A blocking lot-streaming flow shop (BLSFS) scheduling problem is to schedule a number of jobs on more than one machine, where each job is split into a number of sublots while no intermediate buffers exist between adjacent machines. The BLSFS scheduling problem roots from traditional job shop scheduling problems but with additional constraints. It is more difficult to be solved than traditional job shop scheduling problems, yet very popular in real-world applications, and research on the problem has been in its infancy to date. This paper presents a hybrid multi-objective discrete artificial bee colony (HDABC) algorithm for the BLSFS scheduling problem with two conflicting criteria: the makespan and the earliness time. The main contributions of this paper include: (1) developing an initialization approach using a prior knowledge which can produce a number of promising solutions, (2) proposing two crossover operators by taking advantage of valuable information extracted from all the non-dominated solutions in the current population, and (3) presenting an efficient Pareto local search operator based on the Pareto dominance relation. The proposed algorithm is empirically compared with four state-of-the-art multi-objective evolutionary algorithms on 18 test subsets of the BLSFS scheduling problem. The experimental results show that the proposed algorithm significantly outperforms the compared ones in terms of several widely-used performance metrics.
Knowledge-Based Systems – Elsevier
Published: May 15, 2018
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