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

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2006.04.006 Byline: K.-J. Wang (a), S.-M. Wang (b), S.-J. Yang (c) Keywords: Production; Capacity planning and allocation; Genetic algorithms; Semiconductor testing 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. Author Affiliation: (a) Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan, ROC (b) Department of Industrial Engineering, Chung-Yuan Christian University, Chung Li 320, Taiwan, ROC (c) Australian Graduate School of Management, The University of Sydney & The University of New South Wales, Australia Article History: Received 3 March 2005; Accepted 3 April 2006