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Nurses have to work in shifts to sustain continuous health services like other healthcare staff. Nurses have very challenging working conditions, hence the schedules have to be prepared in a fair and balanced way, otherwise, their physical and psychological conditions can be affected negatively. Despite its importance, these schedules are prepared manually in almost all healthcare institutions. The academic studies are not sustainable since they are not transformed into decision support tools. In this study, nurse scheduling problem of cardiovascular surgery service of a hospital in Istanbul is addressed. First, a mixed integer programming model is proposed for the problem. Then a decision support system is prepared for users who has limited technical abilities. This system enables users to prepare fair and balanced shift schedules in a very short time. [ABSTRACT FROM AUTHOR]

Large-scale disasters cause enormous damage to people living in the affected areas. Providing relief quickly to the affected is a critical issue in recovering the effects of a disaster. Pre-disaster planning has an important role on reducing the arrival time of relief items to the affected areas and efficiently allocating them. In this study, a mixed integer programming model is proposed in order to pre-position warehouses throughout a potential affected area and determine the amount of relief items to be held in those warehouses. Time between the strike of the disaster and arrival of relief items at the affected areas is aimed to be minimized. In addition, using probabilistic constraints, the model ensures that relief items arrive at affected areas within a certain time window with certain reliability. Considering instable fault lines on which Istanbul is located, the proposed model is applied to the Istanbul case for pre-positioning warehousesa priorito the possible expected large-scale earthquake. [ABSTRACT FROM PUBLISHER]

In the time of global warming, smart and resilient cities need to have the ability to give quick responses to today's frequent natural disasters. Building a resilient urban transportation system that can timely recover the accessibility of the rescue facilities is vital to the survival chances of citizens. From the perspective of selectively road restoration, we proposed a mixed integer programming model that is based on community hierarchy to provide quick disaster response for damaged urban road networks. This model can efficiently find roads that should give repairing priority to maximize the connectivity in a limited repairing scale. Network community structure has been innovatively introduced into our model, which can remarkably simplify the searching process by extracting critical connectivity information. The model has been tested on square-shaped road networks and Istanbul road networks. The results show that our community structure model gives superior solutions in significantly reduced computational time, and is practical in solving complex real-world large-scale connectivity repairing problems. These findings provide new insights into the understanding of the important role played by mesoscopic topological knowledge of road networks when seeking strategies and solutions for connectivity emergency restoration, which is significant for the development of resilient and sustainable cities. • We propose a community-based MIP approach for connectivity restoration. • The restoration strategies take the advantage of the mesoscopic structure. • The community structure markedly facilitates the optimization process. • The developed algorithms operate on large-scale urban road networks. [ABSTRACT FROM AUTHOR]