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A multiple attribute group decision making framework for the evaluation of lean practices at logistics distribution centers

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  • Zhibin Wu

    (Sichuan University)

  • Jiuping Xu

    (Sichuan University)

  • Zeshui Xu

    (Sichuan University)

Abstract

In recent years, to adapt rapidly to changing market environments and outdo the competition more companies and organizations have adopted lean management practices. One problem that has arisen in these companies and organizations is the need to develop methods to accurately evaluate the lean practices performance. This study proposes a multiple attribute group decision making (MAGDM) framework to facilitate such evaluations. It deals with the consensus process and selection process for MAGDM problems based on the 2-tuple linguistic computation model. The similarity degree and consensus for the linguistic decision matrix are defined using an Euclidian distance function. An algorithm describing the consensus reaching process is presented and its properties analyzed. The entropy method is generalized to a linguistic setting to derive the importance weights for the attributes. One of the main ideas behind the entropy method is that attributes with quite different values are considered more important and therefore have higher weights. Finally, the developed MAGDM framework is applied to a lean practices evaluation problem for a commercial tobacco company’s logistics distribution centers in China.

Suggested Citation

  • Zhibin Wu & Jiuping Xu & Zeshui Xu, 2016. "A multiple attribute group decision making framework for the evaluation of lean practices at logistics distribution centers," Annals of Operations Research, Springer, vol. 247(2), pages 735-757, December.
    • Handle: RePEc:spr:annopr:v:247:y:2016:i:2:d:10.1007_s10479-015-1788-6
    DOI: 10.1007/s10479-015-1788-6
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    References listed on IDEAS

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    Cited by:

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    2. Vadivel Sengazhani Murugesan & Sunil Kumar Jauhar & Aloysius Henry Sequeira, 2022. "Applying simulation in lean service to enhance the operational system in Indian postal service industry," Annals of Operations Research, Springer, vol. 315(2), pages 993-1017, August.
    3. Fabiane Letícia Lizarelli & Jiju Antony & José Carlos Toledo, 2020. "Statistical thinking and its impact on operational performance in manufacturing companies: an empirical study," Annals of Operations Research, Springer, vol. 295(2), pages 923-950, December.
    4. Sujeet Kumar Singh & Shiv Prasad Yadav, 2018. "Intuitionistic fuzzy multi-objective linear programming problem with various membership functions," Annals of Operations Research, Springer, vol. 269(1), pages 693-707, October.
    5. Sinem Buyuksaatci Kiris & Enes Eryarsoy & Selim Zaim & Dursun Delen, 2023. "An integrated approach for lean production using simulation and data envelopment analysis," Annals of Operations Research, Springer, vol. 320(2), pages 863-886, January.
    6. Hengjie Zhang & Wenfeng Zhu & Xin Chen & Yuzhu Wu & Haiming Liang & Cong-Cong Li & Yucheng Dong, 2024. "Managing flexible linguistic expression and ordinal classification-based consensus in large-scale multi-attribute group decision making," Annals of Operations Research, Springer, vol. 341(1), pages 95-148, October.
    7. Rubén Jesús Pérez-López & Jesús Everardo Olguín Tiznado & María Mojarro Magaña & Claudia Camargo Wilson & Juan Andrés López Barreras & Jorge Luis García-Alcaraz, 2019. "Information Sharing with ICT in Production Systems and Operational Performance," Sustainability, MDPI, vol. 11(13), pages 1-18, July.
    8. Min Jiang & Rui Shen & Zhiqing Meng, 2019. "A Concession Equilibrium Solution Method without Weighted Aggregation Operators for Multiattribute Group Decision-Making Problems," Discrete Dynamics in Nature and Society, Hindawi, vol. 2019, pages 1-10, January.

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