Job Scheduling for Processes with Sequence-Dependent Changeover Times

Abstract

Increasing competitiveness in the markets force companies to increase the efficiency in their operations. One way of increasing the efficiency is decreasing the wastes, including the setup times. Setup processes differ in nature depending on the industry and type of manufacturing. For example, companies operating in the Sheet Metal Forming industry face sequence-dependent tool changeovers; i.e. sequencing of jobs affect the total setup time significantly. This increases the complexity of the scheduling problems; thus, effective approaches are required to solve them. Besides the efficiency, another importance of this work is that we include different components of tool changeovers in our calculations.

This thesis work was inspired by a real scheduling problem in the case company. Four different algorithms were considered to solve the sequencing problems with sequence-dependent setup times. We received real data from the customer of the case company and also generated random data set to achieve statistically significant conclusions. Comparative analyses showed that Clustering approach performs better than others. Different sensitivity analyses were conducted, again showing the effectiveness of the Clustering algorithm. So, this algorithm was proposed to the case company to be implemented in their software.