國立虎尾科技大學 |

Robust Planning and Execution.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Robust Planning and Execution./
Author:	Lee, Yooneun.
Description:	1 online resource (120 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
Subject:	Industrial engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9781369991819

Robust Planning and Execution.
Lee, Yooneun.

Robust Planning and Execution. - 1 online resource (120 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Uncertainties in a system environment have an adverse impact on the system. The system will yield suboptimal results, inhibit real-time adjustments, and may incur excessive costs for redesign. One of the most common managerial strategies to deal with such uncertainties is to incorporate redundant buffers in planning to absorb changes in system parameters, but more often than not, this strategy leads to waste of resources. Robust optimization is a method to deal with such parameter uncertainties. It postulates the worst case scenario and obtains a solution that provides a protection against the uncertainty. Plans designed for the worst tend to be overengineered; in many cases, when uncertain parameters are revealed, the degree of uncertainty turns out to be no greater than estimated. This effect may lead to underutilization of resources and inflated costs.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369991819Subjects--Topical Terms:

679492
Industrial engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Robust Planning and Execution.
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099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Lee, Yooneun. $3 1182315
245 1 0 $a Robust Planning and Execution.
264 0 $c 2017
300 $a 1 online resource (120 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Vittaldas V. Prabhu.
502 $a Thesis (Ph.D.) $c The Pennsylvania State University $d 2017.
504 $a Includes bibliographical references
520 $a Uncertainties in a system environment have an adverse impact on the system. The system will yield suboptimal results, inhibit real-time adjustments, and may incur excessive costs for redesign. One of the most common managerial strategies to deal with such uncertainties is to incorporate redundant buffers in planning to absorb changes in system parameters, but more often than not, this strategy leads to waste of resources. Robust optimization is a method to deal with such parameter uncertainties. It postulates the worst case scenario and obtains a solution that provides a protection against the uncertainty. Plans designed for the worst tend to be overengineered; in many cases, when uncertain parameters are revealed, the degree of uncertainty turns out to be no greater than estimated. This effect may lead to underutilization of resources and inflated costs.
520 $a Here we present methods for robust planning and execution in various applications. We first consider the problem of scheduling operating rooms for surgeries. In order to avoid allocation of excessive resources and limit the conservativeness of the robust solution, we apply a cardinality-based robust approach. This enables us to control the level of protection in accordance with the solution quality. Experimental results based on real hospital data indicate that our method outperforms the practical gap-based approach in terms of both overtime and underutilization.
520 $a We next suggest a robust productivity index (RPI) to measure organizational performance in a dynamic environment. Compared to the traditional productivity index, it can be used for monitoring time-dependent performance and detecting exceptions. Efficiencies of service providers are evaluated based on panel data on youth outcomes from a selected community prevention program. The results suggest that our approach not only recognizes patterns of productivity progression, but also enables classification of the innovators.
520 $a Lastly, we study the real-time adaptive control and monitoring algorithm of an adaptive Work in Process (WIP) method. The simulation results suggest benefits of the algorithm as a decision support tool for managing work-in-process and throughput level. Convergence and robustness of the algorithm are also investigated.
520 $a For each of these applications, the proposed method is validated and supported in an appropriate manner with detailed discussion of accuracy.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Industrial engineering. $3 679492
650 4 $a Operations research. $3 573517
655 7 $a Electronic books. $2 local $3 554714
690 $a 0546
690 $a 0796
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The Pennsylvania State University. $b The Harold and Inge Marcus Department of Industrial and Manufacturing Engineering. $3 1182316
773 0 $t Dissertation Abstracts International $g 78-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10629078 $z click for full text (PQDT)