Developing an effective solution method for determining the minimum required number of robots for RPA

Robotic Process Automation (RPA) is a technology increasingly adopted by many companies to automate business processes. Fundamentally, RPA automates routine, repetitive, and rule-based tasks traditionally performed by humans on computers. By implementing this technology, employees can shift their focus to more strategic tasks while RPA software robots (often referred to as "agents" or "bots") perform manual work rapidly, seamlessly, and without error. For robots to operate, they require an environment, which is chosen based on various criteria. These environments may be local (on-premises), cloud-based, or hybrid. Although RPA robots can operate in different environments, costs vary depending on the license type, the number of robots, and additional services utilized. In today's competitive and cost-sensitive market, companies are under pressure to maximize output with minimal resources, making the optimization of RPA robot numbers crucial. This thesis aims to achieve maximum efficiency with minimal robot licensing by considering factors such as the number of processes, processing time, priority level, and repetition frequency, among others. In the introductory chapter, the purpose of the thesis is discussed, emphasizing the need to optimize the number and deployment of RPA robots to ensure task efficiency. Following this, the study examines combinatorial optimization approaches to identify the most effective solution among possible alternatives for executing business processes and optimizing robot numbers. In the context of job scheduling, the goal is to dynamically assign new tasks and reconfigure existing workloads. It has been sought to enable the assignment among robot tasks with integer constraints through the developed mixed-integer programming approach. In conclusion, RPA technology, when implemented correctly, is expected to reduce operational costs, minimize errors, and improve work efficiency. This thesis aims to enable cost reduction and enhanced productivity by ensuring optimal scheduling of robots with minimal resource usage.