Random-Key Optimizer and Linearization for the Quadratic Multiple Constraints Variable-Sized Bin Packing Problem
Natalia A. Santos, Marlon Jeske, Antonio A. Chaves

TL;DR
This paper introduces a linearized model and a novel Ant Colony Optimization algorithm for the complex Quadratic Multiple Constraints Variable-Sized Bin Packing Problem, achieving tighter bounds and better solutions than existing methods.
Contribution
It presents the first linearized model for QMC-VSBPP enabling exact solvers and develops RKO-ACO, a hybrid evolutionary algorithm with adaptive learning for large-scale instances.
Findings
Linearized model yields significantly tighter lower bounds.
RKO-ACO matches or improves best-known solutions.
New upper bounds established for large instances.
Abstract
This paper addresses the Quadratic Multiple Constraints Variable-Sized Bin Packing Problem (QMC-VSBPP), a challenging combinatorial optimization problem that generalizes the classical bin packing problem by incorporating multiple capacity dimensions, heterogeneous bin types, and quadratic interaction costs between items. We propose two complementary methods that advance the current state-of-the-art. First, a linearized mathematical model is introduced to eliminate quadratic terms, enabling the use of exact solvers such as Gurobi to compute strong lower bounds, reported here for the first time for this problem. Second, we develop RKO-ACO, a continuous-domain Ant Colony Optimization algorithm within the Random-Key Optimizer framework, enhanced with adaptive Q-learning parameter control and efficient local search. Extensive computational experiments on benchmark instances show that the…
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