Combining Heuristics Backtracking and Genetic Algorithm to Solve the Container Loading Problem with Weight Distribution
We approach the container loading problem with maximization of the weight distribution. Our methodology consists of two phases. In the first phase, it applies heuristics based on integer linear programming to construct blocks building of small items. A backtracking algorithm chooses the best heuristics. The objective of this phase is to maximize the total volume of the packed boxes. In the second phase, we apply a genetic algorithm on found solution in previous phase in order to maximize its weight distribution. We use a well-known benchmark test to compare our results with other approaches, considering that our algorithm is not yet completely implemented. This paper also presents a case study of our implementation using some real data in a factory of stoves and refrigerators in Brazil. The obtained results are better than the found results by the factory’s system, in reduced time.
KeywordsContainer Loading Problem Weight Distribution Metaheuristics Integer Programming Backtracking Genetic Algorithms
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