Multi-objective scheduling in dynamic of household paper workshop considering energy consumption in production process

Abstract

Aim: The uncertainty and complexity of the production process of household paper are growing sharply in modern factories. Due to the influence of rising energy costs and environmental policies, the demand for reducing production costs and energy consumption is also increasing. Therefore, it is studied that the dynamic shop scheduling problem of household paper production considering simultaneously the cost with energy consumption. Methods: A mathematical model of the multi-objective and multi-constraint household paper scheduling problem is established first. The multi-objective scheduling process is transformed into a multi-agent Markov game process by assigning each objective to each agent. Upon which, a multi-agent game model is constructed for the household paper scheduling problem based on deep reinforcement learning; it is a proposed D3QN algorithm and Nash equilibrium strategy, and the state characteristics and action selection space are proposed according to the characteristics of the household paper production. The model performance has been verified by the actual production data. Results: Results show that the proposed method not only achieves better performance than traditional scheduling methods but also persists in its advantages even when the configuration of the manufacturing system changes. Conclusion: Multi-agent deep reinforcement learning in the Markov game has a good prospect in solving multi-objective dynamic scheduling problems for household paper production.