Genetic based effective column generation for 1-D Cutting Stock problem

A new approach to the One-dimensional Cutting Stock problem using Genetic Algorithms (GA) is developed to optimize the trim loss faced by manufacturing industries like paper and pulp, steel, wooden etc. In this approach, we impose penalty function on the fitness value for evolution of better population. Further, we use adaptive crossover and mutation rate to improve the solution convergence rate by around 50%. The computation experimentation compared with LP based approach proves the feasibility and validity of the algorithm.