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Sustainable service and energy provision based on agile rule induction

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  • Tseng, Tzu-Liang (Bill)
  • Huang, Chun-Che

Abstract

Providing sustainable service and energy has been becoming a trend due to environmental concerns. One of the academic challenges in sustainable service and energy is identified as follows: In the complex service sector, survey data may be complicated, qualitative in nature and in large scale. Numerous attributes which are un-structured in nature and have the impact on service performance are involved. One of the promised solution approaches is the Rough Set (RS) based approach that can deal with qualitative information and provide computational results through an individual object model without any statistical assumption. To date, the traditional RS based approaches still contain a few disadvantages: (i) The decision attribute is only in one level which it could contain or reflect the concept hierarchy if well defined, (ii) Using a two stage process to generate reducts and induct decision rules. In this paper, an extended RS based rule induction approach is proposed while the associated decision tables are not in traditional format. This study contributes implementation of the solution model development to sustainable service and energy provision.

Suggested Citation

  • Tseng, Tzu-Liang (Bill) & Huang, Chun-Che, 2016. "Sustainable service and energy provision based on agile rule induction," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 273-288.
    • Handle: RePEc:eee:proeco:v:181:y:2016:i:pb:p:273-288
    DOI: 10.1016/j.ijpe.2016.07.010
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    References listed on IDEAS

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    Cited by:

    1. Letizia Tebaldi & Barbara Bigliardi & Eleonora Bottani, 2018. "Sustainable Supply Chain and Innovation: A Review of the Recent Literature," Sustainability, MDPI, vol. 10(11), pages 1-29, October.
    2. Mao, Wenxin & Wang, Wenping & Sun, Huifang & Yao, Peiyi & Wang, Xiaolei & Luo, Dang, 2021. "Urban industrial transformation patterns under natural resource dependence: A rule mining technique," Energy Policy, Elsevier, vol. 156(C).

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