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Optimization Models and Algorithms f...

Shi, Yuhui.

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Optimization Models and Algorithms for Prototype Vehicle Test Scheduling.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimization Models and Algorithms for Prototype Vehicle Test Scheduling./
Author:	Shi, Yuhui.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
Description:	148 p.
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
Subject:	Industrial engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10612191
ISBN:	9781369903249

Optimization Models and Algorithms for Prototype Vehicle Test Scheduling.
Shi, Yuhui.

Optimization Models and Algorithms for Prototype Vehicle Test Scheduling. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 148 p.

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

Thesis (Ph.D.)--University of Michigan, 2017.

Automotive makers conduct a series of tests at pre-production phases of each new vehicle model development program. The main goal of those tests is to ensure that the vehicle models meet all design requirements by the time they reach the production phase. These tests target different vehicle components or functions, such as powertrain systems, electrical systems, safety aspects, etc. However, one big issue is that the cost of the resources, mainly prototype vehicles, invested in the testing process is exceedingly expensive. An individual prototype vehicle can cost over 5 times its counterpart's price in the commercial market because many of the parts and the prototype vehicles themselves are highly customized and produced in small batches. Parts needed often require months of lead time, which constrains when vehicle builds can start. That, combined with inflexible time-window constraints for completing tests on those prototypes introduces significant time pressure, an unavoidable and challenging reality. What makes the problem even more difficult is that in addition to the prototype vehicle resources, there are other constrained supporting resources involved during the execution of those tests, such as testing facilities, instruments and equipment like cameras and sensors, human-power availability, etc.

ISBN: 9781369903249Subjects--Topical Terms:

526216
Industrial engineering.

Optimization Models and Algorithms for Prototype Vehicle Test Scheduling.
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100 1 $a Shi, Yuhui. $3 577798
245 1 0 $a Optimization Models and Algorithms for Prototype Vehicle Test Scheduling.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 148 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Advisers: Amy Ellen Mainville Cohn; Marina A Epelman.
502 $a Thesis (Ph.D.)--University of Michigan, 2017.
520 $a Automotive makers conduct a series of tests at pre-production phases of each new vehicle model development program. The main goal of those tests is to ensure that the vehicle models meet all design requirements by the time they reach the production phase. These tests target different vehicle components or functions, such as powertrain systems, electrical systems, safety aspects, etc. However, one big issue is that the cost of the resources, mainly prototype vehicles, invested in the testing process is exceedingly expensive. An individual prototype vehicle can cost over 5 times its counterpart's price in the commercial market because many of the parts and the prototype vehicles themselves are highly customized and produced in small batches. Parts needed often require months of lead time, which constrains when vehicle builds can start. That, combined with inflexible time-window constraints for completing tests on those prototypes introduces significant time pressure, an unavoidable and challenging reality. What makes the problem even more difficult is that in addition to the prototype vehicle resources, there are other constrained supporting resources involved during the execution of those tests, such as testing facilities, instruments and equipment like cameras and sensors, human-power availability, etc.
520 $a An efficient way to conquer the problem is to develop test plans with tight schedules that combine multiple tests on vehicles to fully utilize all available time while balancing the loads of other supporting resources. There are many challenges that need to be overcome in implementing this approach, including complex compatibility relationships between the tests and destructive nature of, e.g., crash tests.
520 $a In this thesis, we show how to mathematically model these test scheduling problems as optimization problems. We develop corresponding solution approaches that enable quick generation of an efficient schedule to execute all tests while respecting all constraints. Our models and algorithms save test planners' and engineers' time, increase q their ability to quickly react to program changes, and save resources by ensuring maximal vehicle utilization.
590 $a School code: 0127.
650 4 $a Industrial engineering. $3 526216
690 $a 0546
710 2 $a University of Michigan. $b Industrial and Operations Engineering. $3 2092213
773 0 $t Dissertation Abstracts International $g 78-11B(E).
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791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10612191