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Optimising resource portfolio planning for capital-intensive industries under process-technology progress

Optimising resource portfolio planning for capital-intensive industries under process-technology progress
Optimising resource portfolio planning for capital-intensive industries under process-technology progress
This paper addresses the problem of resource portfolio planning of firms in high-tech, capital-intensive manufacturing industries. In light of the strategic importance of resource portfolio planning in these industries, we offer an alternative approach to modelling capacity planning and allocation problems that improves the deficiencies of prior models in dealing with three salient features of these industries, i.e. fast technological obsolescence, volatile market demand, and high capital expenditure. This paper first discusses the characteristics of resource portfolio planning problems including capacity adjustment and allocation. Next, we propose a new mathematical programming formulation that simultaneously optimises capacity planning and task assignment. For solution efficiency, a constraint-satisfied genetic algorithm (CSGA) is developed to solve the proposed mathematical programming problem on a real-time basis. The proposed modelling scheme is employed in the context of a semiconductor testing facility. Experimental results show that our approach can solve the resource portfolio planning problem more efficiently than a conventional optimisation solver. The overall contribution is an analytical tool that can be employed by decision makers responding to the dynamic technological progress and new product introduction at the strategic resource planning level.
0020-7343
2625-2648
Yang, Shu-Jung
c7b91fda-ee4f-4ef6-aa45-0bb9c378e5fc
Yang, Feng-Cheng
26488341-0d06-485f-a697-427ca4bfd15e
Wang, Kung-Jeng
1f682579-6277-47bd-bec5-92e7acbeb181
Chandra, Yanto
3812ff86-07e9-48b3-a6d3-bb412af1f830
Yang, Shu-Jung
c7b91fda-ee4f-4ef6-aa45-0bb9c378e5fc
Yang, Feng-Cheng
26488341-0d06-485f-a697-427ca4bfd15e
Wang, Kung-Jeng
1f682579-6277-47bd-bec5-92e7acbeb181
Chandra, Yanto
3812ff86-07e9-48b3-a6d3-bb412af1f830

Yang, Shu-Jung, Yang, Feng-Cheng, Wang, Kung-Jeng and Chandra, Yanto (2009) Optimising resource portfolio planning for capital-intensive industries under process-technology progress International Journal of Production Research, 47, (10), pp. 2625-2648. (doi:10.1080/00207540701644185).

Record type: Article

Abstract

This paper addresses the problem of resource portfolio planning of firms in high-tech, capital-intensive manufacturing industries. In light of the strategic importance of resource portfolio planning in these industries, we offer an alternative approach to modelling capacity planning and allocation problems that improves the deficiencies of prior models in dealing with three salient features of these industries, i.e. fast technological obsolescence, volatile market demand, and high capital expenditure. This paper first discusses the characteristics of resource portfolio planning problems including capacity adjustment and allocation. Next, we propose a new mathematical programming formulation that simultaneously optimises capacity planning and task assignment. For solution efficiency, a constraint-satisfied genetic algorithm (CSGA) is developed to solve the proposed mathematical programming problem on a real-time basis. The proposed modelling scheme is employed in the context of a semiconductor testing facility. Experimental results show that our approach can solve the resource portfolio planning problem more efficiently than a conventional optimisation solver. The overall contribution is an analytical tool that can be employed by decision makers responding to the dynamic technological progress and new product introduction at the strategic resource planning level.

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Accepted/In Press date: 26 May 2007
Published date: 23 March 2009
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 396306
URI: http://eprints.soton.ac.uk/id/eprint/396306
ISSN: 0020-7343
PURE UUID: a42f1ed6-3ed9-45ec-8ae9-6f318c27dfe3

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Date deposited: 18 Jul 2016 14:19
Last modified: 17 Jul 2017 18:50

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Contributors

Author: Shu-Jung Yang
Author: Feng-Cheng Yang
Author: Kung-Jeng Wang
Author: Yanto Chandra

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