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.

Developing a hazardous waste management system with consideration of health, safety, and environment Abstract   This paper presents a novel mathematical model for hazardous waste collection problem. The study aims to introduce a model in order to improve health, safety, environment (HSE) issues for hazardous waste collection problem in addition to considering operational cost. In addition, technologies which will be applied at each facility are investigated. To extend previous networks in this field of study, the vehiclesxe2x80x99 depot are considered,. In order to tackle the problem, xc9x9bconstraint and nondominated sorting genetic algorithm II (NSGAII) methods are applied. According to the results, we can state that NSGAII mean value of comparison metrics are equivalent to the results of xc9x9bconstraint with confidence level of 95%. Moreover, the sum of the distanceu0027s mean from the ideal point for xc9x9bconstraint method is 4% less than NSGAII which can be concluded as an acceptable result for NSGAII algorithm. All in all, The presented model can help managers and decision makers to have a more comprehensive and thorough understanding the HSE issues with respect to the operational cost in the field of waste collection network design.