Novel Meta-heuristic Approaches and Their Applications to Preemptive Operational Planning and Logistics in Disaster Management
A special issue of Algorithms (ISSN 1999-4893).
Deadline for manuscript submissions: closed (30 June 2014) | Viewed by 8499
Special Issue Editors
Interests: soft-computing and machine learning algorithms; meta-heuristics optimization techniques; energy; climate and environmental applications
Special Issues, Collections and Topics in MDPI journals
2. Communications Engineering, University of the Basque Country (UPV/EHU), 48013 Bilbao, Bizkaia, Spain
Interests: machine learning; deep learning; meta-heuristic optimization; explainable artificial intelligence; responsible artificial intelligence; stream learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Let me introduce the Algorithms Special Issue entitled "Novel Meta-heuristic Approaches and Their Applications to Preemptive Operational Planning and Logistics in Disaster Management". Nowadays, there is a generalized and ever-growing concern, across institutions and governments around the globe, with the increased frequency and scale of wide-area disasters, such as forest fires, earthquakes, tsunamis and volcanos. Irrespective of whether these disasters originated from purely natural or human-induced factors, the reality is that more research on operational logistics is widely deemed critical for anticipatively reducing the fatal consequences of these events. In this context, despite the huge research efforts conducted toward predictive risk assessing techniques that focus on the aforementioned disaster events, there is a clear gap between such predictive approaches and the operational logistics that, upon their linkage, would bring about preemptive operations planning and/or logistics (i.e., logistics driven by a priori predictive information concerning the disaster situation at hand).
Bearing this scope in mind, the Special Issue will gravitate toward the use of advanced meta-heuristic optimization approaches as means of properly allocating human, technical, and transport resources based on predictive information concerning the locational severity of a disaster, its geographical probability of occurrence, etc. However, beyond novel algorithmic developments, the Special Issue is open to contributions dealing with conventional meta-heuristic algorithms (e.g., genetic, simulated annealing, PSO, etc.) that are applied, with an emphasis on their practicality, to innovative formulations of operational planning paradigms over wide areas. We hereby invite high quality papers presenting original research on this exciting topic.
Prof. Dr. Miren Nekane Bilbao
Prof. Dr. Sancho Salcedo-Sanz
Dr. Javier Del Ser Lorente
Dr. Sergio Gil-Lopez
Guest Editors
Manuscript Submission Information
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Keywords
- disaster management
- meta-heuristic optimization
- predictive risk modeling
- forest fires
- earthquakes
- operational planning
- logistics
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