Application of a hybrid genetic algorithm and neural network approach in activity-based costing
Introduction
Cost allocation is a very important task involved in many engineering and business decisions. In this sense, activity-based costing (ABC) has received extensive attention during the past decade because it was developed for overcoming the problems of the traditional costing system through more a reasonable cost allocation process. Conventional ABC, however, has some problems to be resolved. The first problem is that ABC does not have general criteria to select relevant cost drivers. This problem is related to the cost-drivers optimization (CDO) problem and associated with the efficiency of costing systems. Second, conventional ABC generally assumes a linear cost function. The linear cost function is a function where the graph of total costs versus a single cost driver forms a straight line within the relevant range. Horngren, Foster, and Datar (1997) pointed out that a cost function, in practice, is not always linear, but sometimes shows nonlinear behavior. They described a nonlinear function as a cost function where the graph of total costs versus a single cost driver does not form a straight line within the relevant range. In this aspect, conventional ABC may distort product costs when a cost behavior shows a nonlinear behavior. Thus, the second problem is associated with cost estimating relationships (CERs), and it is also related to the effectiveness of costing systems.
Prior researchers have endeavored to overcome these problems of conventional ABC. Some of these studies showed that the estimating performance of artificial neural network (ANN) outperforms that of linear regression for optimal cost allocation in ABC (Bode, 1998a, Bode, 1998b, Creese and Li, 1995, Garza and Rouhana, 1995, Lee, 1993, Lee and Ahn, 1993, Smith and Mason, 1997). Other studies proved the efficiency of heuristic search techniques to select optimal cost drivers (Babad and Balachandran, 1993, Levitan and Gupta, 1996). However, they did not consider these two problems simultaneously.
This study proposes a hybrid model composed of genetic algorithms (GAs) and ANN to resolve the above two problems of conventional ABC simultaneously. First, GA portion of the model is proposed as an optimization method of relevant cost drivers. Second, ANN portion of the model is used to reflect a nonlinear cost function for the cost allocation process. In the hybrid model, the GA globally searches and seeks an optimal or near-optimal ANN topology.
The paper is organized as follows: Section 2 reviews related prior studies. Section 3 presents a description of our hybrid model of GA and ANN. In addition, 4 Research data and experiments, 5 Experimental results describe the process of experiments and experimental results. Finally, Section 6 discusses the conclusions and the limitations of the study and future research issues.
Section snippets
Prior research
In general, traditional costing systems, volume-based costing systems usually with a single cost driver, distort the cost allocation process because they allocate costs by only one criterion such as direct labor-hour, machine-hour, or unit of production. Indirect costs, however, do not always behave in proportion to the single cost driver in practice. In addition, if cost drivers are not selected appropriately, traditional approach does not accurately allocate indirect costs to the products.
The hybrid model of GA and ANN
The GA is a search algorithm based on survival of the fittest among string structures (Goldberg, 1989). Recently, the GA has been investigated and shown to be effective in exploring a complex space in an adaptive way, guided by the biological evolution mechanisms of reproduction, crossover, and mutation (Adeli & Hung, 1995).
The first step of the GA is problem representation. The problem must be represented in a suitable form to be handled by the GA. Thus, the problem is described in terms of
Research data and experiments
This study selects a revised case excerpted from Cooper and Kaplan, 1991, Lee, 1993. This is the case of Destin Brass Products Co., which produces valves, pumps, and flow controllers. The initial cost drivers (activities) are ‘Setup labor’, ‘Receiving’, ‘Materials handling’, ‘Engineering’, ‘Packing and shipping’, ‘Maintenance’, and ‘Machine usage’. The description of the research data is as follows:
The possible production range of valves is 3500–10,500 products at intervals of 2000 products,
Experimental results
For validating the proposed model, 25 holdout data items are used. After experiments, the GA selects four cost drivers including Receiving, Materials handling, Maintenance, and Machine usage from all seven drivers. In addition, the GA recommends three hidden nodes in ANN. Table 3 presents mean-absolute-percent-error (MAPE) by different experimental conditions.
The results show that the proposed hybrid model consistently outperforms conventional ANN, regardless of changes in the number of hidden
Concluding remarks
This study has proposed the hybrid AI model to resolve the problems of designing ABC systems. In this study, the GA was used for the step of selecting relevant cost drivers and optimizing the network topology of ANN. The GA globally searched an optimal or near-optimal ANN topology in the hybrid model. The proposed model outperformed the conventional model. From the experimental results, we concluded that the proposed model has advantages when the model analyzes the data with complex and
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