Inventory Optimization in a One Product Recoverable Manufacturing System

Inventory Control;Policy Characterization;Recoverable Manufacturing System;Remanufacturing;Markov Decision Process;Neural Network;Product Life Cycle

Ahiska, Semra Sebnem ; Dr. Russell E. King, Committee Chair,Dr. Thom J. Hodgson, Committee Co-Chair,Dr. Kristin A. Thoney-Barletta, Committee Member,Dr. Jeffrey A. Joines, Committee Member,Ahiska, Semra Sebnem ; Dr. Russell E. King ; Committee Chair ; Dr. Thom J. Hodgson ; Committee Co-Chair ; Dr. Kristin A. Thoney-Barletta ; Committee Member ; Dr. Jeffrey A. Joines ; Committee Member

University:North Carolina State University

关键词: Inventory Control;    Policy Characterization;    Recoverable Manufacturing System;    Remanufacturing;    Markov Decision Process;    Neural Network;    Product Life Cycle;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/4967/etd.pdf?sequence=2&isAllowed=y

美国|英语

来源: null

【 摘 要 】

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.