## 2 Related Works
In particular, in networked systems, unlike serial and arborescent simplifications, distributed vs. centralized policy tuning may lead to drastic cost differences, as evidenced in this work. While desirable for enriching the knowledge about supply system functionality, formal studies are difficult to conduct on more realistic models that cover inherent uncertainties and non-linearities. Consequently, numerical optimization has been proposed as an alternative to searching for optimal solutions. Chu et al. [6] proposed a framework for setting the optimal reorder points and order quantities in tree-like distribution networks. Ivanov [40] studied the impact of disruption propagation in light of the ripple effect in multi-stage supply chains. Song et al. [41] developed an agent-based simulation system to support multi-project scheduling and decision-making processes. Unfortunately, numerous simulations implicate significant computational time and effort due to the high mathematical complexity of real-life problem descriptions. As a remedy to the dimensionality obstacle, the AI techniques are being proposed, e.g., particle swarm optimization in three-echelon systems [42]. However, GAs are probably the most thoroughly investigated, both theoretically and applied in many real-life situations. Ko et al. [13] considered over 150 publications concerning the AI methods used in manufacturing management, order fulfillment, vehicle routing problems, etc., stressing the popularity and usefulness of GAs. In this line of research, Koulouriotis et al. [43] applied GA-based optimization to obtain suitable control parameters in serial distribution systems with backordering. Then, Paul and Rajendran [44] showed how to adjust RSLs and review periods in divergent two-stage supply chains with a single distributor and multiple retailers. Ambekar and Kapoor [45], in turn, investigated food grain distribution systems with arborescent topologies. Moreover, Senoussi et al. [46], after the analysis of several GA-based heuristics for two-echelon systems, pointed out the crossover and mutation operations as crucial operations that are used to tune non-linear models. Meanwhile, GAs have found a variety of applications in real-life transportation problems. For instance, Pal and Kant [47] proposed a GA-based framework to solve the fuel-efficient problem in smart urban areas for both people transport and perishable goods delivery. Murali et al. [48] combined integer programming with GA for the integrated routing and scheduling of freight trains in large-scale systems. Deng et al. [49] considered the feeder-bus network design problem involving multiple criteria, i.e., service costs, lead-time delays, and the intensity of feeder buses. Zhou et al. [50] adopted GAs to solve the pallet dispatching problem in order to minimize multiple partial costs encompassing transportation, inventory, lease, and losses. Furthermore, GAs may support the goods distribution impacted by various uncertainties, such as pre- or post-disaster times [51; 52]. Other nature-inspired techniques, e.g., the whale optimization algorithm [53] or a GA combined with immune algorithms [54], do not yet have a sufficiently well-developed theoretical basis for studying their mechanics and limitations. The modern inventory management systems exhibit complex interactions, which are neither well-understood analytically, nor exhaustively examined numerically. This work explored how CGAs could be tuned with respect to the selection of population size, selection and recombination methods, mutation probability, etc., to obtain high efficiency when solving optimization problems in modern logistic networks with non-trivial topologies subjected to the popular OUT policy control.