Optimization of Standard Wood Cutting : A Case Study of a Wood Furniture Factory

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Authors: Bruno Gonçalves Silva, Kamonmanee Chuaysrinual, Tanapon Klangklaw, Kalyakorn Roekkamyee
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Abstract

Efficient use of raw timber is an important operational issue in furniture manufacturing because component-by-componentcutting can create short residual pieces that are difficult to reuse. This study presents a one-dimensional cuttingstock formulation based on Integer Linear Programming (ILP) for a proof-of-concept case study of a wood furniture factory.The raw material is a standard timber block of 11 × 11 × 200 cm, while the study focuses on longitudinal cutting of fourcomponent types: sofa legs, table legs, chair legs, and chair backrests. Because the chair-leg and chair-backrest cross-sectionspermit three pieces to occupy one 45-cm longitudinal layer, the pattern-generation procedure represents these componentsin groups of three. Constrained enumeration was then used to construct 75 candidate cutting patterns, after which an ILPmodel selected the number of times each pattern should be used to satisfy production demand while minimizing the numberof standard timber units. For the January 2023 production instance, the optimized plan uses 44 standard timber units insteadof 47 under the conventional plan, a reduction of 6.38%. Total geometric residual length decreases from 1,100 cm to 500cm, a reduction of 54.55%. The difference between the two percentages indicates that the principal benefit of optimizationis the improved utilization of residual lengths within each stock unit rather than a proportional reduction in the number ofstock units. The study is intentionally presented as a proof-of-concept; the model does not explicitly represent saw kerf,grain orientation, defects, or timber-quality variation, and validation with longer-term production data is required beforegeneralizing the findings.

Fields of Study & Subjects

Education and Public Policy Evasion and Academic Success Factors Health Education and Validation

Publication details

Journal / source
Statistics, Optimization & Information Computing (University of Cambridge)
Publication date
3 September 2026
Language
EN