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Title: Solving a Real-world Large-scale Cutting Stock Problem: A Clustering-assignment-based Model
Authors: Hao, Xinye
Liu, Changchun 
Liu, Maoqi 
Zhang, Canrong
Zheng, Li
Keywords: Furniture production
Large-scale cutting stock
Material batch feed
Material substitutability
Iterative heuristic.
Issue Date: 6-Oct-2022
Publisher: Informa UK Limited
Citation: Hao, 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.
Abstract: This 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.
Source Title: IISE Transactions
ISSN: 2472-5854
DOI: 10.1080/24725854.2022.2133196
Appears in Collections:Staff Publications

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