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- W2521567997 abstract "The Job shop-scheduling problem (JSSP) is known as the hardest combinatorial optimization problem. To solve it, means to find the schedule that have the optimal total execution time (i.e. makespan) by respecting some constraints; that is why JSSP had capture the interest of some researchers. They have proposed a significant number of metaheuristics by using the computational intelligence, thus to solve a real application based on JSSP, the best method should be acknowledged. The present paper aims to do a comparative study between three population-based metaheuristics for solving the JSSP, which are; the cat swarm optimization algorithm, the Cuckoo search algorithm, and the ant colony optimization, in order to evaluate the effectiveness of the three approaches; they were tested on a set of benchmark instances included in OR-Library. The Collected computation results proved that the ACO is more efficient to solve the JSSP." @default.
- W2521567997 created "2016-09-30" @default.
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- W2521567997 date "2016-09-23" @default.
- W2521567997 modified "2023-09-23" @default.
- W2521567997 title "A Comparative Study of Three Population-Based Metaheuristics for Solving the JSSP" @default.
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- W2521567997 doi "https://doi.org/10.1007/978-3-319-46568-5_24" @default.
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