smart.lab.tunisia@gmail.com +216 71 588 443
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Ameni Azzouz

Informations générales

Ameni Azzouz
Grade

Maître Assistant Habilité

Biographie courte

Ameni Azzouz received the PhD. degree in Business Computing from the higher institute of management of Tunis, University of Tunis in 2017. Currently, she is assistant professor in Business Computing at Tunis Business School (TBS), university of Tunis. Also, Ameni is a Research member at SMART Lab. ISG Tunis, University of Tunis. Her current research interests include scheduling problems in production systems and cloud computing, learning effects, supply chain management, optimization, machine learning and evolutionary computation.

Équipes

Axes de recherche

Bi-level optimization
Combinatorial Optimization
Computational Intelligence
Evolutionary optimization
Metaheuristics
Optimization
Scheduling problems

Publications

  • 2024
    Chin-Chia Wu, Xingong Zhang, Danyu Bai, Ameni Azzouz, Wen-Hsiang Wu, Xin-Rong Chen, Win-Chin Lin

    Sequencing a tri-criteria multiple job classes and customer orders problem on a single machine by using heuristics and simulated annealing method

    Operational Research, 24(1), 2., 2024

    Résumé

    Multiple-job-class sequencing problems solve a group of jobs belonging to multiple classes, where to decrease the processing time, jobs in the same class tend to be performed together with the same setup time. In contrast, customer order scheduling problems focus on completing all jobs (belonging to different classes) in the same order at the same time to reduce shipping costs. Because related studies on multiple-job-class sequencing problems with more than one criterion are quite limited in the current research community, this study investigates tri-criteria scheduling problems with multiple job classes and customer orders on a single machine, where the goal is to minimize a linear combination of the makespan, total completion times of all jobs, and sum of the ranges of all orders. Due to the high complexity of the proposed problem, mixed integer programming is developed to formulate the problem, and a branch-and-bound method along with a lower bound and a property is utilized for finding the optimal schedules. Then, two heuristics and a simulated annealing algorithm are proposed to solve the problem approximately. The simulation results of the proposed heuristics and algorithm are evaluated through statistical methods.

  • Imen Oueslati, Moez Hammami, Issam Nouaouri, Ameni Azzouz, Lamjed Ben Said, Hamid Allaoui

    A Honey Bee Mating Optimization HyperHeuristic for Patient Admission Scheduling Problem

    In proceedings of The 9th International Conference on Metaheuristics and Nature Inspired Computing META Marrakech, Nov 01-04, 2023, 2023

    Résumé

    Hyperheuristics represent a generic method that provides a high level of abstraction, enabling solving several problems in the combinatorial optimization domain while reducing the need for human intervention in parameters tuning. This category consists in managing a set of low-level heuristics and attempting to find the optimal sequence that produces high-quality results. This paper proposes a hyperheuristic that simulates the honey bees mating behavior called “Honey bee Mating Optimization HyperHeuristic”  to solve the Patient Admission Scheduling Problem (PASP). The PASP is an NP-hard problem that represents an important field in the health care discipline. In order to perceive the influence of low-level heuristics on the model’s performance, we implemented two versions of the hyperheuristic that each one works on a different set of low-level heuristics. The results show that one of the versions generates better results than the other, revealing the important role of low-level heuristics’ quality leading to enhancing the hyperheuristic performance.

    Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Dynamic bag-of-tasks scheduling problem in a heterogeneous multi-cloud environment: a taxonomy and a new bi-level multi-follower modeling

    Journal of Supercomputing,1-38,, 2023

    Résumé

    Since more and more organizations deploy their applications through the cloud, an increasing demand for using inter-cloud solutions is noticed. Such demands could inherently result in overutilization of resources, which leads to resource starvation that is vital for time-intensive and life-critical applications. In this paper, we are interested in the scheduling problem in such environments. On the one hand, a new taxonomy of criteria to classify task scheduling problems and resolution approaches in inter-cloud environments is introduced. On the other hand, a bi-level multi-follower model is proposed to solve the budget-constrained dynamic Bag-of-Tasks (BoT) scheduling problem in heterogeneous multi-cloud environments. In the proposed model, the upper-level decision maker aims to minimize the BoT’ makespan under budget constraints. While each lower-level decision maker minimizes the completion time of tasks it received. Experimental results demonstrated the outperformance of the proposed bi-level algorithm and revealed the advantages of using a bi-level scheme with an improvement rate of 32%, 29%, and 21% in terms of makespan for the small, medium, and big size instances, respectively.

    Lung-Yu Li, Win-Chin Lin, Danyu Bai, Ameni Azzouz, Xingong Zhang, Shuenn-Ren Cheng, Ya-Li Wu, Chin-Chia Wu

    Composite heuristics and water wave optimality algorithms for tri-criteria multiple job classes and customer order scheduling on a single machine

    International Journal of Industrial Engineering Computations, 14(2), 265-274., 2023

    Résumé

    Among the well-known scheduling problems, the customer order scheduling problem (COSP) has
    always been of great importance in manufacturing. To reflect the reality of COSPs as much as
    possible, this study considers that jobs from different orders are classified in various classes. This
    paper addresses a tri-criteria single-machine scheduling model with multiple job classes and
    customer orders on which the measurement minimizes a linear combination of the sum of the ranges
    of all orders, the tardiness of all orders, and the total completion times of all jobs. Due to the NPhard complexity of the problem, a lower bound and a property are developed and utilized in a
    branch-and-bound for solving an exact solution. Afterward, four heuristics with three local
    improved searching methods each and a water wave optimality algorithm with four variants of
    wavelengths are proposed. The tested outputs report the performances of the proposed methods

    Win-Chin Lin, Xingong Zhang, Xinbo Liu, Kai-Xiang Hu, Shuenn-Ren Cheng

    Sequencing single machine multiple-class customer order jobs using heuristics and improved simulated annealing algorithms

    RAIRO-Operations Research 57.3 (2023): 1417-1441., 2023

    Résumé

    The multiple job class scheduling problem arises in contexts where a group of jobs belong to multiple classes and in which if all jobs in the same class are operated together, extra setup times would not be needed. On the other hand, the customer order scheduling problem focuses on finishing all jobs from the same order at the same time in order to reduce shipping costs. However, works on customer orders coupled with class setup times do not appear often in the literature. Hence we address here a bicriteria single machine customer order scheduling problem together with multiple job classes. The optimality criterion minimizes a linear combination of the sum of the ranges and sum of tardiness of all customer orders. In light of the high complexity of the concerned problem, we propose a lower bound formula and a property to be used in a branch-and-bound method for optimal solutions. To find approximate solutions, we then propose four heuristics together with a local search method, four cloudy theoretical simulated annealing and a cloudy theoretical simulated annealing hyperheuristic along with five low-level heuristics. The simulation results of the proposed heuristics and algorithms are analyzed.

    Maha Ben Hamida, Ameni Azzouz, Lamjed Ben Said

    An adaptive variable neighborhood search algorithm to solve green flexible job shop problem

    In 2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) (pp. 1403-1408). IEEE., 2023

    Résumé

    Green manufacturing imposes higher expectations on manufacturing engineering, not only with respect to classic competitive factors such as cost, time and quality, but also with sustainable factors such as resources and energy. In this paper, we investigate green flexible job shop scheduling problem (GFJSP) with variable processing speeds. To solve the GFJSP problem, we propose an adaptive Variable Neighborhood Search to minimize the makespan and the total energy consumption. A number of experiments have been conducted to evaluate the performance of our proposed adaptive VNS algorithm. A comparative study was presented and have verified the out performance of the proposed algorithm against other VNS variants.

    Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Dynamic bag-of-tasks scheduling problem in a heterogeneous multi-cloud environment: a taxonomy and a new bi-level multi-follower modeling

    J Supercomput 79, 17716–17753 (2023), 2023

    Résumé

    Since more and more organizations deploy their applications through the cloud, an increasing demand for using inter-cloud solutions is noticed. Such demands could inherently result in overutilization of resources, which leads to resource starvation that is vital for time-intensive and life-critical applications. In this paper, we are interested in the scheduling problem in such environments. On the one hand, a new taxonomy of criteria to classify task scheduling problems and resolution approaches in inter-cloud environments is introduced. On the other hand, a bi-level multi-follower model is proposed to solve the budget-constrained dynamic Bag-of-Tasks (BoT) scheduling problem in heterogeneous multi-cloud environments. In the proposed model, the upper-level decision maker aims to minimize the BoT’ makespan under budget constraints. While each lower-level decision maker minimizes the completion time of tasks it received. Experimental results demonstrated the outperformance of the proposed bi-level algorithm and revealed the advantages of using a bi-level scheme with an improvement rate of 32%, 29%, and 21% in terms of makespan for the small, medium, and big size instances, respectively.

  • Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Efficient bilevel multi-objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment

    Applied Intelligence, 1-29, 2022

    Résumé

    Bag-of-Tasks is a well-known model that processes big-data applications supporting embarrassingly parallel jobs with independent tasks. Scheduling Bag-of-Tasks in a dynamic multi-cloud environment is an NP-hard problem that has attracted a lot of attention in the last years. Such a problem can be modeled using a bi-level optimization framework due to its hierarchical scheme. Motivated by this issue, in this paper, an efficient bi-level multi-follower algorithm, based on hybrid metaheuristics, is proposed to solve the multi-objective budget-constrained dynamic Bag-of-Tasks scheduling problem in a heterogeneous multi-cloud environment. In our proposed model, the objective function differs depending on the scheduling level: The upper level aims to minimize the makespan of the whole Bag-of-Tasks under budget constraints; while each follower aims to minimize the makespan and the execution cost of tasks belonging to the Bag-of-Tasks. Since multiple conflicting objectives exist in the lower level, we propose an improved variant of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) called Efficient NSGA-II (E-NSGA-II), applying a recently proposed quick non-dominated sorting algorithm (QNDSA) with quasi-linear average time complexity. By performing experiments on proposed synthetic datasets, our algorithm demonstrates high performance in terms of makespan and execution cost while respecting budget constraints. Statistical analysis validates the outperformance of our proposal regarding the considering metrics.

    Chin-Chia Wu, Ameni Azzouz, Jia-Yang Chen, Jianyou Xu, Wei-Lun Shen, Lingfa Lu, Lamjed Ben Said, Win-Chin Lin

    A two-agent one-machine multitasking scheduling problem solving by exact and metaheuristics

    Complex & Intelligent Systems, 8(1), 199-212., 2022

    Résumé

    This paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective
    is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another
    agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a
    lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing
    algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to fnd the
    near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for
    the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms.

    Chin-Chia Wu, Win-Chin Lin, Ameni Azzouz, Jianyou Xu, Yen-Lin Chiu, Yung-Wei Tsai, Pengyi Shen

    A bicriterion single-machine scheduling problem with step-improving processing times

    Computers & Industrial Engineering, 171, 108469., 2022

    Résumé

    Time-dependent scheduling problems, where the real processing time of jobs is dependent on the starting time, have received growing attention in recent decades. In particular, scheduling problems on a single machine have been widely studied in many facets that address the learning effect and diverse processing environments or time-dependent processing scheduling. Motivated by this observation, we introduce a variant based on the industrial procedure consideration; that is, owing to due date pressure, the processing time of the remaining jobs should be shortened after a period of manufacturing process. We consider a new single-machine scheduling problem with step-improving processing times where the objective function is to find a schedule to minimize a linear combination of the total weighted completion time and total tardiness of all jobs. The proposed problem without a critical date is an NP-hard problem. Therefore, a mixed integer programming model as well as a branch-and-bound (B&B) along with several dominance properties and a lower bound on the completion of an active partial schedule is utilized for solving the problem under study. Subsequently, four variants of the water wave optimization algorithm and four variants of the simulated annealing algorithms were proposed to solve this problem. The simulation results showed that the branch-and-bound method can solve instance problems for up to twelve jobs. The results also showed that all four variants of the wave optimization algorithm did not perform uniformly better than all three variants of the simulated annealing algorithms.

    Mouna Karaja, Abir Chaabani, Ameni Azzouz, Lamjed Ben Said

    Efficient bi-level multi objective approach for budget-constrained dynamic Bag-of-Tasks scheduling problem in heterogeneous multi-cloud environment.

    Appl Intell 53, 9009–9037 (2023), 2022

    Résumé

    Bag-of-Tasks is a well-known model that processes big-data applications supporting embarrassingly parallel jobs with independent tasks. Scheduling Bag-of-Tasks in a dynamic multi-cloud environment is an NP-hard problem that has attracted a lot of attention in the last years. Such a problem can be modeled using a bi-level optimization framework due to its hierarchical scheme. Motivated by this issue, in this paper, an efficient bi-level multi-follower algorithm, based on hybrid metaheuristics, is proposed to solve the multi-objective budget-constrained dynamic Bag-of-Tasks scheduling problem in a heterogeneous multi-cloud environment. In our proposed model, the objective function differs depending on the scheduling level: The upper level aims to minimize the makespan of the whole Bag-of-Tasks under budget constraints; while each follower aims to minimize the makespan and the execution cost of tasks belonging to the Bag-of-Tasks. Since multiple conflicting objectives exist in the lower level, we propose an improved variant of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) called Efficient NSGA-II (E-NSGA-II), applying a recently proposed quick non-dominated sorting algorithm (QNDSA) with quasi-linear average time complexity. By performing experiments on proposed synthetic datasets, our algorithm demonstrates high performance in terms of makespan and execution cost while respecting budget constraints. Statistical analysis validates the outperformance of our proposal regarding the considering metrics.

  • Chin-Chia Wu, Xingong Zhang, Ameni Azzouz, Wei-Lun Shen, Shuenn-Ren Cheng, Peng-Hsiang Hsu, Win-Chin Lin

    Metaheuristics for two-stage flow-shop assembly problem with a truncation learning function

    Engineering optimization, 53(5), 843-866, 2021

    Résumé

    This study examines a two-stage three-machine flow-shop assembly scheduling model in which job processing time is considered as a mixed function of a controlled truncation parameter with a sum-of-processing-times-based learning effect. However, the truncation function is very limited in the two-stage flow-shop assembly scheduling settings. To overcome this limitation, this study investigates a two-stage three-machine flow-shop assembly problem with a truncation learning function where the makespan criterion (completion of the last job) is minimized. Given that the proposed model is NP hard, dominance rules, lemmas and a lower bound are derived and applied to the branch-and-bound method. A dynamic differential evolution algorithm, a hybrid greedy iterated algorithm and a genetic algorithm are also proposed for searching approximate solutions. Results obtained from test experiments validate the performance of all the proposed algorithms.

  • Ameni Azzouz, Abir Chaabani, Meriem Ennigrou, Lamjed Ben Said

    Handling Sequence-dependent Setup Time Flexible Job Shop Problem with Learning and Deterioration Considerations using Evolutionary Bi-level Optimization

    Applied Artificial Intelligence, 34(6), 433-455, 2020

    Résumé

    Bi-level optimization is a challenging research area that has received significant attention from researchers to model enormous NP-hard optimization problems and real-life applications. In this paper, we propose a new evolutionary bi-level algorithm for Flexible Job Shop Problem with Sequence-Dependent Setup Time (SDST-FJSP) and learning/deterioration effects. There are two main motivations behind this work. On the one hand, learning and deterioration effects might occur simultaneously in real-life production systems. However, there are still ill posed in the scheduling area. On the other hand, bi-level optimization was presented as an interesting resolution scheme easily applied to more complex problems without additional modifications. Motivated by these issues, we attempt in this work to solve the FJSP variant using the bi-level programming framework. We suggest firstly a new bi-level mathematical formulation for the considered FJSP; then we propose a bi-level evolutionary algorithm to solve the problem. The experimental study on well-established benchmarks assesses and validates the advantage of using a bi-level scheme over the compared approaches in this research area to solve such NP-hard problem.

    Ameni Azzouz, Meriem Ennigrou, Lamjed Ben Said

    Solving flexible job-shop problem with sequence dependent setup time and learning effects using an adaptive genetic algorithm

    International Journal of Computational Intelligence Studies, 9(1-2), 18-32., 2020

    Résumé

    For the most schedulling problems studied in literature, job processing times are assumed to be known and constant over time. However, this assumption is not appropriate for many realistic situations where the employees and the machines execute the same task in a repetitive manner. They learn how to perform more efficiently. As a result, the processing time of a given job is shorter if it is scheduled later, rather than earlier in the sequence. In this paper, we consider the flexible job-shop problem (FJSP) with two kinds of constraint, namely, the sequence-dependent setup times (SDST) and the learning effects. Makespan is specified as the objective function to be minimised. To solve this problem, an adaptive genetic algorithm (AGA) is proposed. Our algorithm uses an adaptive strategy based on: 1) the current specificity of the search space; 2) the preceding results of already used operators; 3) their associated parameter settings. We adopt this strategy in order to maintain the balance between exploration and exploitation. Experimental studies are presented to assess and validate the benefit of the incorporation of the learning process to the SDST-FJSP over the original problem.

    Chin-Chia Wu, Danyu Bai, Ameni Azzouz, I-Hong Chung, Shuenn-Ren Cheng, Dwueng-Chwuan Jhwueng, Win-Chin Lin, Lamjed Ben Said

    A branch-and-bound algorithm and four metaheuristics for minimizing total completion time for a two-stage assembly flow-shop scheduling problem with learning consideration

    Engineering Optimization, 52(6), 1009-1036., 2020

    Résumé

    This article addresses a two-stage, three-machine assembly scheduling problem that considers the learning effect. All jobs are processed on two machines in the first stage and move on to be processed on an assembly machine in the second stage. The objective of the study is to minimize the total completion time of the given jobs. Because the problem is NP hard, the authors first established a lower bound and several adjacent propositions using a branch-and-bound algorithm to search for the optimal solution. Four metaheuristics are proposed to approximate the solutions: genetic algorithms, cloud theory-based simulated annealing, artificial bee colonies and iterated greedy algorithms. Four different heuristics are used as seeds in each metaheuristic to obtain high-quality approximate solutions. The performances of all 16 metaheuristics and the branch-and-bound algorithm are then examined and are reported herein.

    Ameni Azzouz, Po-An Pan, Peng-Hsiang Hsu, Win-Chin Lin, Shangchia Liu, Lamjed Ben Said, Chin-Chia Wu

    A two-stage three-machine assembly scheduling problem with a truncation position-based learning effect

    Soft Computing, 24(14), 10515-10533, 2020

    Résumé

    The two-stage assembly scheduling problem has a lot of applications in industrial and service sectors. Furthermore, truncation-based learning effects have received growing attention in connection with scheduling problems. However, it is relatively unexplored in the two-stage assembly scheduling problem. Therefore, we addressed the two-stage assembly with truncation learning effects with two machines in the first stage and an assembly machine in the second stage. The objective function was to complete all jobs as soon as possible (or to minimize the makespan). Due to the NP-hardness of the considered problem, we proposed several dominance relations and a lower bound for the branch-and-bound method for finding the optimal solution. Moreover, we proposed six versions of hybrids greedy iterative algorithm, where three versions of the local searches algorithm with and without a probability scheme are embedded. They include extraction and backward-shifted reinsertion, pairwise interchange and extraction and forward-shifted reinsertion for searching good-quality solutions. The experimental results of all proposed algorithms are presented on small-size and big-size jobs.

  • 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.

  • Abdelkader Dekdouk, Ameni Azzouz, Hiba Yahyaoui, Saoussen Krichen

    Solving energy ordering problem with multiple supply-demand using Bilevel optimization approach

    Procedia Computer Science, 130, 753-759., 2018

    Résumé

    We develop in this paper an energy ordering problem with multiple energy supplying sources and multiple traders trying to satisfy customers’ demands. Such a supply chain network is split of three main layers: the set of energy generation plants (suppliers), a set of traders trying to expect and satisfy customer’s demands dispatched. Following the new investment in renewable energy, customers have the option to choose the nature of its electricity. Customer choice has an impact on the future energy supply chain. For that, we deal with the customer choice in our considered problem. Motivated by this architecture, we propose an evolutionary algorithm-based on bi-level optimization model is developed to handle this problem. The performance of the proposed model is evaluated by numerical experiments based on real-world data.

    Hajer Ben Younes, Ameni Azzouz, Meriem Ennigrou

    Solving Flexible Job Shop Scheduling Problem using Hybrid Bilevel Optimization model

    HIS 2018, 2018

    Résumé

    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 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

    Scheduling problems under learning effects: classification and cartography

    International Journal of Production Research, 56(4), 1642-1661, 2017

    Résumé

    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.

    Hajer Ben Younes, Ameni Azzouz, Meriem Ennigrou

    Solving 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

    Résumé

    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 Said

    A self-adaptive hybrid algorithm for solving flexible job-shop problem with sequence dependent setup time

    Procedia computer science 112 (2017): 457-466., 2017

    Résumé

    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

  • 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.

  • 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).

Projets