{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T09:05:58Z","timestamp":1783933558344,"version":"3.55.0"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T00:00:00Z","timestamp":1781827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002647","name":"Sungkyunkwan University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002647","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Education"},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,7,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>A matrix manufacturing system (MMS) is a highly flexible production system designed to adapt to uncertainties in product demand and shop floor operations, with a focus on maximizing production efficiency and adaptability. Recently, the emergence of the vehicle-as-a-conveyor (VaaC) concept presents an opportunity to fully leverage the flexibility and parallel processing capabilities of MMS. VaaC is a concept in which a vehicle autonomously navigates among workstations and undergoes various processes during production. To ensure efficient operation of an MMS integrated with VaaC (VaaC-MMS), it is crucial to develop an optimization methodology. This paper proposes a methodology for explainable optimization via deep reinforcement learning to enhance dynamic scheduling and resource utilization of the VaaC-MMS. The learning processes and learned policies are interpreted using frequency-map analysis, action-occlusion sensitivity analysis, and SHapley Additive exPlanations (SHAP) based feature attribution. The proposed methodology applies deep Q-network, proximal policy optimization, and asynchronous advantage actor-critic algorithms. To validate the proposed methodology, a case study was conducted focusing on the trim part assembly process in the automotive industry. This paper contributes to the realization of VaaC-MMS and provides a valuable reference for envisioning the factory of the future in the automotive industry.<\/jats:p>","DOI":"10.1093\/jcde\/qwag058","type":"journal-article","created":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T11:44:41Z","timestamp":1781783081000},"page":"35-54","source":"Crossref","is-referenced-by-count":0,"title":["Explainable scheduling in vehicle-as-a-conveyor matrix manufacturing systems via deep reinforcement learning"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4243-4469","authenticated-orcid":false,"given":"Changha","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Industrial Engineering, Sungkyunkwan University , 2066 Seobu-ro, 16419 Suwon-si ,","place":["Republic of 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