A closed loop sustainable supply chain network design for waste electrical & electronic equipment

Supply Chain Management covers the management of all activities starting from the supply of the raw material to the delivery of the final product to the end user. In the rapidly evolving and globalizing world, limited resources and increasing competitiveness push both the nations and the organizations to make difference in the context of supply chain management. Since the carbon dioxide emissions is one of the major causes of global warming mostly made by people, the importance of the concept of the sustainability is recently realised more among the nations and the organisations. The topic of sustainable development has gained importance in many different fields, such as sustainable municipality, sustainable agriculture, sustainable architecture, sustainable production etc. There are negative effects from many factors such as solid waste, chemicals mixed with water, gases which are produced by manufacturing facility activities which have been neglected for many years. Recovery options are considered to be an economic gain by a lot of companies. Moreover, pricing is no longer a unique competitive strategy since customers give today value and prefer environmentally friendly products. In other words, recovery options are considered by manufacturers due to customer demand, regulations and economical return. This study puts forward a sustainable multi-period supply chain network design for minimize the waste of electric and electrical equipment which is the one of the most crucial sectors in terms of waste management. The contribution of this study is to fill the gap about the mathematical closed loop reverse supply chain network design model in multi-product, multi-objective and multi-period aspects for all of three dimensions of sustainability for decision making. The proposed model is optimized with Mixed Integer Linear Programming. It is applied with a sample data set and sensitivity analysis is done with crucial decision variables. The study ends with future directions which gives some beneficial recommendations for other researchers on this topic.