Classified AGV Material Flow and Layout Data Set for Multidisciplinary Investigation

Abstract

Automated Guided Vehicles (AGV) are increasingly used in industry to automate material flow tasks. To efficiently operate systems of AGVs, researchers have proposed many different planning and control methods, e.g., for scheduling, dispatching, and routing. The performance of these methods depends on the characteristics of the system, such as transport distances and station operation frequencies. Even though these characteristics strongly influence the algorithms, no classified collection of layout data was found based on a scientific literature review. In this paper, a data set of 72 material flow and layout compositions from the scientific literature (42) and German industry (30) is presented. Each composition in the data set consists of a transport matrix and a distance matrix. To classify the compositions, a holistic taxonomy was established based on distinguishing criteria for material flow and layout compositions known from the scientific literature. The compositions were classified according to the taxonomy. An analysis of the station operation frequency and transport distance distribution data reveals typical characteristics of the compositions as well as variations between the classified compositions. The aim of this data set is to allow benchmarking of planning and control methods, thus increasing the transparency and traceability of scientific work. Furthermore, the analysis of the layouts and their taxonomy allows to compare the methods of different disciplines. By providing standardized, machine readable formats, automatic testing and optimization will be possible.

Keywords

• AGV
• AMR
• automated guided vehicle
• autonomous mobile robot
• automated material flow
• distance matrix