Genetic algorithms applied to the continuous flow shop problem
Computers and Industrial Engineering
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Computers and Industrial Engineering
Some local search algorithms for no-wait flow-shop problem with makespan criterion
Computers and Operations Research
Design and Analysis of Experiments
Design and Analysis of Experiments
A discrete particle swarm optimization algorithm for the no-wait flowshop scheduling problem
Computers and Operations Research
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers and Industrial Engineering
A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem
INFORMS Journal on Computing
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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This paper studies multi-objective no-wait hybrid flowshop scheduling problems to minimise both makespan and total tardiness. Furthermore, we consider transportation times between machines. This transportation time can be either job-dependent or job-independent. We consider that all transportations are job-independent; and transportations between two machines have to be done by one transporter. This paper presents a new multi-objective electromagnetism algorithm (MOEA). Electromagnetism algorithm is known as a flexible and effective population-based algorithm utilising an attraction/repulsion mechanism to move the particles towards optimality. The algorithm is carefully evaluated for its performance against two available algorithms by means of multi-objective performance measures and statistical tools. The results show that the proposed solution method outperforms the others.