A survey of exact algorithms for the simple assembly line balancing problem
Management Science
Genetic algorithm crossover operators for ordering applications
Computers and Operations Research - Special issue on genetic algorithms
Genetic algorithms for assembly line balancing with various objectives
Computers and Industrial Engineering - Special issue: IE in Korea
Evolutionary algorithms for constrained engineering problems
Computers and Industrial Engineering
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
Computers and Industrial Engineering
Computers and Industrial Engineering
Computers and Industrial Engineering
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
A mathematical model and a genetic algorithm for two-sided assembly line balancing
Computers and Operations Research
A genetic algorithm approach for multi-objective optimization of supply chain networks
Computers and Industrial Engineering
Computers and Operations Research
A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II
Computers and Industrial Engineering
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A solution procedure for type E simple assembly line balancing problem
Computers and Industrial Engineering
Multiobjective memetic algorithms for time and space assembly line balancing
Engineering Applications of Artificial Intelligence
Computers and Industrial Engineering
Multi-objective optimization of stochastic disassembly line balancing with station paralleling
Computers and Industrial Engineering
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Time and space assembly line balancing considers realistic multiobjective versions of the classical assembly line balancing industrial problems involving the joint optimization of conflicting criteria such as the cycle time, the number of stations, and/or the area of these stations. In addition to their multi-criteria nature, the different problems included in this field inherit the precedence constraints and the cycle time limitations from assembly line balancing problems, which altogether make them very hard to solve. Therefore, time and space assembly line balancing problems have been mainly tackled using multiobjective constructive metaheuristics. Global search algorithms in general - and multiobjective genetic algorithms in particular - have shown to be ineffective to solve them up to now because the existing approaches lack of a proper design taking into account the specific characteristics of this family of problems. The aim of this contribution is to demonstrate the latter assumption by proposing an advanced multiobjective genetic algorithm design for the 1/3 variant of the time and space assembly line balancing problem which involves the joint minimization of the number and the area of the stations given a fixed cycle time limit. This novel design takes the well known NSGA-II algorithm as a base and considers the use of a new coding scheme and sophisticated problem specific operators to properly deal with the said problematic questions. A detailed experimental study considering 10 different problem instances (including a real-world instance from the Nissan plant in Barcelona, Spain) will show the good yield of the new proposal in comparison with the state-of-the-art methods.