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2017, Ines Thabet,
A Framework to Support Tunisian Tweets Analysis for Crisis Management
ISCRAM-med 2017, 2017
Abstract
The increasing crisis frequency and the growing impact of their damages require efficient crisis management processes in order to manage crisis effectively and reduce losses. In such context, the need of accurate and updated information about crises is extremely important. In recent years, crisis information has frequently been provided by social media platforms such as Twitter, Facebook, Flickr, etc. In fact, considering the huge amount of shared information, their precision and their real time characteristic, organizations are moving towards the development of crisis management applications that include information provided by social media platforms. Following this view, the main purpose of our work is to propose a framework for Tunisian tweets extraction and analysis. More precisely, we provide an architecture that includes necessary components and tools for Tunisian dialect treatment. The proposed architecture is an extension on the existing AIDR platform. In addition, we specify the functioning of the proposed architecture to enable firstly terms transliteration from Arabic to Latin alphabet, secondly their normalization and finally their translation in order to be treated by existing social media analysis platform.
Abir Chaabani, Slim Bechikh, Lamjed Ben SaidA co-evolutionary decomposition-based chemical reaction algorithm for bi-level combinatorial optimization problems.
International conference on Knowledge Based and Intelligent information and Engineering Systems KES’17, France, 112, 780-789, 2017
Abstract
Bi-level optimization problems (BOPs) are a class of challenging problems with two levels of optimization tasks. The main goal is to optimize the upper level problem which has another optimization problem as a constraint. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level one. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. Recently, a new research field, called EBO (Evolutionary Bi-Level Optimization) has appeared thanks to the promising results obtained by the use of EAs (Evolutionary Algorithms) to solve such kind of problems. However, most of these promising results are restricted to the continuous case. The number of existing EBO works for the discrete (combinatorial case) bi-level problems is relatively small when compared to the field of evolutionary continuous BOP. Motivated by this observation, we have recently proposed a Co-evolutionary Decomposition-Based Algorithm (CODBA) to solve combinatorial bi-level problems. The recently proposed approach applies a Genetic Algorithm to handle BOPs. Besides, a new recently proposed meta-heuristic called CRO has been successfully applied to several practical NP-hard problems. To this end, we propose in this work a CODBA-CRO (CODBA with Chemical Reaction Optimization) to solve BOP. The experimental comparisons against other works within this research area on a variety of benchmark problems involving various difficulties show the effectiveness and the efficiency of our proposal.
Thouraya Sakouhi, ,Computing Semantic Trajectories: Methods and Used Techniques
In: De Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2017. KES-IIMSS-18 2018. Smart Innovation, Systems and Technologies, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-319-59480-4_39, 2017
Abstract
The widespread use of mobile devices generates huge amount of location data. The generated data is useful for many applications, including location-based services such as outdoor sports forums, routine prediction, location-based activity recognition and location-based social networking. Sharing individuals’ trajectories and annotating them with activities, for example a tourist transportation mode during his trip, helps bringing more semantics to the GPS data. Indeed, this provides a better understanding of the user trajectories, and then more interesting location-based services. To address this issue, diverse range of novel techniques in the literature are explored to enrich this data with semantic information, notably, machine learning and statistical algorithms. In this work, we focused, at a first level, on exploring and classifying the literature works related to semantic trajectory computation. Secondly, we capitalized and discussed the benefits and limitations of each approach.
, ,A Multi-Agent based Hyper-Heuristic Algorithm for the Winner Determination Problem
Procedia Computer Science 112:117-126, 2017
Abstract
In this paper we propose a Multi-Agent based Hyper-Heuristic algorithm for theWinner Determination Problem named MAH2- WDP. This algorithm explores a set of cooperating agents to select the appropriate operation using learning techniques. MAH2- WDP is specialized for local search methods and evolutionary methods where the following agents are seeking to improve the search within reinforcement learning: the mediator agent, two local search agents, the perturbation agent and two recombination agents. Our computational study shows that the proposed algorithm performs well on the tested benchmark instances in terms of solution quality. Keywords: Multi-agent; Winner Determination Problem; hyper-heuristic; intensification; diversification; metaheuristics.
Ameni Azzouz, Meriem Ennigrou, Lamjed Ben SaidA hybrid algorithm for flexible job-shop scheduling problem with setup times
International Journal of Production Management and Engineering, 5(1), 23-30, 2017
Abstract
Job-shop scheduling problem is one of the most important fields in manufacturing optimization where a set of n jobs must be processed on a set of m specified machines. Each job consists of a specific set of operations, which have to be processed according to a given order. The Flexible Job Shop problem (FJSP) is a generalization of the above-mentioned problem, where each operation can be processed by a set of resources and has a processing time depending on the resource used. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper addresses the flexible job-shop scheduling problem with sequence-dependent setup times to minimize two kinds of objectives function: makespan and bi-criteria objective function. For that, we propose a hybrid algorithm based on genetic algorithm (GA) and variable neighbourhood search (VNS) to solve this problem. To evaluate the performance of our algorithm, we compare our results with other methods existing in literature. All the results show the superiority of our algorithm against the available ones in terms of solution quality.
Ameni Azzouz, Meriem Ennigrou, Lamjed Ben SaidA self-adaptive evolutionary algorithm for solving flexible job-shop problem with sequence dependent setup time and learning effects
In 2017 IEEE congress on evolutionary computation (CEC) (pp. 1827-1834). IEEE., 2017
Abstract
Flexible job shop problems (FJSP) are among the most intensive combinatorial problems studied in literature. These latters cover two main difficulties, namely, machine assignment problem and operation sequencing problem. To reflect as close as possible the reality of this problem, two others constraints are taken into consideration which are: (1) The sequence dependent setup time and (2) the learning effects. For solving such complex problem, we propose an evolutionary algorithm (EA) based on genetic algorithm (GA) combined with two efficient local search methods, called, variable neighborhood search (VNS) and iterated local search (ILS). It is well known that the performance of EA is heavily dependent on the setting of control parameters. For that, our algorithm uses a self-adaptive strategy based on: (1) the current specificity of the search space, (2) the preceding results of already applied algorithms (GA, VNS and ILS) and (3) their associated parameter settings. We adopt this strategy in order to detect the next promising search direction and maintain the balance between exploration and exploitation. Computational results show that our algorithm is more effective and robust with respect to other well known effective algorithms.
Ameni Azzouz, Meriem Ennigrou, Lamjed Ben SaidScheduling problems under learning effects: classification and cartography
International Journal of Production Research, 56(4), 1642-1661, 2017
Abstract
Traditionally, the processing times of jobs are assumed to be fixed and known throughout the entire process. However, recent empirical research in several industries has demonstrated that processing times decline as workers improve their skills and gain experience after doing the same task for a long time. This phenomenon is known as learning effects. Recently, several researchers have devoted a lot of effort on scheduling problems under learning effects. Although there is increase in the number of research in this topic, there are few review papers. The most recent one considers solely studies on scheduling problems with learning effects models prior to early 2007. For that, this paper focuses on reviewing the most recent advances in this field. First, we attempt to present a concise overview of some important learning models. Second, a new classification scheme for the different model of scheduling under learning effects is proposed and discussed. Next, a cartography showing the relation between some well-known models is proposed. Finally, our viewpoints and several areas for future research are provided.
, Ameni Azzouz, Meriem EnnigrouSolving flexible job shop scheduling problem using hybrid bilevel optimization model
In: Madureira, A., Abraham, A., Gandhi, N., Varela, M. (eds) Hybrid Intelligent Systems. HIS 2018. Advances in Intelligent Systems and Computing, vol 923. Springer, Cham., 2017
Abstract
Flexible Job Shop Problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. This problem is decomposed into two sub-problems: the assignment problem and the scheduling problem. Following this structure, we consider in this work the FJSP as a bilevel problem. For that, we are interested to solve this problem with bilevel optimization method in which the upper level optimizes the assignment problem and the lower level optimizes the scheduling problem. Therefore, we propose, for the first time, an hybrid bilevel optimization model named HB-FJSP based on both exact and approximate methods to solve the FJSP in order to minimize the makespan. The computational results confirm that our model HB-FJSP provides better solutions than other models.
Ameni Azzouz, Meriem Ennigrou, Lamjed Ben SaidA self-adaptive hybrid algorithm for solving flexible job-shop problem with sequence dependent setup time
Procedia computer science 112 (2017): 457-466., 2017
Abstract
The flexible job shop problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. This problem covers two main difficulties, namely, machine assignment problem and operation sequencing problem. To reflect as close as possible the reality of this problem, the sequence dependent setup time is taken into consideration. For solving such a complex problem, we propose a hybrid algorithm based on a genetic algorithm (GA) combined with iterated local search (ILS). It is well known that the performance of an algorithm is heavily dependent on the setting of control parameters. For that, our algorithm uses a self-adaptive strategy based on : (1) the current specificity of the search space, (2) the preceding results of already applied algorithms (GA and ILS) and (3) their associated parameter settings. We adopt this strategy in order to detect the next promising search
Mouna Belhaj, , Lamjed Ben SaidEmotional dynamics and coping mechanisms to generate human-like agent behaviors
Applied Artificial Intelligence, 31(5-6), 472-492., 2017
Abstract
Emotion mechanisms represent an important moderating factor of human behavior. Thus, they are necessary to produce realistic behavioral simulations. This work addresses this challenging issue by incorporating emotional processes into an agent model. We intend to show the potential of emotions and coping mechanisms to produce fast and human-like emotional behaviors, particularly, in emergency situations. We focus on the interplay of emotions and goals and its impact on agent behavior. Emotions constitute heuristics to agent decision making. They induce emotion-specific goals that orient agent goal adoption mechanisms and fasten its behavior selection.
