Student's work helps production factories autonomize

The COVID-19 pandemic presented a challenge to many companies, especially those who hadn’t scaled up their digital production technologies. These technologies enable production factories to make use of flexible systems that can sense and adapt to fluctuating demands. By doing so, they can autonomously make decisions to continue to meet the production needs around them, even as these demands are updated continuously. This is the basis of the fourth industrial revolution, or Industry 4.0.  

Some companies were already implementing these ideas back in 2019 when the pandemic first hit. Those companies fared much better when something caused a change in supply, like the COVID-19 pandemic. With less manpower and less supplies, these companies were still able to continue production. 

This is thanks in part to autonomous mobile robots that replace traditional assembly lines by carrying inventory and finished goods from place to place in the factory. These robots already exist, but deciding the makeup or composition of their robot fleet can be challenging for companies. To help these companies move toward Industry 4.0, Graduate Research Associate Mithun Goutham is creating software that informs the company of the best makeup for their robot fleet. The companies can decide how generous they want to be with their robot purchase and Mithun’s software will guide them. 

This software program works on a high-performance computer, using custom tools to help companies determine the best robot fleet composition for them. The best composition would meet the part delivery requirements (quantity, locations and timing) while also minimizing the energy usage and investment cost. 

Not only does Mithun explore different types of robots that form the fleet, but he has also formulated a path-planning optimization problem for determining the energy-efficient navigation of each robot on the production floor while accounting for turn costs and wait times at intersections. The overall optimization problem becomes large and complicated very quickly and therefore it’s important to find the best methods for improving the convergence time of the algorithm without compromising its ability to find the best option. 

Mithun says, “The work I’m doing deals with systems that use their flexibility to make decisions that improve efficiency, meet the demands of society, and respond to disturbances in real-time without needing human intervention. Such systems are highly complex and interconnected, and dealing with all the associated parameters, variables and constraints makes the challenge exciting.” 

By using Mithun’s software, companies will be able to make better investment decisions and move toward Industry 4.0 in the long run. 

By CAR Writing Intern, Cassie Forsha