Multi-Objective Memetic Search Algorithm for Multi-Objective Permutation Flow Shop Scheduling Problem

Li, Xiangtao; Ma, Shijing

doi:10.1109/access.2016.2565622

Cited by 32 publications

(18 citation statements)

References 49 publications

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“…In PBEDA, a bi-variate probabilistic model is utilized to generate blocks, and the non-dominated sorting technique is employed to filter solutions. For the same problem, Li and Ma [32] proposed a novel multiobjective memetic search algorithm (MMSA), in which a global search and local search strategies are used to find the promising solutions. Recently, Wang and Tang [33] developed a machinelearning based multi-objective memetic algorithm combined with multi-objective local search (MOMA) to solve the above optimization problem.…”

Section: Empirical Comparisonsmentioning

confidence: 99%

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Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns

Han

Gong

Jin

et al. 2019

IEEE Trans. Cybern.

162

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In various flow shop scheduling problems, it is very common that a machine suffers from breakdowns. Under this situation, a robust and stable suboptimal scheduling solution is of more practical interest than a global optimal solution that is sensitive to environmental changes. However, blocking lot-streaming flow shop (BLSFS) scheduling problems with machine breakdowns have not yet been well studied up to date. This paper presents, for the first time, a multiobjective model of the above problem including robustness and stability criteria. Based on this model, an evolutionary multiobjective robust scheduling algorithm is suggested, in which solutions obtained by a variant of single-objective heuristic are incorporated into population initialization and two novel crossover operators are proposed to take advantage of nondominated solutions. In addition, a rescheduling strategy based on the local search is presented to further reduce the negative influence resulted from machine breakdowns.The proposed algorithm is applied to 22 test sets, and compared with the state-of-the-art algorithms without machine breakdowns. Our empirical results demonstrate that the proposed algorithm can effectively tackle BLSFS scheduling problems in the presence of machine breakdowns by obtaining scheduling strategies that are robust and stable.

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Section: Empirical Comparisonsmentioning

confidence: 99%

“…The above state-of-the-art multi-objective algorithms, i.e., INSGA-II [1], PBEDA [31], MMSA [32], and MOMA [33], were selected to compare with our proposed algorithm. As indicated in Section 2, little work has been reported on solving a multi-objective LSFS scheduling problem with machine breakdowns.…”

Section: Empirical Comparisonsmentioning

confidence: 99%

Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns

Han

Gong

Jin

et al. 2019

IEEE Trans. Cybern.

162

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“…(), which is applied to the multiobjective permutation FS problem with sequence‐dependent setup. The same problem is addressed in Li and Li () by means of local search based on decomposition, and in Li and Ma (), by memetic algorithms. Minella et al.…”

Section: Literature Reviewmentioning

confidence: 99%

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

Siqueira

Souza

2019

Int Trans Operational Res

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This paper addresses the multiobjective hybrid flow shop (MOHFS) scheduling problem. In the MOHFS problem considered here, we have a set of jobs that must be performed in a set of stages. At each stage, we have a set of unrelated parallel machines. Some jobs may skip stages. The evaluation criteria are the minimizations of makespan, the weighted sum of the tardiness, and the weighted sum of the earliness. For solving it, an algorithm based on the multiobjective general variable neighborhood search (MO‐GVNS) metaheuristic, named adapted MO‐GVNS, is proposed. This work also presents and compares the results obtained by the adapted MO‐GVNS with those of four other algorithms: multiobjective reduced variable neighborhood search, nondominated sorting genetic algorithm II (NSGA‐II), and NSGA‐III, and another MO‐GVNS from the literature. The results were evaluated based on the Hypervolume, Epsilon, and Spacing metrics, and statistically validated by the Levene test and confidence interval charts. The results showed the efficiency of the proposed algorithm for solving the MOHFS problem.

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“…In [29][30][31][32][33][34][35][36][37], this local search has shown its high effectiveness in obtaining the better quality solutions than the local search based on swap operators. The curial idea of this method is to compare the previous makespan with the new makespan of the sequence, which has inserted a job into all positions of the sequence.…”

Section: Perturbation and Local Search Methodsmentioning

confidence: 99%

A Hybrid Local Search Algorithm for the Sequence Dependent Setup Times Flowshop Scheduling Problem with Makespan Criterion

Wang

2017

Sustainability

Self Cite

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This paper focuses on the flowshop scheduling problem with sequence dependent setup times (FSSP-SDST), which has been an investigated object for decades as one of the most popular scheduling problems in manufacturing systems. A novel hybrid local search algorithm called HLS is presented to solve the flowshop scheduling problem with sequence dependent setup times with the criterion of minimizing the makespan. Firstly, the population is initialized by the Nawaz-Enscore-Hoam based problem-specific method (NEHBPS) to generate high quality individuals of the current population. Then, a global search embedded with a light perturbation is designed to produce a new population. After that, to improve the quality of individuals in the current population, a single insertion-based local search is applied. Meanwhile, a further local search strategy based on the insertion-based local search is used to find better solutions for the individuals which are non-improved. Finally, the heavy perturbation is used to explore potential solutions in the neighbor region. To validate the performance of HLS, we compare our proposed algorithm with other competitive algorithms on Taillard benchmark problems. From the experimental results, it can be concluded that the proposed algorithm outperforms the benchmark algorithms.

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Multi-Objective Memetic Search Algorithm for Multi-Objective Permutation Flow Shop Scheduling Problem

Cited by 32 publications

References 49 publications

Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns

Evolutionary Multiobjective Blocking Lot-Streaming Flow Shop Scheduling With Machine Breakdowns

An MO‐GVNS algorithm for solving a multiobjective hybrid flow shop scheduling problem

A Hybrid Local Search Algorithm for the Sequence Dependent Setup Times Flowshop Scheduling Problem with Makespan Criterion

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