PACKING ALGORITHM STRATEGY FOR NET-TO-GROSS EFFICIENCY OF RENTAL OFFICE DESIGN ( CASE STUDY: JALAN SUDIRMAN, JAKARTA )

• Main Author: Beatricia
• Format: Theses
• Language: Indonesia
• Subjects: Arsitektur
• Online Access: https://digilib.itb.ac.id/gdl/view/45647
• Institution: Institut Teknologi Bandung

Net-to-gross efficiency is a crucial factor for building owner. It defines as a ratio between leasable and overall building area. The value of net-to-gross efficiency indicates the amount of area that can be rented in a building. The higher efficiency value means the higher profit can be gained. The high-efficiency value must be followed by the effective use of space. Because of that, every single floor in a building should have an optimum net-to-gross value, which means the building has high efficiency with efficient space configuration. To achieve an optimum value of net-to-gross efficiency, designers need to determine the profiles and number of modules that can be arranged to achieve a high net-to-gross efficiency value. Packing algorithm is a solution to solve this efficiency issue. Packing algorithm is an optimization method for arranging components in a boundary to achieve maximum compactness and minimum space of residue. A case study in this thesis is rental office design, a typology which requires a high value of leasable area. The office design was applied at Sudirman Street where is well-known as a Central Business District area in Jakarta. At applying packing algorithm to a rental office, it is necessary to specify the area of each of the modules which will be used as components during the simulation. Therefore, a studies of office preferable modules for leased was carried out in eighteen offices in Jakarta. In doing the thesis, simulation played a big role. The simulation was done using Rhinoceros and Grasshopper. The simulation consists of three stages that start from finding net-to-gross efficiency, defining profiles for modules, and applying the packing algorithm to get an optimum efficiency. The first stage goal was to reach minimum 85% net-to-gross efficiency. In this stage, the constraints were site regulations such as building coverage ratio, gross floor area ration, building set back, and the space between buildings. Outputs of this stage were the value of net-to-gross efficiency, profile, and basic form of floor plate. The second stage was a simulation to get the efficient profiles of modules. At this stage, profiles of modules were decided by studies of office preferable area in Jakarta and Francis Duffy theory about office bay. The last stage of simulation was the implementation of packing algorithm. Constraint and component in this stage were floor plate and modules that came from the two previous stages. In this stage, Kangaroo and Galapagos take big roles. Using Kangaroo solver which is a live physics plug-in, all modules that connected to core gained forces to reach the core. Meanwhile, Galapagos was used to determine the number of modules, maximum compactness, and minimum residue of packing area. Packing algorithm was divided into two sections. They were simulation for each module and the combination of all modules. The simulation results packing for each module were the number of modules, area, and residue. They were used as references to determine the number of modules that were used as components in simulation for the combination of all modules. In packing algorithm simulation, several discoveries were obtained such as movements that occurred during the simulation and the results that were affected by compactness of modules. The final result of packing algorithm simulation was a floor plate with spatial configuration arrangement. It was used as the typical floor plan and massing of the tower. Because of this implementation at the beginning of design process, a high-efficiency value was obtained. Even though at the end of design this value got change influenced by several floors of building that require more circulation and utility than typical floor such as commercial and basement area.