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Allocating fixed cost based on consensus data envelopment analysis approach
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AI
DOI：10.1007/s11750-025-00712-3.png)
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Abstract
En 中文
Existing data envelopment analysis (DEA) methods mainly solve the fixed cost allocation problem in an environment with a powerful central decision-maker. However, there is no central decision-maker in some decision-making environments, which brings new challenges to the fixed cost allocation problem. To this end, this paper proposes a consensus DEA method for allocating fixed costs in the absence of a central decision-maker. First, we propose bounded altruism principle, based on which the cost preference is closer to the actual situation. Second, this paper designs a new DEA allocation model that considers the cost preferences of all decision-making units (DMUs), rendering the individual cost schemes reasonable and acceptable. In addition, we propose a consensus reaching process in that all DMUs can reach a consensus in terms of the cost scheme through egoistic and altruistic adjustment. Finally, the proposed method is applied to two empirical studies of truck fleets and insurance alliance to illustrate its feasibility and practicality.
Keywords:
Data envelopment analysis
Fixed cost allocation
Bounded altruism
Preference
Consensus reaching