An Agent-Based Simulation Model for Deadlock Prevention in an Aisle-to-Aisle SBS/RS
| dc.contributor.author | Ecem Eroglu | |
| dc.contributor.author | Banu Yetkin Ekren | |
| dc.contributor.author | Eroglu, Ecem | |
| dc.contributor.author | Ekren, Banu Yetkin | |
| dc.contributor.editor | F Calisir | |
| dc.coverage.spatial | ELECTR NETWORK | |
| dc.date.accessioned | 2025-10-06T16:20:08Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Flexibility and high throughput rate in storage and retrieval systems are essential criteria in today's competitive marketing. Recent developments in information technology enable the intelligent design of systems. This study aims to propose a tier-captive aisle-to-aisle shuttle-based storage and retrieval system (SBS/RS) where shuttles can make autonomous decisions to prevent deadlocks and collisions as well as the efficient process of transactions. Deadlock prevention algorithms are one of the primary concerns in today's autonomous vehicle environment. In the considered tier-captive aisle-to-aisle SBS/RS multiple shuttles can travel between aisles in a dedicated tier. The advantage of this design is that there may be the fewer total number of shuttles running in the system compared to a traditional tier-captive SBS/RS. Due to the complexity of the proposed system and autonomous shuttle-based decision-making target we utilized the advantage of an agent-based modeling approach by simulating the system. Agent definitions roles and behaviors are specified to ensure that no collision and blockage take place in the system. Thanks to the intelligent abilities of agents so that the system can run effectively by using real-time information. | |
| dc.description.sponsorship | Scientific and Technological Research Council of Turkey; Slovenian Research Agency: ARRS [118M180] | |
| dc.description.sponsorship | This work was supported by The Scientific and Technological Research Council of Turkey and Slovenian Research Agency: ARRS [grant number: 118M180]. | |
| dc.identifier.doi | 10.1007/978-3-030-76724-2_11 | |
| dc.identifier.isbn | 978-3-030-76724-2, 978-3-030-76723-5 | |
| dc.identifier.isbn | 9783030767242 | |
| dc.identifier.isbn | 9783030767235 | |
| dc.identifier.issn | 2198-0772 | |
| dc.identifier.uri | http://dx.doi.org/10.1007/978-3-030-76724-2_11 | |
| dc.identifier.uri | https://gcris.yasar.edu.tr/handle/123456789/6214 | |
| dc.identifier.uri | https://doi.org/10.1007/978-3-030-76724-2_11 | |
| dc.language.iso | English | |
| dc.publisher | SPRINGER INTERNATIONAL PUBLISHING AG | |
| dc.relation.ispartof | Global Joint Conference on Industrial Engineering and Its Application Areas (GJCIE) | |
| dc.relation.ispartofseries | Lecture Notes in Management and Industrial Engineering | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.source | INDUSTRIAL ENGINEERING IN THE INTERNET-OF-THINGS WORLD GJCIE 2020 | |
| dc.subject | Agent-based simulation, Deadlock prevention, SBS, RS, Tier-captive, Aisle-to-aisle SBS, RS, Smart decision | |
| dc.subject | SHUTTLE-BASED STORAGE, AUTONOMOUS VEHICLE STORAGE, SYSTEMS, PERFORMANCE, DESIGN | |
| dc.subject | Deadlock Prevention | |
| dc.subject | RS | |
| dc.subject | SBS | |
| dc.subject | Tier-captive | |
| dc.subject | Agent-Based Simulation | |
| dc.subject | Aisle-to-Aisle SBS | |
| dc.subject | Smart Decision | |
| dc.title | An Agent-Based Simulation Model for Deadlock Prevention in an Aisle-to-Aisle SBS/RS | |
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| gdc.description.department | ||
| gdc.description.departmenttemp | [Eroglu, Ecem; Ekren, Banu Yetkin] Yasar Univ, Fac Engn, Dept Ind Engn, Izmir, Turkey | |
| gdc.description.endpage | 146 | |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | |
| gdc.description.startpage | 135 | |
| gdc.description.woscitationindex | Conference Proceedings Citation Index - Science - Conference Proceedings Citation Index - Social Science & Humanities | |
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| gdc.virtual.author | Yetkin Ekren, Banu | |
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| project.funder.name | Scientific and Technological Research Council of Turkey, Slovenian Research Agency: ARRS [118M180] | |
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