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.
| Original language | English |
|---|---|
| Pages (from-to) | 2625-2648 |
| Number of pages | 24 |
| Journal | International Journal of Production Research |
| Volume | 47 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - Jan 2009 |
Bibliographical note
Funding Information:The authors wish to thank the anonymous referee whose comments greatly helped in improving the presentation of this paper. We would also like to acknowledge the help from Nidthida Perm-Ajchariyawong in improving the readability of the paper, and Dr. Shih-Min Wang for the computational effort in the paper. The research of the corresponding author was supported in part by the Australian Graduate School of Management, and the Department of Management and Marketing, University of Melbourne. The research of the second and third authors has been partially supported by the National Science Council of the Republic of China. The research of the final author was partially supported by the School of Marketing, University of New South Wales, and the University of Amsterdam Business School.
Keywords
- Capacity allocation
- Capacity planning
- Genetic algorithms
- Resource portfolio