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To mitigate the impact of subjective biases and evaluation methodologies on the assessment outcomes of transportation outsourcing service providers, this study focuses on small and medium-sized third-party logistics firms. It employs the Total Rank Difference Test (TRDT) method for evaluating and analyzing service providers. The research begins by synthesizing extant evaluation indices from domestic and international sources to construct a comprehensive evaluation system tailored for third-party logistics service providers. This system encompasses ten primary indices, including safety management, cargo damage compensation, emergency response capabilities, and reputation, along with thirty-three secondary indices. To validate the efficacy of this evaluation system, the study utilizes four prominent evaluation techniques—Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), Fuzzy Comprehensive Evaluation (FCE), and Fuzzy Technique for Order Preference by Similarity to an Ideal Solution (Fuzzy-TOPSIS). It incorporates the entropy weighting method to assign index weights, thereby minimizing subjective influence. A consistency-based evaluation model for third-party logistics service providers is established and subjected to the TRDT to assess the reliability of the four evaluation methods. Lastly, the evaluation model is applied to Topchains, a case study, to test its practical utility in assessing transportation outsourcing service providers. The findings reveal that safety management, cargo damage compensation, support services, emergency response, and cooperation quality are pivotal factors in evaluating transportation outsourcing providers. Furthermore, Fuzzy Comprehensive Evaluation and Analytic Hierarchy Process demonstrate strong consistency, yielding stable and reliable results.
Zou Y. Logistics supplier selection model based on DEA-ANP[J]. Journal of Systems Science, 2017, 25(3): 73–78.
Shu FH. Business performance evaluation of listed real estate companies-based on super-efficient DEA. Journal of Shanghai Economic Management Colledge, 2022, 20(1): 34–39.
Sun SL, Li G, Wang YD, et al. Supplier evaluation and selection based on context-dependent DEA-Borda method. Productivity Research, 2022, 36(4): 7–10.
Yuan YB, Liu LX Zhang MY. Material supplier selection in construction based on DEA and TOPSIS. Journal of Civil Engineering and Management, 2016, 33(1): 7–11,17.
Weng X, Dang Y, Tian X, et al. Evaluation of payload components supplier for manned space station: based on monte carlo DEA analysis. Science and Technology Management Research, 2020, 21(18): 63–71.
Hao L, Hu DW, Li C. Supplier selection and order allocation of purchasing management for enterprise supply chain in T-JlT environment. Journal of Highway and Transportation Research and Development, 2018, 35(6): 149–158.
Bodendorf F, Merkl P, Franke J. Intelligent cost estimation by machine learning in supply management: a structured literature review. Computers & Industrial Engineering, 2021, 160: 107601.
Zhang J, Hu LW, Sun Y, et al. Evaluation model of prefabricated building suppliers based on AHP and BP neural network. Journal of Qingdao University of Technology, 2022, 43(1): 18–23.
Zeng Y, Wu B, Tian NS. Risk assessment of power monitoring system based on cloud model and improved evidence theory. Computer Systems and Applications, 2022, 31(8): 55–63.
Tang F, Zhao F, Ning X, et al. Research on the evaluation of science and technology innovation platforms in the railway sector-based on PCA-Grey clustering comprehensive evaluation model. Journal of Beijing Jiaotong University: Social Science Edition, 2022, 21(3): 124–136.
Jing Y, Li KX, Wang X, et al. Passenger flow transport modes of inter-city group with a high-speed railway network. Journal of Tsinghua University: Science and Technology, 2022, 62(7): 1151–1162.
Zhang MS, Liu CT, Li XJ, et al. Design of fuzzy comprehensive evaluation system for performance appraisal based on K-means clustering algorithm. Journal of Jilin University: Engineering and Technology Edition, 2021, 51(5): 1851–1856.
This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, http://creativecommons.org/licenses/by/4.0/).
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