Jin Hanqing. / "July 2004." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (p. 133-139). / 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 dissertations and theses, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

Wong, Chi Ying. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (leaves 71-74). / Abstracts in English and Chinese. / Abstract Page --- p.ii / Acknowledgment Page --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Literature Review --- p.5 / Chapter 3 --- Review of minimax portfolio selection model --- p.11 / Chapter 3.1 --- The I∞ model --- p.11 / Chapter 4 --- Portfolio optimization with group investment limits --- p.16 / Chapter 4.1 --- The model --- p.16 / Chapter 4.2 --- The optimal investment strategy --- p.17 / Chapter 4.2.1 --- All assets are risky --- p.18 / Chapter 4.2.2 --- Some riskfree assets are involved --- p.39 / Chapter 4.3 --- Chapter summary --- p.40 / Chapter 5 --- Tracing out the efficient frontier --- p.41 / Chapter 5.1 --- Properties of the efficient frontier --- p.42 / Chapter 5.2 --- The algorithm --- p.51 / Chapter 5.3 --- Time complexity of the algorithm --- p.56 / Chapter 5.4 --- Chapter summary --- p.57 / Chapter 6 --- Finding the investor's optimal portfolio --- p.58 / Chapter 6.1 --- Investor's portfolio with given A --- p.58 / Chapter 6.2 --- Chapter summary --- p.60 / Chapter 7 --- Numerical experiments --- p.61 / Chapter 7.1 --- Finding the efficient frontier numerically --- p.61 / Chapter 7.2 --- Performance between mean-variance model and I∞ model --- p.64 / Chapter 7.2.1 --- Data analysis --- p.64 / Chapter 7.2.2 --- Experiment description and discussion --- p.65 / Chapter 7.3 --- Chapter summary --- p.67 / Chapter 8 --- Conclusion --- p.68 / Bibliography --- p.71 / Appendix --- p.75 / Chapter A --- Stocks for finding the efficient frontiers with and without bound constraints --- p.75 / Chapter B --- List of companies --- p.77 / Chapter C --- Graphical Results --- p.81

published_or_final_version / Mathematics / Master / Master of Philosophy

Digitized by Kansas Correctional Industries

This paper develops a Bayesian persuasion model examining a manager's incentives to gather information when the manager can disseminate this information selectively to users and when the objectives of the manager and the users are not perfectly aligned. The model predicts that, if the manager can choose the subset of users to receive the information, then the manager may gather more precise information. The paper identifies conditions under which a regime that allows managers to grant access to information selectively maximizes aggregate information. Strikingly, this happens when the objectives of managers and users are sufficiently misaligned. These results call into doubt the common belief that forcing managers to provide unrestricted access to information to all potential users is always beneficial.

Digitized by Kansas Correctional Industries

Efficient and effective berth allocation is essential to guarantee high container throughput in a container terminal. Modern mega-terminals are usually comprised of multiple disjointed berths. However, this type of Berth Allocation Problem (BAP) has not attracted a lot of attention from the academic world due to its great complexity. This research develops new methodologies for solving complex BAPs, in particular, BAPs involving quay crane scheduling in a multiple-berth environment. This research develops a mathematical model and a new Branch and Price algorithm (B&P) which hybridizes the column generation approach and the Branch and Bound method (B&B) to generate optimal multiple-berth plans (MBAP) within acceptable time limits. A new exact algorithm based on the label-correcting concept is designed to obtain all potential columns by defining a new label structure and dominance rules. To accelerate the generation of columns, two heuristics are proposed to distribute vessels among berths and to establish the handling sequence of the vessels allocated to each berth. An early termination condition is also developed to avoid the “tailing off effect” phenomenon during column generation process. The effectiveness and robustness of the proposed methodology are demonstrated by solving a set of randomly generated test problems. Since the Berth Allocation Problem (BAP) and the Quay Crane Scheduling Problem (QCSP) strongly interact, this research also studies the Simultaneous Berth Allocation and Quay Crane Scheduling Problem (BAQCSP). An advanced mathematical model and a new hybrid meta-heuristic GA-TS algorithm which is based on the concept of Genetic Algorithm (GA) are developed to solve the proposed BAQCSP effectively and efficiently. A new crossover operation inspired by the memory-based strategy of Tabu Search (TS) and the mutation operation are implemented to avoid premature convergence of the optimization process. The local search ability of TS is incorporated into the mutation operation to improve the exploitation of the solution space. Comparative experiments are also conducted to show the superiority of the performance of the proposed GA-TS Algorithm over the B&B and the canonical GA. Furthermore, this research extends the scope of BAQCSP to consider the Simultaneous Multiple-berth Allocation and Quay Crane Scheduling Problem (MBAQCSP). A MBAQCSP model is developed consisting of various operational constraints arising from a wide range of practical applications. Since MBAQCSP combines the structures of both MBAP and BAQCSP, the exact B&P proposed for solving MBAP can be modified to optimally solve MBAQCSP. However, the calculation time of B&P increases significantly as the V/B ratio (i.e., vessel number to berth number) grows. In order to eliminate this shortcoming, this research develops a GA-TS Aided Column Generation Algorithm which hybridizes the GA-TS Algorithm proposed for solving BAQCSP with the Column Generation Algorithm to locate the optimal or near optimal solutions of MBAQCSP. The computational results show that the proposed hybrid algorithm locates excellent near optimal solutions to all test problems within acceptable time limits, even problems with high V/B ratios. Finally, this research also shows that the proposed GA-TS Aided Column Generation Algorithm can be easily modified to solve MBAP efficiently. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

No description available.

This thesis gives the formal derivations of the so-called Rubinstein's measures of risk aversion and their multivariate generalizations. The applications of these measures in portfolio selection models are also presented. Assuming that a decision maker's preferences can be represented by a unidimensional von Neumann and Morgenstern utility function, we consider a model with an uninsurable initial random wealth and an insurable risk. Under the assumption that the two random variables have a bivariate normal distribution, the second-order co-variance operator is developed from Stein/Rubinstein first-order covariance operator and is used to derive Rubinstein's measures of risk aversion from the approximations of risk premiums. Rubinstein's measures of risk aversion are proved to be the appropriate generalizations of the Arrow-Pratt measures of risk aversion. In a portfolio selection model with two risky investments having a bivariate normal distribution, we show that Rubinstein's measures of risk aversion can yield the desirable characterizations of risk aversion and wealth effects on the optimal portfolio. These properties of Rubinstein's measures of risk aversion are analogous to those of the Arrow-Pratt measures of risk aversion in the portfolio selection model with one riskless and one risky investment. In multi-dimensional decision problems, we assume that a decision maker's preferences can be represented by a multivariate utility function. From the model with an uninsurable initial wealth vector and insurable risk vector having a joint normal distribution in the wealth space, we derived the matrix measures of risk aversion which are the multivariate extension of Rubinstein's measures of risk aversion. The derivations are based on the multivariate version of Stein/Rubinstein covariance operator developed by Gassmann and its second-order generalization to be developed in this thesis. We finally present an application of the matrix measures of risk aversion in a portfolio selection model with a multivariate utility function and two risky investments. In this model, if we assume that the random returns on the two investments and other random variables have a joint normal distribution, the optimal portfolio can be characterized by the matrix measures of risk aversion. / Business, Sauder School of / Graduate

No description available.