Variable Neighborhood Search for the Bin Packing Problem with Compatible Categories

TL;DR

This paper introduces a new variant of the bin packing problem involving category-based compatibility constraints and proposes a Variable Neighborhood Search metaheuristic to solve large instances efficiently.

Contribution

The paper presents the Bin Packing Problem with Compatible Categories (BPCC), a novel problem formulation, and develops a VNS algorithm that outperforms traditional methods in solving large instances.

Findings

VNS provides high-quality solutions quickly.

The algorithm outperforms linear integer programming in computational experiments.

BPCC models real-world distribution scenarios effectively.

Abstract

Bin Packing with Conflicts (BPC) are problems in which items with compatibility constraints must be packed in the least number of bins, not exceeding the capacity of the bins and ensuring that non-conflicting items are packed in each bin. In this work, we introduce the Bin Packing Problem with Compatible Categories (BPCC), a variant of the BPC in which items belong to conflicting or compatible categories, in opposition to the item-by-item incompatibility found in previous literature. It is a common problem in the context of last mile distribution to nanostores located in densely populated areas. To efficiently solve real-life sized instances of the problem, we propose a Variable Neighborhood Search (VNS) metaheuristic algorithm. Computational experiments suggest that the algorithm yields good solutions in very short times while compared to linear integer programming running on a high-performance computing environment.