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Network and discrete location: models, algorithms, and applications
In: Wiley-Interscience series in discrete mathematics and optimization
In: A Wiley-Interscience publication
APPLICATION OF AN EXPECTED COVERING MODEL TO EMERGENCY MEDICAL SERVICE SYSTEM DESIGN*
In: Decision sciences, Volume 13, Issue 3, p. 416-439
ISSN: 1540-5915
ABSTRACTThe Emergency Medical Service (EMS) Act specifies the fraction of all demands for service that must be reached in a given amount of time for urban and rural areas. The conditions have traditionally been interpreted to apply to the case in which all EMS vehicles are available to respond to demands. A model that considers the probability of a vehicle being busy is formulated and model properties are briefly discussed. The model is then applied to two problems: a 55‐node test case and a 33‐node census tract representation of Austin, Texas. The implications of the new model for EMS system design are discussed as are the limitations of the modeling approach.
An integer L-shaped algorithm for the integrated location and network restoration problem in disaster relief
In: Sanci , E & Daskin , M S 2021 , ' An integer L-shaped algorithm for the integrated location and network restoration problem in disaster relief ' , Transportation Research Part B: Methodological , vol. 145 , pp. 152-184 . https://doi.org/10.1016/j.trb.2021.01.005
Being prepared for potential disaster scenarios enables government agencies and humanitarian organizations to respond effectively once the disaster hits. In the literature, the two-stage stochastic programming models are commonly employed to develop preparedness plans before anticipated disasters. These models can be very difficult to solve as the complexity increases by several sources of uncertainty and interdependent decisions. In this study, we propose an integer L-shaped algorithm to solve the integrated location and network restoration model, which is a two-stage stochastic programming model determining the number and locations of the emergency response facilities and restoration resources under uncertainty. Our algorithm accommodates the second-stage binary decision variables which are required to indicate undamaged and restored roads of the network that can be used for relief distribution. Our computational results show that our algorithm outperforms CPLEX for the larger number of disaster scenarios as the solution time of our algorithm increases only linearly as the number of scenarios increases.
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Modeling Public Sector Facility Location Problems
In: Socio-economic planning sciences: the international journal of public sector decision-making, Volume 46, Issue 2, p. 111
ISSN: 0038-0121