Dynamic AGV Task Allocation in Intelligent Warehouses
- URL: http://arxiv.org/abs/2312.16026v1
- Date: Tue, 26 Dec 2023 12:28:25 GMT
- Title: Dynamic AGV Task Allocation in Intelligent Warehouses
- Authors: Arash Dehghan and Mucahit Cevik and Merve Bodur
- Abstract summary: The booming AGV industry is witnessing widespread adoption due to its efficiency, reliability, and cost-effectiveness.
This paper focuses on enhancing the picker-to-parts system, prevalent in small to medium-sized warehouses, through the strategic use of AGVs.
We propose a novel approach Neural Dynamic Programming approach for coordinating a mixed team of human AGV workers.
- Score: 1.519321208145928
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper explores the integration of Automated Guided Vehicles (AGVs) in
warehouse order picking, a crucial and cost-intensive aspect of warehouse
operations. The booming AGV industry, accelerated by the COVID-19 pandemic, is
witnessing widespread adoption due to its efficiency, reliability, and
cost-effectiveness in automating warehouse tasks. This paper focuses on
enhancing the picker-to-parts system, prevalent in small to medium-sized
warehouses, through the strategic use of AGVs. We discuss the benefits and
applications of AGVs in various warehouse tasks, highlighting their
transformative potential in improving operational efficiency. We examine the
deployment of AGVs by leading companies in the industry, showcasing their
varied functionalities in warehouse management. Addressing the gap in research
on optimizing operational performance in hybrid environments where humans and
AGVs coexist, our study delves into a dynamic picker-to-parts warehouse
scenario. We propose a novel approach Neural Approximate Dynamic Programming
approach for coordinating a mixed team of human and AGV workers, aiming to
maximize order throughput and operational efficiency. This involves innovative
solutions for non-myopic decision making, order batching, and battery
management. We also discuss the integration of advanced robotics technology in
automating the complete order-picking process. Through a comprehensive
numerical study, our work offers valuable insights for managing a heterogeneous
workforce in a hybrid warehouse setting, contributing significantly to the
field of warehouse automation and logistics.
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