Artificial Neural Networks in Manufacturing: Scheduling

Artificial Neural Networks in Manufacturing: Scheduling

George A. Rovithakis (Aristotle University of Thessaloniki, Greece), Stelios E. Perrakis (Technical University of Crete, Greece) and Manolis A. Christodoulou (Technical University of Crete, Greece)
Copyright: © 2006 |Pages: 26
DOI: 10.4018/978-1-59140-670-9.ch014
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In this chapter, a neuroadaptive scheduling methodology, approaching machine scheduling as a control-regulation problem, is presented and evaluated by comparing its performance with conventional schedulers. Initially, after a brief reference to the context of existing solutions, the evaluated controller is thoroughly described. Namely, the employed dynamic neural network model, the subsequently derived continuous time neural network controller and the control input discretization that yield the actual dispatching times are presented. Next, the algorithm guaranteeing system stability and controller-signal boundedness and robustness are evaluated on an existing industrial test case that constitutes a highly nonacyclic deterministic job shop with extremely heterogeneous part-processing times. The major simulation study, employing the idealistic deterministic job-shop abstraction, provides extensive comparison with conventional schedulers, over a broad range of raw-material arrival rates, and through the extraction of several performance indices verifies its superb performance in terms of manufacturing-system stability and low makespan, low average lead times, WIP, inventory, and backlogging costs. Eventually, these extensive experiments highlight the practical value and the potential of the mathematical properties of the proposed neuroadaptive controller algorithm and its suitability for the control of nontrivial manufacturing cells.

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Table of Contents
Joarder Kamruzzaman, Rezaul Begg, Ruhul Sarker
Joarder Kamruzzaman, Rezaul Begg, Ruhul Sarker
Chapter 1
Joarder Kamruzzaman, Ruhul A. Sarker
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Chapter 2
Ruhul A. Sarker, Hussein A. Abbass
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Simultaneous Evolution of Network Architectures and Connection Weights in Artificial Neural Networks
Chapter 3
David Encke
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Neural Network-Based Stock Market Return Forecasting Using Data Mining for Variable Reduction
Chapter 4
Yuehui Chen, Ajith Abraham
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Hybrid-Learning Methods for Stock Index Modeling
Chapter 5
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Financial time-series data is characterized by nonlinearities, discontinuities, and high-frequency multipolynomial components. Not surprisingly... Sample PDF
Application of Higher-Order Neural Networks to Financial Time-Series Prediction
Chapter 6
Masoud Mohammadian, Mark Kingham
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Hierarchical Neural Networks for Modelling Adaptive Financial Systems
Chapter 7
Sumit Kumar Bose, Janardhanan Sethuraman, Sadhalaxmi Raipet
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Forecasting the Term Structure of Interest Rates Using Neural Networks
Chapter 8
Joarder Kamruzzaman, Ruhul A. Sarker, Rezaul K. Begg
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Modeling and Prediction of Foreign Currency Exchange Markets
Chapter 9
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Chapter 10
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Neural Networks in Manufacturing Operations
Chapter 11
M. Imad Khan, Saeid Nahavandi, Yakov Frayman
This chapter presents the application of a neural network to the industrial process modeling of high-pressure die casting (HPDC). The large number... Sample PDF
High-Pressure Die-Casting Process Modelling Using Neural Networks
Chapter 12
Sergio Cavalieri, Paolo Maccarrone, Roberto Pinto
The estimation of the production cost per unit of a product during its design phase can be extremely difficult, especially if information about... Sample PDF
Neural Network Models for the Estimation of Product Costs: An Application in the Automotive Industry
Chapter 13
Tapabrata Ray
Surrogate-assisted optimization frameworks are of great use in solving practical computationally expensive process-design-optimization problems. In... Sample PDF
A Neural-Network-Assisted Optimization Framework and Its Use for Optimum-Parameter Identification
Chapter 14
George A. Rovithakis, Stelios E. Perrakis, Manolis A. Christodoulou
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Artificial Neural Networks in Manufacturing: Scheduling
Chapter 15
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Recognition of Lubrication Defects in Cold Forging Process with a Neural Network
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