Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Bian, Linkan
Committee Member
Marufuzzaman, Mohammad
Committee Member
Medal, Hugh R.
Committee Member
Eksioglu, Burak
Date of Degree
12-9-2016
Document Type
Dissertation - Open Access
Major
Industrial and Systems Engineering
Degree Name
Doctor of Philosophy
College
James Worth Bagley College of Engineering
Department
Department of Industrial and Systems Engineering
Abstract
In this dissertation, we study two important problems related to additive manufacturing (AM). In the first part, we investigate the economic feasibility of using AM to fabricate biomedical implants at the sites of hospitals AM versus traditional manufacturing (TM). We propose a cost model to quantify the supply-chain level costs associated with the production of biomedical implants using AM technology, and formulate the problem as a two-stage stochastic programming model, which determines the number of AM facilities to be established and volume of product flow between manufacturing facilities and hospitals at a minimum cost. We use the sample average approximation (SAA) approach to obtain solutions to the problem for a real-world case study of hospitals in the state of Mississippi. We find that the ratio between the unit production costs of AM and TM (ATR), demand and product lead time are key cost parameters that determine the economic feasibility of AM. In the second part, we investigate the AM facility deployment approaches which affect both the supply chain network cost and the extent of benefits derived from AM. We formulate the supply chain network cost as a continuous approximation model and use optimization algorithms to determine how centralized or distributed the AM facilities should be and how much raw materials these facilities should order so that the total network cost is minimized. We apply the cost model to a real-world case study of hospitals in 12 states of southeastern USA. We find that the demand for biomedical implants in the region, fixed investment cost of AM machines, personnel cost of operating the machines and transportation cost are the major factors that determine the optimal AM facility deployment configuration. In the last part, we propose an enhanced sample average approximation (eSAA) technique that improves the basic SAA method. The eSAA technique uses clustering and statistical techniques to overcome the sample size issue inherent in basic SAA. Our results from extensive numerical experiments indicate that the eSAA can perform up to 699% faster than the basic SAA, thereby making it a competitive solution approach of choice in large scale stochastic optimization problems.
URI
https://hdl.handle.net/11668/19563
Recommended Citation
Emelogu, Adindu Ahurueze, "Optimal Supply Chain Configuration for the Additive Manufacturing of Biomedical Implants" (2016). Theses and Dissertations. 3393.
https://scholarsjunction.msstate.edu/td/3393
Comments
clustering||enhanced sample average approximation||sample average approximation||deployment configuration||large scale supply chain||continuous approximation||Additive manufacturing||biomedical implants||stochastic programming model