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