Türkiye'deki atık piller stokastik geri dönüşüm içın ters lojistik ağı tasarımı ve problem çözme için sağlam optimizasyon

In recent decades, there has been an increasing interest in the mobile electronic devices that use batteries as their source of power. The growing demand for batteries creates environmental concerns for governments and companies because of their content of heavy metals that are usually significantly harmful to the environment. Waste batteries are known as hazardous waste and should therefore be appropriately disposed or recycled. Since finding suitable landfill areas is very hard—because people prefer to not live close to the landfill areas—recycling has become even more popular. In addition to the environmental benefits, recycling waste batteries also has economic advantages. There are a number of mathematical models in the literature that address the design of recycling systems for different products. The design of a reverse logistics system for recycling waste batteries is expressed as a multi-period mixed integer linear programming (MILP) problem to address the collection, transportation, sorting, recycling, and landfill operations of the waste batteries. This model considers various types of collected batteries, several existing and potential facilities with different types and capacity options, various construction and operational costs, and revenue from selling recycled batteries at a secondary market. The objective of this MILP model is to maximize the profit—i.e., the total revenue minus the total cost. The amount of used batteries that are returned by the end users is stochastic. Two-stage stochastic optimization method is used in the literature to address uncertainty in the model. In the first stage, the model makes strategic decisions—e.g., the capacity of the sorting and recycling facilities—while in the second stage, it makes the tactical decisions—e.g., inventory levels in warehouses. We extend the deterministic MILP model by using robust optimization approach to incorporate the uncertainty in the model. We consider four different scenarios for the amount of waste-batteries that are collected from the end users. We use GAMS (General Algebraic Modeling System) optimization package and the CPLEX solver to compare the results of these two approaches. We observe that the two-stage approach has worse results than the robust optimization method with respect to the worst-case performance. We also conduct a sensitivity analysis to show the impact of the amount of collected waste-batteries on the situation of facilities in the model.