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2016Riadh Ghlala, Zahra Kodia, Lamjed Ben Said
BPMN Decision Footprint: Towards Decision Harmony Along BI Process
Conference: International Conference on Information and Software Technologies, 2016
Abstract
Nowadays, one of the companies challenges is to benefit from their Business Intelligence (BI) projects and not to see huge investments ruined. To address problems related to the modelling of these projects and the management of their life-cycle, Enterprise Architecture (EA) Frameworks are considered as an attractive alternative to strengthen the Business-IT alignment. Business Process Model and Notation (BPMN) represents a pillar of these Frameworks to minimize the gap between the expectations of managers and delivered technical solutions. The importance of decision-making in business process has led the Object Management Group (OMG) to announce its new standard: Decision Model and Notation (DMN). In this paper, we propose the BPMN Decision Footprint (BPMNDF), which is a coupling of a BPMN with a novel DMN version. This enhancement has an additional component as a repository of all decisions along the process, used in order to ensure the harmony of decision-making.
, , Moez Hammami,Discussion and review on evolving data streams and concept drift adapting
Evolving Systems, An Interdisciplinary Journal for Advanced Science and Technology Volume 9, pages 1–23, (2018), 2016
Abstract
Recent advances in computational intelligent systems have focused on addressing complex problems related to the dynamicity of the environments. In increasing number of real world applications, data are presented as streams that may evolve over time and this is known by concept drift. Handling concept drift is becoming an attractive topic of research that concerns multidisciplinary domains such that machine learning, data mining, ubiquitous knowledge discovery, statistic decision theory, etc… Therefore, a rich body of the literature has been devoted to the study of methods and techniques for handling drifting data. However, this literature is fairly dispersed and it does not define guidelines for choosing an appropriate approach for a given application. Hence, the main objective of this survey is to present an ease understanding of the concept drift issues and related works, in order to help researchers from different disciplines to consider concept drift handling in their applications. This survey covers different facets of existing approaches, evokes discussion and helps readers to underline the sharp criteria that allow them to properly design their own approach. For this purpose, a new categorization of the existing state-of-the-art is presented with criticisms, future tendencies and not-yet-addressed challenges.
Abir Chaabani, Slim Bechikh, Lamjed Ben SaidA memetic evolutionary algorithm for bi-level combinatorial optimization: a realization between Bi-MDVRP and Bi-CVRP
IEEE Congress on Evolutionary Computation CEC’16, Canada, 1666-1673, 2016
Abstract
Bi-level optimization problems are a class of challenging optimization problems, that contain two levels of optimization tasks. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level problem. 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. In recent decades, it is observed that many efficient optimizations using modern advanced EAs have been achieved via the incorporation of domain specific knowledge. In such a way, the embedment of domain knowledge about an underlying problem into the search algorithms can enhance properly the evolutionary search performance. Motivated by this issue, we present in this paper a Memetic Evolutionary Algorithm for Bi-level Combinatorial Optimization (M-CODBA) based on a new recently proposed CODBA algorithm with transfer learning to enhance future bi-level evolutionary search. A realization of the proposed scheme is investigated on the Bi-CVRP and Bi-MDVRP problems. The experimental studies on well established benchmarks are presented to assess and validate the benefits of incorporating knowledge memes on bi-level evolutionary search. Most notably, the results emphasize the advantage of our proposal over the original scheme and demonstrate its capability to accelerate the convergence of the algorithm.
, ,Routing Challenges and Solutions in Vehicular Ad hoc Networks
Sensors and Transducers 206(11):31-42, 2016
Abstract
Vehicular Ad-hoc Networks (VANETs) are known as a special type of Mobile Ad-hoc Networks (MANETs) specialized in vehicular communications. These networks are based on smart vehicles and basestations, which share data by means of wireless communications. To route these information, a routing protocol is required. Since the VANETs have a particular network features as rapidly changeable topology, designing an efficient routing scheme is a very hard task. In this paper, we mainly focus on surveying new routing protocols dedicated to VANETs. We present unicast, multicast and broadcast protocols. The experimental results are discussed to evaluate the performance of the presented methods.
, , ,A Novel Clustering Algorithm Based on Agent Technology for VANET
Network Protocols and Algorithms 7(4), 2016
Abstract
Vehicular Ad-hoc Network (VANET) is a sub-family of Mobile Ad-hoc Network (MANET).The means 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, varying node density and locations 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 (such as traffic congestion, looking for free parking space etc). In this paper, we propose a novel clustering algorithm based on agent technology to solve the problems mentioned above and improve routing in VANET. Experimental part show promising results regarding the adoption of the proposed approach.
Ameni Azzouz, Meriem Ennigrou, Lamjed Ben SaidFlexible job-shop scheduling problem with sequence-dependent setup times using genetic algorithm
International Conference on Enterprise Information Systems. Vol. 3. SCITEPRESS, 2016., 2016
Abstract
Job shop scheduling problems (JSSP) are among the most intensive combinatorial problems studied in literature. The flexible job shop problem (FJSP) is a generalization of the classical JSSP where each operation can be processed by more than one resource. The FJSP problems cover two difficulties, namely, machine assignment problem and operation sequencing problem. This paper investigates 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 genetic algorithm (GA) 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 GA against the available ones in terms of solution quality.
Mouna Belhaj, , Lamjed Ben SaidModeling and simulation of coping mechanisms and emotional behavior during emergency situations
In Agent and Multi-Agent Systems: Technology and Applications: 10th KES International Conference, KES-AMSTA 2016 Puerto de la Cruz, Tenerife, Spain, June 2016 Proceedings (pp. 163-176). Cham: Springer International Publishing., 2016
Abstract
Emotions shape human behaviors particularly during stressful situations. This paper addresses this challenging issue by incorporating coping mechanisms into an emotional agent. Indeed, coping refers to cognitive and behavioral efforts employed by humans to overcome stressful situations. In our proposal, we intend to show the potential of the integration of coping strategies to produce fast and human-like behavioral responses in emergency situations. Particularly, we propose a coping model that reveals the effect of agent emotions on their action selection processes.
, , Maha Elarbi, , Slim BechikhPractical applications in constrained evolutionary multi-objective optimization
In Recent advances in evolutionary multi-objective optimization (pp. 159-179). Cham: Springer International Publishing, 2016
Abstract
Constrained optimization is applicable to most real world engineering science problems. An efficient constraint handling method must be robust, reliable and computationally efficient. However, the performance of constraint handling mechanism deteriorates with the increase of multi-modality, non-linearity and non-convexity of the constraint functions. Most of the classical mathematics based optimization techniques fails to tackle these issues. Hence, researchers round the globe are putting hard effort to deal with multi-modality, non-linearity and non-convexity, as their presence in the real world problems are unavoidable. Initially, Evolutionary Algorithms (EAs) were developed for unconstrained optimization but engineering problems are always with certain type of constraints. The in-dependability of EAs to the structure of problem has led the researchers to re-think in applying the same to the problems incorporating the constraints. The constraint handling techniques have been successfully used to solve many single objective problems but there has been limited work in applying them to the multi-objective optimization problem. Since for most engineering science problems conflicting multi-objectives have to be satisfied simultaneously, multi-objective constraint handling should be one of the most active research area in engineering optimization. Hence, in this chapter authors have concentrated in explaining the constrained multi-objective optimization problem along with their applications.
Slim Bechikh, Maha Elarbi, Lamjed Ben SaidMany-objective optimization using evolutionary algorithms: A survey
In Recent advances in evolutionary multi-objective optimization (pp. 105-137). Cham: Springer International Publishing, 2016
Abstract
Multi-objective Evolutionary Algorithms (MOEAs) have proven their effectiveness and efficiency in solving complex problems with two or three objectives. However, recent studies have shown that the performance of the classical MOEAs is deteriorated when tackling problems involving a larger number of conflicting objectives. Since most individuals become non-dominated with respect to each others, the MOEAs’ behavior becomes similar to a random walk in the search space. Motivated by the fact that a wide range of real world applications involves the optimization of more than three objectives, several Many-objective Evolutionary Algorithms (MaOEAs) have been proposed in the literature. In this chapter, we highlight in the introduction the difficulties encountered by MOEAs when handling Many-objective Optimization Problems (MaOPs). Moreover, a classification of the most prominent MaOEAs is provided in an attempt to review and describe the evolution of the field. In addition, a summary of the most commonly used test problems, statistical tests, and performance indicators is presented. Finally, we outline some possible future research directions in this research area.
, ,A Novel Clustering Algorithm Based on Agent Technology for VANET
International Journal of Network Protocols and Algorithms, Vol 8, N2, pp1-19, 2016., 2016
Abstract
Vehicular Ad-hoc Network (VANET) is a sub-family of Mobile Ad-hoc Network (MANET).The means 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, varying node density and locations 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 (such as traffic congestion, looking for free parking space, etc.). In this paper, we propose a novel clustering algorithm based on agent technology to solve the problems mentioned above and improve routing in VANET. Experimental part show promising results regarding the adoption of the proposed approach.
