Special Issue "Novel Meta-heuristic Approaches and Their Applications to Preemptive Operational Planning and Logistics in Disaster Management"

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A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 June 2014)

Special Issue Editors

Guest Editor
Prof. Dr. Miren Nekane Bilbao

University of the Basque Country UPV/EHU 48013 Bilbao, Spain
Guest Editor
Prof. Dr. Sancho Salcedo-Sanz

Universidad de Alcalá 28871 Alcalá de Henares, Spain
Guest Editor
Dr. Javier Del Ser Lorente (Website1, Website2)

TECNALIA RESEARCH & INNOVATION 48170 Zamudio Bizkaia, Spain
Interests: machine learning; meta-heuristic optimization; predictive analytics; resource allocation; wireless network design; cognitive radio; risk contention; logistics; clustering
Guest Editor
Dr. Sergio Gil-Lopez (Website)

TECNALIA RESEARCH & INNOVATION 48170 Zamudio Bizkaia, Spain

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

Submission

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed Open Access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 300 CHF (Swiss Francs). English correction and/or formatting fees of 250 CHF (Swiss Francs) will be charged in certain cases for those articles accepted for publication that require extensive additional formatting and/or English corrections.

Keywords

  • disaster management
  • meta-heuristic optimization
  • predictive risk modeling
  • forest fires
  • earthquakes
  • operational planning
  • logistics

Published Papers (1 paper)

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Research

Open AccessArticle Bio-Inspired Meta-Heuristics for Emergency Transportation Problems
Algorithms 2014, 7(1), 15-31; doi:10.3390/a7010015
Received: 4 December 2013 / Revised: 28 January 2014 / Accepted: 30 January 2014 / Published: 11 February 2014
Cited by 8 | PDF Full-text (251 KB) | HTML Full-text | XML Full-text
Abstract
Emergency transportation plays a vital role in the success of disaster rescue and relief operations, but its planning and scheduling often involve complex objectives and search spaces. In this paper, we conduct a survey of recent advances in bio-inspired meta-heuristics, including genetic [...] Read more.
Emergency transportation plays a vital role in the success of disaster rescue and relief operations, but its planning and scheduling often involve complex objectives and search spaces. In this paper, we conduct a survey of recent advances in bio-inspired meta-heuristics, including genetic algorithms (GA), particle swarm optimization (PSO), ant colony optimization (ACO), etc., for solving emergency transportation problems. We then propose a new hybrid biogeography-based optimization (BBO) algorithm, which outperforms some state-of-the-art heuristics on a typical transportation planning problem. Full article

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