【Title】Remanufacturing strategies for a technology product

【Time】2011-11-4 15:00-17:00

【Location】Weilun 453，Tsinghua SEM

【Langage】Chinese

【Abstract】

Remanufacturing is a large, growing sector of the US economy, with an increasing number of firms turning to remanufacturing to offer an expanded product portfolio and to improve profits. Motivated by current remanufacturing practices, we investigate remanufacturing strategies for a technology product with lost sales, capturing the key characteristics of such systems, including short product lifetimes, mismatched supply and demand, heterogeneous qualities of returned products, and eroding resale and salvage values of products. We introduce a general (GEN) remanufacturing framework where a firm has two types of production capacity at its disposal: regular production that has a lower remanufacturing cost but a longer (1-period) lead-time and expedited production that has a higher remanufacturing cost but a shorter (zero) lead time. We analyze the policy structure of GEN and develop an efficient myopic policy. We further specialize GEN into manufacturing-to-stock (RTS) and remanufacturing-to-order (RTO) systems. RTS remanufactures selected returns using only regular production and stocks remanufactured inventory to meet demand. We show that the optimal policy for RTS is characterized by quality prioritization (i.e., returned items should be remanufactured in decreasing order of their qualities) and quality-dependent base-stock levels (i.e., the cumulative quantity to be remanufactured from the first $i$ best quality returns should not exceed a quality $i$-dependent threshold). In contrast, RTO holds selected returns as inventories and remanufactures them only via expedited production after observing demand. As the optimal policy structure for RTO appears intractable, we propose a myopic policy that resembles the quality-dependent base-stock policy for RTS. In addition, exploiting the characteristics of both systems, we propose a hybrid (HYB) system that switches from RTS to RTO at an appropriate time in the product lifetime. Our numerical study shows that HYB not only outperforms RTS and RTO, but also performs close to optimality of GEN over a wide range of practical settings.

【Bio】

Prof. Xu is Professor of Management Science and Supply Chain Management at Pennsylvania State University. She currently serves as the Director of the PhD Program in Smeal College of Business. She served as the Chair of the Intercollege Dual-Title Degree Graduate Program in Operations Research at Penn State from 1998-2007. Prof. Xu’s primary research interests are centered on design, performance evaluation, simulation and optimization of stochastic operating systems and their applications in supply chain management and service systems, telecommunication, information technology, and reliability. In particular, she is interested in production and inventory systems, risk management of supply chain, revenue management, queueing control, stochastic ordering of multivariate stochastic processes, maintenance policies and risk analysis in reliability systems. Prof. Xu has published widely in leading academic journals, including Management Science, Operations Research, Mathematics of Operations Research, Manufacturing & Service Operations Management, Production and Operations Management, among others. Her research is currently supported by NSF grants CMMI-0825960 and CMMI-1000183 and the Competitive Research Program in Smeal College of Business. She has served as an Associate Editor and an editorial board member in several academic journals. She has also held visiting positions in several institutions in the US and abroad.