Authors:
Houssem Eddine Nouri
1
;
Olfa Belkahla Driss
2
and
Khaled Ghédira
1
Affiliations:
1
Higher Institute of Management of Tunis, Tunisia
;
2
Higher Institute of Management of Tunis and University of Manouba, Tunisia
Keyword(s):
Scheduling, Flexible Job Shop, Genetic Algorithm, Tabu search, Holonic Multiagent.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Distributed and Mobile Software Systems
;
Enterprise Information Systems
;
Evolutionary Programming
;
Industrial Applications of Artificial Intelligence
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Multi-Agent Systems
;
Problem Solving
;
Scheduling and Planning
;
Software Engineering
;
Symbolic Systems
Abstract:
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 app
roach is efficient in comparison to other approaches.
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