The multi-objective sustainable closed-loop supply chain network studied involves characteristics that produce high complexity due to the interaction of downstream and upstream strategic, tactical, and operational decisions, as well as sustainability elements. For this reason, a matheuristic algorithm, the Kernel search, is presented to solve large instances of the problem. After the algorithm parameter tuning, several instances are solved. A comparison with an augmented epsilon-constraint method is conducted in terms of speed and quality. The results show that the Kernel search matheuristic outperforms in the selected metrics, achieving an average improvement of 72% in computational time and from 0.47% to 28.18% in quality metrics. The solutions obtained deliver Pareto fronts in terms of economic, environmental, and social objectives.
Rodríguez-Escoto et al. (2026) studied this question.