Recent Advances in Multi-dimensional Packing Problems

Crainic, Teodor Gabriel; Perboli, Guido; Tadei, Roberto

doi:10.5772/33302

Cited by 13 publications

(7 citation statements)

References 59 publications

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“…Furthermore, we integrated the heuristic solver xflp 5 that was developed for real-world container-loading problems in 3D and was inspired by Martello et al (2000); de Castro Silva et al (2003);and Crainic et al (2012). We configured the solver for single-bin packing with rotation of items.…”

Section: Offline 3d Bin Packingmentioning

confidence: 99%

BED-BPP: Benchmarking dataset for robotic bin packing problems

Kagerer

Beinhofer²,

Stricker³

et al. 2023

The International Journal of Robotics Research

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Many algorithms that were developed for solving three-dimensional bin packing problems use generic data for either experiments or evaluation. However, none of these datasets became accepted for benchmarking 3D bin packing algorithms throughout the community. To close this gap, this paper presents the benchmarking dataset for robotic bin packing problems (BED-BPP), which is based on realistic data. We show the variety of the dataset by elaborating an n-gram analysis. Besides, we propose an evaluation function, which contains a stability check that uses rigid body simulation. We demonstrated the application of our dataset on four different approaches, which we integrated in our software environment.

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Section: Offline 3d Bin Packingmentioning

confidence: 99%

BED-BPP: Benchmarking dataset for robotic bin packing problems

Kagerer

Beinhofer²,

Stricker³

et al. 2023

The International Journal of Robotics Research

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“…(2011a) reviewed heuristics for the bin packing problem, grouping them as one phase (items are packed directly into bins), two phase (first, strips of items are created and then the strips are packed into bins) and local search heuristics. Crainic et al. (2012) reviewed and compared models and approaches for solving the two‐ and three‐dimensional knapsack and bin packing problems. Delorme et al. (2016) reviewed mathematical models and exact algorithms for the SBSBPP and SSSCSP. Oliveira et al.…”

Section: Cutting and Packing Problemsmentioning

confidence: 99%

An introduction to the two‐dimensional rectangular cutting and packing problem

Oliveira

Gamboa

Silva

2022

Int Trans Operational Res

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Cutting and packing problems have been widely studied in the last decades, mainly due to the variety of industrial applications where the problems emerge. This paper presents an overview of the solution approaches that have been proposed for solving two‐dimensional rectangular cutting and packing problems. The main emphasis of this work is on two distinct problems that belong to the cutting and packing problem family. The first problem aims to place onto an object the maximum‐profit subset of items, that is, output maximization, while the second one aims to place all the items using as few identical objects as possible, that is, input minimization. The objective of this paper is not to be exhaustive but to provide a solid grasp on two‐dimensional rectangular cutting and packing problems by describing their most important solution approaches.

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“…For a general overview over 2D-KP, we refer to the survey papers (Cheng et al 1994;Crainic et al 2012;Dyckhoff 1990;Hinxman 1980;Hopper and Turton 2001), and in particular to the most recent one by Iori et al (2020). Further, it is important to note that the two-dimensional Cutting Stock Problem (2CSP) is closely related to 2D-KP.…”

Section: Proofmentioning

confidence: 99%

The maximum diversity assortment selection problem

et al. 2021

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In this article, we introduce the Maximum Diversity Assortment Selection Problem (MDASP), which is a generalization of the two-dimensional Knapsack Problem (2D-KP). Given a set of rectangles and a rectangular container, the goal of 2D-KP is to determine a subset of rectangles that can be placed in the container without overlapping, i.e., a feasible assortment, such that a maximum area is covered. MDASP is to determine a set of feasible assortments, each of them covering a certain minimum threshold of the container, such that the diversity among them is maximized. Thereby, diversity is defined as the minimum or average normalized Hamming distance of all assortment pairs. MDASP was the topic of the 11th AIMMS-MOPTA Competition in 2019. The methods described in this article and the resulting computational results won the contest. In the following, we give a definition of the problem, introduce a mathematical model and solution approaches, determine upper bounds on the diversity, and conclude with computational experiments conducted on test instances derived from the 2D-KP literature.

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Recent Advances in Multi-dimensional Packing Problems

Cited by 13 publications

References 59 publications

BED-BPP: Benchmarking dataset for robotic bin packing problems

BED-BPP: Benchmarking dataset for robotic bin packing problems

An introduction to the two‐dimensional rectangular cutting and packing problem

The maximum diversity assortment selection problem

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