Tight Approximation Algorithms for Two-dimensional Guillotine Strip Packing

In the Strip Packing problem (SP), we are given a vertical half-strip \([0,W]\times[0,\infty)\) and a set of \(n\) axis-aligned rectangles of width at most \(W\) . The goal is to find a non-overlapping packing of all rectangles into the strip such that the height of the packing is minimized. A well-studied and frequently used practical constraint is to allow only those packings that are guillotine separable, i.e., every rectangle in the packing can be obtained by recursively applying a sequence of edge-to-edge axis-parallel cuts (guillotine cuts) that do not intersect any item of the solution. In this paper, we study approximation algorithms for the Guillotine Strip Packing problem (GSP), i.e., the Strip Packing problem where we require additionally that the packing needs to be guillotine separable. This problem generalizes the classical Bin Packing problem and also makespan minimization on identical machines, and thus it is already strongly \(\mathsf{NP}\) -hard. Moreover, due to a reduction from the Partition problem, it is \(\mathsf{NP}\) -hard to obtain a polynomial-time \((3/2-\varepsilon)\) -approximation algorithm for GSP for any \(\varepsilon>0\) (exactly as Strip Packing ). We provide a matching polynomial time \((3/2+\varepsilon)\) -approximation algorithm for GSP. Furthermore, we present a pseudo-polynomial time \((1+\varepsilon)\) -approximation algorithm for GSP. This is surprising as it is \(\mathsf{NP}\) -hard to obtain a \((5/4-\varepsilon)\) -approximation algorithm for (general) Strip Packing in pseudo-polynomial time. Thus, our results essentially settle the approximability of GSP for both the polynomial and the pseudo-polynomial settings.

Authors

Institutions

Publication Details

Journal
ACM Transactions on Algorithms
Published
2025-05-22
DOI
https://doi.org/10.1145/3736723
Primary Topic
Optimization and Packing Problems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tight Approximation Algorithms for Two-dimensional Guillotine Strip Packing

Aditya Lonkar, Arnab Maiti, Andreas Wiese, Amatya Sharma et al.
ACM Transactions on Algorithms
Optimization and Packing Problems
article

Tight Approximation Algorithms for Two-dimensional Guillotine Strip Packing

Aditya Lonkar, Arnab Maiti, Andreas Wiese, Amatya Sharma, Arindam Khan
article en

Abstract

In the Strip Packing problem (SP), we are given a vertical half-strip \([0,W]\times[0,\infty)\) and a set of \(n\) axis-aligned rectangles of width at most \(W\) . The goal is to find a non-overlapping packing of all rectangles into the strip such that the height of the packing is minimized. A well-studied and frequently used practical constraint is to allow only those packings that are guillotine separable, i.e., every rectangle in the packing can be obtained by recursively applying a sequence of edge-to-edge axis-parallel cuts (guillotine cuts) that do not intersect any item of the solution. In this paper, we study approximation algorithms for the Guillotine Strip Packing problem (GSP), i.e., the Strip Packing problem where we require additionally that the packing needs to be guillotine separable. This problem generalizes the classical Bin Packing problem and also makespan minimization on identical machines, and thus it is already strongly \(\mathsf{NP}\) -hard. Moreover, due to a reduction from the Partition problem, it is \(\mathsf{NP}\) -hard to obtain a polynomial-time \((3/2-\varepsilon)\) -approximation algorithm for GSP for any \(\varepsilon>0\) (exactly as Strip Packing ). We provide a matching polynomial time \((3/2+\varepsilon)\) -approximation algorithm for GSP. Furthermore, we present a pseudo-polynomial time \((1+\varepsilon)\) -approximation algorithm for GSP. This is surprising as it is \(\mathsf{NP}\) -hard to obtain a \((5/4-\varepsilon)\) -approximation algorithm for (general) Strip Packing in pseudo-polynomial time. Thus, our results essentially settle the approximability of GSP for both the polynomial and the pseudo-polynomial settings.

ACM Transactions on Algorithms
Georgia Institute of Technology (US), University of Washington (US), University of Michigan (US), Seattle University (US), Indian Institute of Science Bangalore (IN), Technical University of Munich (DE)
Openalex Percentile: Top 85%
Optimization and Packing Problems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.