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A novel Hopfield neural network approach for minimizing total weighted tardiness of jobs scheduled on identical machines
[article]
2012
arXiv
pre-print
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They have well-defined deadlines, and relative priorities quantified by non-negative real weights. The objective is to find schedules which minimize the total weighted tardiness (TWT) of all jobs. ...
We show how this problem can be mapped into quadratic form and present a polynomial time heuristic solution based on the Hopfield Neural Network (HNN) approach. ...
We suggest a novel heuristic for the TWT problem, based on the Hopfield Neural Network approach which is shown to perform better than existing simple heuristics and has desirable scaling characteristics ...
arXiv:1208.4583v1
fatcat:a3a2c2xu5zczxbyiws4f5ayueu
Improvements to the Hopfield Neural Network Solution of the TWT Scheduling Problem
2013
Periodica Polytechnica Electrical Engineering
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This paper explores novel, polynomial time, heuristic, approximate solutions to the NP-hard problem of finding the optimal job schedule on identical machines which minimizes total weighted tardiness (TWT ...
quality of the solution, we call the latter Perturbed Smart Hopfield Neural Network (PSHNN). ...
and weights, allowing pre-emption (the stopping and resumption of work items), on identical machines in a way that the total weighted tardiness (TWT) of all jobs is minimal. ...
doi:10.3311/ppee.2090
fatcat:bq3atf7tlrds7e7mbvp3ox6rw4
Preface: Multiprocessor Scheduling, Theory and Applications
[chapter]
2007
Multiprocessor Scheduling, Theory and Applications
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Chapter 1 is a tutorial on theory of cyclic scheduling. It is included for those readers who are unfamiliar with this area of scheduling theory. ...
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 ...
Two mixed integer programming (MIP) formulations are suggested, the first one aimed to minimize the total tardiness while the second minimizing the sum of total earliness/tardiness for parallel machine ...
doi:10.5772/5211
fatcat:i4hco3bepjeo3i2eps3g46miga
Lagrangian relaxation neural networks for job shop scheduling
2000
IEEE Transactions on Robotics and Automation
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In fact, the digital implementation of LRNN for job shop scheduling is similar to the traditional LR approaches. ...
When applying the network for job shop scheduling, the separability of problem formulation is fully exploited, and a new neuron-based dynamic programming is developed making innovative use of the subproblem ...
The problem is to determine operation beginning times so that the total weighted part earliness and tardiness is minimized. ...
doi:10.1109/70.833193
fatcat:o4ljmcwidrbxtmamidyw4q6sdm
A review of scheduling problem and resolution methods in flexible flow shop
2019
International Journal of Industrial Engineering Computations
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This paper gives a comprehensive guide for the reader with respect to future research work on the FFS scheduling problem. ...
The Flexible flow shop (FFS) is defined as a multi-stage flow shops with multiple parallel machines. ...
Acknowledgment The authors would like to thank the reviewers for their constructive comments and the Ministry of Higher ...
doi:10.5267/j.ijiec.2018.4.001
fatcat:kiq2jjjsmzgtpoaudrq4qc7ndi
Identical Parallel Machine Scheduling with Dynamical Networks using Time-Varying Penalty Parameters
[chapter]
2007
Multiprocessor Scheduling, Theory and Applications
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They tried to find the sequence of jobs processed on each machine with the objective of minimizing weighted tardiness. ...
Introduction The classical identical parallel machine scheduling problem can be stated as follows: Given n jobs and m machines, the problem is to assign each job on one of the identical machines during ...
, 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. ...
doi:10.5772/5228
fatcat:i2itldkfgnchhivujyyxe2n6fu
Research on Adaptive Job Shop Scheduling Problems Based on Dueling Double DQN
2020
IEEE Access
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[49, 50] investigated unrelated parallel machine scheduling with Q and R learning in order to minimize the mean weighted tardiness. ...
Yang and Wang [33] presented a new adaptive neural network and heuristics hybrid approach for job-shop scheduling. Aiming at the job-shop scheduling problem with bottlenecks, Varela et al. ...
For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited. ...
doi:10.1109/access.2020.3029868
fatcat:ijx3novui5cdzi6xg25fhzyimy
An extensive and systematic literature review for hybrid flowshop scheduling problems
2022
International Journal of Industrial Engineering Computations
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In the HFSP, a series of jobs are processed respectively in a set of stages that at least one of these stages has more than one parallel machine (identical, uniform or unrelated). ...
Hybrid flowshop scheduling problem (HFSP) is a mixture of two classical scheduling problems as parallel machine scheduling (PMS) and flowshop scheduling (FS). ...
Han et al. (2019b) applied a novel method integrating SA and Hopfield neural network algorithms to solve HFSP with a public buffer and indicated performance efficiency of this method compared with different ...
doi:10.5267/j.ijiec.2021.12.001
fatcat:hfmfq3h73femphrdw4wgpedsfq
AI-based methods to resolve Real time scheduling for embedded systems
2021
International Journal of Cognitive Informatics and Natural Intelligence
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aware scheduling algorithms for ES. ...
We end this survey by a discussion putting the light on the main challenges and the future directions. ...
The proposed algorithm tries to minimize five parameters that are the total tardiness of tasks, the total number of utilized processors, the total completion time, the total waiting time of tasks, and ...
doi:10.4018/ijcini.290308
fatcat:2hax7lts4vanbin4iqxy4cthwm
Lagrangian relaxation neural networks for job shop scheduling
Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)
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In fact, the digital implementation of LRNN for job shop scheduling is similar to the traditional LR approaches. ...
When applying the network for job shop scheduling, the separability of problem formulation is fully exploited, and a new neuron-based dynamic programming is developed making innovative use of the subproblem ...
The problem is to determine operation beginning times so that the total weighted part earliness and tardiness is minimized. ...
doi:10.1109/robot.1998.677428
dblp:conf/icra/LuhZW98
fatcat:zrm34t4sfnatbojmkcp7rqpv3u
EFFICIENT MONITORING AND RESOURCE MANAGEMENT WITH SENSOR NETWORKS
2013
unpublished
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In this case one possible objective is to minimize the total tardiness (TT) or to uniformly distribute the number of jobs over capacity. ...
Hopfield neural network to solve the quadratic optimization tasks. ...
fatcat:nkycowbxlzds3drtnechprtjoe
Applying neural network based algorithms in communication technology
2019
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1,29-31) 29 The next day, he saw Jesus coming towards him and said, 'Look, there is the lamb of God that takes away the sin of the world. 30 It was of him that I said, "Behind me comes one who has passed ...
Ő veszi el a világ bűneit. 30 Róla mondtam: A nyomomba lép valaki, aki nagyobb nálam, mert előbb volt, mint én. 31 Én sem ismertem, de azért jöttem vízzel keresztelni, hogy megismertessem Izraellel" (Jn ...
B.1 Hopfield Neural Network (HNN) Throughout the dissertation the Hopfield Neural Network is used as one main type of RNN. ...
doi:10.15774/ppke.itk.2019.006
fatcat:fs4k5akncrfo5ayggrfotianem
Combined optimization algorithms applied to pattern classification
[article]
2014
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Our findings make contributions to a) the field of Machine Learning, as the proposed method is applicable in training feedforward neural networks, and to b) the field of circuit complexity by proposing ...
The depth vs size problem of neural networks is one of the hardest problems in theoretical computing, with very little progress over the past decades. ...
This novel approach gives future directions for how to treat higher levels in neural networks. ...
doi:10.18745/th.14326
fatcat:pjtzcd7mfvfz3et2nkers4wihi