مدل سازی اکتشافی برای فراهم سازی پایدار در یک زنجیره تامین با استفاده از کلان داده
ترجمه نشده

مدل سازی اکتشافی برای فراهم سازی پایدار در یک زنجیره تامین با استفاده از کلان داده

عنوان فارسی مقاله: مدل سازی اکتشافی برای تهیه و فراهم سازی پایدار در یک زنجیره تامین با استفاده از کلان داده
عنوان انگلیسی مقاله: Heuristic modeling for sustainable procurement and logistics in a supply chain using big data
مجله/کنفرانس: کامپیوترها و تحقیق در عملیات - Computers & Operations Research
رشته های تحصیلی مرتبط: مهندسی صنایع، مهندسی فناوری اطلاعات
گرایش های تحصیلی مرتبط: لجستیک و زنجیره تامین، مدیریت سیستم های اطلاعات
کلمات کلیدی فارسی: فراهم سازی پایدار، کلان داده، حمل و نقل پایدار، MILP (برنامه غیر خطی عدد صحیح مختلط)، MILP (برنامه خطی عدد صحیح مختلط)، اکتشافی
کلمات کلیدی انگلیسی: Sustainable procurement، Big data، Sustainable transportation، MILP (Mixed Integer Non Linear Program)، MILP (Mixed Integer Linear Program)، Heuristic
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.cor.2017.05.008
دانشگاه: Department of Management Studies, Vishwakarma Bhavan, Indian Institute of Technology Delhi, New Delhi 110016, India
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 3/976 در سال 2018
شاخص H_index: 133 در سال 2019
شاخص SJR: 1/859 در سال 2018
شناسه ISSN: 0305-0548
شاخص Quartile (چارک): Q1 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 45
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
کد محصول: E11319
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Literature review


3- Proposed joint sustainable procurement and logistics model


4-Solution methodology


5- Computational experiments


6- Conclusion and future scope of work


References

نمونه متن انگلیسی مقاله

Abstract


Drastic climate change has enforced business organizations to manage their carbon emissions. Procurement and transportation is one of the supply chain business operations where carbon emissions are huge. This paper proposes an environmentally sustainable procurement and logistics model for a supply chain. The proposed models are of MINLP (Mixed Integer Non Linear Program) and MILP (Mixed Integer Linear Program) form requiring a variety of the real time parameters from buyer and supplier side such as costs, capacities, lead-times and emissions. Based on real time data, the models provide an optimal sustainable procurement and transportation decision. It is also shown that large sized problems possessing essential 3V's of big data, i.e., volume, variety and velocity consume non-polynomial time and cannot be solved optimally. Therefore, a heuristic (H-1) is also proposed to solve the large sized problems involving big data. T-test significance is also conducted between optimal and heuristic solutions obtained using 42 randomly generated data instances possessing essential characteristics of big data. Encouraging results in terms of solution quality and computational time are obtained.


Introduction


The global concern over environmental threats caused by various business operations has led researchers and practitioners to explore variety of approaches to reduce overall carbon footprint of a firm. Therefore, the business organizations have started reframing their strategic and operational policies to improve the environmental performance of the products or/and overall manufacturing processes starting from procurement of products till the delivery of finished goods. Hence, a complete integration and successful coordination among all the members of supply chain including raw material suppliers, manufacturers, distributors, and users is required [1]. Low carbon approach has been becoming the trend of the world economy. Carbon emission regulatory policies such as carbon cap, carbon tax, carbon offset, carbon cap and trade are being increasingly applied to various business organizations all over the globe. The globalization of business activities have led to increased demand of products and services worldwide. Therefore, the production, transportation, storage and consumption of increasing demand of products and services have further added to environmental problems. In this information age, lot of data generates at both supplier and buyer side. However, most of the supply chain decisions still do not incorporates the big data characteristics into the decision making models. Therefore, it is important to jointly consider big data in supply chain modeling. Data available at supplier and buyer‟s side are mostly voluminous and also possesses variety and velocity characteristics of big data. In view of this, for effective and efficient decision making these available data should be utilized considering big data while modeling. Hence, supply chain modeling using big data provides a competitive edge to the business organization and makes the supply chain resilient and sustainable [2,3]. As much the big data is essential for decision making in highly volatile and competitive markets, it is equally challenging to store and analyse big data. This is the major reason that despite the huge scope of big data, there are very few attempts made so far to develop models using big data in supply chain modelling [4,5].

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