Sequencing with earliness and tardiness penalties: a review
Operations Research
Computers and Industrial Engineering
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
New Generic Hybrids Based upon Genetic Algorithms
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
A Micro-Genetic Algorithm for Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Computers and Operations Research
Multi-objective optimization of structures topology by genetic algorithms
Advances in Engineering Software - Special issue on evolutionary optimization of engineering problems
The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms
Evolutionary Computation
Multicriteria Scheduling: Theory, Models and Algorithms
Multicriteria Scheduling: Theory, Models and Algorithms
Ant colony optimization for multi-objective flow shop scheduling problem
Computers and Industrial Engineering
An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers
Computers and Operations Research
A tool for multiobjective evolutionary algorithms
Advances in Engineering Software
Two-phase sub population genetic algorithm for parallel machine-scheduling problem
Expert Systems with Applications: An International Journal
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Multi-objective optimization with fuzzy measures and its application to flow-shop scheduling
Engineering Applications of Artificial Intelligence
Solving bi-objective flow shop problem with hybrid path relinking algorithm
Applied Soft Computing
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This paper presents a bi-objective flowshop scheduling problem with sequence-dependent setup times. The objective functions are to minimize the total completion time and the total earliness/tardiness for all jobs. An integer programming model is developed for the given problem that belongs to an NP-hard class. Thus, an algorithm based on a Multi-objective Immune System (MOIS) is proposed to find a locally Pareto-optimal frontier of the problem. To prove the efficiency of the proposed MOIS, different test problems are solved. Based on some comparison metrics, the computational results of the proposed MOIS is compared with the results obtained using two well-established multi-objective genetic algorithms, namely SPEA2+ and SPGA. The related results show that the proposed MOIS outperforms genetic algorithms, especially for the large-sized problems.