## 1 Introduction
Criterion weights play a crucial role in the MCDM process, as they determine the relative importance of each objective and directly affect the final ranking or selection of optimal mechanism design variants [13; 14]. [PERSON] et al. integrate several performance measures into a unified optimization strategy, establishing a comprehensive design methodology. However, the study does not explicitly address the effects of joint friction on power losses, leaving room for further refinement by incorporating tribological effects. The study by [PERSON] et al. [15] confirms the importance of the tribological characteristics of joints within the machine's kinematic chain, as well as the need to minimize power losses due to friction, as it focuses on analyzing joint wear in mechanical systems using a numerical model based on Archard's wear equation. The research shows that the presence of clearance in revolute joints leads to increased contact forces, vibrations, and localized wear, which significantly affects the service life and precision of mechanisms. The study by [PERSON] et al. [16] investigates autonomous control of underground loaders using deep reinforcement learning, emphasizing the importance of optimizing energy efficiency and bucket filling performance through intelligent control strategies. Although their approach differs from structural optimization, their application of multi-agent systems that adaptively manage the loading process based on real-time sensor data highlights the growing relevance of energy-focused methods in the design of loaders. [PERSON] et al. [17] develop a collaborative optimization approach to reduce energy consumption in excavators using multi-objective algorithms that integrate kinematic and energy-based criteria. Their methodology can be adapted to consider friction-based losses as part of a broader efficiency evaluation. However, along with setting the criteria, the optimization process must also take into account numerous kinematic constraints that include specified working reaches, predetermined bucket angles during transport and unloading, and the requirement to ensure the declared digging force. One of the major constraints that must be considered is the elimination of \"dead points\"--kinematic positions in which controlled force transmission is lost in the manipulator drive mechanisms [18]. Research in the field of optimal synthesis of loader manipulator mechanisms has primarily focused on mechanisms with Z-kinematics, due to their widespread use in practice. This kinematic configuration is analyzed in paper [19], which presents an optimization procedure for the manipulator drive mechanism based on a single criterion: minimizing the change in the bucket angle to prevent uncontrolled material spillage during the transition from the transport to the unloading position. In addition to the objective function, the paper also defines the kinematic constraint functions in vector loop form, which makes it possible to apply the Newton-Raphson method in solving the defined optimization problem. Within the optimal synthesis process, the objective functions are modeled as nonlinear equations, resulting from the complex interconnections between the geometric, kinematic, and dynamic parameters of the manipulator drive mechanisms, while the constraints are modeled in the form of inequalities. The authors of study [20] define six nonlinear objective sub-functions for optimizing manipulator drive mechanisms. Optimal solutions are obtained using a genetic algorithm, recognized as an effective method for exploring complex solution spaces  
In [21], the authors present an algorithm for the multi-criteria optimization of Z-kinematics drive mechanisms, where the following criteria are defined as the objective functions: minimum change in the bucket angle, maximum mechanism ratio of the arm and bucket drive mechanisms, and minimum strokes of the boom and bucket hydraulic cylinders.