scheduling models we consider are minimizing maximum lateness (Lmax) on a single machine and in a job shop with random breakdowns. We show that predictable scheduling provides high predictability with minor sacrifices in realized schedule Lmax.

Degree

Ph.D.

Advisors

Uzsoy, Purdue University.

Subject Area

Industrial engineering

COinS