Paul E. Torgersen
Virginia Tech
8 Papers
14 Citations
Paul E. Torgersen is an academic researcher from Virginia Tech. The author has contributed to research in topics: Higher education & Productivity. The author has an hindex of 2, co-authored 8 publications.
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Papers
Bidding-Work Loading Game
TL;DR: In this article, a management game is developed which allows participants to schedule and load work under the constraint of a fixed work load capacity. But the game requires a trade-off between the security of a commitment to a relatively full work load against the potential for more lucrative work opportunities that would otherwise have to be ignored.
5
Engineering education; the “P” word and continuous quality improvement
TL;DR: In this article, a modest beginning is described at Virginia Tech, where accountability for undergraduate education is discussed. But, as institutions of higher education are increasingly being required to restructure and become more productive, so also is accountability for undergrads.
4
A Machine Release Scheme for the Job Shop Work Center
TL;DR: In this paper, the authors view the job shop as a network of queues with each work center then defined as a single or multichannel service facility and the effect of learning at the service facility will be seen as a reduction of queue congestion and a lessening of unit waiting time.
2
A reexamination of Barnard's theory of organization
TL;DR: The Functions of the Executive (Barnard, 1938) as discussed by the authors is a series of eight lectures at the Lowell Institute in Boston by a practicing executive, Chester I. Barnard, who provided an historical bridge between mechanistic and somewhat harsh scientific management era of Frederic Taylor and the more recent behavioral insights that now characterize management thought.
1
The development and design of the surgical scheduler's hospital management game using GASP II
Richard O. Hoffman,Paul E. Torgersen +1 more
- 01 Jan 1971
TL;DR: This paper presents a game designed to introduce the participants to the scheduling function of a hospital's surgical service, set within a probabilistic environment, includes an adaptive learning feature, and uses GASP II simulation language.