Abstract
The one-dimensional bin packing problem is one of typical combinatorial optimization problems. This problem has been solved so far by using several metaheuristic methods such as grouping genetic algorithm, variable neighborhood search method and perturbation MBS' method, and by using branch-and-bound based methods such as bin packing solution procedure. In this paper, a design of the genetic algorithm is proposed for the bin packing problem in such a way that diverse offsprings are generated. The crossover operation is designed so as to inherit the combination of items in each parent's bin. The mutation operation is designed by embedding a heuristic rule in such a way that items fit the capacity constraint as much as possible. It is observed from computational experiments for many benchmarks of this problem that the proposed genetic algorithm finds optimal solutions most frequently among other metaheuristic methods.
