Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/43604
Title: Target-based Optimization in Operations Management
Authors: LONG ZHUOYU
Keywords: inventory management; project selection; target; risk management
Issue Date: 29-May-2013
Source: LONG ZHUOYU (2013-05-29). Target-based Optimization in Operations Management. ScholarBank@NUS Repository.
Abstract: In this thesis, we investigate the decision criteria for two classical problems in operations management, inventory control and project management, by taking into account the effect of aspiration level such as profit target. Different to the existing approach that maximizes the probability of the profit reaching targets, we optimize a new target-oriented decision criterion. In inventory management, we study both single-period and multiple-period problems. For the single-period (newsvendor) problem, the results from our theoretical model happen to be consistent with existing findings in newsvendor experiments. For the multi-period problem, we incorporate the financing decisions, lending/borrowing activities, to smooth out consumptions over time. We show that if borrowing and lending are unrestricted, the optimal financing policy derived from the target-based criterion is to finance consumptions at the target levels for all periods except the last. Moreover, the optimal inventory policy preserves the structure of base-stock policy or (s,S) policy, and could be achieved with relatively modest computational effort. Under restricted financing, we show that the optimal policies are indeed as the same as those that maximize expected additive-exponential utilities, and can be obtained by an efficient algorithm. In project management, we consider a project selection problem where each project has uncertain return with partially characterized probability distribution. The model captures correlation and interaction effects such as synergies. We solve the model using binary search, and obtain solutions of the subproblems from Benders decomposition techniques. As a simple alternative, we describe a greedy heuristic, which routinely provides project portfolios with near optimal underperformance risk.
URI: http://scholarbank.nus.edu.sg/handle/10635/43604
Appears in Collections:Ph.D Theses (Open)

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