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An Integrated Analytic Hierarchy Process—Slack Based Measure-Data Envelopment Analysis Model for Evaluating the Efficiency of Logistics Service Providers Considering Undesirable Performance Criteria

Author

Listed:
  • Patricija Bajec

    (Faculty of Maritime Studies and Transport, University of Ljubljana, Pot pomorščakov 4, 6320 Portorož, Slovenia)

  • Danijela Tuljak-Suban

    (Faculty of Maritime Studies and Transport, University of Ljubljana, Pot pomorščakov 4, 6320 Portorož, Slovenia)

Abstract

Sustainable concerns are reputed to be of the utmost priority among governments. Consequently, they have become more and more of a concern among supply chain partners. Logistics service providers (LPs), as significant contributors to supply chain success but also one of the greatest generator of emissions, play a significant role in reducing the negative environmental impact. Thus, the performance evaluations of LPs should necessarily involve such a measure which, firstly, represents a balance between all three pillars of sustainability and, secondly, consider the desirable and undesirable performance criteria. This paper proposes an integrated analytic hierarchy process (AHP) and slack-based measure (SBM) data envelopment analysis (DEA) model, based on the assumption of a variable return to scale (VRS). An AHP pairwise comparison enables selecting the most influential input/output variables. Output-oriented SBM DEA provides simultaneously evaluation of both the undesirable and desirable outputs. The proposed model was tested on a numerical example of 18 LPs. The comparison of output Charnes, Cooper and Rhodes (CCR) and SBM DEA models resulted in a higher number of inefficient LPs when the SBM DEA model was applied. Moreover, efficiency scores of inefficient LPs were lower in SBM DEA model. The proposed model is fair to those LPs that are environmentally friendly.

Suggested Citation

  • Patricija Bajec & Danijela Tuljak-Suban, 2019. "An Integrated Analytic Hierarchy Process—Slack Based Measure-Data Envelopment Analysis Model for Evaluating the Efficiency of Logistics Service Providers Considering Undesirable Performance Criteria," Sustainability, MDPI, vol. 11(8), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:8:p:2330-:d:223934
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    Cited by:

    1. Yuxia Guo & Huiying Mao & Heping Ding & Xue Wu & Yujia Liu & Hongjun Liu & Shuling Zhou, 2022. "Data-Driven Coordinated Development of the Digital Economy and Logistics Industry," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    2. Yifei Zhang & Sheng Li & Fang Zhang, 2020. "Does an Emissions Trading Policy Improve Environmental Efficiency? Evidence from China," Sustainability, MDPI, vol. 12(6), pages 1-16, March.
    3. Patricija Bajec & Danijela Tuljak-Suban & Eva Zalokar, 2021. "A Distance-Based AHP-DEA Super-Efficiency Approach for Selecting an Electric Bike Sharing System Provider: One Step Closer to Sustainability and a Win–Win Effect for All Target Groups," Sustainability, MDPI, vol. 13(2), pages 1-24, January.
    4. Loske, Dominic & Klumpp, Matthias, 2021. "Human-AI collaboration in route planning: An empirical efficiency-based analysis in retail logistics," International Journal of Production Economics, Elsevier, vol. 241(C).
    5. Yongming Zhu & Xiaoyu Zhou & Junjie Li & Fan Wang, 2022. "Technological Innovation, Fiscal Decentralization, Green Development Efficiency: Based on Spatial Effect and Moderating Effect," Sustainability, MDPI, vol. 14(7), pages 1-16, April.
    6. Gennitsaris, Stavros & Sagani, Angeliki & Sofianopoulou, Stella & Dedoussis, Vassilis, 2023. "Integrated LCA and DEA approach for circular economy-driven performance evaluation of wind turbine end-of-life treatment options," Applied Energy, Elsevier, vol. 339(C).

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