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Research society has developed a number of models and tools to support emergency management. The proposed models are mainly designed for indoor applications oriented to provide guidance directly to people in danger. Only a few of them deal with outdoor scenarios as well as with providing directions to field commanders or rescue teams. Additionally, load balancing techniques for the optimal allocation of a number of entities into a number of resources are understudied creating a gap in the corresponding research. In this paper, we propose a load balancing model oriented to assist field commanders and rescue teams in a post-emergency scenario. The proposed system could be applied either for indoor or outdoor applications. The module builds on top of the solution provided for the known Santa Fe Bar Problem (SFBP). It consists of an intelligent technique aiming to distribute a number of entities into a finite number of resources. A set of predictors undertake the responsibility of estimating the load of each resource. These predictors are adopted to select the appropriate resource for each entity. A case study deals with the distribution of injured persons into a number of hospitals and presents the functionality of the proposed module. Finally, numerical results reveal computational and time requirements of our system. [ABSTRACT FROM AUTHOR]

A Recommendation System (RS) aims to deliver meaningful recommendations to users for items (e.g., music and books), which are of high interest to them. We consider an RS which directly communicates with a set of providers in order to access the information of the items (e.g., descriptions), rate them according to the user's preferences, and deliver an Item List (IL). The RS is enhanced with a mechanism, which sequentially observes the rating information (e.g., similarity degree) of the items and decides when to deliver the IL to the user, without exhausting the entire set of providers. Hence, the RS saves time and resources. We propose two mechanisms based on the theory of optimal stopping. Both mechanisms deliver an IL, which sufficiently matches to the user's needs having examined a partial set of items. That is, the number of items in the delivered IL is optimal, producing a high level of user satisfaction, i.e., Quality of Recommendation (QoR). Our simulations reveal the efficiency of the mechanisms and quantify the benefits stemming from their adoption. [ABSTRACT FROM AUTHOR]