Tackling the Container Loading Problem: A Hybrid Approach Based on Integer Linear Programming and Genetic Algorithms

Abstract

This paper presents a novel hybrid approach for solving the Container Loading (CL) problem based on the combination of Integer Linear Programming (ILP) and Genetic Algorithms (GAs). More precisely, a GA engine works as a generator of reduced instances for the original CL problem, which are formulated as ILP models. These instances, in turn, are solved by an exact optimization technique (solver), and the performance measures accomplished by the respective models are interpreted as fitness values by the genetic algorithm, thus guiding its evolutionary process. Computational experiments performed on standard benchmark problems, as well as a practical case study developed in a metallurgic factory, are also reported and discussed here in a manner as to testify the potentialities behind the novel approach.