A New Framework for Job Shop Integrated Scheduling and Vehicle Path Planning Problem

With the development of manufacturing industry, traditional fixed process processing methods cannot adapt to the changes in workshop operations and the demand for small batches and multiple orders. Therefore, it is necessary to introduce multiple robots to provide a more flexible production mode. Currently, some Job Shop Scheduling Problems with Transportation (JSP-T) only consider job scheduling and vehicle task allocation, and does not focus on the problem of collision free paths between vehicles. This article proposes a novel solution framework that integrates workshop scheduling, material handling robot task allocation, and conflict free path planning between robots. With the goal of minimizing the maximum completion time (Makespan) that includes handling, this paper first establishes an extended JSP-T problem model that integrates handling time and robot paths, and provides the corresponding workshop layout map. Secondly, in the scheduling layer, an improved Deep Q-Network (DQN) method is used for dynamic scheduling to generate a feasible and optimal machining scheduling scheme. Subsequently, considering the robot’s position information, the task sequence is assigned to the robot path execution layer. Finally, at the path execution layer, the Priority Based Search (PBS) algorithm is applied to solve conflict free paths for the handling robot. The optimized solution for obtaining the maximum completion time of all jobs under the condition of conflict free path handling. The experimental results show that compared with algorithms such as PPO, the scheduling algorithm proposed in this paper has improved performance by 9.7% in Makespan, and the PBS algorithm can obtain optimized paths for multiple handling robots under conflict free conditions. The framework can handle scheduling, task allocation, and conflict-free path planning in a unified optimization process, which can adapt well to job changes and then flexible manufacturing.