The University of Southampton
University of Southampton Institutional Repository

A resource portfolio model for equipment investment and allocation of semiconductor testing industry

A resource portfolio model for equipment investment and allocation of semiconductor testing industry
A resource portfolio model for equipment investment and allocation of semiconductor testing industry
Profitable but risky semiconductor testing market has led companies in the industry to carefully seek to maximize their profits by developing a proper resource portfolio plan for simultaneously deploying resources and selecting the most profitable orders. Various important factors, such as resource investment alternatives, trade-offs between the price and speed of equipment and capital time value, further increase the complexity of the simultaneous resource portfolio problem. This study develops a simultaneous resource portfolio decision model as a non-linear integer programming, and proposes a genetic algorithm to solve it efficiently. The proposed method is employed in the context of semiconductor testing industry to support decisions regarding equipment investment alternatives (including new equipment procurement, rent and transfer by outsourcing, and phasing outing) for simultaneous resources (such as testers and handlers) and task allocation. Experiments have showed that our approach, in contrast to an optimal solution tool, obtains a near-optimal solution in a relatively short computing time.
0377-2217
390-403
Wang, K.-J.
80013d6b-7ea5-4c50-993e-d5fc2899354d
Wang, S.-M.
c226028d-1bbc-45a4-a41c-44ffb6567d91
Yang, S.-J.
defa92ea-044b-4b03-983b-322a30a47286
Wang, K.-J.
80013d6b-7ea5-4c50-993e-d5fc2899354d
Wang, S.-M.
c226028d-1bbc-45a4-a41c-44ffb6567d91
Yang, S.-J.
defa92ea-044b-4b03-983b-322a30a47286

Wang, K.-J., Wang, S.-M. and Yang, S.-J. (2007) A resource portfolio model for equipment investment and allocation of semiconductor testing industry. European Journal of Operational Research, 179 (2), 390-403. (doi:10.1016/j.ejor.2006.04.006).

Record type: Article

Abstract

Profitable but risky semiconductor testing market has led companies in the industry to carefully seek to maximize their profits by developing a proper resource portfolio plan for simultaneously deploying resources and selecting the most profitable orders. Various important factors, such as resource investment alternatives, trade-offs between the price and speed of equipment and capital time value, further increase the complexity of the simultaneous resource portfolio problem. This study develops a simultaneous resource portfolio decision model as a non-linear integer programming, and proposes a genetic algorithm to solve it efficiently. The proposed method is employed in the context of semiconductor testing industry to support decisions regarding equipment investment alternatives (including new equipment procurement, rent and transfer by outsourcing, and phasing outing) for simultaneous resources (such as testers and handlers) and task allocation. Experiments have showed that our approach, in contrast to an optimal solution tool, obtains a near-optimal solution in a relatively short computing time.

Text
S0377221706002190 - Other
Restricted to Repository staff only

More information

Accepted/In Press date: 3 April 2006
Published date: 1 June 2007
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 396308
URI: http://eprints.soton.ac.uk/id/eprint/396308
ISSN: 0377-2217
PURE UUID: 9f544b22-7a7e-46be-a42f-4896c58faf65

Catalogue record

Date deposited: 18 Jul 2016 14:24
Last modified: 04 Nov 2019 19:47

Export record

Altmetrics

Contributors

Author: K.-J. Wang
Author: S.-M. Wang
Author: S.-J. Yang

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×