A Genetic Algorithm for Solving the Three-Dimensional Bin Set Selection Problem

This paper presents a genetic algorithm (GA) with novel crossover and mutation operators to solve the three-dimensional bin set selection problem (3D-BSSP). The goal is to find the optimal bin set from standard-sized bins to minimize packaging and shipping costs for an e-commerce company. We utilize minimal feasible bin sets (MFBS) to ensure, that each order can be packed. Evaluations with various item dimension distributions show that the GA achieves near-optimal solutions faster than a mixed-integer programming model.