Abstract
In robotic systems, 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 a Single Robot (FJSPT-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 FJSPT-SR 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 a hybrid metaheuristic approach based on clustered holonic multiagent model for the FJSPT-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 FJSPT-SR. New upper bounds are found, showing the effectiveness of the presented approach.
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