A Heuristic Algorithm Using Variable Depth Neighborhood for the Input-Output Scheduling Problem in Automated Warehouses
Kazumiti NUMATA, Taishi HAMAKAWA
In this paper, we propose a new heuristic algorithm for the Input-Output Scheduling Problem (IOSP) at large scale automated warehouses. The proposed method separates IOSP into two subproblems, the one is to arrange all given tasks into a good partition, and the other is to generate a good tour for each group. It searches the best grouping of tasks by variable depth neighborhood local search, generating the optimum tour for each test group by enumeration. The small group size in IOSP makes it possible to enumerate tours. The results of numerical experiments on the same instances that some existing method solved show that the proposed method well competes with existing one under the criterion that considers both schedule length and solution time.