DEA Evaluation Method Based on Interval Intuitionistic Bayesian Network and Its Application in Enterprise Logistics

Mengdi Xu,Zeshui Xu,Wei Zhou,Shousheng Liu

doi:10.1109/access.2019.2929201

Abstract

Data envelopment analysis (DEA) is a model for evaluating the effectiveness of relative effectiveness of decision-making units with multiple input and output data based on non-parametric modeling using mathematical programming (including linear programming, multi-parameter programming, stochastic programming, etc.). Due to the complexity of real life, sometimes it is hard to get the exact value of the input and output data directly. The fuzzy DEA (FDEA) proposed to solve this problem well and is widely used in practice. However, the data of FDEA have certain subjectivity, and in addition, some indicators cannot be quantified intuitively. Owing to the complexity of society, there are often some causal relationships in the indicator system. As a method of combining uncertainty and graph theory for uncertainty reasoning, Bayesian network (BN) can effectively deal with the causal chain problem existing in the index and discover the potential relationship between data. The BN is often used to process accurate numerical information and does not handle uncertain information with ambiguity favorably. In order to solve the above issue, the interval-valued intuitionistic fuzzy number (IVIFN) is introduced into the BN to construct the interval-valued intuitionistic fuzzy BN (IVIFBN). Then, based on the index data obtained by the IVIFBN, the crossover efficiency is proposed, and the super efficiency interval FDEA (SEIFDEA) model is constructed. According to the different optimisms of the decision makers, the Hurwitz decision criterion is introduced for sorting. In addition, the model is applied to the performance evaluation system of logistics enterprises. Compared with the traditional DEA model, the validity and superiority of the model in fuzzy environment are verified.

Highlights

With the progress of the times and the continuous development of society, logistics has continuously entered our sights and gradually affected our lives
By combining the above practical problems in the data envelopment analysis (DEA) model and the excellent characteristics of Bayesian network (BN), we propose a super-efficient cross-DEA (SECDEA) model based on the BN in the interval intuitionistic fuzzy environment
We briefly introduce some basic probabilities and theories: An economic system can be regarded as a unit within a certain range, through the input of certain factors of production to output a certain result of the process, so as to maximize the effectiveness of the results, such units are called Decision Making units (DMU)

Summary

INTRODUCTION

With the progress of the times and the continuous development of society, logistics has continuously entered our sights and gradually affected our lives. M. Xu et al.: DEA Evaluation Method Based on Interval Intuitionistic Bayesian Network and Its Application in Enterprise Logistics the evaluation value of the cross-efficiency. Aiming at the defects of the traditional FDEA model, Agarwal [7] proposed a FDEA model based on α-cut method to deal with the efficiency metric and ordering problem of given fuzzy input and output data. FDEA is combined with fuzzy analytic hierarchy process (FAHP) for the performance evaluation ranking problem of decision-making units in fuzzy environments [11]. By combining the above practical problems in the DEA model and the excellent characteristics of BN, we propose a super-efficient cross-DEA (SECDEA) model based on the BN in the interval intuitionistic fuzzy environment. We transform the incomplete information collected and convert it into interval direct fuzzy probability through BN as the original data of DEA evaluation method.

THE CONCEPT OF BAYESIAN NETWORK

INTERVAL-VALUED INTUITIONISTIC

IVIF BAYESIAN NETWORK MULTI-ATTRIBUTE DECISION MAKING METHOD

CASE STUDY

CONCLUSION