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The Task Scheduling Problem: A NeuroGenetic Approach

Anurag Agarwal, Selcuk Colak, Jason Deane, Terry Rakes
2014 Journal of Business & Economics Research  
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

Anurag Agarwal, Hasan Pirkul, Varghese S. Jacob
2003 European Journal of Operational Research  
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

Orhan Engin, Cengiz Gunaydin
2011 International Journal of Computational Intelligence Systems  
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]

Eugene Levner
2007 Multiprocessor Scheduling, Theory and Applications  
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]

Jeff Kinnison, Nathaniel Kremer-Herman, Douglas Thain, Walter Scheirer
2018 arXiv   pre-print
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

Panneerselvam Senthilkumar, Sockalingam Narayanan
2010 Intelligent Information Management  
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)  
.......... 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

Tomasz Ujazdowski, Robert Piotrowski
2022 IEEE Access  
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]

Natalia Vesselinova, Rebecca Steinert, Daniel F. Perez-Ramirez, Magnus Boman
2020 arXiv   pre-print
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

Achraf BERRAJAA
2021 International Journal of Advanced Computer Science and Applications  
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

Natalia Vesselinova, Rebecca Steinert, Daniel F. Perez-Ramirez, Magnus Boman
2020 IEEE Access  
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]

Christopher Streiffer, Huan Chen, Theophilus Benson, Asim Kadav
2017 arXiv   pre-print
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

Ellur Anand, Ramasamy Panneerselvam
2015 Intelligent Information Management  
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

Kate A. Smith
1999 INFORMS journal on computing  
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]

Yassine Yaakoubi, François Soumis, Simon Lacoste-Julien
2021 arXiv   pre-print
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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