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Using Genetic Algorithms to Optimize the Selection of Cost Drivers in Activity‐based Costing

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  • Alan Levitan
  • Mahesh Gupta

Abstract

In this paper, we address a cost‐drivers optimization (CDO) problem in which two separate but interrelated decisions (i.e. the number of cost drivers needed and which cost drivers to use) are considered. It is desirable to have (1) an optimal selection of cost drivers in order to provide better indication of product costs and (2) an optimal number of cost drivers in order to avoid excessive control costs and to minimize information costs associated with data collection, storage and processing. The objective of the CDO problem is to balance savings in information costs with loss of accuracy. We propose an heuristic procedure based on genetic algorithms as an alternative with the potential to address more generalized objective functions. Genetic algorithms represent an innovative and promising heuristic approach which does produce results superior to published alternatives. The development and implementation of the algorithm is supported with the literature review and comparative analysis. We also comment on the complexity and experimental design issues for addressing large and practical problems.

Suggested Citation

  • Alan Levitan & Mahesh Gupta, 1996. "Using Genetic Algorithms to Optimize the Selection of Cost Drivers in Activity‐based Costing," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 5(3), pages 129-145, September.
    • Handle: RePEc:wly:isacfm:v:5:y:1996:i:3:p:129-145
    DOI: 10.1002/(SICI)1099-1174(199609)5:33.0.CO;2-S
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    References listed on IDEAS

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    1. Glover, Fred & Greenberg, Harvey J., 1989. "New approaches for heuristic search: A bilateral linkage with artificial intelligence," European Journal of Operational Research, Elsevier, vol. 39(2), pages 119-130, March.
    2. Gupta, Mahesh C. & Gupta, Yash P. & Kumar, Anup, 1993. "Minimizing flow time variance in a single machine system using genetic algorithms," European Journal of Operational Research, Elsevier, vol. 70(3), pages 289-303, November.
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    Cited by:

    1. James R. Coakley & Carol E. Brown, 2000. "Artificial neural networks in accounting and finance: modeling issues," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 9(2), pages 119-144, June.

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