Affiliations: [a] Transportation College, Jilin University, Changchun, Jilin, China
| [b]
Pan Asia Technical Automotive Center, Shanghai, China
| [c] Newcastle Business School, Northumbria University, Newcastle upon Tyne, United Kingdom
Correspondence:
[*]
Corresponding author. Gendao Li, Newcastle Business School, Northumbria University, Newcastle upon Tyne, NE1 8ST, United Kingdom. E-mail: [email protected].
Abstract: The efficient operation of Intelligent Warehousing System does not rely on individual resource scheduling in stages but multi-type resources collaborative scheduling. In this paper, a collaborative scheduling model for stackers, automated guided vehicles and picking workstations in outbound process is abstracted into a hybrid flow-shop scheduling problem within an automated warehouse scene. Considering the impacts of uncertain factors related to scheduling, the objective function of this model is minimizing the makespan based on the triangular fuzzy processing time. A genetic algorithm is designed to obtain feasible solution of this model with the form of vector coding and the approach of ranking fuzzy numbers. Example analysis shows that the validity of the model and algorithm is verified. Within different resource allocation schemes, their evaluating indexes are significantly different, which are the likely completion time of system operation, the capability coordination degree and the initial investment. Furthermore, the increase of picking workstations is contributed much more to reducing the likely completion time and to improving the capability coordination degree than that of automated guided vehicles.
