Browsing by Subject "Decision making -- Mathematical models"
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Item A Sensitivity Model For the Analysis of Capital Budgeting Decisions(Texas Tech University, 1973-12) Lara, Mario A. B. d.Not Available.Item Design of inexact reasoning systems for management problem diagnosis(Texas Tech University, 1990-05) Jung, Dong-GillHuman decision-making becomes more complicated when decision problems arise in less-than-perfect situations-- situations with information imperfection. In these situations, decision quality degrades severely because of the limitation of a human's reasoning capabilities. Promoted by advances in modern computing technology, intelligent decision aid systems have surfaced as a solution to solve that problem. The core of such decision aid systems is an inexact reasoning system. The purpose of this research was to design a robust and efficient reasoning system that can handle the problems with information imperfection effectively. The problem domain of focus was managerial problem diagnosis at a strategic decision level. The research question was whether the new inexact reasoning architecture can help managers to diagnose their problems in a more robust and efficient way than existing inexact reasoning architectures. The task of designing a robust and efficient inexact reasoning architecture was performed by synthesizing the knowledge in two major fields of modern computing technology: the representation of imperfect knowledge and information, and the connectionist computational architectures. Design of the inexact reasoning system, named as GIROS, involved: i) formulation of design criteria; ii) conceptualization and functional specification; iii) architectural design; iv) detailed design; and v) coding and verification. Detailed design required designing a series of algorithms for the functional specification of GIROS. To accomplish the research purpose and to answer the research question, prototype system development was adopted as a base for the methodology of the research. A checklist of the functional capabilities of inexact reasoning systems was developed as a framework for the comparative evaluation of CIROS and the selected inexact reasoning systems. Then, a comparative evaluation was done based on the framework. The evaluation and a demonstration of the use of CIROS seemed sufficient to conclude that GIROS is a robust and efficient inexact reasoning architecture. Moreover, CIROS can handle more diverse types of information imperfection than the selected inexact reasoning systems. CIROS can perform inexact inferencing more efficiently than many other inexact reasoning architectures. And GIROS has its own justification/explanation facility, a capability that is nonexistent in the connectionist architectures.Item Development of a mathematical model for undergraduate mathematics curriculum decision-making(Texas Tech University, 1968-06) Thompson, Paul Edward,Not availableItem Integrated mathematical and financial modeling with applications to product distribution, warehouse location and capacity problems(Texas Tech University, 1985-05) Cokelez, SadikThe main objective of this study is to develop effective integrated models in product distribution system design. The integrated mixed Integer linear programming model developed in this paper concurrently assesses the optimal solution of interrelated problems. Conventional optimization models treat such problems separately. This research has combined the existing models of subproblems with minor modifications to achieve an overall objective. Existing models were drawn from the areas of production operations management, operations research, finance, and statistics. The research has produced general guidelines for: 1. formulation of integrated decision models and their applications to product mix and distribution system design, 2. warehouse location and capacity under diverse situations. This thesis has contributed to production operations management and operations research decisions by developing integrated models with capabilities that serve to: 1. provide a high degree of coordination, adaptability, and flexibility, 2. provide cost-effective model usage, 3. prevent suboptimality caused by treating the individual models separately. The integrated decisions regarding which warehouses to operate and what quantity to ship from each warehouse have been the cornerstones of product distribution system design. The warehouse location problem has attracted much attention. Without warehouses, shipping direct from factory to customers may result in higher costs due to the inability to ship bulk and in long shipping time. Also, the warehouses act as collection points for several factories, thereby enabling a mix of products to be shipped to customers. Existing warehouse location models do not integrate production decisions and are useful only to agencies or middlemen who are in the transportation or warehousing businesses, not producers themselves. The models that treat production problems individually may give suboptimal results and artificially-generated subjective supply figures; they suffer from the artificial restrictions Imposed by individual models such as subjectively predetermined supply figures, subjectively predetermined warehouse capacity ranges or meeting all the demand even when it is not profitable to do so. The integrated mixed Integer linear model developed in this research is more comprehensive. For this reason, there was a need for a more sophisticated and realistic integrated model capable of handling diverse problems without imposing the artificial restrictions mentioned above. This research developed a unified and highly coordinated mixed integer programming model to address product mix, transportation, warehouse location, warehouse capacity and overcapacity Issues concurrently. This unified model allows insertion, deletion, and choice of individual models and it is very flexible. It has also been shown through test problems that profits were much higher using the integrated decision model developed in this paper than using conventional optimization techniques. Finally, this study extended the warehouse location problem by analyzing various factors affecting warehouse location and distribution Analyzing techniques required experiments on the computer, followed by comprehensive mathematical proofs. The effects of an Increase or decrease in distances among possible warehouse sites on the degree of warehouse centralization were analyzed. In addition, the effects of changes in resource consumption of products were studied. The analysis ended with a study of relationship between warehouse location costs and warehouse distribution and appropriate conclusions were drawn.Item John Foster Dulles' perceptions of the People's Republic of China: a study of belief systems and perception in the analysis of foreign policy decision-making(Texas Tech University, 1973-12) Gilbert, Jerry D.Not availableItem Study on the relationship of training data size to error rate and the performance comparison for two decision tree algorithms(Texas Tech University, 2004-08) Zheng, JianjunThe decision tree model is a well accepted and widely used classification technique in the data mining field because of its advantages with fast construction, accuracy, and understandability. The decision tree model can be induced through algorithms, such as C4.5 and CART. This thesis research studies the relationship of training data size to error rate for the C4.5 and CART algorithms, and also compares the performance of both of them. Several conclusions are drawn from the results of this thesis research; for example, the well accepted 66.7:33.3 splitting ratio in the literature can be increased to 80:20 for large data sets with more than 1000 samples to generate more accurate decision tree models. This thesis research also shows that the performance of C4.5 and CART on small data sets are similar, but differ on large data sets; therefore, large data sets are more suitable for comparing different algorithms.