A fast layered path planning algorithm for job shop scheduling problem

Abstract

Job shop scheduling problem (JSP) is a classical system resource optimisation problem and also an NP hard problem. The search algorithm based on Akers obstacle graph model is an effective algorithm to solve JSP, which first removes part of jobs from the original schedule, then constructs obstacle graph and finds the shortest path from the graph, and finally reinserts the jobs according to the shortest path decoding method to get the new schedule. Although the new scheduling can achieve good results, it is time-consuming to find the shortest path. Therefore, it is necessary to further study how to quickly plan the shortest path. This study presents a fast layered path search algorithm for solving the obstacle graph of job shop scheduling. The algorithm designs a node expansion method and a delay distance formula. The obstacles generated by different machines in the obstacle graph are layered. When the nodes expand, the extended nodes are compared with the parent layer nodes to quickly avoid closely arranged obstacles, and multiple child nodes are generated at one time through node expansion to improve the node expansion ability. At the same time, node expansion method and delay distance formula can be well integrated with A* algorithm. Finally, the test verifies that the algorithm can spend less time to find the shortest path.