A Genetic Algorithm Approach to Simultaneous Scheduling of Processing Machines and Multiple-load Automated Guided Vehicles

In this paper, a new genetic algorithm (GA) approach to simultaneous scheduling of processing machines and multiple-load automated guided vehicles (AGVs) is proposed. The proposed method is based on a GA for job shop scheduling problems. In the proposed method, a chromosome is composed of operation sequence genes and AGV assignment genes. Operation sequence genes contain processing operations, pickup operations and delivery operations. A decoding scheme considering processing and transportation is introduced. We applied the proposed method to a set of 82 problems which are designed to evaluate methods for simultaneous scheduling of machines and multiple-load AGVs. The result is compared with that of random search and schedule by using a dispatching rule. The proposed method outperforms random search in 54 out of 82 problems while the proposed method obtains better schedule than that of the dispatching rule in 31 out of 82 problems. These simulation results indicate the effectiveness of the proposed method.