Network traffic congestion is a crucial issue facing mobile operators at various locations depending on the time of day and year. Traffic congestion generally occurs at peak times in urban or suburban areas. This is because the evolved NodeBs’ (eNBs) maximum capacity is very quickly exceeded by the number of users since the resource blocks (RBs) and the bandwith are limited. To best manage network congestion, it is necessary to optimize the allocation of the network resources. The resource allocation problem, which is known to be NPhard, is commonly formulated as an optimization problem under constraints. The objective is to minimize the number of uncovered users for a service required on an eNB in a given scenario. The territory under study is the city of Lomé, the data used for the analysis come from the geographical data of the OpenStreetMap (OSM) database. We propose an approach allowing the dynamic configuration of the network based on a model for predicting users mobility and a heuristic for optimizing Long Term Evolution (LTE) network resources. A Greedy algorithm and a Tabu Search algorithm have been implemented and tested. The results showed the potential of the Tabu Search algorithm to outperform the Greedy algorithm.

Several studies have shown that the layout of an area's infrastructure has a strong impact on the mobility of the mobile network users. Each district in a city has one or more different type of activity areas. Depending on the type of activity areas that a district covers, several profiles emerge. These profiles are closely linked to the impact a district can have on LTE users mobility. In the current work, we propose a first approach to determine the profile of a district in a territory. The territory under study is the city of Lomé, the data used for this analysis come from the geographical data of the OSM database. An alternative approach is proposed that, in the case of missing data, determines the profile of a new district from knowledge built from other districts in the same study area. To validate the proposed approach, evaluations were conducted considering several types of distance (Mahalanobis distance, Euclidian distance,…). It appears that with the K-NN algorithm, using manhattan distance, we have 61 % accuracy in determining the profile of a new district based on the nearest district's profiles.

The quay crane scheduling problem (QCSP) in the Lebanese port (Tripoli) is discussed in this study. It consists in assigning each crane to a set of bays for a given vessel, while sequencing the unloading of these bays. In a previous study, we discussed two exacts methods whose main drawback is the difficulty to obtain results for large instances. That's why, we propose a genetic algorithm which enables us to overcome this and to get quickly near optimal solutions. We have tested and validated our method on real instances from the port of Tripoli.

In this paper, we propose a Mixed Integer Linear Programming model for solving a hoist scheduling problem with several transportation resources. This model complements initial work that neglected the risk of collisions between hoists. This new model identifies and manages the various possible collision situations, and it is intended to be integrated as a solution evaluation module in a hybrid algorithm addressing the broader and more complex joint problem of sizing transport resources and scheduling surface treatment workshops. In this global approach, an evolutionary algorithm first generates partially feasible solutions, whose total feasibility is then verified a posteriori, thanks to the proposed new model. This model is validated through tests performed on instances of the literature.

This paper addresses the scheduling problem in port of Tripoli-Lebanon for a single quay crane with multiple yard trucks, all containers that will be unloaded from the vessel are in the same bay. The objective is to reduce the completion time of all containers from the vessel to their store location, we used a mixed integer linear programming and a dynamic programming algorithm to solve the problem. Finally, we have compared and validated our results on real instances from the port.

With the rapid growth of cell phone networks during the last decades, call detail records (CDR) have been used as approximate indicators for large scale studies on human and urban mobility. Although coarse and limited, CDR are a real marker of human presence. In this paper, we use more than 800 million CDR to identify weekly patterns of human mobility through mobile phone data. Our methodology is based on the classification of individuals into six distinct presence profiles where we focus on the inherent temporal and geographical characteristics of each profile within a territory. Then, we use an event-based algorithm to cluster individuals and we identify 12 weekly patterns. We leverage these results to analyze population estimates adjustment processes and as a result, we propose new indicators to characterize the dynamics of a territory. Our model has been applied to real data coming from more than 1.6 million individuals and demonstrates its relevance. The product of our work can be used by local authorities for human mobility analysis and urban planning.