Multi-load AGVs scheduling by application of modified memetic particle swarm optimization algorithm

Abstract

The automated guided vehicles (AGVs) are extensively applied for material handling operations in the flexible manufacturing system (FMS) facilities. The scheduling decisions for the multi-load AGVs serving in the FMS with minimum travel time, waiting time and time to serve jobs are highly significant from the sustainable profits point of view. The present study proposes a combination of particle swarm optimization (PSO) for global search and memetic algorithm (MA) for local search termed as the modified memetic particle swarm optimization algorithm (MMPSO) for scheduling of multi-load AGVs in FMS. The newly proposed algorithm is applied for the generation of initial feasible solutions for scheduling of multi-load AGVs with minimum travel and minimum waiting time in the FMS. From the computational experiments, it is observed that the proposed MMPSO algorithm performs an effective and efficient exploration and exploitation process and further yields promising results for the multi-load AGVs scheduling problem in the FMS facility.