Abstract:

To improve the safety of urban medical waste recycling and disposal, an optimization methodology for the medical waste transportation network was proposed, where the facility location, vehicle-routing and vehicle acquisition were simultaneously optimized. Firstly, according to the environmental transmission characteristic of medical waste viruses and uncertainty of urban emergency response time, BP neural network model was used to simulate the dynamic evolution process of risk, and a four-dimensional prediction model was designed. Secondly, introducing vehicle volume and capacity constraints, an optimization model, minimizing the total cost and risk, was developed by two-commodity flow formulation. The solution procedure was also developed by improving the NSGA-II algorithm based on the complexity of model. Finally, a case study in Shanghai and several tests were provided to demonstrate the workability. The computational results show that the new model and approach can provide multiple efficient plans within 672 seconds, and they are sensitive to some parameters. Compared to the traditional risk assessment, new model can provide a reduction of 3.47% and 13.04% in total cost and risk respectively. Using the risk prediction technique, a decrement of 7.41% in total risk can be achieved when comparing to current policy. New algorithm can reduce the CPU time by at least 49.44% and keep stable performance in solving problems of different scales while comparing to traditional multi-objective optimal methods.

Key words: four-dimensional risk prediction, medical waste transportation network, back propagation (BP) neural network, multi-objective, non-dominant sorting genetic algorithm Ⅱ(NSGA-Ⅱ)