<abstract xmlns="http://www.w3.org/1999/xhtml">

<bold>Aim/purpose</bold> ‒ 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 <italic>n</italic> firms for computing efficiency scores, the resolution of <italic>n</italic> similar problems is necessary. Therefore, the multi-objective linear programming (MOLP) problem is used to simplify the complexity.
<bold>Design/methodology/approach</bold> ‒ 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.
<bold>Findings</bold> ‒ 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&amp;D) efficiencies to show its practical application.
<bold>Research implications/limitations</bold> ‒ 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.
<bold>Originality/value/contribution</bold> ‒ 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.
</abstract>

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.

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

Journal of Economics and Management

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

Aim/purpose ‒ The traditional data envelopment analysis (DEA) is popularly used to evaluate the relative efficiency among public or private firms by maximising...

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

Publicado en línea: 23 jul 2021

Páginas: 156 - 177

Recibido: 15 abr 2019

Aceptado: 17 jun 2019

DOI: https://doi.org/10.22367/jem.2019.37.08

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

© 2019 Yuh Wen Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

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

Publicado en línea: 23 jul 2021

Páginas: 156 - 177

Recibido: 15 abr 2019

Aceptado: 17 jun 2019

DOI: https://doi.org/10.22367/jem.2019.37.08

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

© 2019 Yuh Wen Chen, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

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