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Metaheuristics

Description

Aucune description disponible pour cet axe de recherche.

Membres

Ameni Azzouz Ameni Azzouz
Amel ZIDI Amel ZIDI
Meriem Ennigrou Meriem Ennigrou
Mouna Karaja Mouna Karaja
Rihab Said Rihab Said
Houyem Ben Hassen Houyem Ben Hassen

Publications

  • 2019
    Chin-Chia Wu, Ameni Azzouz, I-Hong Chung, Win-Chin Lin, Lamjed Ben Said

    A two-stage three-machine assembly scheduling problem with deterioration effect

    International Journal of Production Research, 57(21), 6634-6647., 2019

    Résumé

    The two-stage assembly scheduling problem has received growing attention in the research community. Furthermore, in many two-stage assembly scheduling problems, the job processing times are commonly assumed as a constant over time. However, it is at odds with real production situations some times. In fact, the dynamic nature of processing time may occur when machines lose their performance during their execution times. In this case, the job that is processed later consumes more time than another one processed earlier. In view of these observations, we address the two-stage assembly linear deterioration scheduling problem in which there are two machines at the first stage and an assembly machine at the second stage. The objective is to complete all jobs as soon as possible (or to minimise the makespan, implies that the system can yield a better and efficient task planning to limited resources). Given the fact that this problem is NP-hard, we then derive some dominance relations and a lower bound used in the branch-and-bound method for finding the optimal solution. We also propose three metaheuristics, including dynamic differential evolution (DDE), simulated annealing (SA) algorithm, and cloud theory-based simulated annealing (CSA) algorithm for find near-optimal solutions. The performances of the proposed algorithms are reported as well.

    Houyem Ben Hassen, Jihene Tounsi, Rym Ben Bachouch

    An Artificial Immune Algorithm for HHC Planning Based on multi-Agent System

    Procedia Computer Science, 164, 251-256, 2019

    Résumé

    This paper presents the home health care routing and scheduling problem as the vehicle routing problem with time windows (VRPTW). we propose a dynamic approach for home care planning to ensure the continuity of care for patients. The proposed approach aims to optimize the care plan route of each caregiver according to their skills, availabilities and preferences. We aim also to minimize the violation of time windows in order to maximize patient and caregiver’s satisfaction. The optimal plan route is generated with a population-based algorithm which is the Artificial Immune Algorithm (AIS). A multi-agent approach is used to ensure communication and coordination between the different actors.

  • Abir Chaabani, Slim Bechikh, Lamjed Ben Said

    A new co-evolutionary decomposition-based algorithm for bi-level combinatorial optimization

    Applied Intelligence, 48(9), 2847-2872, 2018

    Résumé

    Bi-Level Optimization Problems (BLOPs) 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. The latter is called the lower level problem. In this way, the evaluation of each upper level solution requires finding an (near) optimal solution to the corresponding lower level problem, which is computationally very expensive. Many real world applications are bi-level by nature, ranging from logistics to software engineering. Further, proposed bi-level approaches have been restricted to solve linear BLOPs. This fact has attracted the evolutionary computation community to tackle such complex problems and many interesting works have recently been proposed. Unfortunately, most of these works are restricted to the continuous case. Motivated by this observation, we propose in this paper a new Co-evolutionary Decomposition Algorithm inspired from Chemical Reaction Optimization algorithm, called E-CODBA (Energy-based CODBA), to solve combinatorial bi-level problems. Our algorithm is based on our previous works within this research area. The main idea behind E-CODBA is to exploit co-evolution, decomposition, and energy laws to come up with good solution(s) within an acceptable execution time. The statistical analysis of the experimental results on the Bi-level Multi-Depot Vehicle Routing Problem (Bi-MDVRP) show the out-performance of our E-CODBA against four recently proposed works in terms of effectiveness and efficiency.

    Ines Seghir, Ines Ben Jaafar, khaled Ghedira

    A Multi-Agent Based Optimization Method for Combinatorial Optimization Problems

    International Journal of Artificial Intelligence Tools, 2018

    Résumé

    This paper introduces a Multi-Agent based Optimization Method for Combinatorial Optimization Problems named MAOM-COP. In this method, a set of agents are cooperatively interacting to select the appropriate operators of metaheuristics using learning techniques. MAOM-COP is a flexible architecture, whose objective is to produce more generally applicable search methodologies. In this paper, the MAOM-COP explores genetic algorithm and local search metaheuristics. Using these metaheuristics, the decision-maker agent, the intensification agents and the diversification agents are seeking to improve the search. The diversification agents can be divided into the perturbation agent and the crossover agents. The decision-maker agent decides dynamically which agent to activate between intensification agents and crossover agents within reinforcement learning. If the intensification agents are activated, they apply local search algorithms. During their searches, they can exchange information, as they can trigger the perturbation agent. If the crossover agents are activated, they perform recombination operations. We applied the MAOM-COP to the following problems: Quadratic assignment, graph coloring, winner determination and multidimensional knapsack. MAOMCOP shows competitive performances compared with the approaches of the literature.

  • Abir Chaabani, Slim Bechikh, Lamjed Ben Said

    A 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

    Résumé

    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.

    Ameni 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

    Résumé

    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 Said

    A 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

    Résumé

    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 Said

    Flexible job-shop scheduling problem with sequence-dependent setup times using genetic algorithm

    International Conference on Enterprise Information Systems. Vol. 3. SCITEPRESS, 2016., 2016

    Résumé

    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.

    Samira Harrabi, Ines ben jaafar, Khaled ghedira

    Novel Optimized Routing Scheme for VANETs »

    The 7th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (Elsevier, EUSPN-2016), 2016

    Résumé

    The Vehicular ad -hoc networks (VANETs) are a specific type of Mobile ad-hoc networks (MANETs). However, the main problem related to it is the potential high speed of moving vehicles. This special property causes frequent changing in network topology and instability of communication routes. Consequently, some of the challenges that researchers focus on are routing protocols for VANETs. They have proved that the existing MANET proactive routing protocols are the most used for vehicular communication. Yet, they are not as adequate as they are for VANETs. The main problem with these protocols in dynamic environment is their route instability. This paper combines multi-agent system approach and PSO algorithm to solve the above mentioned problems. We carried out a set of simulations tests to evaluate the performance of our scheme. The simulation part shows promising results regarding the adoption of the proposed scheme.

  • Ameni Azzouz, Meriem Ennigrou, Boutheina JLIFI

    Diversifying TS using GA in multi-agent system for solving flexible job shop problem

    12th International Conference on Informatics in Control, Automation and Robotics (ICINCO). Vol. 1. IEEE, 2015., 2015

    Résumé

    No doubt, the flexible job shop problem (FJSP) has an important significance in both fields of production management and combinatorial optimization. For this reason, FJSP continues to attract the interests of researchers both in academia and industry. In this paper, we propose a new multi-agent model for FJSP. Our model is based on cooperation between genetic algorithm (GA) and tabu search (TS). We used GA operators as a diversification technique in order to enhance the searching ability of TS. The computational results confirm that our model MAS-GATS provides better solutions than other models.

  • Ameni Azzouz, Meriem Ennigrou, Boutheina JLIFI, Khaled Ghedira

    Combining tabu search and genetic algorithm in a multi-agent system for solving flexible job shop problem

    n International Conference on Enterprise Information Systems (Vol. 3, pp. 47-53), 2012

    Résumé

    The Flexible Job Shop problem (FJSP) is an important extension of the classical job shop scheduling problem, in that each operation can be processed by a set of resources and has a processing time depending on the resource used. The objective is to minimize the make span, i.e., the time needed to complete all the jobs. This works aims to propose a new promising approach using multi-agent systems in order to solve the FJSP. Our model combines a local optimization approach based on Tabu Search (TS) meta-heuristic and a global optimization approach based on genetic algorithm (GA).