Investigation of Genetic Algorithm Parameters and Comparison of Heuristic Arrangements for Container Packing Problem
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Abstract
The container packing problem (CPP) has gained a great deal of attention from researchers. CPP is included in the NP-complete problem, which means that the problem is very difficult to find the best solution in a reasonable time. The total numbers of the solutions depend on the number of the containers arranged (n!) multiplied by six ways of turning each box (6th). Genetic algorithm (GA) is one of the stochastic search methods that are suitable for solving NP-complete problems. The aims of this work were to find the optimal GA parameters and mechanisms (including population size, number of generations, probabilities of crossover and mutation and types of crossover and mutation) for CPP and to compare two approaches of heuristic arrangement (wall-building and guillotine cutting). Two different sizes of packing problem (100 and 500 various sizes of boxes) were considered in a sequential experiment. The results obtained from the effective designed experiments showed that only some GA parameters were statistically significant. It was also found that wall-building approach produced better solutions than guillotine cutting approach.
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