Airlift scheduling for the upgraded command and control system of military airlift command.

April 1984This report describes a conceptual design for automation of the scheduling of airlift activities as part of the current upgrade of the MAC C2 System. It defines the airlift scheduling problem in generic terms before reviewing the current procedures used by MAC; and then a new scheduling system aimed at handling a very busy and dynamic wartime scenario, is introduced. The new system proposes "Airlift Scheduling Workstations" where MAC Airlift Schedulers would be able to manipulate symbolic information on a computer display to create and quickly modify schedules for aircraft, crews, and stations. For certain sub-problems in generating schedules, automated decision support algorithms would be used interactively to speed the search for feasible and efficient solutions. Airlift Scheduling Workstations are proposed to exist at each "Scheduling Cell", a conceptual organizational unit which has been given sole and complete responsibility for developing the schedule of activities for a specific set of airlift resources-aircraft by tail number, aircrew by name, and stations by location. A Mission Scheduling Database is located at each cell to support the Airlift Scheduling Workstation, and requires information communicated by Airlift Task Planners, and, Airlift Operators at many other locations. These locations would have smaller workstations with local databases, and database management software to assist Task Planners and Operators in viewing current committed and planned schedule information of particular interest to them, and to allow them to send information to the Mission Scheduling Database. The Command and Control processes for Airlift have been structured into a three level hierarchy in this report: Task Planning, Mission Scheduling, and Schedule Execution. Task Planners deal with Airlift Users and Mission Schedulers, but not Airlift Operators. Task Planning has three sub-processes: Processing User Requests; Assigning Requirements and Resources; and Monitoring Task Status. Task planning does not create missions, schedule the missions, or route aircraft. Mission Schedulers deal with Task Planners and Airlift Operators, but not Airlift Users. Mission Scheduling combines several sub-processes to allow efficient schedules to be quickly generated at the ASW (Airlift Scheduling Workstation). These sub-processes are: Mission Generation, Schedule Map Generation (for each type of aircraft), Crew Mission Sequence Generation, Station Schedule Generation, Management of Schedule Status, and Monitoring Schedule Execution and Resource Status. It is important that all these processes be co-located and processed by the Airlift Scheduling Cell. Schedule Execution is performed by Airlift Operators assigned by the scheduling process. It has three sub-processes: Monitor Assigned Schedules, Report Resources Assigned to Schedule, Report Local Capability Status. The assignment of local resources such as aircraft by tail, and crew by name is actually another scheduling process, but has not been studied in this report. Airlift Operators do not deal with Task Planners, but may deal with Airlift Users to finalize details of the scheduled operations. This three level hierarchy is compatible with the current organizational structures of MAC Command and Control. However, it is clear that both the current organizational structures and procedures of MAC Command and Control for both tactical and strategic airlift will be significantly affected by the introduction of the automated scheduling systems envisioned here. These changes will occur in an evolutionary manner after the upgraded MAC C2 system is introduced.Prepared for the Electronic Systems Division, Air Force Systems Command, USAF, Hanscom Air Force Base, Bedford, M

Statistical techniques to forecast the demand for air transportation

August 1977Includes bibliographical references (p. 76-78)Introduction and objectives: For some time now regression models, often calibrated using the ordinary least-squares (OLS) estimation procedure, have become common tools for forecasting the demand for air transportation. However, in recent years more and more decision makers have begun to use these models not only to forecast traffic, but also for analyzing alternative policies and strategies. Despite this increase in scope for the use of these models for policy analysis, few analysts have investigated in depth the validity and precision of these models with respect to their expanded use. In order to use these models properly and effectively it is essential not only to understand the underlying assumptions and their implications which lead to the estimation procedure, but also to subject these assumptions to rigorous scrutiny. For example, one of the assumptions that is built into the ordinary least-squares estimation technique is that the explanatory variables should not be correlated among themselves. If the variables are fairly collinear, then the sample variance of the coefficient estimators increases significantly, which results in inaccurate estimation of the coefficients and uncertain specification of the model with respect to inclusion of those explanatory variables. As a corrective procedure, it is a common practice among demand analysts to drop those explanatory variables out of the model for which the t-statistic is insignificant. This is not a valid procedure since if collinearity is present the increase in variance of the coefficients will result in lower values of the t-statistic and rejection from the demand model of those explanatory variables which in theory do explain the variation in the dependent variable. Thus, if one or more of the assumptions underlying the OLS estimation procedure are violated, the analyst must either use appropriate correction procedures or use alternative estimation techniques. The purpose of the study herein is three-fold: (1) develop a "good" simple regression model to forecast as well as analyze the demand for air transportation; (2) using this model, demonstrate the application of various statistical tests to evaluate the validity of each of the major assumptions underlying the OLS estimation procedure with respect to its expanded use of policy analysis; and, (3) demonstrate the application of some advanced and relatively new statistical estimation procedures which are not only appropriate but essential in eliminating the common problems encountered in regression models when some of the underlying assumptions in the OLS procedure are violated. The incentive for the first objective, to develop a relatively simple single equation regression model to forecast as well as analyze the demand for air transportation (as measured by revenue passenger miles in U.S. Domestic trunk operations), stemmed from a recently published study by the U.S. Civil Aeronautics Board [CAB, 1976]. In the CAB study a five explanatory variable regression equation was formulated which had two undesirable features. The first was the inclusion of time as an explanatory variable. The use of time is undesirable since, from a policy analysis point of view, the analyst has no "control" over this variable, and it is usually only included to act as a proxy for other, perhaps significant, variables inadvertently omitted from the equation. The second undesirable feature of the CAB model is the "delta log" form of the equation (the first difference in the logs of the variables),which allowed a forecasting interval of only one year into the future. This form was the result of the application of a standard correction procedure for collinearity among some of the explanatory variables. In view of these two undesirable features, it was decided to attempt to improve on the CAB model. In addition to the explanatory variables considered in the CAB study a number of other variables were analyzed to determine their appropriateness in the model. Sections II and III of this report describe the total set of variables investigated as well as a method for searching for the "best" subset. Then, Section IV outlines the decisions involved in selecting the appropriate form of the equation. The second objective of this study is to describe a battery of statistical tests, some common and some not so common, which evaluate the validity of each of the major assumptions underlying the OLS estimation procedure with respect to single equation regression models. The major assumptions assessed in Section V of this report are homoscedasticity, normality, autocorrelation, and multicollinearity. The intent here is not to present all of the statistical tests that are available, for to do so would be the purpose of regression textbooks, but to scrutinize these four major assumptions enough to remind the analyst that it is essential to investigate in depth the validity and precision of the model with respect to its expanded use of policy analysis. It is hopeful that the procedure outlined in this report sets an example to demand modeling analysts of the essential elements used in the development of reliable forecasting tools. The third and ultimate objective of this work is to demonstrate the use of some advanced corrective procedures in the event that any of the four above mentioned assumptions have been violated. For example, the problem of autocorrelation can be resolved by the use of generalized least-squares(GLS), which is demonstrated in Section VI of this report; and the problem of multicollinearity , usually corrected by employing the cumbersome and restrictive delta log form of equation, has been eliminated by using Ridge regression (detailed in Section VII). Finally, in Section VIII an attempt is made to determine the "robustness" of a model by first performing an examination of the residuals using such techniques as the "hat matrix", and second by the application of the recently developed estimation procedures of Robust regression. Although the techniques of Ridge and Robust regression are still in the experimental stages, sufficient research has been performed to warrant their application to significantly improve the currently operational regression models

Data Science in a Pandemic

Data Science has the potential to provide humanity with critical insight into the massive data being collected during a pandemic. The COVID-19 pandemic presented that opportunity, and Data Science supported an international audience promptly, reliably, effectively, and frequently during that difficult time. The most significant contributions were data visualizations and data dashboards, however, other tools, such as predictive and prescriptive analytics, were equally critical to the effort. The urgency at the start of the pandemic was to quickly communicate information to citizens, governments, and institutions. The change in modality from traditional statistical metrics and tables to data visualizations was extremely significant and helpful to so many. This paper reviews these contributions by demonstrating how the COVID-19 story unfolded through author-generated data visualizations and dashboards, and by providing the community with open-source access to the scripts that generated these visualizations. The open-source access to the (R language) scripts reflects this article’s novelty in the literature. Using publicly available datasets from multiple sources, and employing R toolkits, the author validates the role that Data Science can play in a pandemic, and that can be implemented by anyone with some basic knowledge of scripting languages, like R. The intent is to provide these valuable tools to the community and to demonstrate their effectiveness in the likely event when there is another crisis

A Lesson Plan for Communicating the Sustainability of an Enterprise

Sustainability is the ability to endure. For any organization or any enterprise, sustainability is the ability to remain productive long term while minimizing waste and creating value. An organization can achieve sustainability if it aligns itself with the product and service needs of its customers and wants and interests of its multiple stakeholders. The enterprise, whether it is an ecological, environmental, human, or service enterprise, must possess five “abilities” to be sustainable: availability; dependability; capability; affordability; and marketability. This paper presents a lesson plan or strategy for how an enterprise should communicate/promote sustainability to its stakeholders based on these five abilities

Can Scalability be a Marketing Liability for Sustainability?

A common principle of modern business marketing is that growth is good. It is usually thought that all businesses should market themselves with the goal of increasing their revenues and gaining market share. Scalability is developing products or services that people want and figuring out how to produce and promote many of them for lower costs while selling more of them (Dudnik 2010). It is the purpose of this paper to show that some businesses, especially small ventures with unique value propositions, should not necessarily seek to grow or scale up. There are numerous examples of new ventures failing for various reasons, and many of these have to do with growth. While new ventures frequently offer creative solutions to market needs, there are also aspects of these businesses that will be crucial to maintain while scaling up. Consequently, some new ventures may succeed based on aspects of the organization that are not feasible to scale, and thus a more conservative growth strategy should be undertaken

A Lesson Plan For Sustainability In Higher Education

Much anxiety surrounds the future of higher education in the United States. With escalating costs, tuition, and class sizes, and the increasing exclusion of many poor and minority students, higher education needs to become more accessible and sustainable. This paper defines five “abilities” for the sustainability of higher education - availability, dependability, capability, affordability, and marketability. The literature indicates that components of each of these abilities are lacking at many institutions. To remedy this problem, the authors developed a framework for sustainability based on these five abilities, and a case study at a public university in the United States was used to validate the framework’s applicability to education

Presentations from the 1992 MIT/industry cooperative research program annual meeting

Cover titleMay 1992Includes bibliographical reference

Presentations from the 1995 MIT/industry cooperative research program annual meeting.

Cover titl

Presentations from the MIT/Industry Cooperative Research Program Annual Meeting, 1991

Cover titleMay 1991Includes bibliographical reference