Deep Reinforcement Learning Algorithm: Dynamic Job Shop Scheduling Problem with Order Rejection and Inventory

This paper addresses a dynamic job shop scheduling problem (DJSSP) based on a real stone paper factory, incorporating parallel machines, order rejection, tardiness costs, machine deterioration, and inventory costs. Orders arrive at variable times, with some potentially rejected due to capacity constraints. Also, raw materials must be purchased at appropriate times and quantities. The model optimizes three objectives: profit maximization, inventory cost variance minimization, and machine workload variance minimization. A heuristic solution method based on a double-network deep reinforcement learning (DRL) algorithm, complemented by an artificial neural network for inventory management, was developed. Evaluation against public benchmarks and other DRL algorithms highlights the impact of inventory cost variance and workload balancing. Real-case evaluation demonstrated that multi-objective optimization reduced inventory cost variance by 9.8%, machine workload variance by 21.87%, and raw material holdings by 21.7%, while slightly decreasing profit (3.2%) and slightly increasing purchase frequency (4.7%). Sensitivity analysis highlighted the individual effects of each objective on profit, workload, and makespan, confirming the practical benefits of the proposed approach.