{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T16:59:40Z","timestamp":1742403580597,"version":"3.37.3"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T00:00:00Z","timestamp":1705622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Horizon Europe program","award":["HORIZON-CL4-2022-HUMAN-01-14-101092612 SUN","HORIZON-CL4-2021-TWIN-TRANSITION-01-07-101057294 AIDEAS"]},{"name":"Generalitat Valenciana","award":["PROMETEO\/2021\/065"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"In automated storage and retrieval systems (AS\/RSs), the utilization of intelligent algorithms can reduce the makespan required to complete a series of input\/output operations. This paper introduces a simulation optimization algorithm designed to minimize the makespan in a realistic AS\/RS commonly found in the steel sector. This system includes weight and quality constraints for the selected items. Our hybrid approach combines discrete event simulation with biased-randomized heuristics. This combination enables us to efficiently address the complex time dependencies inherent in such dynamic scenarios. Simultaneously, it allows for intelligent decision making, resulting in feasible and high-quality solutions within seconds. A series of computational experiments illustrates the potential of our approach, which surpasses an alternative method based on traditional simulated annealing.<\/jats:p>","DOI":"10.3390\/a17010046","type":"journal-article","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T16:04:57Z","timestamp":1705680297000},"page":"46","source":"Crossref","is-referenced-by-count":1,"title":["A Biased-Randomized Discrete Event Algorithm to Improve the Productivity of Automated Storage and Retrieval Systems in the Steel Industry"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4507-4789","authenticated-orcid":false,"given":"Mattia","family":"Neroni","sequence":"first","affiliation":[{"name":"\u201cEnzo Ferrari\u201d Engineering Department, University of Modena and Reggio Emilia, 41125 Modena, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7871-966X","authenticated-orcid":false,"given":"Massimo","family":"Bertolini","sequence":"additional","affiliation":[{"name":"\u201cEnzo Ferrari\u201d Engineering Department, University of Modena and Reggio Emilia, 41125 Modena, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1392-1776","authenticated-orcid":false,"given":"Angel A.","family":"Juan","sequence":"additional","affiliation":[{"name":"Research Center on Production Management and Engineering, Universitat Polit\u00e8cnica de Val\u00e8ncia, 03801 Alcoy, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.ejor.2004.08.007","article-title":"Combining path relinking and genetic algorithms for the multiple-level warehouse layout problem","volume":"169","author":"Zhang","year":"2006","journal-title":"Eur. 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