Sequencing to minimize work overload in assembly lines with product options
Management Science
A genetic alorithm for multiple objective sequencing problems in mixed model assembly lines
Computers and Operations Research
Multiobjective evolutionary algorithm test suites
Proceedings of the 1999 ACM symposium on Applied computing
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
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
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
A scatter search based hyper-heuristic for sequencing a mixed-model assembly line
Journal of Heuristics
A new evolutionary algorithm for non-linear economic dispatch
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
An effective shuffled frog-leaping algorithm for resource-constrained project scheduling problem
Computers and Operations Research
A solution procedure for type E simple assembly line balancing problem
Computers and Industrial Engineering
Research on edge detection algorithm of rotary kiln infrared color image
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
An improved shuffled frog-leaping algorithm with extremal optimisation for continuous optimisation
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers' demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems.