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Abstract：
Medical waste generation is rapidly rising, which may cause a serious risk for both humans and environment if it is not properly managed. Designing an efficient and reliable medical waste reverse supply chain (MWRSC) is extremely beneficial to society. Most studies on this issue have only considered the generated waste and have not reported the uncertainty in the amount of medical waste generation and other MWRSC parameters. Sustainability criteria and environmental issues in choosing treatment technology are rarely considered as well. In this research, a linear programming model under uncertainty is developed to design an MWRSC. The proposed model is multi-item and multi-period with three objective functions. The first objective function minimizes total costs, the second objective function is relevant to the best treatment technology selection and the third objective function minimizes the total medical waste stored. A robust possibilistic programming approach is utilized to handle imprecise parameters in the model and a fuzzy goal programming method is employed to build up a multi-objective model. A real case study is conducted to illustrate the potential of the proposed model which involves different attributes and problems, such as the location and capacity of facilities, and the type of treatment technology. Furthermore, the transferring amount of medical waste among different nodes is calculated.
摘要：
医疗废物产生量迅速上升,如果管理不当，可能会对人类和环境造成严重风险。设计高效可靠的医疗废物逆向供应链(MWRSC)对社会极为有利。关于这个问题的大多数研究只考虑了产生的废物，没有报告医疗废物产生量和其他MWRSC参数的不确定性。在选择处理技术时很少考虑可持续性标准和环境问题。在这项研究中,建立了不确定条件下的线性规划模型来设计MWRSC。所提出的模型是具有三个目标函数的多项目多周期模型。第一个目标函数最小化总成本，第二个目标函数与最佳处理技术选择相关，第三个目标函数使储存的医疗废物总量最小化。利用鲁棒的可能性规划方法来处理模型中的不精确参数，并采用模糊目标规划方法来建立多目标模型。进行了一个真实的案例研究，以说明所提出的模型的潜力，其中涉及不同的属性和问题，例如设施的位置和容量，以及处理技术的类型。此外，计算医疗废物在不同节点之间的转移量。
