Houssem Eddine Nouri

Simultaneous scheduling of machines and transport robots in flexible job shop environment using hybrid metaheuristics based on clustered holonic multiagent model

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.

Hybrid metaheuristics for scheduling of machines and transport robots in job shop environment

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.

A Classification Schema for the Job Shop Scheduling Problem with Transportation Resources: State-of-the-Art Review

The Job Shop scheduling Problem (JSP) is one of the most known problems in the domain of the production task scheduling. The Job Shop scheduling Problem with Transportation resources (JSPT) is a generalization of the classical JSP consisting of two sub-problems: the job scheduling problem and the generic vehicle scheduling problem. In this paper, we make a state-of-the-art review of the different works proposed for the JSPT, where we present a new classification schema according to seven criteria such as the transportation resource number, the transportation resource type, the job complexity, the routing flexibility, the recirculation constraint, the optimization criteria and the implemented approaches.

Hybrid Metaheuristics within a Holonic Multiagent Model for the Flexible Job Shop Problem

Abstract The Flexible Job Shop scheduling Problem (FJSP) is an extension of the classical Job Shop scheduling Problem (JSP) that allows to process operations on one machine out of a set of alternative machines. It is an NP-hard problem consisting of two sub-problems which are the assignment and the scheduling problems. This paper proposes a hybridization of two metaheuristics within a holonic multiagent model for the FJSP. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a cluster agents set uses a local search technique to guide the research in promising regions. Numerical tests are made to evaluate our approach, based on two sets of benchmark instances from the literature of the FJSP, which are the Brandimarte and Hurink data. The experimental results show the efficiency of our approach in comparison to other approaches.

A Holonic Multiagent Model Based on a Combined Genetic Algorithm─Tabu Search for the Flexible Job Shop Scheduling Problem

The Flexible Job Shop scheduling Problem (FJSP) is an extension of the classical Job Shop scheduling Problem (JSP) that allows to process operations on one machine out of a set of alternative machines. It is an NP-hard problem consisting of two sub-problems which are the assignment and the scheduling problems. This paper proposes a holonic multiagent model based on a combined genetic algorithm and tabu search for the FJSP. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a cluster agents set uses a local search technique to guide the research in promising regions. Numerical tests are made to evaluate our approach, based on two sets of benchmark instances from the literature of the FJSP: Kacem and Hurink. The experimental results show the efficiency of our approach in comparison to other approaches.

Genetic Algorithm Combined with Tabu Search in a Holonic Multiagent Model for Flexible Job Shop Scheduling Problem

The Flexible Job Shop scheduling Problem (FJSP) is an extension of the classical Job Shop scheduling Problem (JSP) presenting an additional difficulty caused by the operation assignment problem on one machine out of a set of alternative machines. The FJSP is an NP-hard problem composed by two complementary problems, which are the assignment and the scheduling problems. In this paper, we propose a combination of a genetic algorithm with a tabu search in a holonic multiagent model for the FJSP. In fact, firstly, a scheduler agent applies a genetic algorithm for a global exploration of the search space. Then, 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 final population. To evaluate our approach, numerical tests are made based on two sets of well known benchmark instances in the literature of the FJSP: Kacem and Brandimarte. The experimental results show that our approach is efficient in comparison to other approaches.

Simultaneous Scheduling of Machines and a Single Moving Robot in a Job Shop Environment by Metaheuristics based Clustered Holonic Multiagent Model

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.

MATP: A Multi-agent Model for the University Timetabling Problem

This paper proposes a multi-agent model for solving the university course timetabling problem. It is composed of cooperating agents enabling highly distributed processing of the problem and incorporating constraints that have not been considered by previous works. The aim of our model is to provide a best solution satisfying hard and soft constraints while reducing temporal complexity. To analyze the efficiency of our model, we give experimental results based on real instances of the Higher Business School of Tunis by analyzing the variation effect of the lecture and teacher numbers on the messages number and the CPU execution time, and the variation effect of the assignment priority score on the percentage of teacher’s preferences satisfaction.

A Metaheuristic Hybridization Within a Holonic Multiagent Model for the Flexible Job Shop Problem

The Flexible Job Shop scheduling Problem (FJSP) is an extension of the classical Job Shop scheduling Problem (JSP) that allows to process operations on one machine out of a set of alternative machines. It is an NP-hard problem consisting of two sub-problems which are the assignment and the scheduling problems. This paper proposes a hybridization of a genetic algorithm with a tabu search within a holonic multiagent model for the FJSP. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a cluster agents set uses a local search technique to guide the research in promising regions. Numerical tests are made to evaluate our approach, based on two sets of benchmark instances from the literature of the FJSP: Brandimarte and Hurink. The experimental results show the efficiency of our approach in comparison to other approaches.

Metaheuristics based on Clustering in a Holonic Multiagent Model for the Flexible Job Shop Problem

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.