# GA-Based Optimization Method for Mobile Crane Repositioning Route Planning
## 2 Current State of Mobile Crane Operation Planning  
Many studies have been conducted to establish mobile crane operation plans. Crane operation planning starts with the selection of an appropriate crane by considering the site conditions. Existing studies have identified essential factors that need to be considered for the selection of an appropriate crane (i.e., work clearance, lift height, lift performance of the crane, ground condition, site accessibility, etc.) Considering these conditions, [PERSON] and [PERSON] [3] used fuzzy logic to select the crane type. [PERSON] and [PERSON] [4] developed a stochastic artificial neural network (ANN)-based crane type selection system. [PERSON] et al. [5] developed an algorithm for selecting an appropriate crane by considering the geometric shape based on the payload and boom/jib of the crane. [PERSON] et al. [16] argued that the wind direction is crucial for crane selection owing to the characteristics of the lift work and proposed a building information modeling (BIM)-based crane selection method. Thus, based on existing studies, the optimal crane type can be selected by considering various site conditions. Once the mobile crane selection is completed, feasible locations where the crane can be installed/placed to operate on the site are searched. As the feasible locations must be considered according to the crane type and site conditions while selecting the crane, a study has been conducted to select the crane and feasible locations simultaneously [17]. The method proposed by [PERSON] finds feasible working areas for the crane based on the lift performance and maximum lift range of a particular crane. [PERSON] and [PERSON] [8] identified the work areas that minimized the potential spatial conflicts between the crane and the structure. [PERSON] et al. [7] automated the feasible working area search procedure for a crane by considering the minimum and maximum working ranges of the crane and the rotational range of the vehicle body in a computer-aided design environment. Furthermore, [PERSON] et al. [18] proposed a method for finding a work area to increase the collaborative productivity of cranes when multiple cranes are operated on a site. Based on the outcomes of the existing studies, workers can determine the feasible working areas of the crane by considering the lift performance of the crane, structure, three-dimensional geometry of the crane, shape, and weight of the load, etc. However, existing studies have focused on lift locations and load characteristics. Thus, there is a lack of consideration for loading areas. Owing to the nature of the construction site, materials must be loaded in the designated places. Therefore, for feasible working areas of a crane, the loading areas must be included in the working range of the crane. Therefore, a method to find feasible working areas by considering the loading locations, lift locations, and working range of the crane is required. Once the feasible working areas are identified, the crane can be positioned at an appropriate location to lift loads. However, the manager must review the adequacy of the lift path because the lift path is directly linked to productivity and occurrence of safety accidents. [PERSON] et al. [9] used the hill-climbing and A* algorithm to propose a method that searches for the optimal lift path when two cranes collaborate to lift a load. [PERSON] et al. [10] investigated the GA-based optimal lift path. [PERSON] and [PERSON] [11] considered the shape of the payload and lifting behavior to search the lift path. [PERSON] and [PERSON] [19] proposed an algorithm to find a lift path by considering the smoothness of the crane motion. Moreover, [PERSON] et al. [20] proposed a GA-based method for finding a lift path that can increase the time and energy efficiencies by considering the characteristics of the crane operator.