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
Computers and Operations Research
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
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
Mathematical and Computer Modelling: An International Journal
Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes
Computers and Operations Research
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In this paper we consider a multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times by minimizing total weighted tardiness and maximum completion time simultaneously. Whereas these kinds of problems are NP-hard, thus we proposed a multi-population genetic algorithm (MPGA) to search Pareto optimal solution for it. This algorithm comprises two stages. First stage applies combined objective of mentioned objectives and second stage uses previous stage's results as an initial solution. In the second stage sub-population will be generated by re-arrangement of solutions of first stage. To evaluate performance of the proposed MPGA, it is compared with two distinguished benchmarks, multi-objective genetic algorithm (MOGA) and non-dominated sorting genetic algorithm II (NSGA-II), in three sizes of test problems: small, medium and large. The computational results show that this algorithm performs better than them.