Robust Optimization for the Two-Dimensional Strip-Packing Problem with Variable-Sized Bins

The two-dimensional strip-packing problem (2D-SPP) emerges as a notable variant of the cutting and packing (C&P) problem, aiming to optimize the arrangement of small rectangular items within unique strips with a fixed width and infinite height to minimize the usage of height. Despite extensive a...

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Main Authors: Kaiyuan Liu, Hongyu Zhang, Chong Wang, Hui Li, Yongquan Chen, Qiong Chen
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/11/23/4781
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author Kaiyuan Liu
Hongyu Zhang
Chong Wang
Hui Li
Yongquan Chen
Qiong Chen
author_facet Kaiyuan Liu
Hongyu Zhang
Chong Wang
Hui Li
Yongquan Chen
Qiong Chen
author_sort Kaiyuan Liu
collection DOAJ
description The two-dimensional strip-packing problem (2D-SPP) emerges as a notable variant of the cutting and packing (C&P) problem, aiming to optimize the arrangement of small rectangular items within unique strips with a fixed width and infinite height to minimize the usage of height. Despite extensive academic exploration, applying 2D-SPP solutions in industrial settings remains challenging. Two significant issues, often overlooked in academia yet frequently encountered in industrial contexts, are the uncertain demand for items, exacerbated by the bullwhip effect, and the need for diverse types of strips to cater to varying customer needs. Our paper addresses this academia–industry gap by proposing a robust optimization model for the uncertain 2D-SPP with variable-sized bins, aiming to manage the demand fluctuations within a box uncertainty set framework. Additionally, we employ the contiguous one-dimensional relaxation technique in conjunction with column generation to tighten the lower bound of the problem, thereby augmenting solution accuracy. Furthermore, we leverage the Karush–Kuhn–Tucker (KKT) condition to transform the model into a more tractable form, subsequently leading to an exact solution. Based on datasets from a real-life plastic-cutting company, comprehensive experiments validate the effectiveness and efficiency of our proposed relaxation method and algorithm, showcasing the potential for an improved industrial application of 2D-SPP solutions.
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spelling doaj.art-6e1cee26c185409387eb7d0bbbe772f82023-12-08T15:21:45ZengMDPI AGMathematics2227-73902023-11-011123478110.3390/math11234781Robust Optimization for the Two-Dimensional Strip-Packing Problem with Variable-Sized BinsKaiyuan Liu0Hongyu Zhang1Chong Wang2Hui Li3Yongquan Chen4Qiong Chen5Department of Logistics and Transportation, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaDepartment of Logistics and Transportation, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaDepartment of Logistics and Transportation, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, ChinaShenzhen Institute of Artificial Intelligence and Robotics for Society, Chinese University of Hong Kong, Shenzhen 518172, ChinaShenzhen Institute of Artificial Intelligence and Robotics for Society, Chinese University of Hong Kong, Shenzhen 518172, ChinaNavigation College, Jimei University, Xiamen 361021, ChinaThe two-dimensional strip-packing problem (2D-SPP) emerges as a notable variant of the cutting and packing (C&P) problem, aiming to optimize the arrangement of small rectangular items within unique strips with a fixed width and infinite height to minimize the usage of height. Despite extensive academic exploration, applying 2D-SPP solutions in industrial settings remains challenging. Two significant issues, often overlooked in academia yet frequently encountered in industrial contexts, are the uncertain demand for items, exacerbated by the bullwhip effect, and the need for diverse types of strips to cater to varying customer needs. Our paper addresses this academia–industry gap by proposing a robust optimization model for the uncertain 2D-SPP with variable-sized bins, aiming to manage the demand fluctuations within a box uncertainty set framework. Additionally, we employ the contiguous one-dimensional relaxation technique in conjunction with column generation to tighten the lower bound of the problem, thereby augmenting solution accuracy. Furthermore, we leverage the Karush–Kuhn–Tucker (KKT) condition to transform the model into a more tractable form, subsequently leading to an exact solution. Based on datasets from a real-life plastic-cutting company, comprehensive experiments validate the effectiveness and efficiency of our proposed relaxation method and algorithm, showcasing the potential for an improved industrial application of 2D-SPP solutions.https://www.mdpi.com/2227-7390/11/23/4781robust optimizationtwo-dimensional strip-packing problemvariable-sized binscolumn generationcontiguous relaxationone-dimensional bin-packing problem
spellingShingle Kaiyuan Liu
Hongyu Zhang
Chong Wang
Hui Li
Yongquan Chen
Qiong Chen
Robust Optimization for the Two-Dimensional Strip-Packing Problem with Variable-Sized Bins
Mathematics
robust optimization
two-dimensional strip-packing problem
variable-sized bins
column generation
contiguous relaxation
one-dimensional bin-packing problem
title Robust Optimization for the Two-Dimensional Strip-Packing Problem with Variable-Sized Bins
title_full Robust Optimization for the Two-Dimensional Strip-Packing Problem with Variable-Sized Bins
title_fullStr Robust Optimization for the Two-Dimensional Strip-Packing Problem with Variable-Sized Bins
title_full_unstemmed Robust Optimization for the Two-Dimensional Strip-Packing Problem with Variable-Sized Bins
title_short Robust Optimization for the Two-Dimensional Strip-Packing Problem with Variable-Sized Bins
title_sort robust optimization for the two dimensional strip packing problem with variable sized bins
topic robust optimization
two-dimensional strip-packing problem
variable-sized bins
column generation
contiguous relaxation
one-dimensional bin-packing problem
url https://www.mdpi.com/2227-7390/11/23/4781
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