Due to the high demand of iron ore products in the steel industry, they have the largest share in dry bulk trading per year, above coal and grains. Approximately 9000 Cape-size bulk carriers with capacities up to 400 000 tonnes (DWT) transport the annual demand of iron ore to destination ports. Grabs are employed extensively to unload iron ore from ship holds. A fast and reliable unloading process is required to maintain a minimized cost for port operators and to deliver iron ore products to customers on time. In practice, many factors, such as moisture, varying material properties over the cargo depth and grab’s dynamics, contribute in creating challenges for achieving the desired performance during the unloading process. A solution for improving the unloading process is to enhance the design of grabs by using simulation-based methods. This enables a higher mass of iron ore to be collected per grab cycle, thus minimizing the total unloading time of a bulk carrier. Virtual prototyping of grabs is a novel simulation-based method that allows for evaluating the design performance in an affordable way. The virtual prototype of a grab as it interacts with bulk material are co-simulated at full-scale by coupling two different solvers: Discrete Element Method (DEM) and MultiBody Dynamics (MBD). The co-simulation requires virtual crane operator, CAD model of grab connected to a crane, and calibrated DEM material model as inputs. Over the past decade, reliable DEM calibration procedures have been developed to model free-flowing bulk solids, such as iron ore pellets, sand and gravel. However, due to moisture content the majority of iron ore products show cohesive and stress-history dependent behaviours, which should be considered in the calibration procedure. Additionally, considering particle size and shape of such fine iron ore products, the extreme computation time of DEM simulations is a challenge to be solved. Furthermore, a grab is often used to handle a broad variety of iron ore cargoes that are different in their properties, such as moisture content, shear strength and bulk density. The variability of bulk solid properties influences the grabbing process considerably, and thus, the grab’s efficiency. The primary objective of this dissertation is to develop an accurate co-simulation of grab and cohesive iron ore, and utilizing it for optimizing virtual prototypes. Once properties of an iron ore product in interaction with equipment are characterized, a reliable multi-variable calibration procedure needs to be employed to set various input parameters of a DEM material model, including continuous and categorical variables. Furthermore, once proper scaling rules are applied on the DEM simulation, a full scale grab-material co-simulation can be set up to be validated. Next, by determining the optimal settings of design variables the effect of bulk cargo variation on the grab’s efficiency can be minimized. This is the fundamental strategy of robust grab design. Bulk terminal operators value grabs that are optimized for multiple objectives, including a maximized efficiency with a minimized deviation.