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Resource portfolio planning of make-to-stock products using a constraint programming-based genetic algorithm

  • Wang, S.M.
  • Chen, J.C.
  • Wang, K.-J.
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    The investment on facilities for manufacturing high-tech products requires a large amount of capital. Even though the demands of such products change dramatically, a company is forced to implement some make-to-stock policies apart from a regular make-to-order production, so that the capacity of expensive resources can be highly utilized. The inherent characteristics to be considered include finite budget for investing resources, lump demands of customers, long production horizon, many types of products to mix simultaneously, time value of capital and asset, technology innovation of resources, efficient usage of multiple-function machines, and limited capacity of resources. In addition to revenue gained from products and the salvage/assets of resources, a decision maker also needs to consider costs regarding inventory, backorder, and resource acquisition-related costs through procurement, renting, and transfer. This study thus focuses on the following issues: (i) how to decide on resources portfolio regarding the way and timing of acquisting resources, and (ii) how to allocate resources to various orders in each production period. The goal is to maximize the long-term profit. This study formulates the problem as a non-linear mixed integer mathematical programming model. A constraint programming-based genetic algorithm is developed. It has been demonstrated to solve the problem efficiently.

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    Article provided by Elsevier in its journal Omega.

    Volume (Year): 35 (2007)
    Issue (Month): 2 (April)
    Pages: 237-246

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    Handle: RePEc:eee:jomega:v:35:y:2007:i:2:p:237-246
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    1. Swaminathan, Jayashankar M., 2000. "Tool capacity planning for semiconductor fabrication facilities under demand uncertainty," European Journal of Operational Research, Elsevier, vol. 120(3), pages 545-558, February.
    2. Bashyam, T. C. A., 1996. "Competitive capacity expansion under demand uncertainty," European Journal of Operational Research, Elsevier, vol. 95(1), pages 89-114, November.
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