In response to resource shortages caused by the COVID-19 pandemic, many communities have been leasing health facilities such as hospitals, clinics, and other centers in order to meet the needs of their patients. The goals have been two-folded: leasing costs had to be optimized and patients had to be served as soon as possible. Decisions as to when to lease which services at which facility locations shaped the success of these goals. At the heart of these decisions lies a complex optimization problem, which we call the Online Non-metric Facility Service Leasing problem (non-metric OFSL), a generalization of the well-known Online Non-metric Facility Leasing problem (non-metric OFL) in which facility locations are leased for different facility-time durations. In non-metric OFSL, each facility location may provide a number of services leased for different service-time durations. Additionally, each service is associated with a dormant fee that needs to be paid for each day at which the service is not leased. The optimization goal is to minimize the total leasing costs, dormant fees, and the distances between patients and the facilities they are connected to. We develop the first online algorithm for non-metric OFSL, evaluated using the notion of competitive analysis. The latter is a worst-case analysis used to measure the quality of online algorithms, in which the online algorithm's output is compared to the optimal offline solution for all instances of the problem. © 2021 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved

Many markets have seen a shift from the idea of buying and moved to leasing instead. Arguably, the latter has been the major catalyst for their success. Ten years ago, research realized this shift and initiated the study of 'online leasing problems' by introducing leasing to online optimization problems. Resources required to provide a service in an 'online leasing problem' are no more bought but leased for different durations. In this paper, we provide an overview of results that contribute to the understanding of 'online resource leasing problems'.