Optimization of Automated Guided Vehicles (AGV) Fleet Size With Incorporation of Battery Management

<p>An important aspect in manufacturing automation is material handling. To facilitate material handling, automated transport systems are implemented and employed. The AGV (automated guided vehicle) has become widely used for internal and external transport of materials. A critical aspect in the use of AGVs is determining the number of vehicles required for the system to meet the material handling requirements.</p> <p>Several models and simulations have been applied to determine the fleet size. Most of these models and simulations do not incorporate the battery usage of the vehicles and the effect it can have on the throughput and the number of AGVs required for the system. The goal of this research is to develop a simulation model to determine the optimized number of AGVs that is capable of increasing throughput while meeting the material handling requirements of the system. This model incorporates the battery management aspect and issues, which are usually omitted in AGV research. This includes the charging options and strategies, the number and location of charging stations, maintenance, and extended charging. The analysis entails studying various scenarios by applying different charging options and strategies and changing different parameters to achieve improved throughput and an optimized AGV fleet size.</p> <p>The results clearly show that battery management can have a significant effect on the average throughput and the AGV usage. It is important that the battery management of the AGVs is addressed adequately to run an AGV system efficiently.</p>

Name thesis:degree_name

Doctor of Philosophy (PhD)

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Engineering Management & Systems Engineering

Year dc:date.available

2019

Author dc:creator

• Hamdy, Ahmed

Contributors dc:contributor

• Ghaith Rabadi
• Resit Unal
• Ali Ardalan

• Automated guided vehicles
• Battery management
• Fleet size
• Material handling
• Optimization
• Simulation
• Business Administration, Management, and Operations
• Industrial Engineering
• Operational Research

Identifier

9781392884119

OAI identifier oai:identifier

oai:digitalcommons.odu.edu:emse_etds-1169