Integrated risk/cost planning models for the US Air Traffic system

A prototype network planning model for the U.S. Air Traffic control system is described. The model encompasses the dual objectives of managing collision risks and transportation costs where traffic flows can be related to these objectives. The underlying structure is a network graph with nonseparable convex costs; the model is solved efficiently by capitalizing on its intrinsic characteristics. Two specialized algorithms for solving the resulting problems are described: (1) truncated Newton, and (2) simplicial decomposition. The feasibility of the approach is demonstrated using data collected from a control center in the Midwest. Computational results with different computer systems are presented, including a vector supercomputer (CRAY-XMP). The risk/cost model has two primary uses: (1) as a strategic planning tool using aggregate flight information, and (2) as an integrated operational system for forecasting congestion and monitoring (controlling) flow throughout the U.S. In the latter case, access to a supercomputer is required due to the model's enormous size

PORTFOLIO OPTIMIZATION ALGORITHMS

A milestone in Portfolio Theory is represented by the Mean-Variance Model introduced in 1952 by Harry Markowitz. During the years, mathematicians have developed several different models extending, improving and diversifying the Mean-Variance Model. This paper will briefly present some of these extensions and the resulted models. The aim is to search and identify some connections between portfolio theory and energy production. Analyzing the Mean-Variance Model and its extensions we can conclude that from practical point of view the minimax model is the easiest to be implemented, because the analytical solution is computed with low effort. This model, like all others from Portfolio Theory, has a high sensitivity for mean. We consider that this model fits to our goal (energy optimization) and we intend to implement it in our future research project

A study of frequent pattern and association rule mining: with applications in inventory update and marketing.

Wong, Chi-Wing.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 149-153).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- MPIS --- p.3Chapter 1.2 --- ISM --- p.5Chapter 1.3 --- MPIS and ISM --- p.5Chapter 1.4 --- Thesis Organization --- p.6Chapter 2 --- MPIS --- p.7Chapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Related Work --- p.10Chapter 2.2.1 --- Item Selection Related Work --- p.11Chapter 2.3 --- Problem Definition --- p.22Chapter 2.3.1 --- NP-hardness --- p.25Chapter 2.4 --- Cross Selling Effect by Association Rules --- p.28Chapter 2.5 --- Quadratic Programming Method --- p.32Chapter 2.6 --- Algorithm MPIS_Alg --- p.41Chapter 2.6.1 --- Overall Framework --- p.43Chapter 2.6.2 --- Enhancement Step --- p.47Chapter 2.6.3 --- Implementation Details --- p.48Chapter 2.7 --- Genetic Algorithm --- p.60Chapter 2.7.1 --- Crossover --- p.62Chapter 2.7.2 --- Mutation --- p.64Chapter 2.8 --- Performance Analysis --- p.64Chapter 2.8.1 --- Preparation Phase --- p.65Chapter 2.8.2 --- Main Phase --- p.69Chapter 2.9 --- Experimental Result --- p.77Chapter 2.9.1 --- Tools for Quadratic Programming --- p.77Chapter 2.9.2 --- Partition Matrix Technique --- p.78Chapter 2.9.3 --- Data Sets --- p.81Chapter 2.9.4 --- Empirical Study for GA --- p.84Chapter 2.9.5 --- Experimental Results --- p.92Chapter 2.9.6 --- Scalability --- p.102Chapter 2.10 --- Conclusion --- p.106Chapter 3 --- ISM --- p.107Chapter 3.1 --- Introduction --- p.107Chapter 3.2 --- Related Work --- p.108Chapter 3.2.1 --- Network Model --- p.108Chapter 3.3 --- Problem Definition --- p.112Chapter 3.4 --- Association Based Cross-Selling Effect --- p.117Chapter 3.5 --- Quadratic Programming --- p.118Chapter 3.5.1 --- Quadratic Form --- p.119Chapter 3.5.2 --- Algorithm --- p.128Chapter 3.5.3 --- Example --- p.129Chapter 3.6 --- Hill-Climbing Approach --- p.134Chapter 3.6.1 --- Efficient Calculation of Formula of Profit Gain --- p.134Chapter 3.6.2 --- FP-tree Implementation --- p.135Chapter 3.7 --- Empirical Study --- p.136Chapter 3.7.1 --- Data Set --- p.137Chapter 3.7.2 --- Experimental Results --- p.138Chapter 3.8 --- Conclusion --- p.141Chapter 4 --- Conclusion --- p.147Bibliography --- p.15