5,752 research outputs found
Towards the Design of Heuristics by Means of Self-Assembly
The current investigations on hyper-heuristics design have sprung up in two
different flavours: heuristics that choose heuristics and heuristics that
generate heuristics. In the latter, the goal is to develop a problem-domain
independent strategy to automatically generate a good performing heuristic for
the problem at hand. This can be done, for example, by automatically selecting
and combining different low-level heuristics into a problem specific and
effective strategy. Hyper-heuristics raise the level of generality on automated
problem solving by attempting to select and/or generate tailored heuristics for
the problem at hand. Some approaches like genetic programming have been
proposed for this. In this paper, we explore an elegant nature-inspired
alternative based on self-assembly construction processes, in which structures
emerge out of local interactions between autonomous components. This idea
arises from previous works in which computational models of self-assembly were
subject to evolutionary design in order to perform the automatic construction
of user-defined structures. Then, the aim of this paper is to present a novel
methodology for the automated design of heuristics by means of self-assembly
MINIMUM FLOW TIME SCHEDULE GENETIC ALGORITHM FOR MASS CUSTOMIZATION MANUFACTURING USING MINICELLS
Minicells are small manufacturing cells dedicated to an option family and organized in a multi-stage configuration for mass customization manufacturing. Product variants, depending on the customization requirements of each customer, are routed through the minicells as necessary. For successful mass customization, customized products must be manufactured at low cost and with short turn around time. Effective scheduling of jobs to be processed in minicells is essential to quickly deliver customized products. In this research, a genetic algorithm based approach is developed to schedule jobs in a minicell configuration by considering it as a multi-stage flow shop. A new crossover strategy is used in the genetic algorithm to obtain a minimum flow time schedule
Cell Production System Design: A Literature Review
Purpose In a cell production system, a number of machines that differ in function are housed in the same cell. The task of these cells is to complete operations on similar parts that are in the same group. Determining the family of machine parts and cells is one of the major design problems of production cells. Cell production system design methods include clustering, graph theory, artificial intelligence, meta-heuristic, simulation, mathematical programming. This article discusses the operation of methods and research in the field of cell production system design.
Methodology: To examine these methods, from 187 articles published in this field by authoritative scientific sources, based on the year of publication and the number of restrictions considered and close to reality, which are searched using the keywords of these restrictions and among them articles Various aspects of production and design problems, such as considering machine costs and cell size and process routing, have been selected simultaneously.
Findings: Finally, the distribution diagram of the use of these methods and the limitations considered by their researchers, shows the use and efficiency of each of these methods. By examining them, more efficient and efficient design fields of this type of production system can be identified.
Originality/Value: In this article, the literature on cell production system from 1972 to 2021 has been reviewed
High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm
We implement a master-slave parallel genetic algorithm (PGA) with a bespoke
log-likelihood fitness function to identify emergent clusters within price
evolutions. We use graphics processing units (GPUs) to implement a PGA and
visualise the results using disjoint minimal spanning trees (MSTs). We
demonstrate that our GPU PGA, implemented on a commercially available general
purpose GPU, is able to recover stock clusters in sub-second speed, based on a
subset of stocks in the South African market. This represents a pragmatic
choice for low-cost, scalable parallel computing and is significantly faster
than a prototype serial implementation in an optimised C-based
fourth-generation programming language, although the results are not directly
comparable due to compiler differences. Combined with fast online intraday
correlation matrix estimation from high frequency data for cluster
identification, the proposed implementation offers cost-effective,
near-real-time risk assessment for financial practitioners.Comment: 10 pages, 5 figures, 4 tables, More thorough discussion of
implementatio
Anticipatory Buffer Control and Quality Selection for Wireless Video Streaming
Video streaming is in high demand by mobile users, as recent studies
indicate. In cellular networks, however, the unreliable wireless channel leads
to two major problems. Poor channel states degrade video quality and interrupt
the playback when a user cannot sufficiently fill its local playout buffer:
buffer underruns occur. In contrast to that, good channel conditions cause
common greedy buffering schemes to pile up very long buffers. Such
over-buffering wastes expensive wireless channel capacity.
To keep buffering in balance, we employ a novel approach. Assuming that we
can predict data rates, we plan the quality and download time of the video
segments ahead. This anticipatory scheduling avoids buffer underruns by
downloading a large number of segments before a channel outage occurs, without
wasting wireless capacity by excessive buffering. We formalize this approach as
an optimization problem and derive practical heuristics for segmented video
streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed
measurements show that our solution essentially eliminates playback
interruptions without significantly decreasing video quality
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