{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T15:34:41Z","timestamp":1765812881908,"version":"3.48.0"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012456","name":"National Social Science Foundation of China","doi-asserted-by":"publisher","award":["2024-SKJJ-C-027"],"award-info":[{"award-number":["2024-SKJJ-C-027"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Naval University of Engineering independent research projects","award":["202350A010"],"award-info":[{"award-number":["202350A010"]}]},{"name":"Naval University of Engineering independent research projects","award":["2025500330"],"award-info":[{"award-number":["2025500330"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>In smart manufacturing environments, production scheduling is highly susceptible to multi-source disruptions. However, traditional methods often struggle to accurately characterize the complex interdependencies between control points and disruptions, along with their systemic propagation effects, thereby constraining the proactivity and precision of scheduling optimization. This paper proposes a novel data-driven approach that integrates Weighted Association Rule Mining (WARM) with a two-layer directed weighted complex network to achieve precise identification of critical control points in production scheduling. First, a production loss function integrating delay duration and resource idle cost is constructed, and the max-pooling method is applied to map control point weights, thereby quantifying their intrinsic importance. Subsequently, under the constraint that association rule antecedents are restricted to control points, an improved Apriori algorithm is employed to mine directed \u201cControl Point-Disruption\u201d association rules. These rules are then used to construct a two-layer directed weighted complex network. Furthermore, by combining weighted PageRank and edge betweenness centrality analyses, critical control points and high-risk propagation paths are identified from the dual dimensions of node influence and path propagation capability. A case study conducted in a crankshaft production workshop demonstrates that the proposed method effectively identifies low-frequency yet high-impact hidden nodes often overlooked by traditional rules. The resulting scheduling optimization scheme reduces the occurrence rate of high-impact disruptions by 53% and significantly improves key performance indicators such as on-time delivery rate and equipment utilization. This research provides new theoretical support and a technical pathway for manufacturing enterprises to suppress system disturbances through flexible interventions targeting high-betweenness paths.<\/jats:p>","DOI":"10.3390\/systems13121122","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T15:15:08Z","timestamp":1765811708000},"page":"1122","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Method for Identifying Critical Control Points in Production Scheduling for Crankshaft Production Workshop by Integrating Weighted-ARM with Complex Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Luwen","family":"Yuan","sequence":"first","affiliation":[{"name":"Department of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan 430033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ge","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan 430033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Management Engineering and Equipment Economics, Naval University of Engineering, Wuhan 430033, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"ref_1","first-page":"100599","article-title":"Simulation optimization applied to production scheduling in the era of industry 4.0: A review and future roadmap","volume":"39","author":"Ghasemi","year":"2024","journal-title":"J. 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