Development of a Model for Evaluating the Efficiency of Transport Companies: PCA–DEA–MCDM Model

Abstract

The efficiency of transport companies is a very important factor for the companies themselves, as well as for the entire economic system. The main goal of this paper is to develop an integrated model for determining the efficiency of representative transport companies over a period of eight years. An original model was developed that includes the integration of DEA (Data Envelopment Analysis), PCA (Principal Component Analysis), CRITIC (Criteria Importance Through Inter criteria Correlatio), Entropy and MARCOS (Measurement Alternatives and Ranking according to the COmpromise Solution) methods in order to determine the final efficiency of transport companies based on 10 input–output parameters. The results showed that the most efficient business performance was achieved in the period 2014–2017, followed by slightly less efficient results. Then, extensive sensitivity analysis and comparative analysis were performed, which confirmed, to some extent, the previously obtained results. In the sensitivity analysis, 30 scenarios with changes in the weights of criteria were created, while the comparative analysis was carried out with three other MCDM (Multi-Criteria Decision-Making) methods. Finally, the rank correlation index was determined using the Spearman and WS (Wojciech Salabun) correlation coefficients. According to the final results, very efficient years can be separated that can be the benchmark for furthering the business.

Data-Driven Evaluation and Optimisation of Livelihood Improvement Efficiency

“…Compared with the existing literature [36,[49][50][51][52], this study can reduce the dimensionality of the complex evaluation index system while retaining the information of the original variables, overcome the limitation of the number of indicators in the DEA evaluation method, and improve the accuracy of the evaluation results. Moreover, in contrast to the existing literature [41][42][43][44][45][46][47], this study used the Tobit method to analyse the influencing factors of LIE. This contributes to the completeness of research on LIE evaluation and optimisation, improves the scientificity of the judgment of the influencing factors, and lays the foundation for policy recommendations.…”

“…Poldaru et al used PCA-DEA to evaluate the quality of life of residents of Estonian counties [43], whereas Sarra et al applied this method to evaluate the well-being of residents of Italian cities [44]. In addition, the combination of the two methods has been widely used in efficiency evaluation in other fields, such as the operational efficiency of transportation companies [45], the management efficiency of banks [46], and the work performance of governments [47]. When the dependent variable has data truncation characteristics, ordinary least-squares calculations can result in biased parameter estimates; thus, Tobin proposed a restricted dependent variable model in 1958 [48].…”

Novel Scheme for Robust Confusion Component Selection Based on Pythagorean Fuzzy Set

“…The TOPSIS technique [12] is a powerful MCDM approach that is used to find the best feasible solution in various decision-making applications. Different researchers have introduced different extensions to the TOPSIS system for explaining decision problems in various fuzzy contexts in the last few years, such as in [14] Yue extended for the intuitionistic fuzzy environment, Joshi in [15] extended for the interval-valued fuzzy environment, but all of the above extensions were unable to solve decision problems using Pythagorean fuzzy information.…”

“…Researchers have introduced various MCDM methods in the past few decades to successfully tackle decision-making problems [8][9][10][11]. TOPSIS [12] is a well-known MCDM technique that is utilized to locate the optimal result which is nearer to a positive ideal solution (PIS) and a long way from a negative ideal solution (NIS). Many scholars have successfully employed the TOPSIS approach to tackle MCDM issues in different fuzzy environments [13][14][15][16].…”

Analysis of Mutual Influence Relationships of Purchase Intention Factors of Electric Bicycles: Application of DEMATEL Taking into Account Information Uncertainty and Expert Confidence

“…MCDM overcomes several assumptions in the use of traditional statistical theory (e.g., the sample data needs to conform to a normal distribution, and the assumption that variables are independent of each other). MCDM allows the use of a small sample of expert interview data to generate reliable analytical results through consistency or consensus testing [18][19][20]. In this study, we have three main implementation stages.…”