We propose in this paper a Blocking Iterated Greedy algorithm (BIG) which makes an adjustment between two relevant destruction and construction stages to solve the blocking flow shop scheduling problem and minimize the maximum completion time (makespan). The greedy algorithm starts from an initial solution generated based on some well-known heuristic. Then, solutions are enhanced till some stopping condition and through the above mentioned stages. The effectiveness and efficiency of the proposed technique are deduced from all the experimental results obtained on both small randomly generated instances and on Taillard's benchmark in comparison with state-of-the-art methods.

The objective of this work is to present and evaluate metaheuristics 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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