Multi-objective optimisation for AGV and machine integrated scheduling problem considering battery consumption rate

With the advancement of eco-friendly manufacturing and smart production, researchers have increasingly focused on the automatic guide vehicle (AGV) and machine integrated scheduling problem, considering energy consumption. However, the current research overlooks the varying battery consumption rates of AGV under different operational conditions. This paper addresses this gap by dissecting AGV battery usage into load and no-load scenarios and develops a multi-objective optimisation model for the integrated scheduling problem. An improved Non-Dominated Sorting Genetic Algorithm (I-NSGA-II) is presented to solve the model. In the algorithm, a novel two-segment real number encoding approach for machine/AGV assignment and process operations is proposed. The Taguchi analysis was used to discuss the key parameters of the algorithm, and experiments were conducted to perform sensitivity analysis on the model. Simulation results demonstrate that the proposed algorithm outperforms three other widely recognised algorithms in the benchmark.