Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Beam-ACO applied to assembly line balancing
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Multiobjective memetic algorithms for time and space assembly line balancing
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
We present an extension of a multi-objective algorithm based on Ant Colony Optimisation to solve a more realistic variant of a classical industrial problem: Time and Space Assembly Line Balancing. We study the influence of incorporating some domain knowledge by guiding the search process of the algorithm with preferences-based dominance. Our approach is compared with other techniques, and every algorithm tackles a real-world instance from a Nissan plant. We prove that the embedded expert knowledge is even more justified in a real-world problem.