Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
Computers and Operations Research
Computational experience with a branch-and-cut algorithm for flowshop scheduling with setups
Computers and Operations Research
Hybrid flow shop scheduling: a survey
Computers and Industrial Engineering
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Computers and Operations Research
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
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Computers and Operations Research
A survey of very large-scale neighborhood search techniques
Discrete Applied Mathematics
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Evolutionary Computation
Design and Analysis of Experiments
Design and Analysis of Experiments
Minimizing total earliness and tardiness on a single machine using a hybrid heuristic
Computers and Operations Research
Parallel machine total tardiness scheduling with a new hybrid metaheuristic approach
Computers and Operations Research
Computers and Operations Research
Computers and Operations Research
A hybrid electromagnetism-like algorithm for single machine scheduling problem
Expert Systems with Applications: An International Journal
Integrating simulation and optimization to schedule a hybrid flow shop with maintenance constraints
Computers and Industrial Engineering
Two-phase sub population genetic algorithm for parallel machine-scheduling problem
Expert Systems with Applications: An International Journal
Covering pareto sets by multilevel evolutionary subdivision techniques
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Bi-objective group scheduling in hybrid flexible flowshop: A multi-phase approach
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Solving flexible flow-shop problem with a hybrid genetic algorithm and data mining: A fuzzy approach
Expert Systems with Applications: An International Journal
Multi-objective genetic-based algorithms for a cross-docking scheduling problem
Applied Soft Computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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This paper considers the problem of sequence-dependent setup time hybrid flowshop scheduling with the objectives of minimizing the makespan and sum of the earliness and tardiness of jobs, and present a multi-phase method. In initial phase, the population will be decomposed into several subpopulations. In this phase we develop a random key genetic algorithm and the goal is to obtain a good approximation of the Pareto-front. In the second phase, for improvement the Pareto-front, non-dominant solutions will be unified as one big population. In this phase, based on the local search in Pareto space concept, we propose multi-objective hybrid metaheuristic. Finally in phase 3, we propose a novel method using e-constraint covering hybrid metaheuristic to cover the gaps between the non-dominated solutions and improve Pareto-front. Generally in three phases, we consider appropriate combinations of multi-objective methods to improve the total performance. The hybrid algorithm used in phases 2 and 3 combines elements from both simulated annealing and a variable neighborhood search. The aim of using a hybrid metaheuristic is to raise the level of generality so as to be able to apply the same solution method to several problems. Furthermore, in this study to evaluate non-dominated solution sets, we suggest several new approaches. The non-dominated sets obtained from each phase and global archive sub-population genetic algorithm presented previously in the literature are compared. The results obtained from the computational study have shown that the multi-phase algorithm is a viable and effective approach.