Doctoral Dissertations

Date of Award

12-2000

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Industrial Engineering

Major Professor

Ken Kirby

Committee Members

Rupy Sawhney, William Hamel, Hampton Liggett

Abstract

The allocation of cutting tools to machines is an important concern for managers of flexible manufacturing systems. This research was conducted to study the impact of four tool allocation strategies on five performance measures, contingent upon three part-type selection rules. In addition, the average tool inventory and tool consumption rates were evaluated for each tool policy and selection rule. The four tool allocation policies consisted of the bulk exchange, tool migration, tool sharing, and resident tooling. The five performance measures consisted of the average flowtime of parts, the average machine utilization, the robot utilization, the percentage of parts late, and the mean lateness. Simulation was used to study the impact of the tooling strategies on the performance measures. Analysis of variance procedures, graphical comparison charts and Bonferroni multiple comparison tests were used to analyze the data. The results show that clustering tools, based on group technology, is the preferred method for allocating cutting tools to machines. Tool sharing was the preferred tool allocation strategy. Also, tool allocation policies that require tool changes, after a part's machining cycle, increase part flowtimes because parts are delayed in the system due to the increase in tool changing activities. In addition, tool allocation strategies based on tool clustering methods reduced the utilization of resources. The results of this study show that bulk exchange produced lower tool consumption rates per production period during the early periods of production. During the middle and later production periods, tool sharing produced lower tool consumption rates. This study concluded that grouping tools based on the commonality of tool usage results in a lower average inventory per production period. Furthermore, this study showed that the uneven distribution of part-types to machine, under tool clustering methods, affected the average mean lateness of part-type. Moreover, no part-type selection rule outperformed another on ail performance measures. The earliest due date rule produced the lowest mean lateness values for all tool policies. Tool policies that produce low mean flowtimes may not produce low mean lateness values. Managerial implications are discussed with respect to the findings from this study. Further research is needed to evaluate flexible manufacturing systems, which include using different part-type selection rules, machine failures, and hybrids of tool allocation strategies.

Download
Files over 3MB may be slow to open. For best results, right-click and select "save as..."

Share

COinS