Balanced scheduling for medical waste treatment in public health emergencies via Simulation-Driven Mixed Integer Linear Programming
提出一种仿真驱动的混合整数线性规划模型，用于优化公共卫生突发事件中医疗废物处理的调度，降低处理成本并确保各处理站负荷均衡。
This study introduces a Simulation-Driven Mixed Integer Linear Programming (SD-MILP) model developed to optimize the scheduling of medical waste treatment during emergencies. Our approach integrates simulation to reflect the complexities of the real world, providing solutions that are adaptable to dynamic conditions. Initially, we formulate the problem using an MILP model to optimize waste allocation and alleviate operational pressures. We then incorporate a simulation mechanism within the MILP framework, which simulates waste generation to address uncertainties in epidemic transmission and the rehabilitation process. Through computational experiments conducted on benchmark instances, we evaluate the model’s performance. The results confirm its efficacy in reducing waste treatment costs, including transportation, fixed expansion costs and temporary overload operating costs at treatment stations, while ensuring equitable load distribution among treatment stations.