An Improved Heuristic Based Genetic Algorithm for Bin Packing Problem

Abstract

The NP-complete bin packing problem is a widely studied grouping problem that serves to model several useful and practical problems, e.g., batch-processing machine scheduling, industrial and transportation logistics, etc. Due to the complexity involved to solve this class of problems, usually two main strategies are adopted: sub-optimal building heuristics and optimization models using metaheuristics algorithms. The building heuristics are computationally efficient, however, usually obtaining non-optimal solutions or local minima. Otherwise, adapted metaheuristics to handle this problem allows an effective global search which augments the chance to obtain optimal or quasi-optimal solutions, however with a high computational cost. This work develops a heuristic based genetic algorithm aiming to obtain a hybrid approach constructed to explore the best features of each strategy. Special encoding handling and specific operators are included additionally to the final model to enhance the behavior and performance of the hybrid model. Numerical experimental using well-established benchmarks for one-dimensional bin packing problem are carried out to compare the versions of the presented hybrid methods with high-quality methods presented in the literature. The results indicate the potential for the presented strategy to solve the one-dimensional bin packing problems.