A mixed integer linear programming model to optimize reverse logistics activities of end-of-life vehicles in Turkey - Demirel, Eray; Demirel, Neslihan; Gökçen, Hadi (Journal of cleaner production, 2016)
There are legislations encumbering manufacturers to incorporate environmental factors into their decisions in several industries. Automotive is one of these sectors and in many countries, particularly in those who are a member of the European Union, there are regulations to guarantee the recycling of end-of-life vehicles (ELVs). In Turkey, recovery of ELVs is regulated with Regulation About Controlling of ELVs which was issued by the Turkish Republic Ministry of Environment and Forestry in Official Gazette in 2009. Manufacturers are responsible for free take-back of ELVs from end-users, depolluting, dismantling, shredding and recycling of ELVs. In this paper, in order to comply with related regulations and manage the recovery of ELVs efficiently, we presented a mixed integer linear programming (MILP) model for network design including the different actors taking part in ELV recovery system. The proposed framework is justified by a real case performed in Ankara, the capital and second largest city of Turkey. We also presented a modeling approach for the projection of car ownership and number of ELVs and generated scenario analyzes based on the long-term average developments in the number of ELVs. The case study and analyzes provided important insights on how logistics network behave over time. The results demonstrated that the number of facilities to be located and the system cost increase while the number of ELVs are getting higher in the future. •A MILP model of end-of-life vehicle (ELV) recycling network is presented.•Sales revenue of reusable parts and scrap metals is considered in the model.•The proposed framework is justified by a real case performed in Turkey.•Future developments in the number of car ownership and ELVs are determined.•Scenario analyses are generated based on the projections of the number of ELVs.