Binary integer formulation for mixed-model assembly line balancing problem
Computers and Industrial Engineering
A Taxonomy of Hybrid Metaheuristics
Journal of Heuristics
Mixed model assembly line design in a make-to-order environment
Computers and Industrial Engineering
2-ANTBAL: An ant colony optimisation algorithm for balancing two-sided assembly lines
Computers and Industrial Engineering
Balancing of mixed-model two-sided assembly lines
Computers and Industrial Engineering
A unified view on hybrid metaheuristics
HM'06 Proceedings of the Third international conference on Hybrid Metaheuristics
Parameter control in evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A hybrid genetic algorithm for parameter identification of bioprocess models
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
Multi-objective optimization of stochastic disassembly line balancing with station paralleling
Computers and Industrial Engineering
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In this paper, we propose a hybrid genetic algorithm to solve mixed model assembly line balancing problem of type I (MMALBP-I). There are three objectives to be achieved: to minimize the number of workstations, maximize the workload smoothness between workstations, and maximize the workload smoothness within workstations. The proposed approach is able to address some particular features of the problem such as parallel workstations and zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively. We aim to improve its exploring capability by sequentially hybridizing the three well known heuristics, Kilbridge & Wester Heuristic, Phase-I of Moodie & Young Method, and Ranked Positional Weight Technique, with genetic algorithm. The proposed hybrid genetic algorithm is tested on 20 representatives MMALBP-I and the results are compared with those of other algorithms.