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The dramatic increase in medical waste has put a severe strain on sorting operations. Traditional manual order picking is extremely susceptible to infection spread among workers and picking errors, while automated medical waste sorting systems can handle large volumes of medical waste efficiently and reliably. This paper investigates the optimization problem in the automated medical waste sorting system by considering the operational flow of medical waste. For this purpose, a mixed-integer programming model is developed to optimize the assignment among medical waste, presorting stations, and AGVs. An effective variable neighborhood search based on dynamic programming algorithm is proposed, and extensive numerical experiments are conducted. It is found that the proposed algorithm can efficiently solve the optimization problem, and the sensitivity analysis gives recommendations for the speed setting of the conveyor.

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This study investigates a variant of the vehicle routing problem (VRP) for customized on-demand bus service platforms. In this problem, the platform plans customized bus routes upon receiving a batch of orders released by passengers and informs the passengers of the planned pick-up and drop-off locations. The related decision process takes into account some passenger-side time window-related requirements, walking limits, the availability and capacities of various types of buses. A mixed-integer linear programming model of this new VRP variant with floating targets (passengers) is formulated. To solve the model efficiently, a solution method is developed that combines the branch-and-bound and column generation algorithms and includes embedded acceleration techniques such as the multi-labeling algorithm. Experiments based on real data from Dalian, China are conducted to validate the effectiveness of the proposed model and efficiency of the algorithm; the small-scale experimental results demonstrate our algorithm can obtain optimal results in majority of instances. Additionally, sensitivity analysis is conducted and model extensions are investigated to provide customized bus service platform operators with potentially useful managerial insights; for example, a platform need not establish as many candidate stops as possible, a wide range of walking distance may not bring early arrival at destinations for customers, more mini-buses should be deployed than large buses in our real-world case. Moreover, the rolling horizon based context and zoning strategies are also investigated by extending our proposed methodology.