40 research outputs found
Solving Pallet loading Problem with Real-World Constraints
Efficient cargo packing and transport unit stacking play a vital role in
enhancing logistics efficiency and reducing costs in the field of logistics.
This article focuses on the challenging problem of loading transport units onto
pallets, which belongs to the class of NP-hard problems. We propose a novel
method for solving the pallet loading problem using a branch and bound
algorithm, where there is a loading order of transport units. The derived
algorithm considers only a heuristically favourable subset of possible
positions of the transport units, which has a positive effect on computability.
Furthermore, it is ensured that the pallet configuration meets real-world
constraints, such as the stability of the position of transport units under the
influence of transport inertial forces and gravity.Comment: 8 pages, 1 figure, project report pape
TS2PACK: A Two-Level Tabu Search for the Three-dimensional Bin Packing Problem
Three-dimensional orthogonal bin packing is a problem NP-hard in the strong sense where a set of boxes must be orthogonally packed into the minimum number of three-dimensional bins. We present a two-level tabu search for this problem. The first-level aims to reduce the number of bins. The second optimizes the packing of the bins. This latter procedure is based on the Interval Graph representation of the packing, proposed by Fekete and Schepers, which reduces the size of the search space. We also introduce a general method to increase the size of the associated neighborhoods, and thus the quality of the search, without increasing the overall complexity of the algorithm. Extensive computational results on benchmark problem instances show the effectiveness of the proposed approach, obtaining better results compared to the existing one
Hybrid Approach for Solving Real-World Bin Packing Problem Instances Using Quantum Annealers
Efficient packing of items into bins is a common daily task. Known as Bin
Packing Problem, it has been intensively studied in the field of artificial
intelligence, thanks to the wide interest from industry and logistics. Since
decades, many variants have been proposed, with the three-dimensional Bin
Packing Problem as the closest one to real-world use cases. We introduce a
hybrid quantum-classical framework for solving real-world three-dimensional Bin
Packing Problems (Q4RealBPP), considering different realistic characteristics,
such as: i) package and bin dimensions, ii) overweight restrictions, iii)
affinities among item categories and iv) preferences for item ordering.
Q4RealBPP permits the solving of real-world oriented instances of 3dBPP,
contemplating restrictions well appreciated by industrial and logistics
sectors.Comment: 9 pages, 24 figure
Solving Logistic-Oriented Bin Packing Problems Through a Hybrid Quantum-Classical Approach
The Bin Packing Problem is a classic problem with wide industrial
applicability. In fact, the efficient packing of items into bins is one of the
toughest challenges in many logistic corporations and is a critical issue for
reducing storage costs or improving vehicle space allocation. In this work, we
resort to our previously published quantum-classical framework known as
Q4RealBPP, and elaborate on the solving of real-world oriented instances of the
Bin Packing Problem. With this purpose, this paper gravitates on the following
characteristics: i) the existence of heterogeneous bins, ii) the extension of
the framework to solve not only three-dimensional, but also one- and
two-dimensional instances of the problem, iii) requirements for item-bin
associations, and iv) delivery priorities. All these features have been tested
in this paper, as well as the ability of Q4RealBPP to solve real-world oriented
instances.Comment: 7 pages, 7 figures, paper accepted for being presented in the
upcoming 26th IEEE International Conference on Intelligent Transportation
Systems - ITSC 202
Extreme-Point-based Heuristics for the Three-Dimensional Bin Packing problem
One of the main issues in addressing three-dimensional packing problems is finding an efficient and accurate definition of the points at which to place the items inside the bins, because the performance of exact and heuristic solution methods is actually strongly influenced by the choice of a placement rule. We introduce the extreme point concept and present a new extreme point-based rule for packing items inside a three-dimensional container. The extreme point rule is independent from the particular packing problem addressed and can handle additional constraints, such as fixing the position of the items. The new extreme point rule is also used to derive new constructive heuristics for the three-dimensional bin-packing problem. Extensive computational results show the effectiveness of the new heuristics compared to state-of-the-art results. Moreover, the same heuristics, when applied to the two-dimensional bin-packing problem, outperform those specifically designed for the proble
Stable bin packing of non-convex 3D objects with a robot manipulator
Recent progress in the field of robotic manipulation has generated interest
in fully automatic object packing in warehouses. This paper proposes a
formulation of the packing problem that is tailored to the automated
warehousing domain. Besides minimizing waste space inside a container, the
problem requires stability of the object pile during packing and the
feasibility of the robot motion executing the placement plans. To address this
problem, a set of constraints are formulated, and a constructive packing
pipeline is proposed to solve for these constraints. The pipeline is able to
pack geometrically complex, non-convex objects with stability while satisfying
robot constraints. In particular, a new 3D positioning heuristic called
Heightmap-Minimization heuristic is proposed, and heightmaps are used to speed
up the search. Experimental evaluation of the method is conducted with a
realistic physical simulator on a dataset of scanned real-world items,
demonstrating stable and high-quality packing plans compared with other 3D
packing methods