Fast alternatives to Perlin's bias and gain functions
Graphics gems IV
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
A genetic algorithm for multi-level, multi-machine lot sizing and scheduling
Computers and Operations Research
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Constraint Handling in Genetic Algorithms: The Set Partitioning Problem
Journal of Heuristics
Constraint handling in genetic algorithms using a gradient-based repair method
Computers and Operations Research
Computers and Operations Research
Production Planning by Mixed Integer Programming (Springer Series in Operations Research and Financial Engineering)
Computers and Operations Research
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In this paper we present a genetic algorithm with new components to tackle capacitated lot sizing and scheduling problems with sequence dependent setups that appear in a wide range of industries, from soft drink bottling to food manufacturing. Finding a feasible solution to highly constrained problems is often a very difficult task. Various strategies have been applied to deal with infeasible solutions throughout the search. We propose a new scheme of classifying individuals based on nested domains to determine the solutions according to the level of infeasibility, which in our case represents bands of additional production hours (overtime). Within each band, individuals are just differentiated by their fitness function. As iterations are conducted, the widths of the bands are dynamically adjusted to improve the convergence of the individuals into the feasible domain. The numerical experiments on highly capacitated instances show the effectiveness of this computational tractable approach to guide the search toward the feasible domain. Our approach outperforms other state-of-the-art approaches and commercial solvers.