{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T19:24:42Z","timestamp":1769282682796,"version":"3.49.0"},"reference-count":52,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T00:00:00Z","timestamp":1691712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Manufacturing companies face a significant challenge when developing their master production schedule, navigating unforeseen disruptions during daily operations. Moreover, fluctuations in demand pose a substantial risk to scheduling and are the main cause of instability and uncertainty in the system. To address these challenges, employing flexible systems to mitigate uncertainty without incurring additional costs and generate sustainable responses in industrial applications is crucial. This paper proposes a product-driven system to complement the master production plan generated by a mathematical model. This system incorporates intelligent agents that make production decisions with a function capable of reducing uncertainty without significantly increasing production costs. The agents modify or determine the forecasted production quantities for each cycle or period. In the case study conducted, a master production plan was established for 12 products over a one-year time horizon. The proposed solution achieved an 11.42% reduction in uncertainty, albeit with a 2.39% cost increase.<\/jats:p>","DOI":"10.3390\/a16080386","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T10:33:23Z","timestamp":1691750003000},"page":"386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Reducing Nervousness in Master Production Planning: A Systematic Approach Incorporating Product-Driven Strategies"],"prefix":"10.3390","volume":"16","author":[{"given":"Patricio","family":"S\u00e1ez","sequence":"first","affiliation":[{"name":"Department of Statistics, Universidad de Concepci\u00f3n, Concepci\u00f3n 4030000, Chile"}]},{"given":"Carlos","family":"Herrera","sequence":"additional","affiliation":[{"name":"Department of Industrial Engineering, Universidad de Concepci\u00f3n, Concepci\u00f3n 4030000, Chile"}]},{"given":"Victor","family":"Parada","sequence":"additional","affiliation":[{"name":"Department of Informatics Engineering, Universidad de Santiago de Chile, Santiago 8320000, Chile"},{"name":"Instituto Sistemas Complejos de Ingenier\u00eda (ISCI), Santiago 8320000, Chile"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.procir.2017.03.176","article-title":"Implementation of an Adapted Holonic Production Architecture","volume":"63","year":"2017","journal-title":"Procedia CIRP"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1016\/j.ifacol.2018.08.420","article-title":"Evolution of holonic control architectures towards Industry 4.0: A short overview","volume":"51","author":"Cardin","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.conengprac.2019.03.009","article-title":"The model-based product agent: A control oriented architecture for intelligent products in multi-agent manufacturing systems","volume":"86","author":"Kovalenko","year":"2019","journal-title":"Control Eng. 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