An Algorithm for Exchanging Target Asset Pairs using the Kidney Exchange Model

Since chemical, biological, radiological, nuclear, and high yield explosive (CBRNE) attacks can cause catastrophic damage, it is important to detect and eliminate the means of attack at the origin. In surveillance operations, efficient allocation of friendly intelligence assets and enemy targets is critical for continuous and reliablemonitoring. In this research, we investigate a mathematical model for exchanging target–asset pairs when there are sudden changes in various operational environments. For this task, we refer to the kidney exchange model as a benchmark. In particular, the methods for constructing and solving the target–asset exchange problem in near realtime are presented. Additionally, we introduce the methodology and results for obtaining a feasible solution of the weapon target assignment problem using the exchange model. Our method can facilitate decisions in reconnaissance operations, especially when countless targets and assets are intricately intertwined in future battlefield scenarios

Malmquist Productivity Analysis using DEA frontier in Stata

In this presentation, the author presents a procedure and an illustrative application of a user-written Malmquist Productivity Analysis(MPA) using Data Envelopment Analysis (DEA) frontier in Stata. MPA measures the productivity changes for units between time periods. MPA has been used widely for assessing the productivity changes of public and private sectors, such as banks, airlines, hospitals, universities, defense firms, and manufacturers when the panel data are available. The MPA using DEA frontier in Stata will allow Stata users to conduct not only the stochastic approach for productivity analysis using SFA frontier but also non-stochastic approach using DEA frontier also suggested by the author. The user-written MPA approach in Stata will provide some possible future extensions of Stata programming in the productivity analysis.

An Efficient Data Envelopment Analysis with Large Data Set in Stata

In this presentation, the author presents an approach to improve the computational efficiency of Data Envelopment Analysis(DEA) with large data set in Stata. Since the author written "dea" program in Stata was presented in the DC09 Stata conference, the author reviewed various comments and requests by the Stata users and updated the code significantly in terms of computation time and model variants. The presentation illustrates an approach to reduce the computation time and improve the accuracy of DEA results using the 5 inputs and 1 output data set of 365 DMUs.

Data envelopment analysis

In this article, we introduce a user-written data envelopment analysis command for Stata. Data envelopment analysis is a linear programming method for assessing the efficiency and productivity of units called decision-making units. Over the last decades, data envelopment analysis has gained considerable attention as a managerial tool for measuring performance of organizations, and it has been used widely for assessing the efficiency of public and private sectors such as banks, airlines, hospitals, universities, defense firms, and manufacturers. The dea command in Stata will allow users to conduct the standard optimization procedure and extended managerial analysis. The dea command developed in this article selects the chosen variables from a Stata data file and constructs a linear programming model based on the selected dea options. Examples are given to illustrate how one could use the code to measure the efficiency of decision-making units

Data envelopment analysis

In this article, we introduce a user-written data envelopment analysis command for Stata. Data envelopment analysis is a linear programming method for assessing the efficiency and productivity of units called decision-making units. Over the last decades, data envelopment analysis has gained considerable attention as a managerial tool for measuring performance of organizations, and it has been used widely for assessing the efficiency of public and private sectors such as banks, airlines, hospitals, universities, defense firms, and manufacturers. The dea com- mand in Stata will allow users to conduct the standard optimization procedure and extended managerial analysis. The dea command developed in this article selects the chosen variables from a Stata data file and constructs a linear programming model based on the selected dea options. Examples are given to illustrate how one could use the code to measure the efficiency of decision-making units. Copyright 2010 by StataCorp LP.dea, data envelopment analysis, linear programming, nonparametric, efficiency, decision-making units

Technology Prediction for Acquiring a Must-Have Mobile Device for Military Communication Infrastructure

The smartphone is a must-have mobile device for the military forces to accomplish critical missions and protect critical infrastructures. This paper explores the applicability of a technology prediction methodology to manage technological obsolescence while pursuing the acquisition of advanced commercial technology for military use. It reviews the Technology Forecasting using Data Envelopment Analysis (TFDEA) methodology and applies an author-written Stata program for smartphone technology forecasting using TFDEA. We analyzed smartphone launch data from 2005 to 2020 to predict the adoption of smartphone technology and discuss the pace of technological change. The study identifies that the market is undergoing reorganization as new smartphone models expand the market and increase their technical performance. The average rate of technological change, the efficiency change, and the technology change were 1.079, 1.004, and 1.011 each, respectively, which means that the technology progressed over the period. When dividing before and after 2017, technological change and efficiency change generally regressed except for Huawei, Xiaomi, and Oppo. This means that Chinese smartphones are expanding the global market in all directions and the technology is reaching maturity and market competition is accelerating