透過您的圖書館登入
IP:18.224.51.67
  • 學位論文

以約略集合為基礎的規則歸納解決方法應用於綠色運輸車隊選擇

A Rough Set-Based Rule Induction for Green Transportation Fleet Selection

指導教授 : 黃俊哲
若您是本文的作者,可授權文章由華藝線上圖書館中協助推廣。

摘要


近年來,綠色運輸已經成為運輸業經營的重要議題。然而,採用新的科技來降低碳排放量,將會增加相對應的採購成本。因此,運輸車隊選擇所需配置的綠色車款是目前運輸業面臨的問題。對於解決質性性質的運輸車隊選擇,約略集合是一個很好的方法。然而,以往約略集合有以下缺點:(1) 使用兩個階段來產生決策規則。(2) 比較屬性折減(reduct)時,限制在相同屬性數量的reduct。(3) 單一概念階層僅找尋到某些可能的規則,全部的結果皆存在同一階層。(4) 僅提出一次性的方法。 本研究提供一個完整的解決方法來找到最後的階層,其有明確的階層決策屬性被包含在決策規則中,並在比較季節性決策規則的基礎上,提出企業經營策略。本研究有助於從階層決策屬性中歸納出比較季節性的決策規則。其結果可以解決受季節性影響的綠色運輸車隊選擇,並提出建議的企業策略,包括市場份額、庫存管理和綠色車隊分配,從而提升企業的競爭優勢。

並列摘要


In the resent years, the greening transportation is perceived as important to transport operations. However, adopting a new technology to reduce carbon intensity increases corresponding purchase costs. Therefore, the transportation industry faces the problem of the transportation fleet selection to allocate desired greening vehicles. For the nature of qualitative in hierarchical transportation fleet selection, the rough set approach is one of the promised solutions. However, previous rough set approaches have following weakness: (1) Used two stages to generate reducts and induct decision rules. (2) Comparison of the reducts is restricted to the same number of attributes selected in the reducts. (3) Finding certain rules and possible rules on the single concept level and obtain all outcomes are in the same level only. (4) Provide one shot solution only. This study provides a total solution, which finds the final level with the specific hierarchical decision attribute inducted in the decision rule set. And the proposed solution approach provides business strategies based on the comparison of seasonal decision rule sets. This study contributes in induct seasonality decision rule sets with the hierarchical decision attribute. The results can resolve green transport fleet selection of transport seasonal demand and provide business strategy to advise market share, inventory management, green fleet allocation, and consequently enhance the competitive advantage.

參考文獻


Bae, S. H., Sarkis, J., & Yoo, C. S. (2011). Greening transportation fleets: Insights from a two-stage game theoretic model. Transportation Research Part E: Logistics and Transportation Review, 47(6), 793-807.
Bektaş, T., & Laporte, G. (2011). The Pollution-Routing Problem. Transportation Research Part B: Methodological, 45(8), 1232-1250.
Breault, J. L. (2001) Data mining diabetic databases: are rough sets a useful addition? Proceedings of the Computing Science and Statistics, 33.
Ceder, A. (2011). Optimal Multi-Vehicle Type Transit Timetabling and Vehicle Scheduling. Procedia - Social and Behavioral Sciences, 20(0), 19-30.
Chevre, N., Gagne, F., Gagnon, P., & Blaise, C. (2003). Application of rough sets analysis to identify polluted aquatic sites based on a battery of biomarkers: a comparison with classical methods. Chemosphere, 51(1), 13-23.

延伸閱讀