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A general methodological approach for determining the selection of military training simulations with respect to military training requirements has not been developed. This thesis undertakes a literature review, which indicated that there was a need for a multi-criteria decision making model to assist acquisition and/or training planners in making training selection decisions. The Analytical Hierarchy Process (AHP) Model was selected from a multi-criteria decision-making model candidate list for evaluation of its efficacy in selecting military training simulations based upon the military training requirements. Four separate trainee populations, Alpha, Beta, Charlie, and Delta, were evaluated. Results from the Alpha study case showed evidence of the AHP model providing consistency between the participants' preferred choice and their demographic background. This indicates that the AHP model may be a useful multi-criteria decision-making method for acquisition and/or training planners. These results indicate that decision-makers should: I) allow for more than a low-level of effort on the front-end when creating the necessary AHP input, 2) reflect on the selection of attributes as a critical step in establishing the AHP model hierarchy, and 3) consider the level of detail needed for input into the AHP model. Further, results from the Beta, Charlie, and Delta populations indicate that an approach has been developed which is consistent across groups and displays strong alternative preferences that are consistent.

Analytics and Decision Support in Health Care Operations Management -- Contents -- Tables & Figures -- Acknowledgments -- The Author -- Introduction -- Chapter-by-Chapter Revisions for the Third Edition -- Chapter 1: Introduction to Analytics and Decision Support in Health Care Operations Management -- Learning Objectives -- Historical Background and the Development of Decision Techniques -- The Health Care Manager and Decision Making -- Importance of Health Analytics: Information Technology (IT) and Decision Support Techniques -- The Scope of Health Care Services, and Recent Trends -- Health Care Services Management -- Distinctive Characteristics of Health Care Services -- Patient Participation -- Simultaneous Production and Consumption -- Perishable Capacity -- The Intangible Nature of Health Care Outputs -- The High Levels of Judgment Called Upon, and the Heterogeneous Nature of Health Care -- Big Data and Data Flow in Health Care Organizations -- Summary -- Key Terms -- Chapter 1 Supplement: Data Analytics in MS Excel: Creating and Manipulating Pivot Tables -- Exercises -- Chapter 2: Predictive Analytics -- Learning Objectives -- Steps in the Predictive Analytics Process -- Identify the Goal of the Predictive Analytics -- Establish a Time Horizon -- Select a Predictive Analytics Technique -- Conduct the Prediction -- Monitor Accuracy -- Predictive Analytics Techniques -- Judgmental Predictions -- Time-Series Technique -- Techniques for Averaging -- Techniques for Trend -- Predictive Techniques for Seasonality -- Accuracy of Predictive Analytics -- Prediction Control -- Summary -- Key Terms -- Exercises -- Chapter 3: Decision Making in Health Care -- Learning Objectives -- The Decision Process -- What Causes Poor Decisions? -- The Decision Level and Decision Milieu -- Decision Making under Uncertainty -- Payoff Table.

AbstractReal‐life decision problems are usually so complex that they cannot be modelled with a single objective function, thus creating a need for clear and efficient techniques for handling multiple criteria to support the decision process. A widely used technique and one commonly taught in general OR/MS courses is goal programming, which is clear and appealing. On the other hand, goal programming is strongly criticized by multiple‐criteria optimization specialists for its non‐compliance with the efficiency (Pareto‐optimality) principle. In this paper we show how the implementation techniques of goal programming can be used to model the reference point method and its extension, aspiration/reservation‐based decision support. Thereby we show a congruence between these approaches and suggest how the GP model with relaxation of some traditional assumptions can be extended to an efficient decision support technique meeting the efficiency principle and other standards of multiobjective optimization theory.

New patterns of U instruction & res are spreading in recent yrs. The RAND Corporation adapted the technique of war gaming to the consideration of diplomatic questions, & today the technique is variously adapted to seminar use. Suggestive procedures have also arisen in connection with psychoanalysis & the study of small groups. It is increasingly obvious that modes of group problem solving are needed that improve the probability of realistic, comprehensive, & timely solutions. The 5 intellectual tasks involved in every problem are distinguished: goal clarification, trend description, analysis of conditioning factors, projection of future developments, invention, & evaluation of policy alternatives. The new audio-visualprint techniques of modern technology are at hand to provide comprehensive & condensed means of storage & recall of information in the seminar room environment. Procedures are proposed by which full knowledge can be taken of these methods in continuing decision seminars, & the results made subject to continual assessment. AA-IPSA.

"Decision Support Systems" published on by Oxford University Press.

This is the second of two papers in which multiple criteria location problems (MCLPs) are discussed. In this paper two major approaches to locational decisionmaking are overviewed: optimizing decision rules (utility-function-based methods) and satisficing decision rules (goal-programming methods). Their advantages and disadvantages are discussed. From these two concepts a quasi-satisficing decision rule is developed and operationalized through a reference point method. A framework for an interactive decision support system (DSS) for tackling MCLPs is proposed. The system integrates a network model with the quasi-satisficing approach. It is argued that the DSS data and analytical components can be effectively integrated by means of the interactive decision support concept that involves a feedback exchange of information between a decisionmaker and a computer-based support system. This concept allows for the exploration of the locational decision problem and the alternative solutions both in decision space and in criterion outcome space.