Design of Intelligent Logistics Drivers Evaluation System-Based on Entropy-AHP Method

Based on the analysis of current research in performance evaluation of logistics and the practical operation mode of x company, this paper integrates all the factors that may affect performance evaluation of distribution and finally selects 11 indicators to make a comprehensive performance evaluation of drivers. These indicators are classified as four dimensions: total amount of work, transportation quality, service level and execution. Entropy weight method and analytic hierarchy process (AHP) are adopted to determine the comprehensive weight of each indicator, which is also enriched by the introduction of region factor. Besides, the drivers' individual relative progress factor is added into this evaluation model to better measure their efforts. In the empirical analysis of x company, the rationality and performability of the model are tested by comparing with the previous performance evaluation result. The result showed that this improved model could fully reflect the performance of logistics drivers and make effective distinctions between them. Also, this model can provide a basis for subsequent salary assessment and task allocation priority. What's more, it has practical significance for encouraging drivers to carry out tasks obeying the algorithm instructions.

[1]  Mark S. Squillante,et al.  The Growth and Performance Diagnostics Initiative: A Multi-Dimensional Framework for Sales Performance Analysis and Management , 2011 .

[2]  Tao Lu,et al.  Logistics system performance evaluation of the fresh and live agricultural products with an application of analytic network process , 2010, 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM).

[3]  Bart de Langhe,et al.  Bang for the Buck: Gain-Loss Ratio as a Driver of Judgment and Choice , 2015, Manag. Sci..

[4]  Ning Zhang,et al.  Safety Assessment Model for Dangerous Goods Transport by Air Carrier , 2018 .

[5]  Zheng Wang,et al.  Environmental vulnerability assessment for mainland China based on entropy method , 2018, Ecological Indicators.

[6]  Aleksandra Gulc,et al.  Models and Methods of Measuring the Quality of Logistic Service , 2017 .

[7]  Thomas L. Saaty,et al.  The Modern Science of Multicriteria Decision Making and Its Practical Applications: The AHP/ANP Approach , 2013, Oper. Res..

[8]  Mauricio Camargo,et al.  A framework for measuring logistics performance in the wine industry , 2012 .

[9]  Gi-Tae Yeo,et al.  Weighing the Key Factors to Improve Vietnam's Logistics System , 2018, The Asian Journal of Shipping and Logistics.

[10]  Ling Liu,et al.  Comprehensive Evaluation of Logistics Performance for Agricultural Products Distribution Center , 2010, 2010 2nd International Conference on E-business and Information System Security.

[11]  Rabin Shrestha,et al.  Performance evaluation of electric distribution centers using Data Envelopment Analysis , 2010, North American Power Symposium 2010.

[12]  Jian Rong,et al.  Evaluation of cooperative systems on driver behavior in heavy fog condition based on a driving simulator. , 2019, Accident; analysis and prevention.

[13]  Jin-Xiao Chen,et al.  A new approach to overall performance evaluation based on multiple contexts: An application to the logistics of China , 2018, Comput. Ind. Eng..

[14]  Xavier Drèze,et al.  Real-Time Evaluation of E-mail Campaign Performance , 2009, Mark. Sci..

[15]  Cynthia Barnhart,et al.  Airline-Driven Performance-Based Air Traffic Management: Game Theoretic Models and Multicriteria Evaluation , 2016, Transp. Sci..

[16]  Pengjian Shang,et al.  Comparison of transfer entropy methods for financial time series , 2017 .

[17]  K. Cullinane,et al.  Evaluating the sustainability of national logistics performance using Data Envelopment Analysis , 2019, Transport Policy.