Cycle time calculation of shuttle-lift-crane automated storage and retrieval system

Abstract

This article deals with cycle time calculation of Automated Storage and Retrieval Systems (AS/RS). Cycle time has a high impact on the operating performance of an AS/RS, and its knowledge is essential, both at the operational and design level. The novelty of this work concerns the peculiar kind of system that is considered, as the focus is on the Shuttle-Lift-Crane AS/RS. This solution, common in the steel sector, is used to store bundles of long metal bars, which are automatically handled by cranes, lifts, and shuttles. The functioning of these machines, which can operate in parallel and independently, is stochastically modeled, and the probability distribution function of the cycle time is computed, both for single and dual command cycles. The model, assessed via discrete event simulation, ensures a high average accuracy of 96% and 98%, under single and dual command cycles, respectively.

Keywords:

• Analytical and numerical modeling
• automated warehouses
• cycle-time analysis
• steel sector
• shuttle-lift-crane-based automated storage and retrieval systems

Additional information

Notes on contributors

Francesco Zammori

Francesco Zammori graduated with distinction in 2004 in management engineering and completed his post-graduate studies in 2009, when he received a PhD in mechanical engineering at the University of Pisa (Italy). From 2012 he has been an assistant professor at the University of Parma (Italy), where he teaches management accounting, operations management, data bases and information systems. His research interests mainly concern operation and project management, modelling and simulation of manufacturing systems, machine learning and optimization techniques. His research activity has led to the publication of more than 40 works, most of which are indexed on Scopus Database.

Mattia Neroni

Mattia Neroni was born in November 1993 in Reggio Emilia (Italy). He studied at scientific high school and piano conservatory at the same time. He achieved a Bachelor’s degree and then a Master’s degree both in management engineering and both at University of Parma (Italy). During his studies he spent a year abroad working in London (UK). Currently, he has just completed his PhD at the Department of Engineering and Architecture at the University of Parma. His PhD is focused on the development and validation of algorithms for performance improvement in logistics, and his research interest mainly consists in data science, operational research and operations management. He is currently co-author of seven scientific publications, and four other publications under revision. Recently, he was a research guest for 3 months at a technical University in Stuttgart (Germany), namely the Hochschule fur Technik, and an invited speaker at a major conference (Moscow International Logistic Forum 2020).

Davide Mezzogori

Davide Mezzogori graduated with distinction in 2015 in management engineering and received a PhD in industrial engineering in 2019 at the University of Parma (Italy). From 2019 he has been a post-graduate research fellow at the Department of Architecture and Engineering of the University of Parma, where he is involved in the OPEN DIGILAB4U international project, for the development of a serious game for supply chain and operation management education. His main research interests concern the application of machine and Deep Learning algorithms to Industrial problems, as well as the development of metaheuristics for operation management. He has applied these techniques both academically and industrially; the obtained results have been published in prestigious International Journals and/or presented at International Conferences.