Decentralized exam timetabling: A solution for conducting exams during pandemics

Traditionally, exams were scheduled into designated timeslots within a centralized campus. However, the outbreak of COVID-19 disrupted this practice, forcing universities to prioritize the safety of individuals during exams. In response, a shift to online assessments occurred, bringing forth new challenges such as academic dishonesty and inadequate infrastructure. This study presents a novel approach to decentralized in-person exams for geographically dispersed students. A mixed-integer non-linear programming model is developed, followed by a mixed-integer linear reformulation, to optimally determine the scheduling of exams, test center locations, and student allocation, while addressing the incorporation of both hard and soft constraints. Given the complexity of the problem, we have implemented meta-heuristic algorithms to solve larger instances, including genetic and simulated-annealing algorithms. Additionally, we have devised a hybrid algorithm that combines the strengths of population-based and local-search algorithms. For evaluation purposes, we used real data collected from our department during the fall semester of 2022. The results demonstrate the superior performance of the hybrid algorithm compared to others, in terms of both effectiveness and adaptability. It remains consistent across different scenarios, effectively addressing real-world complexities. Finally, a comparison between centralized and decentralized scenarios underscores the advantages of the latter approach during the pandemic.