Doctoral Dissertations

Date of Award

8-1996

Degree Type

Dissertation

Degree Name

Doctor of Philosophy

Major

Management Science

Major Professor

Kenneth Gilbert

Committee Members

Mary Keitnaker, Melissa Bowers, Charles Noon

Abstract

The focus of this research is the development of a solution approach that will aid decision makers in understanding the tradeoffs associated with sequencing to meet due dates versus sequencing to level component usage for the mixed-model sequencing problem with specific customer due dates. In this problem, the product due dates are determined by the customer and each product on the assembly line is associated with a specific customer due date. This method of manufacturing leads to 'build-to-order' production rather than 'build-to-stock' production.

The problem of sequencing in the 'build-to-order' environment with specific customer due dates is a new research area that is the main focus of this work. Mixed-model sequencing techniques have been developed for use in the Just-In-Time, repetitive manufacturing, build-to-stock environment. A majority of these scheduling methods are designed to optimize component usage by keeping a constant rate of usage of parts in the system. Other techniques incorporate the goal of leveling the work load at each station on the assembly line. Along with studying the due date, part usage tradeoff, this research also introduces a new sequencing goal: reducing the time spread of producing a customer’s order. This new goal is currently an important issue in industry, but is not studied in the mixed-model sequencing literature.

This research adds insight to the problem of determining which items and how many items should be sequenced late to better meet the goal of leveling the component usage for a specified time period. A set of new sequencing methods are designed and tested on two populations with varying results. Also, a new sequencing goal for leveling component usage over the short term as well as for the long run, is discussed and incorporated into the algorithms.

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