A Robust Optimization Model for ClosedLoop Supply Chain Network Under Uncertain Returns. An effective closedloop supply chain (CLSC) is increasingly important for corporate sustainable development, where used products are returned, remanufactured andor recycled. Optimized planning of CLSC network is required and is influenced by uncertainties of recovery. So, this study seeks to establish a robust optimal model for CLSC network, considering both uncertainties in quantity and quality of returned products. After the general framework of CLSC is discussed, the measurements for uncertain quantity and quality of returned products are formulated mathematically, respectively as a number of discrete scenarios and Quality Index. A mixed integral linear programming (MILP) model for a CLSC network design is established and then translated into a robust optimization model based on regret value, to determine facilitiesxe2x80x99 locations and quantity of flows between facilities in the network. A numerical example is given, and the simulation results show that the operation strategies of the CLSC are relatively stable under different recycling scenarios. Therefore, the optimization model for CLSC network has good robustness.

An Improved BiObjective Stochastic Model with SAAbased Solution Method for Reverse Logistics Design of Hazardous Materials Nowadays, the unprecedented advancement in technology and rapid development of economy have not only brought opportunities for improving peopleu0027s living standards but also resulted in a tremendous increase on waste generation. The treatment of hazardous materials from both industrial sectors and households has become one of the most significant challenges in waste management. Because of the negative impact on the environment and the safety issues posed to the nearby residents, the design of a hazardous material management system is, however, a complicated decisionmaking problem. Thence, an improved biobjective stochastic model is proposed to design a singleproduct multistage reverse logistics system for hazardous materials under uncertainties from different input information. Moreover, a sample average approximation (SAA) based solution method is also proposed so as to find out the Pareto optimal solutions to the model under a stochastic environment. The results of the numerical experimentations illustrate the tradeoff between economic efficiency and system risk in reverse logistics design of hazardous materials. In addition, the impact from uncertainties related to the input information is also revealed.