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2017Ameni Azzouz, Meriem Ennigrou, Lamjed Ben Said
A 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.
Maha Elarbi, Slim Bechikh, , Lamjed Ben Said,A new decomposition-based NSGA-II for many-objective optimization
IEEE transactions on systems, man, and cybernetics: systems, 48(7), 1191-1210, 2017
Abstract
Multi-objective evolutionary algorithms (MOEAs) have proven their effectiveness and efficiency in solving problems with two or three objectives. However, recent studies show that MOEAs face many difficulties when tackling problems involving a larger number of objectives as their behavior becomes similar to a random walk in the search space since most individuals are nondominated with respect to each other. Motivated by the interesting results of decomposition-based approaches and preference-based ones, we propose in this paper a new decomposition-based dominance relation to deal with many-objective optimization problems and a new diversity factor based on the penalty-based boundary intersection method. Our reference point-based dominance (RP-dominance), has the ability to create a strict partial order on the set of nondominated solutions using a set of well-distributed reference points. The RP-dominance is subsequently used to substitute the Pareto dominance in nondominated sorting genetic algorithm-II (NSGA-II). The augmented MOEA, labeled as RP-dominance-based NSGA-II, has been statistically demonstrated to provide competitive and oftentimes better results when compared against four recently proposed decomposition-based MOEAs on commonly-used benchmark problems involving up to 20 objectives. In addition, the efficacy of the algorithm on a realistic water management problem is showcased.
Maha Elarbi, Slim Bechikh, Lamjed Ben SaidOn the importance of isolated solutions in constrained decomposition-based many-objective optimization
In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 561-568), 2017
Abstract
During the few past years, decomposition has shown a high performance in solving Multi-objective Optimization Problems (MOPs) involving more than three objectives, called as Many-objective Optimization Problems (MaOPs). The performance of most of the existing decomposition-based algorithms has been assessed on the widely used DTLZ and WFG unconstrained test problems. However, the number of works that have been devoted to tackle the problematic of constrained many-objective optimization is relatively very small when compared to the number of works handling the unconstrained case. Recently there has been some interest to exploit infeasible isolated solutions when solving Constrained MaOPs (CMaOPs). Motivated by this observation, we firstly propose an IS-update procedure (Isolated Solution-based update procedure) that has the ability to: (1) handle CMaOPs characterized by various types of difficulties and (2) favor the selection of not only infeasible solutions associated to isolated sub-regions but also infeasible solutions with smaller Constraint Violation (CV) values. The IS-update procedure is subsequently embedded within the Multi-Objective Evolutionary Algorithm-based on Decomposition (MOEA/D). The new obtained algorithm, named ISC-MOEA/D (Isolated Solution-based Constrained MOEA/D), has been shown to provide competitive and better results when compared against three recent works on the CDTLZ benchmark problems.
