Please use this identifier to cite or link to this item: https://doi.org/10.1080/24725854.2022.2133196
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dc.titleSolving a Real-world Large-scale Cutting Stock Problem: A Clustering-assignment-based Model
dc.contributor.authorHao, Xinye
dc.contributor.authorLiu, Changchun
dc.contributor.authorLiu, Maoqi
dc.contributor.authorZhang, Canrong
dc.contributor.authorZheng, Li
dc.date.accessioned2022-10-10T02:18:17Z
dc.date.available2022-10-10T02:18:17Z
dc.date.issued2022-10-06
dc.identifier.citationHao, Xinye, Liu, Changchun, Liu, Maoqi, Zhang, Canrong, Zheng, Li (2022-10-06). Solving a Real-world Large-scale Cutting Stock Problem: A Clustering-assignment-based Model. IISE Transactions : 1-29. ScholarBank@NUS Repository. https://doi.org/10.1080/24725854.2022.2133196
dc.identifier.issn2472-5854
dc.identifier.issn2472-5862
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/231799
dc.description.abstractThis study stems from a furniture factory producing products by cutting and splicing operations. We formulate the problem into an assignment-based model, which reflects the problem accurately but is intractable due to a large number of binary variables and severe symmetry in the solution space. To overcome these drawbacks, we reformulate the problem into a clustering-assignment-based model (and its variation), which provides lower (upper) bounds of the assignment-based model. According to the classification of the board types, we categorize the instances into three cases: Narrow Board, Wide Board, and Mixed Board. We prove that the clustering-assignment-based model can obtain the optimal schedule for the original problem in the Narrow Board case. Based on the lower and upper bounds, we develop an iterative heuristic to solve instances in the other two cases. We use industrial data to evaluate the performance of the iterative heuristic. On average, our algorithm can generate high-quality solutions within one minute. Compared with the greedy rounding heuristic, our algorithm has obvious advantages in terms of computational efficiency and stability. From the perspective of the total costs and practical metrics, our method reduces costs by 20.90% and cutting waste by 4.97%, compared with factory’s method.
dc.publisherInforma UK Limited
dc.sourceElements
dc.subjectFurniture production
dc.subjectLarge-scale cutting stock
dc.subjectMaterial batch feed
dc.subjectMaterial substitutability
dc.subjectIterative heuristic.
dc.typeArticle
dc.date.updated2022-10-10T01:04:46Z
dc.contributor.departmentINST OF OPERATIONS RESEARCH & ANALYTICS
dc.description.doi10.1080/24725854.2022.2133196
dc.description.sourcetitleIISE Transactions
dc.description.page1-29
dc.published.statePublished
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