A proposed method using GPU based SDO to optimize retail warehouses

TL;DR

This paper presents a GPU-based system design optimization method for retail warehouses that significantly improves throughput by optimizing order picking sequences using parallel computing techniques.

Contribution

It introduces a novel GPU-accelerated approach for warehouse optimization, demonstrating substantial throughput improvements in a real retail case study.

Findings

Over 20% increase in throughput achieved

Effective clustering and sequencing of orders

Successful implementation of distributed GPU computing

Abstract

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.