Task Sequencing and Scheduling of Autonomous Mobile Robots in Flexible Manufacturing Systems Using Metaheuristic Optimization

¹ Department of Computer Science Engineering, SIMATS Engineering, Chennai, Tamil Nadu 602105, India
² Department of Mathematics, Sri Ramakrishna Institute of Technology, Coimbatore, Tamil Nadu 641010, India
³ Department of Automobile Engineering, Kongu Engineering College, Erode, Tamil Nadu 638060, India
⁴ Independent Researcher, IEEE Senior Member, San Jose, California, USA
⁵ Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai, Tamil Nadu 600123, India
⁶ Department of Mechanical Engineering, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu 600073, India

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Published online: 2 March 2026

Abstract

The research mentioned in this document deals with the issue of scheduling and path planning for autonomous mobile robots (AMRs) in a manufacturing setup where multiple robots and feeders are present. In this context, two metaheuristic methods are put forward: VNS-AMR, which is based on the Variable Neighborhood Search (VNS), and VND-AMR, which utilizes the Variable Neighborhood Descent (VND) technique. With the help of the proposed algorithms, an initial solution is generated first by a controlled greedy heuristic (HSI) and then local search procedures are applied with respect to the previously defined neighborhood structures. Compliance with constraints on release times, robot capacities, and replenishment requirements is ensured. The methods are tested on case studies which are based on data from literature and take into account various numbers of feeders, robot capacities, and subtasks. The results show that both metaheuristics are capable of finding feasible solutions in a short time, for both small and large scale problems, which is the main advantage over conventional scheduling methods, alongside reduced idle times for robots and good allocation of tasks.