Multi-objective data envelopment analysis: A game of multiple attribute decision-making

• Authors: Chen Yuh Wen
• Languages of publication: EN

Abstracts

EN

Aim/purpose ‒ The traditional data envelopment analysis (DEA) is popularly used to evaluate the relative efficiency among public or private firms by maximising each firm’s efficiency: the decision maker only considers one decision-making unit (DMU) at one time; thus, if there are n firms for computing efficiency scores, the resolution of n similar problems is necessary. Therefore, the multi-objective linear programming (MOLP) problem is used to simplify the complexity. Design/methodology/approach ‒ According to the similarity between the DEA and the multiple attribute decision making (MADM), a game of MADM is proposed to solve the DEA problem. Related definitions and proofs are provided to clarify this particular approach. Findings ‒ The multi-objective DEA is validated to be a unique MADM problem in this study: the MADM game for DEA is eventually identical to the weighting multi-objective DEA. This MADM game for DEA is used to rank ten LCD companies in Taiwan for their research and development (R&D) efficiencies to show its practical application. Research implications/limitations ‒ The main advantage of using an MADM game on the weighting multi-objective DEA is that the decision maker does not need to worry how to set these weights among DMUs/objectives, this MADM game will decide the weights among DMUs by the game theory. However, various DEA models are eventually evaluation tools. No one can guarantee us with 100% confidence that their evaluated results of DEA could be the absolute standard. Readers should analyse the results with care. Originality/value/contribution ‒ A unique link between the multi-objective CCR DEA and the MADM game for DEA is established and validated in this study. Previous scholars seldom explored and developed this breathtaking view before.

Keywords

EN

Data Envelopment Analysis (DEA); Multi-Objective Linear Programming (MOLP); Multiple Attribute Decision Making (MADM); Research and Development (R&D) efficiency

• Publisher: Uniwersytet Ekonomiczny w Katowicach
• Journal: Journal of Economics and Management
• Year: 2019
• Volume: 37
• Pages: 156-177

Contributors

author

Chen Yuh Wen

• Institute of Industrial Engineering and Management. College of Engineering. Da-Yeh University, Chang Hwa, Taiwan

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Identifiers

ISSN

1732-1948