A bi-objective data-driven chance-constrained optimization for sustainable urban medical waste management

The processing and transportation of medical waste pose uncertain threats to the surrounding people and the environment in urban road networks. This paper aims to mitigate such risks under an emergency system with uncertain response times. In more detail, we first formulate an integrated pollution-population risk assessment that estimates the dynamic impact on the exposed population by embedding the emergency response time into the risk measure. Given the variability in traffic conditions, the response time is uncertain, which also affects the associated risks. Taking this randomness into consideration, a bi-objective chance-constrained model is developed to seek optimal facility locations, vehicle acquisitions, as well as route and tour plans, such that both the risk and cost are simultaneously minimized. To meet practical restrictions on medical waste collection, continuously accumulative vehicle load and volume constraints are added to the two-commodity flow formulation. Then, we propose a comprehensive solution procedure that integrates a Back Propagation Neural Network approach within the fuzzy chance constraint framework to address uncertainties. Two multi-objective methods, an augmented-constraint solution technique and a nearest-neighbor Non-Dominated Sorting Genetic Algorithm II (NSGA-II) algorithm, are implemented respectively for small- and large-scale problem instances. A series of numerical experiments are conducted on a real-life situation in Shanghai city of China to demonstrate the workability of the proposed model and approach. The numerical results show that our recommended system can effectively prevent the overall capacity shortage, reduce the total cost and risk respectively by more than 8% and 11%, as well as lower the transportation risk and distance respectively by nearly 15% and 23%.