## 4 Analysis of the Impact of Loading Strategies on Operation Time  
This part of the paper presents the analysis of the terminal container handling system considering the number of unproductive manipulations. The objective is to demonstrate that the number of unproductive manipulations is influenced by at least two factors:  
* storage position occupancy,
* adopted cargo distribution strategy. Storage location occupancy is a feature that is easily determined. Comparison of the number of occupied positions with the number of available ones is simply required, calculating the data in TEU or pieces of freight units of a given type. After one of the -freight unit forms is adopted, it should continue to be used. In this paper, calculations are conducted using container units. The adopted cargo distribution strategy is a broader issue. Different approaches in this regard were mentioned earlier. In reality, terminal operators also apply their strategies. In most cases, these are based on observations and focus on accelerating only one operation, e.g., train unloading, followed by a series of unproductive manipulations that are usually not considered any further. The authors of this paper tested the results of the two papers at one of the terminals in Poland. The first strategy referred to the fastest handling of a training delivering cargo, which in practice meant placing containers picked up from carriages in the first available position within a block to fill container blocks. The second approach enabled longer train handling to stack containers by weight (heaviest in a lower position, lighter in a higher position, and empty contained separately). The study lasted for about a week for each method and involved reading the number of moves above each of the containers from the terminal operating system. It should be noted that only 25% of the container had a known date of cargo delivery to the destination before arriving at the terminal; however, the date specified only the day and not the day and time. In principle, this means a complete absence of information on determining which of the containers should be stacked lower (for a longer stand), and which should be stacked higher up. Next, by combining storage position occupancy and the adopted cargo distribution strategy and proceeding with the mass service theory, the authors determined the average freight unit handling time. For this purpose, the procedure was adopted according to the M/m/n method. The study lasted for two weeks, testing the first strategy for 6 working days and the second strategy also for 6 days. When open 14 h/day, the terminal accepts approximately 60 containers (the analysis involved 50 to 70 containers arriving daily). In total, 55% of these are 40\({}^{\prime}\) and 45\({}^{\prime}\) containers, approx. 40% are 20\({}^{\prime}\) containers with the rest being 30\({}^{\prime}\) containers. Three or four handling machines are operated at the facility. These are the popular reach stackers. The number of the equipment in operation depends on the number of employees on a working shift. The storage yard occupancy level during the study ranged from 35% to 45%. The times of individual operations (time of picking up and putting down a freight unit depending on the level of the container) were also analyzed. Freight unit handling times were determined by combining that information based on the mass service theory. Table 2 shows the average freight unit handling times according to the first strategy (placing containers from carriages in the first free position, with the provision that the storage block is to be full). The average repetition rate was 20%, which means that 20 containers in every 100 required translocations to handle the unit underneath (see Figure 7). Table 3 shows the average freight unit handling time according to the second strategy (putting down containers from carriages and filling the stack so that heavier containers are lower and lighter ones are higher) (see Figure 8).