Key Words: inventory systems, product returns, closed-loop supply chain, remanufacturing, optimal strategies, approximation algorithms. / Product returns have become a significant part for many manufacturing, distribution, and retail systems. These products are returned under different operational conditions that require different remanufacturing effort/costs. In this thesis, we study several inventory models with remanufacturing, in particular, remanufacturing inventory systems with multiple types of returns, with non-identical processing leadtime, with random yields and with history dependent returns. Optimal manufacturing/remanufacturing policies for such inventory systems are fully or partially characterized. We have also developed effective algorithms or easy-to-implement heuristics for controlling such systems. / Tao, Zhijie. / Adviser: Sean X. Zhou. / Source: Dissertation Abstracts International, Volume: 73-01, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 136-140). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

No description available.

No description available.

LD2668 .T4 1966 S836 / Master of Science

Tsan-Ming Choi. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 117-118). / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Literature Review --- p.3 / Chapter 1.3 --- Summary of Classic Results --- p.6 / Chapter Chapter 2 --- Two-Stage Single Ordering 一 Unknown Mean and Variance --- p.12 / Chapter 2.1 --- Mathematical Model --- p.12 / Chapter 2.2 --- "Order Quantities, Expected Profits and Expected Quantities of Goods Sold" --- p.16 / Chapter 2.3 --- Benefits from Forecast Update --- p.21 / Chapter 2.4 --- Applying the Model --- p.25 / Chapter 2.5 --- Application Example --- p.25 / Chapter Chapter 3 --- Two-Stage Single Ordering with Ordering Cost Difference --- p.28 / Chapter 3.1 --- Mathematical Model --- p.28 / Chapter 3.2 --- Order Quantity and Expected Profit --- p.31 / Chapter 3.3 --- Two-Stage Dynamic Programming Formulation --- p.33 / Chapter 3.4 --- Application Example --- p.37 / Chapter 3.5 --- Sensitivity Studies --- p.39 / Chapter Chapter 4 --- Two-Stage Two-Ordering with Ordering Cost Difference --- p.44 / Chapter 4.1 --- Mathematical Model --- p.44 / Chapter 4.2 --- Dynamic Programming Formulation --- p.47 / Chapter 4.3 --- Optimal Order Quantities --- p.51 / Chapter 4.4 --- Application Example --- p.52 / Chapter 4.5 --- Sensitivity Studies --- p.54 / Chapter Chapter 5 --- Multiple-Stage Single Ordering with Ordering Cost Difference --- p.60 / Chapter 5.1 --- Mathematical Model --- p.61 / Chapter 5.2 --- Order Quantity and Expected Profit --- p.64 / Chapter 5.3 --- Dynamic Programming Formulation --- p.64 / Chapter 5.4 --- Approximation by Taylor Series Expansion --- p.72 / Chapter 5.5 --- Approximation by Polynomial --- p.76 / Chapter 5.6 --- Comparison of Taylor Series and Polynomial Approximations --- p.84 / Chapter 5.7 --- Application Examples --- p.85 / Chapter 5.8 --- Sensitivity Studies --- p.91 / Chapter 5.9 --- Extension from Two Stages to Multiple Stages --- p.98 / Chapter 5.10 --- Non-Monotonicity of Cutting Points --- p.99 / Chapter Chapter 6 --- Real World Applications --- p.102 / Chapter 6.1 --- Background --- p.102 / Chapter 6.2 --- Two-Stage Cases --- p.105 / Chapter 6.3 --- Multiple-Stage Cases --- p.109 / Chapter Chapter 7 --- Conclusion and Further Studies --- p.115 / References --- p.117 / Appendix --- p.119

The first essay considers a dynamic non-stationary inventory problem in which replenishment is made in fixed lot sizes (e.g., in full truckloads or full containers). We consider two separate cases: one with exogenous pricing and the other with endogenous pricing. In the first case (exogenous pricing), we show that when the ordering cost contains only a variable component, the reorder-point lot-size policy or (r, Q) policy is optimal for both single-stage and multi-echelon inventory systems. In the presence of a fixed cost, we establish the optimality of batch based (s, S ) policies for the single-stage inventory system. In the second case (endogenous pricing), we show that when the demand function has the additive form and there is only a variable ordering cost, the (r,Q) list-price policy is optimal for the single-stage system, where inventory replenishment follows an (r,Q) policy and the optimal price in each period depends on the order-up-to level. / The second essay analyzes a periodic-review, stochastic, inventory-control system in which the fixed order-cost is a step function of the order size. In particular, if the order size is within a specified limit, C, then the setup cost is K1; otherwise it is K2, where K2 ≥ K1. This cost structure is motivated from some industrial applications and transportation/production contracts used in practice. Under the condition that K1 ≤ K 2 ≤ K1, we introduce a new concept called C - (K1 ≤ K 2) convexity, which enables us to partially characterize the structure of an optimal ordering policy. For the general condition K 1 ≤ K2 , the analysis is facilitated with a different notion called strong K-convexity. Based on this analysis, we provide a partial characterization of the optimal policy and construct an easy-to-implement heuristic method that has near-optimal performance in random test instances. Our study extends or redevelops (with different techniques) several existing results in the literature. / The third essay studies a firm's periodic-review production/inventory ordering decisions when the next period's setup cost depends on the quantity produced/ ordered in the current period. In particular, if the current period's production/order quantity exceeds a specified threshold value, the system starts the next period in a "warm" state and no fixed setup cost is incurred; otherwise the state is considered "cold" and a positive setup cost is required for production/ ordering. We develop a dynamic programming formulation of the problem and provide a partial characterization of the optimal policy under the assumption that the demands follow a Polya or Uniform distribution. We use the structural results to develop fairly simple heuristic policies, which perform highly effectively in our computational experiments. / With increased globalization and competition in the current market, supply chain has become longer and more complicated than ever before. An effective and efficient supply chain is crucial and essential to a successful firm. In a supply chain, inventories are a very important component as the investment in inventories is enormous. This dissertation consists of three essays related to stochastic inventory management. / Yang, Yi. / Adviser: Youhua Chen. / Source: Dissertation Abstracts International, Volume: 73-04, Section: B, page: . / Thesis (Ph.D.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 145-151). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest dissertations and theses, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

Zhang Han. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 44-46). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background Study --- p.4 / Chapter 3 --- "The Cost Function under the (s,c2,c1, S) Policy" --- p.8 / Chapter 4 --- "Determination of the Optimal (s,c2,c1,s) Policy" --- p.16 / Chapter 4.1 --- "The Auxiliary Function lγ(s, c2, C1, 5)" --- p.16 / Chapter 4.2 --- Optimizing Parameters of s(γ) and s(γ) --- p.18 / Chapter 4.3 --- Optimizing Parameters c2 (γ) and c1 (γ) --- p.21 / Chapter 5 --- "Algorithm for Computing the Optimal (s, c2, C1, S) Policy" --- p.27 / Chapter 5.1 --- Brief Description of the Algorithm --- p.27 / Chapter 5.2 --- The Statement of the Algorithm --- p.28 / Chapter 5.3 --- Interpretation of the Algorithm --- p.29 / Chapter 6 --- "The Optimality of the (s, c2，c1, S) Policy" --- p.31 / Chapter 6.1 --- Average Cost Criterion --- p.31 / Chapter 6.2 --- The Proof of Optimality --- p.34 / Chapter 6.3 --- "Optimality Proof for the Modified (s,c2,C1,S) Policy in the Re- laxed Model" --- p.38 / Chapter 6.3.1 --- Introduction of the Relaxed Model --- p.38 / Chapter 6.3.2 --- Average Cost Criteria for the Relaxed Model --- p.38 / Chapter 7 --- Conclusion --- p.42 / Bibliography --- p.43

Yu, Yikun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (p. 117-120). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Review --- p.6 / Chapter 3 --- System with Controllable Returns --- p.13 / Chapter 3.1 --- Problem Description --- p.13 / Chapter 3.2 --- The Model with Controllable Returns --- p.17 / Chapter 3.3 --- Numerical Experiments --- p.25 / Chapter 3.4 --- Summary --- p.34 / Chapter 4 --- System with Controllable Returns and Price Dependent Demand --- p.36 / Chapter 4.1 --- System without Disposals --- p.37 / Chapter 4.2 --- The System with Disposals --- p.40 / Chapter 4.3 --- Numerical Experiments --- p.47 / Chapter 4.4 --- Summary --- p.54 / Chapter 5 --- Conclusion --- p.56 / Chapter 6 --- Appendix --- p.58

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