## 1 Introduction  
One of the key areas of economic and entrepreneur activity in modern production-inventory systems, irrespective of the scale, i.e., regional or international, is logistics. Nevertheless, the current conditions in which the logistic companies operate, highly influenced by globalization, make the processes associated with the goods distribution and transportation difficult to analyze formally in precise terms. They are also not amenable to the standard optimization methods due to the high computational complexity. In fact, most of the scientific research in the field until now has focused on the basic configurations, such as single-stage systems [1; 2], serial chain [3; 4], and tree-like structures [5; 6; 7]. Meanwhile, in real systems, more complex configurations in the form of logistic networks have been established [8]. The non-linearities, delays, and couplings in the network node interaction typically do not permit obtaining a closed-form solution to the associated optimization problem. Moreover, the traditional models and numerical methods, e.g., involving the Markov chains, are hardly applicable to the actual, high-dimensional cases [9]. Similarly, the application of heuristics requires precise development for a specific scenario and type of network [10]. As an alternative, one might consider customizing artificial intelligence (AI) approaches to complex logistic systems [11; 12]. Even though the exact solution may not always be attained with the desired accuracy, the relieved computational and implementation burden provided by a properly tuned AI method makes it an attractive choice for practical management problems. According to Min [11], the recent literature displays an increasing interest in the AI approaches for process optimization in logistics. The fundamental AI methods, such as agent-based systems, expert systems, and fuzzy logic, find a wide range of applications in the decision-making problems related to scheduling processes. In addition, more sophisticated approaches, such as evolutionary algorithms, have been gradually employed in the planning stage of real-life systems in which the space of potential solutions should be considered in order to find the optimum [13]. These methods, depending on their specifics and implementation choices, can solve a given optimization problem by self-adjusting to the changes in the system variables and constraints. Among other promising approaches one can also point out machine learning, which finds relatively widespread acceptance in the industrial framework [14], and simulation-based optimization [15; 16]. The computational intelligence approach is recommended for complex systems in which the optimal solution cannot be formulated in an analytical way, e.g., owing to model non-linearities, and the evaluation of the fitness function requires substantial resources and is time-consuming [17]. Among the attractive AI methods, genetic algorithms (GAs) are relatively well investigated regarding general optimization problems. They form a kind of metaheuristics based on the phenomenon of biological evolution that explores the space of alternative problem solutions to reach the best one, which is quantified through a predefined fitness function [18]. Nowadays, GAs are increasingly used in real-life problems. Jauhar and Pant [19] and Lee [20] note that despite the invention of numerous modern soft computing techniques, GAs are still widely used by scientists in optimization problems. Besides industrial applications, many organizational challenges might be addressed using GAs, e.g., traffic flow control [21]. These real-life problems require a different implementation and tuning philosophy of GAs compared to the benchmark ones due to the substantial computational investment in assessing the population fitness [18]. In order to obtain a solution in a reasonable time, the number of fitness function evaluations needs to be greatly constrained compared to the benchmark cases.