A comparative evaluation of heuristic line balancing techniques
Management Science
A survey of exact algorithms for the simple assembly line balancing problem
Management Science
Optimally balancing large assembly lines with `FABLE'
Management Science
Fast, effective algorithms for simple assembly line balancing problems
Operations Research
Eureka: a hybrid system for assembly line balancing
Management Science
An Ant Algorithm with a New Pheromone Evaluation Rule for Total Tardiness Problems
Real-World Applications of Evolutionary Computing, EvoWorkshops 2000: EvoIASP, EvoSCONDI, EvoTel, EvoSTIM, EvoROB, and EvoFlight
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
2-ANTBAL: An ant colony optimisation algorithm for balancing two-sided assembly lines
Computers and Industrial Engineering
EvoCOP'08 Proceedings of the 8th European conference on Evolutionary computation in combinatorial optimization
Information Sciences: an International Journal
Multiple-colony ant algorithm for parallel assembly line balancing problem
Applied Soft Computing
Beam-ACO applied to assembly line balancing
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
A Branch, Bound, and Remember Algorithm for the Simple Assembly Line Balancing Problem
INFORMS Journal on Computing
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The present work is focused on the assembly line balancing design problems whose objective is to minimize the number of stations needed to manufacture a product in a line given a fixed cycle time, equivalent to a fixed production rate. The problem is solved using an ACO metaheuristic implementation with different features, obtaining good results. Afterwards, an adaptation of the previous implementation is used to solve a real case problem found in a bike assembly line with a hierarchical multi-objective function and additional constraints between tasks.