# Path Planning Optimization of the Load Transport Process Using Heuristic Algorithms
## 1 Introduction
The method of data processing and the BIM (Building Information Modeling) concept were used to optimize the location of the cranes, and the boundary contour method was used to detect collisions and ensure work safety. Optimization of the storage area on construction sites where a tower crane is located is presented in [8]. The presented mathematical model was used to calculate the optimal positions due to the cost minimization criterion. The optimal positions of the cranes on the construction site were calculated while the algorithm was running, which allowed for the free movement of obstacles in the analyzed working area. Reference [9] presents a method for optimizing the trajectory of rotating cranes based on the integration of the A* algorithm with a time-optimal approach, which allows smooth obstacle avoidance and minimizes the movement time. The A* algorithm is modified to better control the rotation angles and the movement of the crane rope, taking into account different priorities for individual movement elements, in accordance with the dynamics and constraints. The trajectory is expressed by a polynomial function, which provides effective load tilt damping, as shown by the simulation results. The A* algorithm was also used in [10] to develop an improved hybrid version with multi-body constraints that addresses the problems of traditional path planning methods for non-holonomic mobile robots, such as approaching too close to obstacles, unnecessary backtracking, and excessive turns. By dynamically weighting the heuristic function and optimizing the path costs, the algorithm increases the minimum obstacle distance by more than 50%, reduces the number of unnecessary backtracking and turning, and shortens the total navigation time by 14.2% on average, thus improving the robot navigation performance. In [11], the metaheuristic optimization algorithm Grey Wolf Optimizer (GWO) was applied in the process of designing offshore cranes. GWO allowed for automatic configuration of design parameters, which increased the maximum safe lifting capacity of the crane and reduced its weight, and its effectiveness was confirmed in comparison with other evolutionary algorithms. In turn, in [12], a new algorithm Puma Optimizer (PO) was presented, inspired by puma intelligence, which, thanks to the hyper-heuristic mechanism, automatically adjusts the exploration and exploitation phases to the characteristics of the problem. PO proved to be more effective than other optimization algorithms, obtaining better results in 27 out of 33 benchmarks and in tasks such as community detection and feature selection. In [13], a heuristic algorithm for optimizing the container retrieval process was proposed, which minimizes the number of their movements and shortens the crane operation time. The algorithm's effectiveness was confirmed based on tests conducted on 70 problems, and its computational efficiency allows for quick decision-making in practical applications. The problem of planning lifting routes for all-terrain cranes in complex environments was presented in [14], where energy and time costs and operational risk were optimized. For this purpose, a parallel genetic algorithm was used, which generates safe lifting routes, meeting the requirements for collision avoidance and operational constraints. The authors of [15] presented an algorithm based on simulated annealing (SA) for optimizing the retrieval phase in automatic warehouses for metal bundles. This algorithm improves operational efficiency by minimizing the differences between the ordered and retrieved material in terms of quality and weight, which increases the number of missions performed per hour. While significant efforts have been made in the field of crane optimization, much of the research does not translate directly into broader applications. This paper aims to fill this gap by presenting potential areas where optimization models can be applied, such as load-handling automation in complex industrial environments.