Solving a Mobile Robot Scheduling Problem using Metaheuristic Approaches

Flexible Manufacturing Systems (FMS) comprised a number of machine tools alongside other material handling devices that forms a synergistic combination of productivity-efficiency transport and flexibility. In FMS, mobile robots are commonly deployed in the material handling system for the purpose of increasing the manufacturing process’ productivity and efficiency. Due to the necessity of navigating from one location to another, it is crucial to properly designed the AMR’s schedule in accordance to the real-time situation prior to planning its path. A reliable, efficient, and optimally scheduling is the most important in such manufacturing system. This paper presents the metaheuristic approaches i.e., Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to deal with the NP-hard problem of scheduling mobile robot in Job-Shop FMS environment. These algorithms are developed to find the feasible solution for the integrated problem with the goal to obtain a minimum completion time of all tasks (or makespan). The results indicated that the performance of GA provided the better solution quality in terms of minimal makespan, while PSO exhibited better convergence times.