Overcoming Healthcare Transportation Barriers: A Case Study

Transportation remains a major barrier in receiving cancer treatment in Canada. The situation is especially alarming for those living in rural areas and in the light of COVID pandemic, poses another risk in the long list of health challenges to patients with pre-existing conditions. In this dissertation we set out find a solution to this problem by providing a framework for a personalized healthcare transportation system tailored to the needs of this population. A three-step approach is proposed. First, a review of literature and initiatives employed by global transportation providers is conducted to identify major methods used for healthcare industry. Second, a transportation strategy is proposed, and key performance indicators identified through analysis of data and interviews with industry best practices in order to determine key aspects of such operations having the most impact on the overall service level. Finally, a discrete event simulation is provided and tested through various scenarios to understand how such operations would behave in real life and how they react as the environment evolves through time. A case study of a major nonprofit organization for whom this strategy was originally outlined is provided for further context. In the end, the key findings from this research are formulated as a decision-making tool for future guidelines in managing similar operations. Keywords: Transportation; Strategy; Simulation; Healthcare; Ride Sharing; Breast Cance

Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem

The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid adaptive large neighborhood search (hybrid ALNS) algorithm for the MF-HDARP. The computational experiments show that the algorithm produces high quality solutions on our generated instances and on HDARP benchmarks instances. Computational experiments also highlight that the newest components added to the standard ALNS algorithm enhance intensification and diversification during the search process

The dial-a-ride problem with electric vehicles and battery swapping stations

The Dial-a-Ride Problem (DARP) consists of designing vehicle routes and schedules for customers with special needs and/or disabilities. The DARP with Electric Vehicles and battery swapping stations (DARP-EV) concerns scheduling a fleet of EVs to serve a set of pre-specified transport requests during a certain planning horizon. In addition, EVs can be recharged by swapping their batteries with charged ones from any battery-swap stations. We propose three enhanced Evolutionary Variable Neighborhood Search (EVO-VNS) algorithms to solve the DARP-EV. Extensive computational experiments highlight the relevance of the problem and confirm the efficiency of the proposed EVO-VNS algorithms in producing high quality solutions