Environmental regulations or the necessity for a green image due to growing environmental concerns as well as the potential economical benefits of product recovery have pushed manufacturers to integrate product recovery management with their manufacturing process. Consequently, production planning and inventory control of recoverable manufacturing systems has gained significant interest among researchers who aim to contribute to industrial practice. This dissertation considers inventory optimization of a single product recoverable manufacturing system where stochastic demand is met by either newly manufactured items or remanufactured items. Lead times and set up costs for manufacturing and remanufacturing are considered. The inventory optimization problem for this system is formulated as a Markov decision process (MDP) and through an empirical study, optimal or near-optimal policy characterizations under several cost configurations and several lead time cases for manufacturing and remanufacturing are determined. The effects of a change in cost parameters of the system on the optimal policy structure as well as policy parameter values are investigated. Results indicate that the existence of set up cost for either manufacturing or remanufacturing has a significant effect on policy structure. Consequently, an MDP-based search procedure is introduced to determine the inventory policy characterizations given that appropriate policy structures under certain cost configurations are known. Further, a neural network analysis is performed to determine the functional relationships between cost parameters of the system and the inventory policy parameter values. Results indicate that the policy characterizations found by either MDP-based search methodology or the formulae provided by neural network are optimal or near-optimal with small deviations (usually, less than 1%) from optimal cost. Finally, the optimal inventory policies are investigated through the entire product life cycle of a remanufacturable product. Benefiting from the MDP analysis, the optimal or near-optimal policy characterizations with only a few parameters are determined for every stage of the product life cycle. The effects of a change in the demand and return rates on the optimal inventory policies are investigated. Further, the performance of these long-run policy characterizations is evaluated in a finite-horizon setting, and the importance of frequently revising the inventory policies over the product life cycle is illustrated numerically.
| Identifer | oai:union.ndltd.org:NCSU/oai:NCSU:etd-03192008-154625 |
| Date | 28 March 2008 |
| Creators | Ahiska, Semra Sebnem |
| Contributors | Dr. Russell E. King, Dr. Thom J. Hodgson, Dr. Kristin A. Thoney-Barletta, Dr. Jeffrey A. Joines |
| Publisher | NCSU |
| Source Sets | North Carolina State University |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://www.lib.ncsu.edu/theses/available/etd-03192008-154625/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dis sertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to NC State University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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