تاثیر مالیات کربن بر طراحی شبکه لجستیک معکوس
ترجمه نشده

تاثیر مالیات کربن بر طراحی شبکه لجستیک معکوس

عنوان فارسی مقاله: تاثیر مالیات کربن بر طراحی شبکه لجستیک معکوس
عنوان انگلیسی مقاله: Effect of carbon tax on reverse logistics network design
مجله/کنفرانس: مهندسی صنایع و کامپیوتر – Computers & Industrial Engineering
رشته های تحصیلی مرتبط: مهندسی صنایع، مدیریت
گرایش های تحصیلی مرتبط: لجستیک و زنجیره تامین، مدیریت نوآوری و فناوری
کلمات کلیدی فارسی: لجستیک معکوس؛ تولید مجدد؛ طراحی شبکه؛ برنامه ریزی خطی مختلط با اعداد صحیح؛ شناسه کربن
کلمات کلیدی انگلیسی: Reverse Logistics; Remanufacturing; Network Design; Mixed Integer Linear Programming; Carbon Footprint
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.cie.2019.106184
دانشگاه: Indian Institute of Technology, Kharagpur, W. Bengal, India
صفحات مقاله انگلیسی: 44
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 5.321 در سال 2019
شاخص H_index: 121 در سال 2020
شاخص SJR: 1.649 در سال 2019
شناسه ISSN: 0360-8352
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: دارد
کد محصول: E15097
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست مطالب (انگلیسی)

Abstract

1- Introduction

2- Literature review

3- Problem description

4- Mathematical modelling

5- Results

6- Conclusion

Acknowledgements

References

بخشی از مقاله (انگلیسی)

Abstract

Reverse logistics network design (RLND) is getting momentum as more organizations realize the benefits of recycling or remanufacturing of their end-of-life products. Similarly, there is an impetus for organizations to become more environmentally conscious or green. This environmental context has driven many organizations to invest in green technologies, with a recent emphasis on reducing greenhouse gas emissions. This environmental investment situation and decision can be addressed through the integration of facility location, operational planning, and vehicle type selection, while simultaneously accounting for carbon emissions from vehicles, inspection centers, and remanufacturing centers in a reverse logistics (RL) context. In the current study, we present a mixed-integer linear programming (MILP) model to solve a multi-tier multi-period green RL network, including vehicle type selection. This research integrates facility locations, vehicle type selection with emissions producing from transportation and operations at various processing centers. Prior research does not account for carbon emissions for this design problem type. Valuable managerial insights are obtained when incorporating carbon emissions cost.

Introduction

Throughout the history of corporate environmentalism, environmental actions and concerns have evolved from a localized, pollution emissions perspective, to a global concern on general environmental sustainability through such efforts as the United Nations Global Compact (Kell, 2003). During the past three decades, there have been many international conferences and treaties, including the recent Conference of Parties (COP) emphasizing the need to rein in global climate change greenhouse gas (GHG) emissions (Boucher et al., 2016). There is a consensus among world leaders for the need to limit GHG emissions. Global organizations recognize the need to consider inter-generational sustainability as a means of survival, given that a significant share of the economy heavily burdens the natural resource base, which is continuously depleting. Among many popular corporate environmental sustainability initiatives remanufacturing and its supporting activities will play a vital role to extend the life of resources and materials; while seeking to limit pollutant emissions (Kerr & Ryan, 2001; Diener & Tillman, 2015). In addition to this environmental benefit, business benefits also exist. Firms can strategically distinguish themselves from competitors by reducing their costs, adding value to their supply chain and end customers while achieving environmental sustainability through RL and remanufacturing efforts (Kumar, Chinnam, & Murat, 2017).