735 research outputs found
On three soft rectangle packing problems with guillotine constraints
We investigate how to partition a rectangular region of length and
height into rectangles of given areas using
two-stage guillotine cuts, so as to minimize either (i) the sum of the
perimeters, (ii) the largest perimeter, or (iii) the maximum aspect ratio of
the rectangles. These problems play an important role in the ongoing Vietnamese
land-allocation reform, as well as in the optimization of matrix multiplication
algorithms. We show that the first problem can be solved to optimality in
, while the two others are NP-hard. We propose mixed
integer programming (MIP) formulations and a binary search-based approach for
solving the NP-hard problems. Experimental analyses are conducted to compare
the solution approaches in terms of computational efficiency and solution
quality, for different objectives
Scheduling of data-intensive workloads in a brokered virtualized environment
Providing performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource management solutions that consider the brokered nature of these workloads, as well as the special demands of their intra-dependent components. In this paper, we present an offline mechanism for scheduling batches of brokered data-intensive workloads, which can be extended to an online setting. The objective of the mechanism is to decide on a packing of the workloads in a batch that minimizes the broker's incurred costs, Moreover, considering the brokered nature of such workloads, we define a payment model that provides incentives to these workloads to be scheduled as part of a batch, which we analyze theoretically. Finally, we evaluate the proposed scheduling algorithm, and exemplify the fairness of the payment model in practical settings via trace-based experiments
A heuristic for solving the irregular strip packing problem with quantum optimization
We introduce a novel quantum computing heuristic for solving the irregular
strip packing problem, a significant challenge in optimizing material usage
across various industries. This problem involves arranging a set of irregular
polygonal pieces within a fixed-height, rectangular container to minimize
waste. Traditional methods heavily rely on manual optimization by specialists,
highlighting the complexity and computational difficulty of achieving
quasi-optimal layouts. The proposed algorithm employs a quantum-inspired
heuristic that decomposes the strip packing problem into two sub-problems:
ordering pieces via the traveling salesman problem and spatially arranging them
in a rectangle packing problem. This strategy facilitates a novel application
of quantum computing to industrial optimization, aiming to minimize waste and
enhance material efficiency. Experimental evaluations using both classical and
quantum computational methods demonstrate the algorithm's efficacy. We evaluate
the algorithm's performance using the quantum approximate optimization
algorithm and the quantum alternating operator ansatz, through simulations and
real quantum computers, and compare it to classical approaches.Comment: 30 pages, 12 figure
On Semantic Word Cloud Representation
We study the problem of computing semantic-preserving word clouds in which
semantically related words are close to each other. While several heuristic
approaches have been described in the literature, we formalize the underlying
geometric algorithm problem: Word Rectangle Adjacency Contact (WRAC). In this
model each word is associated with rectangle with fixed dimensions, and the
goal is to represent semantically related words by ensuring that the two
corresponding rectangles touch. We design and analyze efficient polynomial-time
algorithms for some variants of the WRAC problem, show that several general
variants are NP-hard, and describe a number of approximation algorithms.
Finally, we experimentally demonstrate that our theoretically-sound algorithms
outperform the early heuristics
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
Automating the packing heuristic design process with genetic programming
The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains
Application-Specific Heterogeneous Network-on-Chip Design
Cataloged from PDF version of article.As a result of increasing communication demands, application-specific and scalable Network-on-Chips (NoCs) have emerged to connect processing cores and subsystems in Multiprocessor System-on-Chips. A challenge in application-specific NoC design is to find the right balance among different tradeoffs, such as communication latency, power consumption and chip area. We propose a novel approach that generates latency-aware heterogeneous NoC topology. Experimental results show that our approach improves the total communication latency up to 27% with modest power consumption. © 2013 The Author 2013. Published by Oxford University Press on behalf of The British Computer Society
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