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Research in warehouse optimization has gotten increased attention in the lastfew years due to e-commerce. The warehouse contains a waste range of differentproducts. Due to the nature of the individual order, it is challenging to planthe picking list to optimize the material flow in the process. There are alsochallenges in minimizing costs and increasing production capacity, and thiscomplexity can be defined as a multidisciplinary optimization problem with anIDF nature. In recent years the use of parallel computing using GPGPUs hasbecome increasingly popular due to the introduction of CUDA C and accompanyingapplications in, e.g., Python. In the case study at the company in the field ofretail, a case study including a system design optimization (SDO) resulted inan increase in throughput with well over 20% just by clustering differentcategories and suggesting in which sequence the orders should be picked duringa given time frame. The options provided by implementing a distributedhigh-performance computing network based on GPUs for subsystem optimizationhave shown to be fruitful in developing a functioning SDO for warehouseoptimization. The toolchain can be used for designing new warehouses orevaluating and tuning existing ones.

A proposed method using GPU based SDO to optimize retail warehouses