Multi-objective evolutionary algorithm-enabled multi-stage collaborative scheduling for automotive production

In the automotive industry, considering all the process workshops as a whole in terms of production scheduling becomes much more significant for the enhancement and optimization of overall productivity, efficiency, resource utilization, and coordination among factories. However, the complicated operational interdependencies between workshops make it hard to acquire a global objective. This paper aims to model the collaborative scheduling problem for a multi-stage automotive production process first, involving critical decision variables from four main workshops, stamping, welding, painting, and assembling. Then, the multi-objective evolutionary algorithm based on Pareto optimal subspace learning (MOEA/PSL) associated with an encoding and decoding strategy based on a random key is designed to solve the model for minimizing the total cost and weighted tardiness. Finally, a real-life case study is carried out to illustrate the effectiveness and superiority of the proposed method via experimental comparison using practical data and simulated instances for further analysis.