# Path Planning Optimization of the Load Transport Process Using Heuristic Algorithms
## 1 Introduction  
The load transport process in an industrial hall or on a construction site is one of the basic and key operations without which efficient work would be impossible. The primary motivation for optimizing these processes lies in the need to enhance efficiency, safety, and precision in cargo transport, especially in industries such as construction, manufacturing, and logistics, where the cargo can range from heavy construction materials to delicate electronic components. The calculated optimization models can directly benefit operations involving heavy cranes, gantries, and automated guided vehicles (AGVs). The main goal of research on this issue is to improve efficiency, safety, and comfort at work. The increase in computational capabilities makes it possible to optimize the duty cycle of transport machines, not only in the case of repetitive activities but also in the case of single transfers. The continuous development of information technology and the growing interest in cyber-physical systems determine the increasingly common work without human intervention or with only supervisory oversight. Determination of the optimal path in this case can be sent directly to the control system, which can reduce manual control of the machine. In the literature on crane optimization studies, two main divisions can be distinguished: crane construction or its duty cycle. The minimization of impacts caused by the Coriolis force at the stage of selecting the system parameters and crane structure is presented in reference [1]. The optimization was confirmed by dynamic analysis, which allowed for a significant reduction in the value of dynamic overloads already at the design stage, resulting in a cheaper and lighter structure. Based on the mathematical model, optimization criteria were formulated for each crane mechanism. In addition to minimizing Coriolis force impacts, the focus was also on reducing load fluctuations. Reference [2]presents the optimization of the construction design of work platforms. A 3D model was developed in the SolidWorks environment, and then a dynamic analysis was carried out to determine the construction parameters of the lift. The results of the analysis were compared with the mathematical model and validated. A simulation model that allows determining the interaction between mobile cranes and the accompanying work crews is presented in reference [3]. Optimal crane and crew configurations have been determined to minimize construction costs and unloading time. Optimization was carried out based on a multi-functional genetic algorithm. The authors of [4] present a method to optimize the configuration of a gantry crane using a two-phase model. Minimization of costs during reloading operations was adopted as the objective criterion, taking into account all the limitations resulting from the nature of the crane's operation. Based on experimental verification, it was found that the proposed method can effectively reduce operating costs while maintaining the implementation time. The application of the genetic algorithm during the load positioning stage is presented in reference [5]. The optimization was based on the method of selecting the driving function of the crane platform rotation. As a result of the applied method, the desired final position of the load and the minimization of the crane's duty cycle time were obtained. Reference [6] presents the issue of synchronizing the work of a quay crane and a shipyard truck. The objective function was to minimize the total time needed to complete the unloading and transport operations of all containers. Optimal solutions were obtained using the adaptive particle swarm algorithm (APSO), in which all parameters were selected automatically. The issue of precise operation of mobile cranes, taking into account the complexity and dynamics of construction sites, is shown in reference [7], where an approach to determining the location of cranes based on simulation studies is presented.