Abstract
The permutation flow shop problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a biogeography based optimization (BBO) based on memetic algorithm, named HBBO is proposed for PFSSP. Firstly, to make BBO suitable for PFSSP, a new LRV rule based on random key is introduced to convert the continuous position in BBO to the discrete job permutation. Secondly, the NEH heuristic was combined with the random initialization to initialize the population with certain quality and diversity. Thirdly, a fast local search is used for enhancing the individuals with a certain probability. Fourthly, the pair wise based local search is used to enhance the global optimal solution and help the algorithm to escape from local minimum. Additionally, simulations and comparisons based on PFSSP benchmarks are carried out, showing that our algorithm is both effective and efficient. Key words: Biogeography based optimization, permutation flow shop scheduling, memetic algorithm, local search.
Highlights
Scheduling problems play an important role in both manufacturing systems and industrial process for improving the utilization of resources, and it is crucial to develop efficient scheduling technologies (Stadtler, 2005)
In order to enhance the local search ability and get a better solution, we propose a new fast local search to enhance the makespan of every vector with the certain probability
To test the performance of the proposed hybrid BBO (HBBO) for the permutation flow shop scheduling problem, computational simulations are carried out with some well-studied problems taken from the OR-Library
Summary
Scheduling problems play an important role in both manufacturing systems and industrial process for improving the utilization of resources, and it is crucial to develop efficient scheduling technologies (Stadtler, 2005). In the paper (Stützle, 1998), an ant-colony based algorithm was proposed to solve PFSSP by combining the fast local search to enhance the solutions.
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