Sami Faiz

Tabu-based GIS for solving the vehicle routing problem

Abstract Besides being a hard combinatorial problem, the VRP is also a spatial problem. Hence, effective decision making in this field strongly requires the integration of GIS and optimization systems (GIS-O). This article integrates GIS and optimization tools for solving the vehicle routing problem with loading and distance requirements (DCVRP). A general outline of the multi-step integration is pointed out showing the interaction of the GIS and the spatial optimization according to the loose coupling strategy. The computational performance of the TS-VRP algorithm for the DCVRP turned out to be quite efficient on both computation time and solution quality. The Tunisian case study well illustrates the incentive behind using such a spatial decision support system that allows the management of the problem from the data acquisition to the visualization of possible simulation scenarios in a more realistic way.

MASIR: A Multi-agent System for Real-Time Information Retrieval from Microblogs During Unexpected Events

Microblogs have proved their potential to attract people from all over the world to express voluntarily what is happening around them during unexpected events. However, retrieving relevant information from the huge amount of data shared in real time in these microblogs remain complex. This paper proposes a new system named MASIR for real-time information retrieval from microblogs during unexpected events. MASIR is based on a decentralized and collaborative multi-agent approach analyzing the profiles of users interested in a given event in order to detect the most prominent ones that have to be tracked in real time. Real time monitoring of these users enables a direct access to valuable fresh information. Our experiments shows that MASIR simplifies the real-time detection and tracking of the most prominent users by exploring both the old and fresh information shared during the event and outperforms the standard centrality measures by using a time-sensitive ranking model.

Integration of Heterogeneous Spatial Databases for Disaster Management

The response phase in a disaster case is often considered to be the most critical in terms of saving lives and dealing with irreversible damage. The timely provision of geospatial information is crucial in the decision-making process. Thus, there is a need for the integration of heterogeneous spatial databases which are inherently distributed and created under different projects by various organizations. The integration of all relevant data for timely decision making is a challenging task due to syntactic, schematic and semantic heterogeneity. This paper aims to propose a framework for the integration of heterogeneous spatial databases using novel approaches, such as web services and ontologies. We focus on providing solutions for the three levels of heterogeneity, in order to be able to interrogate the content of the different databases conveniently. Based on the proposed framework, we implemented a use case using heterogeneous data belonging to La Rochelle city in France.

Towards geographic databases enrichment

The geographic database (GDB) is the backbone of the geographic information system (GIS). Indeed, all kinds of data managements are based and strongly affected by the type, relevancy and scope of the stored data. Nevertheless, this dataset is sometimes insufficient to make the adequate decision. Then, it is of primary interest to provide other data sources to complement the inherent GDB. In this context, we propose a four staged semantic data enrichment approach consisting of: text segmentation, theme identification, delegation and text filtering. Besides, a refinement is eventually executed to enhance the data enrichment results.

Towards a New Ubiquitous Learning Environment Based on Blockchain Technology

Ubiquitous learning environments have an increasing trend considering the huge number of connected smart devices dedicated to educational services. Ubiquitous learning (U-learning) provides to students the possibility to learn at anyplace and anytime within the collaborative environment using interactive multimedia system that enables effective communication among teacher and learners. The architecture of ubiquitous learning environment (ULE) still suffers from the problems of vulnerability. Blockchain (BC) technology recently explored to provide much more privacy and security using essentially peer-to-peer (P2P) networks has a significant role in the development of decentralized topologies. This paper expounds a novel BC-based architecture for ULE that preserves the benefits of security and privacy. The architecture offers new opportunities to design secured collaborative learning system. It provides data exchange with BC using trust methods within the decentralized topology. The evaluation of this implemented system demonstrates its efficiency while it delivers security and privacy for ULE.

Simulated annealing-based decision support system for routing problems.

The spatial character intrinsic to the routing field requires the integration of geographic information systems (GIS) and optimization approaches to handle spatial and non-spatial data in transportation applications. Motivated by the need to better support decision making in logistic area, we develop an interactive spatial decision support system (SDSS) for solving the vehicle routing problems by coupling the simulated annealing method with Quantum GIS (QGIS). In this paper, the evoked variants of VRP are detailed and formulated mathematically. The SDSS architecture is designed for the VRPs showing the interaction of GIS and SA approach according to the tight coupling strategy. A VRP variant termed the Open VRP (OVRP) is selected to show the system effectiveness. The computational performance of the SDSS for the OVRP, based on a set of benchmark instances, turned out to be effective on both computation time and solution quality.

Estimating Forest Fire Losses Using Stochastic Approach: Case Study of the Kroumiria Mountains (Northwestern Tunisia)

ABSTRACT Kroumiria Mountains (northwestern Tunisia) have experienced major fires, making them the main loss reason of Tunisian forested areas. The ability of accurately forecasting or modeling forest fire areas may significantly aid optimizing fire-fighting strategies. However, there are still limitations in the empirical study of forest fire loss estimation because the poor availability and low quality of fire data. In this study, a stochastic approach based on Markov process was developed for the prediction of burned areas, using available meteorological data sets and GIS layers related to the forest under analysis. The Self-organizing map (SOM) was initially used to classify spatiotemporal factors influencing the fire behavior. Subsequently, the SOM clusters were incorporated into a Hidden Markov Model (HMM) framework to model their corresponding burned areas. Results achieved using a database of 829 forest fires records between 1985 and 2016, showed the appropriateness of the HMM approach for the prediction of burned areas compared with a state-of-the art machine learning methods. The transition probability matrix (TPM) and the emission probability matrix (EPM) were also analyzed to further understand the spatiotemporal patterns of fire losses.

Extending F-Perceptory to model fuzzy objects with composite geometries for GIS

When analyzing spatial issues, geographers are often confronted with many problems with regard to the imprecision of the available information. It is necessary to develop representation and design methods which are suited to imprecise spatiotemporal data. This led to the recent proposal of the F-Perceptory approach. F-Perceptory models fuzzy primitive geometries that are appropriate in representing homogeneous regions. However, the real world often contains cases that are much more complex, describing geographic features with composite structures such as a geometry aggregation or combination. From a conceptual point of view, these cases have not yet been managed with F-Perceptory. This article proposes modeling fuzzy geographic objects with composite geometries, by extending the pictographic language of F-Perceptory and its mapping to the Unified Modeling Language (UML) necessary to manage them in object/relational databases. Until now, the most commonly used object modeling tools have not considered imprecise data. The extended F-Perceptory is implemented under a UML-based modeling tool in order to support users in fuzzy conceptual data modeling. In addition, in order to properly define the related database design, an automatic derivation process is implemented to generate the fuzzy database model.

On Solving the Multi-depot Vehicle Routing Problem

Problems associated with seeking the lowest cost vehicle routes to deliver demand from a set of depots to a set of customers are called Multi-depot Vehicle Routing Problems (MDVRP). The MDVRP is a generalization of the standard vehicle routing problem which involves more than one depot. In MDVRP each vehicle leaves a depot and should return to the same depot they started with. In this paper, the MDVRP is tackled using a iterated local search metaheuristic. Experiments are run on a number of benchmark instances of varying depots and customer sizes. The numerical results show that the proposed algorithm is competitive against state-of-the-art methods.

Towards Association Rules as a Predictive Tool for Geospatial Areas Evolution

Although it was basically presented as an exploratory tool rather than a predictive tool, numerous follow-up researchers have enhanced association rule mining, which contributes in making it a powerful predictive tool. In this context, this paper review the main advances in this datamining technique, then attempts to describe how they can, practically, be harnessed to deal with problems such as the prediction of geographical areas evolution.