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Khaled Ghedira - One of the best experts on this subject based on the ideXlab platform.

  • Solving the flexible job shop problem by hybrid metaheuristics-based Multiagent Model
    Journal of Industrial Engineering International, 2018
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic Multiagent Model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

  • Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment
    Applied Intelligence, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic Multiagent Model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

  • MDAI - Optimizing Robot Movements in Flexible Job Shop Environment by Metaheuristics Based on Clustered Holonic Multiagent Model
    Modeling Decisions for Artificial Intelligence, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    In systems based robotic cells, the control of some elements such as transport robot has some difficulties when planning operations dynamically. The Flexible Job Shop scheduling Problem with Transportation times and Many Robots (FJSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs have to be transported between them by several transport robots. This paper proposes hybrid metaheuristics based on clustered holonic Multiagent Model for the FJSPT-MR. Computational results are presented using a set of literature benchmark instances. New upper bounds are found, showing the effectiveness of the presented approach.

  • ICAART (2) - Simultaneous Scheduling of Machines and a Single Moving Robot in a Job Shop Environment by Metaheuristics based Clustered Holonic Multiagent Model
    Proceedings of the 8th International Conference on Agents and Artificial Intelligence, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    In systems based robotic cells, the control of some elements such as transport robot has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and a Single Robot (JSPT-SR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by a single transport robot. Hence, the JSPT-SR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic Multiagent Model for the JSPT-SR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using benchmark data instances from the literature of JSPT-SR. New upper bounds are found, showing the effectiveness of the presented approach.

  • Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic Multiagent Model
    Computers & Industrial Engineering, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    Display Omitted Hybrid metaheuristics is proposed to schedule machines and transport robots.A genetic algorithm is applied by a scheduler agent to explore the search space.A local search is used by cluster agents to guide the search in promising regions.A new disjunctive graph is presented to Model simultaneously this problem.Computational results are presented using three sets of benchmark instances. In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Flexible Job Shop scheduling Problem with Transportation times and Many Robots (FJSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs have to be processed on a set of alternative machines and additionally have to be transported between them by several transport robots. Hence, the FJSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the flexible job shop scheduling problem and the robot routing problem. This paper proposes hybrid metaheuristics based on clustered holonic Multiagent Model for the FJSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using three sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

Houssem Eddine Nouri - One of the best experts on this subject based on the ideXlab platform.

  • Solving the flexible job shop problem by hybrid metaheuristics-based Multiagent Model
    Journal of Industrial Engineering International, 2018
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic Multiagent Model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

  • Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment
    Applied Intelligence, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic Multiagent Model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

  • MDAI - Optimizing Robot Movements in Flexible Job Shop Environment by Metaheuristics Based on Clustered Holonic Multiagent Model
    Modeling Decisions for Artificial Intelligence, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    In systems based robotic cells, the control of some elements such as transport robot has some difficulties when planning operations dynamically. The Flexible Job Shop scheduling Problem with Transportation times and Many Robots (FJSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs have to be transported between them by several transport robots. This paper proposes hybrid metaheuristics based on clustered holonic Multiagent Model for the FJSPT-MR. Computational results are presented using a set of literature benchmark instances. New upper bounds are found, showing the effectiveness of the presented approach.

  • ICAART (2) - Simultaneous Scheduling of Machines and a Single Moving Robot in a Job Shop Environment by Metaheuristics based Clustered Holonic Multiagent Model
    Proceedings of the 8th International Conference on Agents and Artificial Intelligence, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    In systems based robotic cells, the control of some elements such as transport robot has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and a Single Robot (JSPT-SR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by a single transport robot. Hence, the JSPT-SR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic Multiagent Model for the JSPT-SR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using benchmark data instances from the literature of JSPT-SR. New upper bounds are found, showing the effectiveness of the presented approach.

  • Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic Multiagent Model
    Computers & Industrial Engineering, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    Display Omitted Hybrid metaheuristics is proposed to schedule machines and transport robots.A genetic algorithm is applied by a scheduler agent to explore the search space.A local search is used by cluster agents to guide the search in promising regions.A new disjunctive graph is presented to Model simultaneously this problem.Computational results are presented using three sets of benchmark instances. In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Flexible Job Shop scheduling Problem with Transportation times and Many Robots (FJSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs have to be processed on a set of alternative machines and additionally have to be transported between them by several transport robots. Hence, the FJSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the flexible job shop scheduling problem and the robot routing problem. This paper proposes hybrid metaheuristics based on clustered holonic Multiagent Model for the FJSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using three sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

Olfa Belkahla Driss - One of the best experts on this subject based on the ideXlab platform.

  • Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment
    Applied Intelligence, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and Many Robots (JSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by several transport robots. Hence, the JSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic Multiagent Model for the JSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using two sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

  • MDAI - Optimizing Robot Movements in Flexible Job Shop Environment by Metaheuristics Based on Clustered Holonic Multiagent Model
    Modeling Decisions for Artificial Intelligence, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    In systems based robotic cells, the control of some elements such as transport robot has some difficulties when planning operations dynamically. The Flexible Job Shop scheduling Problem with Transportation times and Many Robots (FJSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs have to be transported between them by several transport robots. This paper proposes hybrid metaheuristics based on clustered holonic Multiagent Model for the FJSPT-MR. Computational results are presented using a set of literature benchmark instances. New upper bounds are found, showing the effectiveness of the presented approach.

  • ICAART (2) - Simultaneous Scheduling of Machines and a Single Moving Robot in a Job Shop Environment by Metaheuristics based Clustered Holonic Multiagent Model
    Proceedings of the 8th International Conference on Agents and Artificial Intelligence, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    In systems based robotic cells, the control of some elements such as transport robot has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and a Single Robot (JSPT-SR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by a single transport robot. Hence, the JSPT-SR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic Multiagent Model for the JSPT-SR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using benchmark data instances from the literature of JSPT-SR. New upper bounds are found, showing the effectiveness of the presented approach.

  • Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic Multiagent Model
    Computers & Industrial Engineering, 2016
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    Display Omitted Hybrid metaheuristics is proposed to schedule machines and transport robots.A genetic algorithm is applied by a scheduler agent to explore the search space.A local search is used by cluster agents to guide the search in promising regions.A new disjunctive graph is presented to Model simultaneously this problem.Computational results are presented using three sets of benchmark instances. In real manufacturing environments, the control of some elements in systems based on robotic cells, such as transport robots has some difficulties when planning operations dynamically. The Flexible Job Shop scheduling Problem with Transportation times and Many Robots (FJSPT-MR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs have to be processed on a set of alternative machines and additionally have to be transported between them by several transport robots. Hence, the FJSPT-MR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the flexible job shop scheduling problem and the robot routing problem. This paper proposes hybrid metaheuristics based on clustered holonic Multiagent Model for the FJSPT-MR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using three sets of benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

  • GECCO (Companion) - Metaheuristics based on Clustering in a Holonic Multiagent Model for the Flexible Job Shop Problem
    Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015
    Co-Authors: Houssem Eddine Nouri, Olfa Belkahla Driss, Khaled Ghedira
    Abstract:

    The Flexible Job Shop scheduling Problem (FJSP) is a generalization of the classical Job Shop scheduling Problem (JSP) allowing to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the machine assignment and the operation scheduling problems. In this paper, we propose how to solve the FJSP by metaheuristics based on clustering in a holonic Multiagent Model. Firstly, a Neighborhood-based Genetic Algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Secondly, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. To evaluate our approach, numerical tests are made based on three sets of well known benchmark instances from the literature of the FJSP, which are Kacem, Brandimarte, Hurink. The experimental results show the efficiency of our approach in comparison to other approaches.

H. N. Aung - One of the best experts on this subject based on the ideXlab platform.

  • Multiagent system for real-time operation of a microgrid in real-time digital simulator
    IEEE Transactions on Smart Grid, 2012
    Co-Authors: Thillainathan Logenthiran, Ashwin M Khambadkone, Dipti Srinivasan, H. N. Aung
    Abstract:

    This paper presents a Multiagent system (MAS) for real-time operation of a microgrid. The proposed operational strategy is mainly focused on generation scheduling and demand side management. In generation scheduling, schedule coordinator agent executes a two-stage scheduling: day-ahead and real-time scheduling. The day-ahead scheduling finds out hourly power settings of distributed energy resources (DERs) from a day-ahead energy market. The real-time scheduling updates the power settings of the distributed energy resources by considering the results of the day-ahead scheduling and feedback from real-time operation of the microgrid in real-time digital simulator (RTDS). A demand side management agent performs load shifting before the day-ahead scheduling, and does load curtailing in real-time whenever it is necessary and possible. The distributed Multiagent Model proposed in this paper provides a common communication interface for all components of the microgrid to interact with one another for autonomous intelligent control actions. Furthermore, the Multiagent system maximizes the power production of local distributed generators, minimizes the operational cost of the microgrid, and optimizes the power exchange between the main power grid and the microgrid subject to system constraints and constraints of distributed energy resources. Outcome of simulation studies demonstrates the effectiveness of the proposed Multiagent approach for real-time operation of a microgrid.

Thillainathan Logenthiran - One of the best experts on this subject based on the ideXlab platform.

  • Multiagent system for real-time operation of a microgrid in real-time digital simulator
    IEEE Transactions on Smart Grid, 2012
    Co-Authors: Thillainathan Logenthiran, Ashwin M Khambadkone, Dipti Srinivasan, H. N. Aung
    Abstract:

    This paper presents a Multiagent system (MAS) for real-time operation of a microgrid. The proposed operational strategy is mainly focused on generation scheduling and demand side management. In generation scheduling, schedule coordinator agent executes a two-stage scheduling: day-ahead and real-time scheduling. The day-ahead scheduling finds out hourly power settings of distributed energy resources (DERs) from a day-ahead energy market. The real-time scheduling updates the power settings of the distributed energy resources by considering the results of the day-ahead scheduling and feedback from real-time operation of the microgrid in real-time digital simulator (RTDS). A demand side management agent performs load shifting before the day-ahead scheduling, and does load curtailing in real-time whenever it is necessary and possible. The distributed Multiagent Model proposed in this paper provides a common communication interface for all components of the microgrid to interact with one another for autonomous intelligent control actions. Furthermore, the Multiagent system maximizes the power production of local distributed generators, minimizes the operational cost of the microgrid, and optimizes the power exchange between the main power grid and the microgrid subject to system constraints and constraints of distributed energy resources. Outcome of simulation studies demonstrates the effectiveness of the proposed Multiagent approach for real-time operation of a microgrid.