مقاله انگلیسی رایگان در مورد انتخاب تامین کننده در زنجیره تامین زنجیره ای – الزویر 2018

 

مشخصات مقاله
ترجمه عنوان مقاله انتخاب تامین کننده و برنامه ریزی عملیاتی در زنجیره تامین زنجیره ای با عدم اطمینان عرضه
عنوان انگلیسی مقاله Supplier selection and operation planning in biomass supply chains with supply uncertainty
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 33 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
پایگاه داده نشریه الزویر
نوع نگارش مقاله
مقاله پژوهشی (Research article)
مقاله بیس این مقاله بیس نمیباشد
نمایه (index) scopus – master journals – JCR
نوع مقاله ISI
فرمت مقاله انگلیسی  PDF
ایمپکت فاکتور(IF)
3.113 در سال 2017
شاخص H_index 117 در سال 2018
شاخص SJR 1.024 در سال 2018
رشته های مرتبط مهندسی صنایع
گرایش های مرتبط لجستیک و زنجیره تامین
نوع ارائه مقاله
ژورنال
مجله / کنفرانس کامپیوترها و مهندسی شیمی – Computers and Chemical Engineering
دانشگاه Charles Delaunay Institute – University of technology of Troyes – France
کلمات کلیدی زنجیره تامین زیست توده، انتخاب تامین کننده، عدم اطمینان، برنامه ریزی تصادفی
کلمات کلیدی انگلیسی Biomass supply chain, Supplier selection, Uncertainty, Stochastic programming
شناسه دیجیتال – doi
https://doi.org/10.1016/j.compchemeng.2018.07.012
کد محصول E9893
وضعیت ترجمه مقاله  ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید.
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فهرست مطالب مقاله:
Highlights
Abstract
Keywords
1 Introduction
2 Literature review
3 Problem description and model formulation
4 Solution approach
5 Numerical study
6 Conclusions
Appendix A. Data of model parameters
Appendix B. The sequential approach
References

بخشی از متن مقاله:
Abstract

Bioenergy is considered a potential solution to reduce carbon footprint and fight against global warming. However, uncertainty in the harvest of biomass could lead to the instability of feedstock supply that has a significant impact on the sustainability of biomass supply chain. In this paper, we present a two-stage stochastic programming model dealing with supplier selection to stabilize feedstock supply of a biomass supply chain in uncertain environments. The model involves the first stage decisions for the supplier selection and the second-stage decisions for planning transportation, inventory and production operations. To reduce the computational burden for large instances, we propose an enhanced and regularized L-shaped decomposition algorithm to solve the model. The applicability of this model and the performance of the solution method are evaluated by numerical studies. Sensitivity analysis shows that the values of some parameters related to suppliers have significant impacts on the optimal expected cost and supplier selection.

Introduction

Nowadays, humanity is facing enormous challenges of global warming and the exhaustion of fossil resources according to Shafiee and Topal (2009). The use of fossil fuel is one of the major causes of global warming that affects severely human life and increases natural disaster in recent decades. Therefore, renewable energy will play a significant role in the energy transition and appears to be a very promising solution to sustainable development in the future. The various renewable resources require different technologies to capture energy such as solar, hydro-power, wind, geothermal, marine and biological resources. They are clean energy sources and do not contribute to global warming, greenhouse effect and air pollution. However, the exploitation of renewable energy sources requires much time, high costs of investment and incentive policies from the government. Bioenergy is generated from sources such as wood, agricultural products, animal and plant wastes. In contrast to wind and solar power, biomass can be stored in existing infrastructures in response to various demand according to Kaut et al. (2015). Nowadays, bioenergy industry has to deal with challenges related to low energy density, high harvest, and transportation costs of biomass to compete with fossil energy. The research in Eks¸ioglu et al. (2009) shows that an ˘ efficient supply chain design and management could reduce the biofuel price significantly.

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