Optimal Distribution and Waste Management of COVID-19 Vaccines from Vaccination Centers’ Satisfaction Perspective – A Fuzzy Time Window-based VRP
Abbas Maleki
Ph.D. Candidate, IEBIS /CHOIR Group, University of Twente.
The COVID-19 pandemic has underscored the critical need for effective public health strategies, particularly in implementing widespread vaccination programs. The urgency and scale of these programs have brought global attention to the development of essential policies and tactics to ensure efficient and equitable vaccine distribution. However, a significant and often overlooked consequence of these efforts is the dramatic increase in medical waste production, which, if not properly managed, poses severe risks to both public health and the environment. The mishandling of medical waste, including used syringes, vials, and personal protective equipment, can lead to the spread of infectious diseases and environmental contamination, making its management an integral component of any public health initiative.
In response to these challenges and, we proposed a comprehensive and sustainable fuzzy multi-objective, multi-period, multi-product, and location-allocation model that integrates both the vaccine distribution phase and the medical waste management process. This model is designed to optimize the logistics of vaccine distribution while simultaneously addressing the complexities of medical waste collection, transportation, and disposal. Moreover, the model introduces a novel social aspect by incorporating a fuzzy version of the time window constraint from the perspective of vaccination centers. This enhancement allows for the integration of both the satisfaction level and the priority of the nodes that must be visited, ensuring that the model not only meets logistical objectives but also aligns with the social and public health priorities of the vaccination centers. By accounting for the variability and uncertainty inherent in real-world operations, the fuzzy time window enables a more flexible and responsive approach to scheduling and routing.
Abbas Maleki is a Ph.D. researcher in the Industrial Engineering & Business Information Systems (IEBIS) Section at the University of Twente. He holds bachelor's (2017-2021) and master's (2021-2024) degree in Industrial Engineering - Systems Optimization from the University of Tehran. His main research interest lies in integrating AI-based and optimization-based techniques to develop decision-support tools to deal with stochasticity in healthcare systems. Abbas started his Ph.D. in June 2024, titled “Design and Analysis of Drone-based Medical Items Transportation” supervised by Dr. Amin Asadi working at CHOIR.
Estimating Energy Consumption and Charging Duration of Electric Vehicle in Multigraph
Asal Karimpour
Ph.D. Candidate, IEBIS/CHOIR Group, University of Twente.
One of the most significant human-induced environmental challenges is the rise in greenhouse gas (GHG) emissions, leading to global warming, pollution, environmental harm, and health risks to animals. Transportation, as a key part of the supply chain, plays a major role in these environmental challenges. The impact of this sector is especially significant with the growing use of green vehicles like Electric Vehicles (EVs). EVs are relatively new but increasingly important modes of transportation, and their deployment has grown rapidly over the past decade. Designing an efficient routing scheme for EV fleets is crucial, especially given their need for charging stations. The best route is the route with the least energy consumption because of a shortage of charging stations, the relatively long charging duration, and the charging cost. The existence of time limitations adds complexity to the routing process, especially due to the formation of queues at charging stations. Including a queuing system in the routing plan leads to more precise time calculations and a more efficient routing scheme. In addition to these factors, the transportation network may present alternative paths, known as a multigraph. Multigraph is a road graph where at least one pair of nodes is connected by parallel edges as alternative paths. When planning routes on a multigraph, various criteria such as distance, travel time, travel cost, and energy consumption must be evaluated. The presence of alternative paths requires a more sophisticated approach to route planning, ensuring that the chosen path optimally balances all these factors, especially in the context of electric vehicles.
In June 2024, Asal joined the IEBIS section and CHOIR group at the University of Twente as a PhD candidate. Her doctoral thesis focuses on Nurse-Centric Scheduling in Home Care. She obtained her bachelor's and master's degrees in Industrial Engineering. Her master's thesis addressed the rechargeable electric vehicle routing problem, and she published a paper based on this research in COR in 2023. After completing her master’s, Asal worked in industry as a manufacturing planner. After gaining valuable experience, Asal decided to continue my academic journey by pursuing a PhD at the University of Twente.