Research in the aviation domain, driving distractions, anesthesia administration, and nuclear power plant control rooms show that Concurrent Task Management (CTM) is a process that every human operator performs when interacting with complex environments. The need for understanding concurrent task management in a broader perspective more applicable and generalizable to different domains, led to the development of the Augmented Stage Model (ASM) of human information processing and the development of a test bed where hypotheses deriving from the augmented stage model can be tested. The ASM is an elaboration of the current Stage Model attempting to explain CTM in terms of those basic stages of human information processing and drawing on relevant, recent psychological research. One question that arises from the creation of the augmented stage model is to what degree the augmented stage model can be justified by actual human CTM performance. A corollary of this question is to what degree can CTM performance be explained by performance in simple tests that are derived directly from the stages of the model. To answer this question, 94 participants were tested on several standard cognitive tests suggested by the ASM: i.e. simple and complex reaction time, decision making, working memory, and intelligence. Performance in the cognitive tests was compared to participants' CTM performance in a multitasking simulator called the Task Management Environment (TME). The findings indicated that basic cognitive abilities, except for working memory, do not correlate significantly with CTM performance as calculated by the TME. Performance on three working memory tests was shown to predict up to 47% of the variation in CTM performance. This suggests that simple cognitive abilities do not predict CTM performance. Although, cognitive abilities might be a component of CTM, a combination of them might prove to better predict CTM performance. / Graduation date: 2004

The research presented in this dissertation addresses the Multi-Mode Resource-Constrained Project Scheduling Problem (MMRCPSP) in the presence of resource unavailability. This research is motivated by the scheduling of engineering design tasks in automotive product development to minimize the project completion time, but addresses a general scheduling situation that is applicable in many contexts. The current body of MMRCPSP research typically assumes that, 1) individual resource units are available at all times when assigning tasks to resources and, 2) before assigning tasks to resources, there must be enough resource availability over time to complete the task without interruption. In many situations such as assigning engineering design tasks to designers, resources are not available over the entire project-planning horizon. In the case of engineering designers and other human resources, unavailability may be due to several reasons such as vacation, training, or being scheduled to do other tasks outside the project. In addition, when tasks are scheduled they are often split to accommodate unavailable resources and are not completed in one continuous time segment. The objectives of this research are to obtain insight into the types of project scheduling situations where task splitting may result in significant makespan improvements, and to develop a fast and effective scheduling heuristic for such situations. A designed computational experiment was used to gain insight into when task splitting may provide significant makespan improvements. Problem instances were randomly generated using a modification of a standard problem generator, and optimally solved with and without task splitting using a branch and bound algorithm. In total 3,880 problem instances were solved with and without task splitting. Statistical analysis of the experimental data reveals that high resource utilization is the most important factor affecting the improvements obtained by task splitting. The analysis also shows that splitting is more helpful when resource unavailability occurs in multiple periods of short duration versus fewer periods of long duration. Another conclusion from the analysis indicates that the project precedence structure and the number (not amount) of resources used by tasks do not significantly affect the improvements due to task splitting. Using the insights from the computational testing, a new heuristic is developed that can be applied to large problems. The heuristic is an implementation of a simple priority rule-based heuristic with a new parameter used to control the number of task splits. It is desirable to obtain the majority of task splitting benefits with the smallest number of split tasks. Computational experiments are conducted to evaluate its performance against known optimal solutions for small sized problems. A deterministic version of the heuristic found optimal solutions for 33% of the problems and a stochastic version found optimal solutions for over 70%. The average percent increase in makespan compared to optimal was 7.58% for the deterministic heuristic and less than 2% for the stochastic versions demonstrating acceptable performance. / Graduation date: 2003

Thesis (Ph. D.)--University of California, San Diego, and San Diego State University, 2003. / Vita. Includes bibliographical references (leaves 98-102).

Thesis (Ph. D.)--Ohio State University, 2008. / Title from first page of PDF file. Includes bibliographical references (p. 93-103).

Thesis (Ph. D.)--West Virginia University, 2004. / Title from document title page. Document formatted into pages; contains ix, 70 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 42-47).

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1997. / Vita. Abstract. "UMI number : 9812512"--Verso of t.p. Includes bibliographical references (p. 64-73). Available electronically via Internet.

Dissertation (Ph.D.)--The University of North Carolina at Greensboro, 2009. / Directed by Gerald G. Duffy; submitted to the Dept. of Curriculum and Instruction. Title from PDF t.p. (viewed May 17, 2010). Includes bibliographical references (p. 133-145).

Thesis (M.S. in Computer Science)--Naval Postgraduate School, March 2006. / Thesis Advisor(s): Rudolph P. Darken. "March 2006." Includes bibliographical references (p. 45-47). Also available online.

Thesis (Ph. D.)--University of Hong Kong, 2006.

Thesis (M.A.I.S.)--Oregon State University, 2008. / Printout. Includes bibliographical references (leaves 49-52). Also available on the World Wide Web.