Unit sized transfer batch scheduling with setup times
Computers and Industrial Engineering
Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
A genetic alorithm for multiple objective sequencing problems in mixed model assembly lines
Computers and Operations Research
Two-machine flowshop group scheduling problem
Computers and Operations Research
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Scheduling groups of tasks with precedence constraints on three dedicated processors
Discrete Applied Mathematics
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Single-machine group scheduling with a time-dependent learning effect
Computers and Operations Research
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering - Special issue: Group technology/cellular manufacturing
Two-machine group scheduling problems in discrete parts manufacturing with sequence-dependent setups
Computers and Operations Research
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Scheduling groups of jobs in the two-machine flow shop
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: 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
Computers and Industrial Engineering
Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes
Computers and Operations Research
Hi-index | 12.05 |
This study strives to minimize multi-objective flexible flowshop considering sequence-dependent setup times. The flowshop scheduling problem made up of n jobs that have to be processed on m machine. But a flexible flowshop scheduling problem should have more than one machine in at least one stage. As this problem is proven to be NP-hard, a multi-phase approach is developed to solve it. Both phases two and three improve their previous phase solutions, in order to tackle with the complexity of being multi-objective optimization, Pareto archive concepts have been implemented here. The parameters of the proposed algorithm are calibrated using a design of experiment (DOE) method. We investigate the performance of our algorithm through comparing two last stage of it with a distinguished benchmark, multi-objective genetic algorithm (MOGA). The computational results support the high performance of our innovative algorithm.