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A Robust Optimization Approach for the Multi-Period Strip Cutting Problem with Multi-Cutter Slitting Machines

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Publicado en:Applied Sciences vol. 15, no. 19 (2025), p. 10387-10410
Autor principal: Hong, Sungwon
Otros Autores: Park, Jongyoon, Lee, Younsoo
Publicado:
MDPI AG
Materias:
Raw materials
Integer programming
Pulp & paper industry
Linear programming
Packing problem
Algorithms
Manufacturing
Cost control
Heuristic
Production costs
Inventory
Optimization
Flexibility
Acceso en línea:Citation/Abstract
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Resumen:This paper studies a multi-period strip cutting problem motivated by the paper industry. The focus is on multi-cutter slitting machines, which allow the simultaneous production of items with different lengths and provide higher cutting flexibility compared to conventional single-cutter machines. We propose a pattern-based mixed-integer programming formulation to evaluate the benefits of multi-cutter machines and compare it with heuristic strategies and a single-cutter benchmark. To address demand uncertainty, we extend the model using robust optimization with budgeted uncertainty sets and derive a tractable reformulation. Computational experiments with real-world data show that multi-cutter machines can substantially reduce raw material usage costs compared to the single-cutter setting. Under demand uncertainty, the budgeted robust model provides lower realized costs and smaller variability than both deterministic and box-type robust models.
ISSN:2076-3417
DOI:10.3390/app151910387
Fuente:Publicly Available Content Database