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 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.
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Nouri, H.E., Driss, O.B., Ghédira, K. (2015). A Holonic Multiagent Model Based on a Combined Genetic Algorithm─Tabu Search for the Flexible Job Shop Scheduling Problem. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Sustainability - The PAAMS Collection. PAAMS 2015. Communications in Computer and Information Science, vol 524. Springer, Cham. https://doi.org/10.1007/978-3-319-19033-4_4
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