A multi-objective optimization model for medical waste recycling network design under uncertainties

Transporting medical waste (MW) generated by medical institutions (MIs) is a process that poses potential threats to the environment and public safety. Therefore, it is vital to find a safe and efficient way to transport this type of waste to disposal centers (DCs). However, there are challenges in the transportation of MW due to random factors such as the generation of waste in an unpredictable manner and unforeseen travel times. In this paper, we propose a bi-level optimization model to minimize site selection costs, transportation costs, time-window penalties costs, and transportation risks under uncertainties. The concepts of "loading reliability", "travel time reliability" and "transportation risk" are adopted in the proposed optimization model. The simulated annealing algorithm (SA) is employed to address the optimal location problem (Upper level), which involves minimizing the construction cost, transportation cost (DC-CP-DC), and transportation risk (estimated using Bayesian method). To tackle the capacity-constrained vehicle routing problem considering transportation risk and time window penalties (Lower level), we propose an improved genetic algorithm named harmony search algorithm (IHSGA). Subsequently, the results from the lower level are looped back to the upper level, fostering mutual influence between the two stages. We demonstrate the effectiveness and correctness of the proposed model and algorithm using S city in China as an illustrative example. Furthermore, a series of sensitivity analyses were conducted to examine the impact of various factors. The findings highlight the pivotal roles of both travel time reliability and loading reliability in designing the medical waste recycling network. In comparison to the general Genetic Algorithm (GA) and CPLEX solver, the modified IHSGA presented in this paper exhibits superior performance.