  Research in warehouse optimization has gotten increased attention in the last
few years due to e-commerce. The warehouse contains a waste range of different
products. Due to the nature of the individual order, it is challenging to plan
the picking list to optimize the material flow in the process. There are also
challenges in minimizing costs and increasing production capacity, and this
complexity can be defined as a multidisciplinary optimization problem with an
IDF nature. In recent years the use of parallel computing using GPGPUs has
become increasingly popular due to the introduction of CUDA C and accompanying
applications in, e.g., Python. In the case study at the company in the field of
retail, a case study including a system design optimization (SDO) resulted in
an increase in throughput with well over 20% just by clustering different
categories and suggesting in which sequence the orders should be picked during
a given time frame. The options provided by implementing a distributed
high-performance computing network based on GPUs for subsystem optimization
have shown to be fruitful in developing a functioning SDO for warehouse
optimization. The toolchain can be used for designing new warehouses or
evaluating and tuning existing ones.
