27,849 research outputs found
Facility layout problem: Bibliometric and benchmarking analysis
Facility layout problem is related to the location of departments in a facility area, with the aim of determining the most effective configuration. Researches based on different approaches have been published in the last six decades and, to prove the effectiveness of the results obtained, several instances have been developed. This paper presents a general overview on the extant literature on facility layout problems in order to identify the main research trends and propose future research questions. Firstly, in order to give the reader an overview of the literature, a bibliometric analysis is presented. Then, a clusterization of the papers referred to the main instances reported in literature was carried out in order to create a database that can be a useful tool in the benchmarking procedure for researchers that would approach this kind of problems
Robust Monotonic Optimization Framework for Multicell MISO Systems
The performance of multiuser systems is both difficult to measure fairly and
to optimize. Most resource allocation problems are non-convex and NP-hard, even
under simplifying assumptions such as perfect channel knowledge, homogeneous
channel properties among users, and simple power constraints. We establish a
general optimization framework that systematically solves these problems to
global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles
general multicell downlink systems with single-antenna users, multiantenna
transmitters, arbitrary quadratic power constraints, and robustness to channel
uncertainty. A robust fairness-profile optimization (RFO) problem is solved at
each iteration, which is a quasi-convex problem and a novel generalization of
max-min fairness. The BRB algorithm is computationally costly, but it shows
better convergence than the previously proposed outer polyblock approximation
algorithm. Our framework is suitable for computing benchmarks in general
multicell systems with or without channel uncertainty. We illustrate this by
deriving and evaluating a zero-forcing solution to the general problem.Comment: Published in IEEE Transactions on Signal Processing, 16 pages, 9
figures, 2 table
Seeing Shapes in Clouds: On the Performance-Cost trade-off for Heterogeneous Infrastructure-as-a-Service
In the near future FPGAs will be available by the hour, however this new
Infrastructure as a Service (IaaS) usage mode presents both an opportunity and
a challenge: The opportunity is that programmers can potentially trade
resources for performance on a much larger scale, for much shorter periods of
time than before. The challenge is in finding and traversing the trade-off for
heterogeneous IaaS that guarantees increased resources result in the greatest
possible increased performance. Such a trade-off is Pareto optimal. The Pareto
optimal trade-off for clusters of heterogeneous resources can be found by
solving multiple, multi-objective optimisation problems, resulting in an
optimal allocation of tasks to the available platforms. Solving these
optimisation programs can be done using simple heuristic approaches or formal
Mixed Integer Linear Programming (MILP) techniques. When pricing 128 financial
options using a Monte Carlo algorithm upon a heterogeneous cluster of Multicore
CPU, GPU and FPGA platforms, the MILP approach produces a trade-off that is up
to 110% faster than a heuristic approach, and over 50% cheaper. These results
suggest that high quality performance-resource trade-offs of heterogeneous IaaS
are best realised through a formal optimisation approach.Comment: Presented at Second International Workshop on FPGAs for Software
Programmers (FSP 2015) (arXiv:1508.06320
A Domain Specific Approach to High Performance Heterogeneous Computing
Users of heterogeneous computing systems face two problems: firstly, in
understanding the trade-off relationships between the observable
characteristics of their applications, such as latency and quality of the
result, and secondly, how to exploit knowledge of these characteristics to
allocate work to distributed computing platforms efficiently. A domain specific
approach addresses both of these problems. By considering a subset of
operations or functions, models of the observable characteristics or domain
metrics may be formulated in advance, and populated at run-time for task
instances. These metric models can then be used to express the allocation of
work as a constrained integer program, which can be solved using heuristics,
machine learning or Mixed Integer Linear Programming (MILP) frameworks. These
claims are illustrated using the example domain of derivatives pricing in
computational finance, with the domain metrics of workload latency or makespan
and pricing accuracy. For a large, varied workload of 128 Black-Scholes and
Heston model-based option pricing tasks, running upon a diverse array of 16
Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both
the makespan and accuracy are generally within 10% of the run-time performance.
When these models are used as inputs to machine learning and MILP-based
workload allocation approaches, a latency improvement of up to 24 and 270 times
over the heuristic approach is seen.Comment: 14 pages, preprint draft, minor revisio
A Feature-Based Analysis on the Impact of Set of Constraints for e-Constrained Differential Evolution
Different types of evolutionary algorithms have been developed for
constrained continuous optimization. We carry out a feature-based analysis of
evolved constrained continuous optimization instances to understand the
characteristics of constraints that make problems hard for evolutionary
algorithm. In our study, we examine how various sets of constraints can
influence the behaviour of e-Constrained Differential Evolution. Investigating
the evolved instances, we obtain knowledge of what type of constraints and
their features make a problem difficult for the examined algorithm.Comment: 17 Page
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