Integrated decisions for supplier selection and lot-sizing considering different carbon emission regulations in Big Data environment

The rising concerns about carbon emissions due to drastic environmental changes globally has increased awareness of customers regarding the carbon footprint of the products they are consuming. Thus, compelled supply chain managers to reformulate strategies for controlling the carbon emissions. The various activities contributing to carbon emissions in a supply chain are procurement, transportation, ordering and holding of inventory. Operational decisions like selection of the right supplier of right lot-sizes can play a vital role in reducing the overall carbon footprint of a supply chain. This paper proposes a mixed-integer nonlinear program (MINLP) for supplier selection along with determining the right lot-sizes in a dynamic setting having multi-periods, multi-products and multi-suppliers with a view of overall reduction in the supply chain cost as well as associated cost of carbon emissions. The model requires a range of real time parameters from both the buyer's and supplier's perspectives such as costs, capacities and carbon caps. These parameters have been mapped with the different dimensions of Big Data viz. volume, velocity and variety. The model provides an optimal supplier selection and lot-sizing policy along with the carbon emissions. For the purpose of evaluating the carbon emissions, three different carbon regulating policies viz., carbon cap-and-trade, strict cap on carbon emission and carbon tax on emissions, have been considered and insights are drawn. The validation of the proposed MINLP has been done using a randomly generated dataset having the essential parameters of Big Data, i.e. volume, velocity, and variety.