Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Industrial Engineering

Major Professor

Fong-Yuen Ding

Committee Members

Denise F. Jackson, Dukwon Kim, Melissa R. Bowers


Mixed model assembly has been widely used in many industries. It is applied in order to effectively deal with increasing product complexity. Sequencing and resequencing on a mixed-model assembly line is also complicated by high product complexity. To improve the performance of a mixed-model assembly system and the supply chain, one can develop efficient sequencing rules to address sequencing problems, and manage product complexity to reduce its negative impact on the production system. This research addresses aspects of sequence alteration and restoration on a mixed-model assembly line for the purpose of improving the performance of a manufacturing system and its supply chain, and addresses product complexity analysis. This dissertation is organized into Parts 1, 2, and 3 based on three submitted journal papers.

Part 1. On a mixed-model assembly line, sequence alteration is generally used to intentionally change the sequence to the one desired by the downstream department; and sequence restoration is generally applied to achieve sequence compliance by restoring to the original sequence that has been unintentionally changed due to unexpected reasons such as rework. Rules and methods for sequence alteration using shuffling lines or sorting lines were developed to accommodate the sequence considerations of the downstream department. A spare units system based on queuing analysis was proposed to restore the unintentionally altered sequence in order to facilitate sequenced parts delivery. A queuing model for the repairs of defective units in the spare units system was developed to estimate the number of spare units needed in this system.

Part 2. Research was conducted on product complexity analysis. Data envelopment analysis (DEA) was first applied to compare product complexity related to product variety among similar products in the same market, two DEA models including their respective illustrative models considering various product complexity factors and different comparison objectives were developed. One of these models compared the product complexity factors in conjunction with sales volume. The third DEA model was developed to identify product complexity reduction opportunities by ranking various product attributes. A further incremental economic analysis considering the changes in costs and market impact by an intended complexity change was presented in order to justify a product complexity reduction opportunity identified by the DEA model.

Part 3. Two extended DEA models were developed to compare the relative complexity levels of similar products specifically in automobile manufacturing companies. Some automobile product attributes that have significant cost impact on manufacturing and the supply chain were considered as inputs in the two extended DEA models. An incremental cost estimation approach was developed to estimate the specific cost change in various categories of production activities associated with a product complexity change. A computational tool was developed to accomplish the cost estimation.

In each of the above stated parts, a case study was included to demonstrate how these developed rules, models, or methods could be applied at an automobile assembly plant. These case studies showed that the methodologies developed in this research were useful for better managing mixed-model assembly and product complexity in an automobile manufacturing system and supply chain.

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