@article {1453,
	title = {Next-day operating room scheduling with uncertain surgery durations: Exact analysis and heuristics},
	journal = {European Journal of Operational Research},
	volume = {286},
	year = {2020},
	pages = {49-62},
	abstract = {<p>Operating rooms are units of particular interest in hospitals as they constitute more than 40\% of total expenses and revenues. Managing operating rooms is challenging due to conflicting priorities and preferences of various stakeholders and the inherent uncertainty of surgery durations. In this study, we consider the next-day scheduling problem of a hospital operating room. Given the list and the sequence of non-identical surgeries to be performed in the next day, one needs to determine the scheduled durations of surgeries where the actual duration of each surgery is uncertain. Our objective is to minimize the weighted sum of expected patient waiting times, room idle time and overtime. First, we provide a reformulation of the objective function in terms of auxiliary functions with a recursive pattern that enables exact analysis of the optimal surgery durations at the expense of high CPU time. Next, we develop and analyze simple-to-use and close-to-optimal scheduling heuristics motivated by practice, for the OR managers to deploy in the field. Our proposed hybrid heuristic attains 1.22\% average performance gap and worst average optimality gap of 2.77\%. Our solution is easy to implement as it does not require any advanced optimization tool, which is the reality of many operating room environments.</p>},
	keywords = {Heuristics, Operating room planning, OR in health services, Scheduling, Stochastic optimization},
	issn = {0377-2217},
	doi = {https://doi.org/10.1016/j.ejor.2020.03.002},
	url = {https://www.sciencedirect.com/science/article/pii/S0377221720302162},
	author = {Taghi Khaniyev and Enis Kay{\i}{\c s} and Refik G{\"u}ll{\"u}}
}
@article {1437,
	title = {Influence maximization with deactivation in social networks},
	journal = {European Journal of Operational Research},
	volume = {278},
	year = {2019},
	pages = {105-119},
	abstract = {<p>In this paper, we consider an extension of the well-known Influence Maximization Problem in a social network which deals with finding a set of k nodes to initiate a diffusion process so that the total number of influenced nodes at the end of the process is maximized. The extension focuses on a competitive variant where two decision makers are involved. The first one, the leader, tries to maximize the total influence spread by selecting the most influential nodes and the second one, the follower, tries to minimize it by deactivating some of these nodes. The formulated bilevel model is solved by complete enumeration for small-sized instances and by a matheuristic for large-sized instances. In both cases, the lower level problem, which is a stochastic optimization problem, is approximated via the Sample Average Approximation method.</p>},
	keywords = {Bilevel modeling, Influence maximization, Matheuristics, Metaheuristics, Stochastic optimization},
	issn = {0377-2217},
	doi = {https://doi.org/10.1016/j.ejor.2019.04.010},
	url = {https://www.sciencedirect.com/science/article/pii/S0377221719303303},
	author = {K{\"u}bra Tan{\i}nm{\i}{\c s} and Necati Aras and I. Kuban Altinel}
}
