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Green-Building-Material Supplier Selection with a Rough-Set-Enhanced Quality Function Deployment

Author

Listed:
  • Zhiying Zhang

    (Business School, Sichuan University, Chengdu 610064, China)

  • Huchang Liao

    (Business School, Sichuan University, Chengdu 610064, China
    Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
    Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada, 18071 Granada, Spain)

  • Jiaying Chang

    (Business School, Sichuan University, Chengdu 610064, China)

  • Abdullah Al-barakati

    (Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

Building material supplier evaluation and selection is a significant strategic-decision problem for reducing construction costs and ensuring the quality of a residential product. As people are increasingly concerning about the green level of a residential product and the competition in the housing market is becoming increasingly fierce, it becomes important to select a green customer-oriented material supplier for property developers. Quality function deployment (QFD) has been proven to be an effective quality-control technique to take customer voices into consideration. However, the relationship matrix in the QFD technique, as a key to translate customer requirements into technical attributes, was subjectively given by decision-makers in previous studies, which failed to reflect customer requirements accurately. The aim of this study is to put forward a neighborhood-rough-set-based quality function deployment model for a green-building-material supplier selection. The neighborhood rough set, as a nonparametric and flexible data-mining approach, can effectively and objectively determine the core relationships between a variety of factors. A rough number-based aggregation approach is applied to effectively and objectively aggregate the evaluations given by a group of experts. Then, the classical double normalization-based multiple aggregation method, which considers two types of normalization methods, three aggregation models, and a comprehensive score formula, is extended in rough-number form in order to rank the alternatives. Afterward, an attempt is made to evaluate and rank eleven alternative building-material suppliers for a repute property developer in mainland China, and the corresponding comparative and sensitive analyses verify the effectiveness and robustness of the proposed hybrid model.

Suggested Citation

  • Zhiying Zhang & Huchang Liao & Jiaying Chang & Abdullah Al-barakati, 2019. "Green-Building-Material Supplier Selection with a Rough-Set-Enhanced Quality Function Deployment," Sustainability, MDPI, vol. 11(24), pages 1-21, December.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:24:p:7153-:d:297756
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    References listed on IDEAS

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    Cited by:

    1. Han Lai & Huchang Liao & Jonas Šaparauskas & Audrius Banaitis & Fernando A. F. Ferreira & Abdullah Al-Barakati, 2020. "Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination," Sustainability, MDPI, vol. 12(8), pages 1-17, April.
    2. Fabio Neves Puglieri & Aldo Roberto Ometto & Rodrigo Salvador & Murillo Vetroni Barros & Cassiano Moro Piekarski & Izabella Morré Rodrigues & Octavio Diegoli Netto, 2020. "An Environmental and Operational Analysis of Quality Function Deployment-Based Methods," Sustainability, MDPI, vol. 12(8), pages 1-18, April.

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