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The Task Scheduling Problem: A NeuroGenetic Approach
2014
Journal of Business & Economics Research
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The machines (resources) are all identical and a task needs only one machine for processing. ...
The augmented neural network approach is itself a hybrid of a heuristic approach and a neural network approach. ...
Agarwal et al. (2003) proposed the augmented neural network (AugNN) approach for solving the task scheduling problem. ...
doi:10.19030/jber.v12i4.8860
fatcat:womlenquwnd2znpmhgxd4zswji
Augmented neural networks for task scheduling
2003
European Journal of Operational Research
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We propose a new approach, called Augmented Neural Networks (AugNN) for solving the task-scheduling problem. This approach is a hybrid of the heuristic and the neural networks approaches. ...
This approach, which we call the Augmented Neural Network (AugNN) approach, is a hybrid of the heuristic and the neural network approaches. ...
Hu (1961) had pioneered the idea of a heuristic approach for solving scheduling problems for the identical machine case with precedence constraints. ...
doi:10.1016/s0377-2217(02)00605-7
fatcat:ycgb77urvrehrnqewqo6of6twm
An adaptive learning approach for no-wait flowshop scheduling problems to minimize make-span
2011
International Journal of Computational Intelligence Systems
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The proposed IALA method for NW-FSSP is compared with Aldowaisan and Allahverdi's 2 results by using Genetic heuristic. ...
s 1 adaptive learning approach (ALA) is improvement for NW-FSSPs. Improvements in adaptive learning approach is similar to neural-network training. ...
Also, Agarwal et al. 30 proposed new heuristics along with an augmented-neural-network formulation for solving the makespan minimization task-scheduling problem for the non-identical machine environment ...
doi:10.2991/ijcis.2011.4.4.11
fatcat:2l4xzf5t5zg7do55ckz7jfcl7e
Preface: Multiprocessor Scheduling, Theory and Applications
[chapter]
2007
Multiprocessor Scheduling, Theory and Applications
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Preface Scheduling theory is concerned with the optimal allocation of scarce resources (for instance, machines, processors, robots, operators, etc.) to activities over time, with the objective of optimizing ...
, approximation algorithms with performance guarantees, heuristics and metaheuristics; novel models and approaches to scheduling; and, last but least, several real-life applications and case studies. ...
Chapter 7 presents a new hybrid metaheuristic for solving the jobshop scheduling problem that combines augmented-neural-networks with genetic algorithm based search. ...
doi:10.5772/5211
fatcat:i4hco3bepjeo3i2eps3g46miga
SHADHO: Massively Scalable Hardware-Aware Distributed Hyperparameter Optimization
[article]
2018
arXiv
pre-print
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We then conduct model search with SHADHO over the course of one week using 74 GPUs across two compute clusters to optimize U-Net for a cell segmentation task, discovering 515 models that achieve a lower ...
Computer vision is experiencing an AI renaissance, in which machine learning models are expediting important breakthroughs in academic research and commercial applications. ...
Acknowledgements This research was supported in part by the Notre Dame Center for Research Computing through access to distributed computing resources. ...
arXiv:1707.01428v2
fatcat:tyxbvw2vs5hplk722kya7p65za
Literature Review of Single Machine Scheduling Problem with Uniform Parallel Machines
2010
Intelligent Information Management
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The single machine scheduling problem with uniform parallel machines consists of n jobs, each with single operation, which are to be scheduled on m parallel machines with different speeds. ...
This paper presents a survey of single machine scheduling problem with uniform parallel machines. ...
Agarwal, Colak, Jacob and Pirkul [28] have proposed new heuristics along with an augmented-neural-netwrok (AugNN) formulation for solving the makespan minimization taskscheduling problem for the non-identical ...
doi:10.4236/iim.2010.28056
fatcat:cugkndehgrgedbxdjuga7mz7e4
Table of Contents
2020
2020 IEEE Symposium Series on Computational Intelligence (SSCI)
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.......... 968
SCM: Cooperative Algorithms, Chair: Mohammed El-Abd Seyedali Mirjalili
Island-based Modified Harmony Search Algorithm with Neighboring Heuristics Methods for Flow Shop
Scheduling with ...
Cruz-Duarte and Hugo Terashima-Marin .......... 3133 A Genetic Programming Hyper-Heuristic Approach to Design High-Level Heuristics for Dynamic Workflow Scheduling in Cloud Kirita-Rose Escott Escott, Hui ...
doi:10.1109/ssci47803.2020.9308155
fatcat:hyargfnk4vevpnooatlovxm4li
Task Scheduling–Review of Algorithms and Analysis of Potential Use in a Biological Wastewater Treatment Plant
2022
IEEE Access
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This review shows the extent to which task scheduling methods are applied in industry. This paper presents methods and algorithms for solving task scheduling problems. ...
The idea of task scheduling is to increase the efficiency of a system by minimising wasted time, evenly loading machines, or maximising the throughput of machines. ...
Network (ANN) [14] , [53] - [57] VOLUME 10, 2022 • Radial Basis Function Neural Networks (RBFNN) [55] • Augmented Neural Networks (AugNN) [56] • . . . ...
doi:10.1109/access.2022.3170105
fatcat:6gfkex5p4za3joeiq77ktcqf3e
Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking
[article]
2020
arXiv
pre-print
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Relevant developments in machine learning research on graphs is surveyed, for this purpose. ...
research networks. ...
In other words, this section is tailored for readers with machine learning background but no familiarity with attention mechanisms, graph neural networks and deep reinforcement learning. ...
arXiv:2005.11081v1
fatcat:ajqghcevqvdrvdlcrknxlzlqdi
Solving the Steel Continuous Casting Problem using an Artificial Intelligence Model
2021
International Journal of Advanced Computer Science and Applications
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We have proposed to solve it a recurrent neural network (RNN) with LSTM cells that we will executed in the cloud. ...
For our problem, we consider several machines at each stage that are the converter stage, the refining stage and the continuous casting stage. ...
The classic flowshop (FS) considers scheduling a set of tasks on one machine at each stage, while the hybrid flowshop (HFS) aims to schedule a flow shop with multiple parallel machines at each stage [ ...
doi:10.14569/ijacsa.2021.01212105
fatcat:l27fnxlh7bh2lkyplrz45lnfxe
Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking
2020
IEEE Access
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Relevant developments in machine learning research on graphs are surveyed for this purpose. ...
research networks. ...
Junaid Shuja, who coordinated the review process, and to the anonymous reviewers for their expeditious and helpful review comments received in preparation of the final version of the manuscript. ...
doi:10.1109/access.2020.3004964
fatcat:v7i7x6p77zfi7dntipxoiolily
DeepConfig: Automating Data Center Network Topologies Management with Machine Learning
[article]
2017
arXiv
pre-print
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In this vision paper, we argue that many data center networking techniques, e.g., routing, topology augmentation, energy savings, with diverse goals actually share design and architectural similarity. ...
We present a design for developing general intermediate representations of network topologies using deep learning that is amenable to solving classes of data center problems. ...
For example, while c-through [33] and FireFly [17] solve broadly identical problems, they leverage different heuristics to account for low-level differences. ...
arXiv:1712.03890v1
fatcat:rjkbk2yzzbat7megrnudwc4yiy
Literature Review of Open Shop Scheduling Problems
2015
Intelligent Information Management
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Directions for future research are discussed in the end. 1 2 , n J J J and a set M which consists of m machines { } 1 2 ...
miscellaneous measures of the open shop scheduling problem. ...
Acknowledgements The authors thank the anonymous referees for their constructive suggestions, which have improved the paper.
References ...
doi:10.4236/iim.2015.71004
fatcat:shukynonjrfaleqiousjru3ari
Neural Networks for Combinatorial Optimization: A Review of More Than a Decade of Research
1999
INFORMS journal on computing
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This article briefly summarizes the work that has been done and presents the current standing of neural networks for combinatorial optimization by considering each of the major classes of combinatorial ...
It has been over a decade since neural networks were first applied to solve combinatorial optimization problems. ...
Gendreau, and Dr. B. Golden for their helpful comments and suggestions. ...
doi:10.1287/ijoc.11.1.15
fatcat:jy5jur2pyjhndpi45argti2f54
Flight-connection Prediction for Airline Crew Scheduling to Construct Initial Clusters for OR Optimizer
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2009.12501v1 fatcat:uif6gvvc5re63ilwf6nibsaq7q
an adapted neural network approach that achieves high accuracy (99.7% overall or 82.5% on harder instances). ...
a 10x speed improvement and up to 0.2% cost saving. ...
The authors would like to thank these organizations for their support and confidence. ...
arXiv:2009.12501v2
fatcat:jmsbhtdwrbbubfah4bicp6b3xe
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