Level schedules for mixed-model, Just-in-Time processes
Management Science
A genetic alorithm for multiple objective sequencing problems in mixed model assembly lines
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Two-sided assembly line balancing to maximize work relatedness and slackness
Computers and Industrial Engineering
A multi-objective scatter search for a mixed-model assembly line sequencing problem
Advanced Engineering Informatics
2-ANTBAL: An ant colony optimisation algorithm for balancing two-sided assembly lines
Computers and Industrial Engineering
An analysis of the equilibrium of migration models for biogeography-based optimization
Information Sciences: an International Journal
Computers and Industrial Engineering
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Biogeography-Based Optimization
IEEE Transactions on Evolutionary Computation
A dynamic programming algorithm for scheduling mixed-model, just-in-time production systems
Mathematical and Computer Modelling: An International Journal
Computers and Industrial Engineering
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This research presents a Pareto biogeography-based optimisation (BBO) approach to mixed-model sequencing problems on a two-sided assembly line where a learning effect is also taken into consideration. Three objectives which typically conflict with each other are optimised simultaneously comprising minimising the variance of production rate, minimising the total utility work and minimising the total sequence-dependent setup time. In order to enhance the exploration and exploitation capabilities of the algorithm, an adaptive mechanism is embedded into the structure of the original BBO, called the adaptive BBO algorithm (A-BBO). A-BBO monitors a progressive convergence metric in every certain generation and then based on this data it will decide whether to adjust its adaptive parameters to be used in the next certain generations or not. The results demonstrate that A-BBO outperforms all comparative algorithms in terms of solution quality with indifferent solution diversification.