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2017Thouraya 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.
Samira Harrabi, Ines Ben Jaafar,Message Dissemination in Vehicular Networks on the Basis of Agent Technology
An International Journal of Wireless Personal Communications, 2017
Abstract
Vehicular Ad hoc Network (VANET) is a sub-family of Mobile Ad hoc Network (MANET). The principal goal of VANET is to provide communications between nearby nodes or between nodes and fixed infrastructure. Despite that VANET is considered as a subclass of MANET, it has for particularity the high mobility of vehicles producing the frequent changes of network topology that involve changing of road and varying node density of vehicles existing in this road. That‘s why, the most proposed clustering algorithms for MANET are unsuitable for VANET. Various searches have been recently published deal with clustering for VANETs, but most of them are focused on minimizing network overhead value, number of created clusters and had not considered the vehicles interests which defined as any related data used to differentiate vehicle from another. In this paper, we propose a novel clustering algorithm based on agent technology to improve routing in VANET.
Samira Harrabi, Ines Ben Jaafar,Reliability and Quality of Service of an Optimized Protocol for Routing in VANETs
In CTRQ 2017: The tenth international conference on communication theory, reliability, and quality of service., 2017
Abstract
Vehicular Ad hoc NETworks (VANETs) are a special kind of Mobile Ad hoc NETworks (MANETs), which can provide scalable solutions for applications such as traffic safety, internet access, etc. To properly achieve this goal, these applications need an efficient routing protocol. Yet, contrary to the routing protocols designed for the MANETs, the routing protocols for the VANETs must take into account the highly dynamic topology caused by the fast mobility of the vehicles. Hence, improving the MANET routing protocol or designing a new one specific for the VANETs are the usual approaches to efficiently perform the routing protocol in a vehicular environment. In this context, we previously enhanced the Destination-Sequenced Distance-Vector Routing protocol (DSDV) based on the Particle Swarm Optimization (PSO) and the Multi-Agent System (MAS). This motivation for the PSO and MAS comes from the behaviors seen in very complicated problems, in particular routing. The main goal of this paper is to carry out a performance evaluation of the enhanced version in comparison to a well-known routing protocol which is the Intelligent Based Clustering Algorithm in VANET (IBCAV). The simulation results show that integrating both the MAS and the PSO is able to guarantee a certain level of quality of service in terms of loss packet, throughput and overhead.
