Proceedings of the 21st international conference on Computers and industrial engineering
Inferential Performance Assessment of Stochastic Optimisers and the Attainment Function
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Computers and Operations Research
Proceedings of the 10th annual conference on Genetic and evolutionary computation
International Journal of Computer Integrated Manufacturing - Global Competitive Manufacturing
Iterated Greedy Algorithms for a Real-World Cyclic Train Scheduling Problem
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Computers and Operations Research
Quality Assessment of Pareto Set Approximations
Multiobjective Optimization
A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem
INFORMS Journal on Computing
A neural network to enhance local search in the permutation flowshop
Computers and Industrial Engineering
Two ant-colony algorithms for minimizing total flowtime in permutation flowshops
Computers and Industrial Engineering - Special issue: Selected papers from the 30th international conference on computers; industrial engineering
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
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Multi-objective optimisation problems have seen a large impulse in the last decades. Many new techniques for solving distinct variants of multi-objective problems have been proposed. Production scheduling, as with other operations management fields, is no different. The flowshop problem is among the most widely studied scheduling settings. Recently, the Iterated Greedy methodology for solving the single-objective version of the flowshop problem has produced state-of-the-art results. This paper proposes a new algorithm based on Iterated Greedy technique for solving the multi-objective permutation flowshop problem. This algorithm is characterised by an effective initialisation of the population, management of the Pareto front, and a specially tailored local search, among other things. The proposed multi-objective Iterated Greedy method is shown to outperform other recent approaches in comprehensive computational and statistical tests that comprise a large number of instances with objectives involving makespan, tardiness and flowtime. Lastly, we use a novel graphical tool to compare the performances of stochastic Pareto fronts based on Empirical Attainment Functions.