Biobjective optimal planning for emergency resource allocation in the maritime oil spill accident response phase under uncertainty Emergency resource allocation is an important issue in emergency response to marine oil spill accident. This paper addresses an emergency resource allocation problem which involves multiple relief resource center and one accident point. To solve this problem, a biobjective model is constructed to minimize the disposal time and the disposal costs. Due to the existence of uncertainty in different aspects involved in the emergency response to the marine oil spill, we also incorporated into the model some fuzzy parameters. An algorithm based on the nondominated sorting genetic algorithmII and the differential evolution algorithm, was presented to obtain the Pareto set. The technique for order preference by similarity to ideal solution method was employed to reach a final compromise solution. A case was studied to demonstrate the effectiveness of the proposed model and the hybrid algorithm, which can support the decision making in the process of the oil spill emergency disposal.

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.