A dynamic operative framework for allocation in automated storage and retrieval systems. (1st March 2023)

• Record Type: Journal Article
• Authors: Bertolini, Massimo; Mezzogori, Davide; Neroni, Mattia; Zammori, Francesco
• Is Part Of: Expert systems with applications. Volume 213:Part A(2023)
• Publication Date: 2023-03-01
• Subjects: Automated Storage and Retrieval Systems (AS/RS) -- Interleaving -- Location assignment -- Performance improvement -- Simulation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118940
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: The focus is on Shuttle-Lift-Crane Automated Storage and Retrieval Systems. Heavy loads are moved and so reducing the total travelled distance is essential. A dynamic operating framework (DOF) is presented. It allows the system to autonomously react to changes in the operating conditions. A set of allocation and interleaving policies is proposed to boost performance. Abstract: Two very important aspects in automated storage and retrieval systems (AS/RS) are productivity (or performance) and maintenance costs. In the literature, as in industry, it is very difficult to find a solution that guarantees satisfactory results in terms of both. Moreover, all the solutions that the scientific community has proposed are static, i.e., the system's behavior does not change as boundary conditions change. In this paper, we propose an innovative solution known as a dynamic operative framework (DOF), which allows the system to react to changes in operating conditions and guarantees good results in terms of productivity, with a consistent reduction in the distance that handling machines travel and a consequent reduction in maintenance and energy consumption costs. To test the proposed solution, we focused on the shuttle-lift crane AS/RS, a common configuration used to store bundles of long metal bars, and we compared the DOF with four benchmark policies that exploit a class-based reorganization of the stock performed during non-working shifts. Many simulation runs' results indicated that the DOF ensures a throughput time aligned to that of the benchmarks, but without needing to reorganize the stock during nonworking shifts. In this way, it leads to consistent savings in terms of energy consumption and maintenance costs.