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Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

This study aims to propose a framework for Halal Food Sustainable Traceability, with the purpose of investigating the implementation of traceability and sustainability within organizations operating in the halal food industry as well as exploring the impact of these practices on organizational performance. This study examines the meat processing sector in Indonesia, focusing on medium to large-scale industrial operations. The rationale for this investigation stems from Indonesia’s substantial potential in the competitive worldwide halal food industry.

The research framework has been developed by an extensive review of relevant literature, with a specific emphasis on the cycle of the halal food sustainable traceability framework. This cycle encompasses four key stages, including the roles played by authorities, the process of standardization, the implementation phase and the importance of collaboration. The study analyses and validates data using partial least square-structural equation modeling and empirically tests the theoretical framework using 109 Indonesian halal food industry data.

The research identifies potential obstacles and difficulties that may arise during different phases of the halal food sustainable traceability framework. Concerns regarding authority, standardization, implementation and collaboration are among these. In addition, strategies for overcoming these obstacles are deliberated upon, including knowledge sharing, transparency, ongoing reporting and strategic collaboration.

The present study introduces a Halal Sustainable Traceability Framework that incorporates the principles of halal, traceability, sustainability and their effects on organizational performance. This study offers significant perspectives on the difficulties and resolutions pertaining to the traceability and sustainability of halal food in Indonesia.