دانلود مقاله ISI انگلیسی شماره 54639
عنوان فارسی مقاله

طراحی شبکه های توزیع تدارکات شهر با کربن سطح پایین با استقرار منابع

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
54639 2016 6 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Low-carbon city logistics distribution network design with resource deployment
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of Cleaner Production, Volume 119, 15 April 2016, Pages 223–228

کلمات کلیدی
استقرار منابع؛ تدارکات شهر ؛ مالیات بر کربن؛ برنامه ریزی شبکه؛ انتشار کربن
پیش نمایش مقاله
پیش نمایش مقاله طراحی شبکه های توزیع تدارکات شهر با کربن سطح پایین با استقرار منابع

چکیده انگلیسی

The Chinese government has now published its emission reduction goal of carbon dioxide by 2020 and any industrial player is obliged to take effective initiatives to decrease its carbon footprint. For the city logistics distribution system, as a significant energy-consuming and pollutant-emitting sector, energy saving and emission reduction are very meaningful especially for megacities like Beijing. With rational hypotheses and parameter design, meanwhile considering the deployment of low-carbon resources, a novel carbon tax-constrained city logistics distribution network planning model is proposed. The model is a bilinear non-convex mixed integer programming and is reduced to a pure linear mixed integer programming through proper linearization. To verify the effectiveness of the model, an empirical study is conducted on a city logistics operator in Beijing and the popular commercial optimization suite IBM ILOG CPLEX is adopted for optimization purpose. Through analysis of the optimization results and comparison with traditional optimization models, it is found that the proposed model can help the city logistics distribution operator save up to 9.2% of operational cost during a full service cycle, and meanwhile cut down its carbon dioxide discharge by around 54.5% or 2135 metric tons at most.

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