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.
Highlights
Scheduling is a field of investigation which has known a significant growth these last years
The proposed GATSþHM is implemented in java language on a 2.10 GHz Intel Core 2 Duo processor and 3 Gb of RAM memory, where we use the integrated development environment (IDE) Eclipse to code the algorithm and the multiagent platform Jade (Bellifemine et al 1999) to create the different agents of our holonic model
Numerical tests are made based on four sets of wellknown benchmark instances in the literature of the flexible job shop scheduling problem (FJSP):
Summary
Scheduling is a field of investigation which has known a significant growth these last years. The crossover operator has an important role in the global process, allowing to combine in each case the chromosomes of two parents to obtain new individuals and to attain new better parts in the search space In this work, this operator is applied with two different techniques successively for the parent’s chromosome vectors MA and OS. Each cluster agent CAi is responsible to apply successively to each cluster CLi a local search technique which is the tabu search algorithm to guide the research in promising regions of the search space and to improve the quality of the final population of the genetic algorithm This local search is executed simultaneously by the set of the CAs agents, where each CA starts the research from an elite solution of its cluster searching to attain new more dominant individual solutions separately in its assigned cluster CLi; see Fig. 8. Crossover probability 1.0. Mutation probability 0.5. Maximum number of iterations 1000
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