Samia Kouki

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This paper deals with solving the permutation flow shop problem (PFSP) which is an NP-hard scheduling problem using grid computing. As the sequential resolution of this problem can be sometimes impossible on a single machine and especially for instances of large data, we are interested in this work by solving the PFSP using parallel programming. In a(More)
The optimization of scheduling problems is based on different criteria to optimize. One of the most important criteria is the minimization of completion time of the last task on the last machine called makespan. In this paper, we present a parallel algorithm for solving the permutation flow shop problem. Our algorithm is a basic parallel distributed(More)
The study of the Permutation Flow Shop Problem (PFSP) is still of great interest in the community. Reducing the execution time of some very costly instances and/or solving new unresolved instances remain a challenge. The PFSP consists on scheduling n jobs in m machines with make span criterion. Our approach to resolve this problem is based on a parallel(More)
This paper deals with the resolution of the Permutation Flow Shop Problem (PFSP) which requires scheduling n jobs through m machines that are placed in series so as to minimize the makespan. In this study, we focus on parallel methods for solving the one-machine PFSP. We present a parallel distributed Algorithm for this problem with extensive computational(More)
In this paper, the Permutation Flow Shop Problem (PFSP) with total completion time is studied. In previous papers, we proposed parallel algorithms to solve the PFSP based on load balancing Branch and Bound approach. In this work, we propose other alternatives based on Genetic Algorithms to solve this problem. However, using efficiently genetic algorithm(More)
Solving NP-hard combinatorial optimization problems by exact search methods, such as Branch-and-Bound, may degenerate to complete enumeration. For that reason, exact approaches limits us to solve only small or moderate size problem instances, due to the exponential increase in CPU time when problem size increases. One of the most promising ways to reduce(More)