Abstract
The objective of this work is to present and evaluate meta-heuristics for the blocking permutation flow shop scheduling problem subject to regular objectives. The blocking problem is known to be NP-hard with more than two machines. We assess the difficulty level of this problem by developing two population-based meta-heuristics: Genetic Algorithm and Artificial Bee Colony algorithm. The final goal is to measure the performance of these proposed techniques and potentially contribute in possible improvements in the blocking benchmark instances. Furthermore, computational tests carried out on randomly generated test problems show that the approaches consistently yields good solutions in a moderate amount of time. Finally, an updated list of best-known solutions for the Taillard's and Ronconi and Henriques's benchmark is exposed: new best-known solutions for the blocking flow shop scheduling problem with makespan, total flow time, and total tardiness criteria are found.
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