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The Permutation Flow Shop Problem (PFSP) is considered as one of the most complex and hard combinatorial optimization scheduling problem to solve. To address the computational complexity of the PFSP, parallel and distributed algorithms using grid computing are considered in this work. Indeed, our contributions focuses on two parallel algorithms: the recent one, called Grid Algorithm with Load Balancing (GALB), is an improvement of our previous algorithm, called Grid Algorithm Updating the Upper Bound (GAUUB), with a new strategy of scheduling jobs leading to a better load balancing between processors. In this paper, we present first, the core idea of GALB algorithm which consists on a dynamic and efficient distribution of the load of all available processors. The objective is to minimize the running times of large and hard instances. Second, we present an empirical study addressing the performance comparison between the GAUUB and GALB algorithms. All conducted experiments used the Taillard's benchmark data sets. Our algorithm (GALB) generated high quality solutions and outperforms the GAUUB algorithm in terms of resolution of new instances, speed, scalability and load balancing.
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