Computers

1456 KiB

Open AccessArticle

The Accurate Method for Computing the Minimum Distance between a Point and an Elliptical Torus

by Xiaowu Li, Zhinan Wu, Linke Hou, Lin Wang and Chunguang Yue

Computers 2016, 5(1), 4; https://0-doi-org.brum.beds.ac.uk/10.3390/computers5010004 - 24 Feb 2016

Cited by 3 | Viewed by 6293

We present an accurate method to compute the minimum distance between a point and an elliptical torus, which is called the orthogonal projection problem. The basic idea is to transform a geometric problem into finding the unique real solution of a quartic equation, [...] Read more.

We present an accurate method to compute the minimum distance between a point and an elliptical torus, which is called the orthogonal projection problem. The basic idea is to transform a geometric problem into finding the unique real solution of a quartic equation, which is fit for orthogonal projection of a point onto the elliptical torus. Firstly, we discuss the corresponding orthogonal projection of a point onto the elliptical torus for test points at six different spatial positions. Secondly, we discuss the same problem for test points on three special positions, e.g., points on the z-axis, the long axis and the minor axis, respectively. Full article

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1535 KiB

Open AccessArticle

Learning Dispatching Rules for Scheduling: A Synergistic View Comprising Decision Trees, Tabu Search and Simulation

by Atif Shahzad and Nasser Mebarki

Computers 2016, 5(1), 3; https://0-doi-org.brum.beds.ac.uk/10.3390/computers5010003 - 17 Feb 2016

Cited by 33 | Viewed by 8815

Abstract

A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation [...] Read more.

A promising approach for an effective shop scheduling that synergizes the benefits of the combinatorial optimization, supervised learning and discrete-event simulation is presented. Though dispatching rules are in widely used by shop scheduling practitioners, only ordinary performance rules are known; hence, dynamic generation of dispatching rules is desired to make them more effective in changing shop conditions. Meta-heuristics are able to perform quite well and carry more knowledge of the problem domain, however at the cost of prohibitive computational effort in real-time. The primary purpose of this research lies in an offline extraction of this domain knowledge using decision trees to generate simple if-then rules that subsequently act as dispatching rules for scheduling in an online manner. We used similarity index to identify parametric and structural similarity in problem instances in order to implicitly support the learning algorithm for effective rule generation and quality index for relative ranking of the dispatching decisions. Maximum lateness is used as the scheduling objective in a job shop scheduling environment. Full article

(This article belongs to the Special Issue Combining Learning and Optimisation)

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643 KiB

Open AccessEditorial

Acknowledgement to Reviewers of Computers in 2015

by Computers Editorial Office

Computers 2016, 5(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/computers5010002 - 21 Jan 2016

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Abstract

The editors of Computers would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2015. [...] Full article

329 KiB

Open AccessLetter

Exponentiated Gradient Exploration for Active Learning

by Djallel Bouneffouf

Computers 2016, 5(1), 1; https://doi.org/10.3390/computers5010001 - 08 Jan 2016

Cited by 22 | Viewed by 9474

Abstract

Active learning strategies respond to the costly labeling task in a supervised classification by selecting the most useful unlabeled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision boundary, rather than exploring new regions that can [...] Read more.

Active learning strategies respond to the costly labeling task in a supervised classification by selecting the most useful unlabeled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision boundary, rather than exploring new regions that can be more informative. In this setting, we propose a sequential algorithm named exponentiated gradient (EG)-active that can improve any active learning algorithm by an optimal random exploration. Experimental results show a statistically-significant and appreciable improvement in the performance of our new approach over the existing active feedback methods. Full article

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Computers, Volume 5, Issue 1 (March 2016) – 4 articles

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