Some electronic devices have a short lifetime, and variety-seeking and consumerism are increasingly growing in today’s societies. Moreover, electronic wastes contain precious substances such as gold, silver, copper, and aluminum. The proper disposal and processing of them by recycling offer considerable advantages to the environment, given the hazardous natures of such devices’ substances. The proposed reverse logistics with waste electrical and electronic equipment (WEEE) is an important task considered by researchers, the use of which offers economic benefits and reduces the environmental impacts of wastes. The present study models the electrical and electronic equipment (EEE) reverse logistics process as a bi-objective mixed-integer programming model under uncertainties. The mathematical model investigates two objectives: an economic objective and an environmental objective. The first is minimizing cost, while the second is maximizing the environmental score by reverse logistics processes in recovering and recycling. The parameters of demand and WEEE return rate which is obtained from the customer were considered as two uncertain parameters. A scenario-based stochastic programming (SSP) approach is applied to deal with the uncertainties. A case study of an electronic equipment manufacturer in Esfahan, Iran was included. The model was solved by a nominal approach and an SSP approach via the epsilon-constraint (EC) and augmented epsilon-constraint (AEC) methods to obtain optimal Pareto solutions and compare the methods. Finally, the optimal results of the two approaches were evaluated. The results indicated that the SSP approach using the AEC method had better outcomes.

一些电子设备的寿命很短，并且在当今社会中寻求品种和消费主义正在日益增长。此外，电子废物还包含诸如金，银，铜和铝的珍贵物质。考虑到此类设备物质的危险性，通过回收利用对它们进行适当的处​​理和处理可为环境带来可观的优势。拟议中的利用废弃电气电子设备进行逆向物流是研究人员考虑的一项重要任务，其使用可带来经济利益并减少废物对环境的影响。本研究将电气和电子设备（EEE）逆向物流过程建模为不确定性下的双目标混合整数规划模型。该数学模型研究了两个目标：经济目标和环境目标。第一个是最小化成本，第二个是通过反向物流过程进行回收和循环利用来最大化环境得分。从客户获得的需求和WEEE回报率参数被视为两个不确定参数。应用基于场景的随机规划（SSP）方法来处理不确定性。其中包括对伊朗伊斯法罕一家电子设备制造商的案例研究。通过标称方法和SSP方法，通过ε约束（EC）和增强ε约束（AEC）方法对模型进行求解，以获得最佳的帕累托解并进行比较。最后，评估了两种方法的最佳结果。