A Neural Network Approach to Cost Minimizatin in a Production Scheduling Setting

A Neural Network Approach to Cost Minimizatin in a Production Scheduling Setting

Kun-Chang Lee (Sungkyunkwan University, Korea) and Tae-Young Paik (Sungkyunkwan University, Korea)
Copyright: © 2006 |Pages: 17
DOI: 10.4018/978-1-59140-902-1.ch014
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Cost managers working in manufacturing firms have suffered from the difficulty of determining an optimal cost control strategy. Though the concept of ABC can provide a theoretically nice scheme for cost control, it has been widely known that cost managers have serious trouble comprehending the ABC scheme and applying it to real cost control situations. In this sense, proposing a heuristic method by which cost managers can obtain an approximate cost control strategy comparable to one obtained by ABC would be very meaningful from the view of both theory and practice. To fulfill this need, we suggest using a multi-layered perceptron (MLP) neural network model with backpropagation learning algorithm, and then approximating the optimal cost control strategy of ABC. The primary concern of this study is to investigate whether such solutions approximated by the MLP would be valid from a statistical perspective. If true, it would mean that cost managers can depend on the neural network method to come up with an optimal cost control strategy comparable to applying ABC. To show the validity of the proposed cost control strategy by using the MLP, this study proposes to solve two problems within the context of a production scheduling situation, using ABC: (1) neural network-based total cost estimation (NNTCE); and (2) neural network-based cycle time estimation (NNCTE). For experimental setup, we assume that two products sharing five types of exogenous variables and three types of endogenous variables are manufactured at the same facility. The MLP neural network approach to NNTCE and NNCTE was generated with a set of 180 training data and 125 test data, all of which were proved to be statistically identical with the ABC results.

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Table of Contents
Alejandro Pazos
Chapter 1
Ana B. Porto, Alejandro Pazos
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Neuroglial Behaviour in Computer Science
Chapter 2
Eduardo D. Martin, Alfonso Araque
Artificial neural networks are a neurobiologically inspired paradigm that emulates the functioning of the brain. They are based on neuronal... Sample PDF
Astrocytes and the Biological Neural Networks
Chapter 3
Paulo Cortez, Miguel Rocha, José Neves
This chapter presents a hybrid evolutionary computation/neural network combination for time series prediction. Neural networks are innate candidates... Sample PDF
Time Series Forecasting by Evolutionary Neural Networks
Chapter 4
Julián Dorado, Nieves Pedreira, Mónica Miguelez
This chapter presents the use of Artificial Neural Networks (ANN) and Evolutionary Computation (EC) techniques to solve real-world problems... Sample PDF
Development of ANN with Adaptive Connections by CE
Chapter 5
Daniel Manrique, Juan Rios, Alfonso Rodriguez-Paton
This chapter describes genetic algorithm-based evolutionary techniques for automatically constructing intelligent neural systems. These techniques... Sample PDF
Self-Adapting Intelligent Neural Systems Using Evolutionary Techniques
Chapter 6
Daniel Rivero, Miguel Varela, Javier Pereira
A technique is described in this chapter that makes it possible to extract the knowledge held by previously trained artificial neural networks. This... Sample PDF
Using Genetic Programming to Extract Knowledge from Artificial Neural Networks
Chapter 7
Marcos G. Pose, Alberto C. Carollo, José M.A. Garda, Mari P. Gomez-Carracedo
This chapter shows several approaches to determine how the most relevant subset of variables can perform a classification task. It will permit the... Sample PDF
Several Approaches to Variable Selection by Means of Genetic Algorithms
Chapter 8
Juan R. Rabunal, Juan Puertas
This chapter proposes an application of two techniques of artificial intelligence in a civil engineering area: the artificial neural networks (ANN)... Sample PDF
Hybrid System with Artificial Neural Networks and Evolutionary Computation in Civil Engineering
Chapter 9
Belén Gonzalez, M. Isabel Martinez, Diego Carro
This chapter displays an example of application of the ANN in civil engineering. Concretely, it is applied to the prediction of the consistency of... Sample PDF
Prediction of the Consistency of Concrete by Means of the Use of Artificial Neural Networks
Chapter 10
J. Sethuraman
Soft computing is popularly referred to as a collection of methodologies that work synergistically and provide flexible information processing... Sample PDF
Soft Computing Approach for Bond Rating Prediction
Chapter 11
Robert Perkins, Anthony Brabazon
The practical application of MLPs can be time-consuming due to the requirement for substantial modeler intervention in order to select appropriate... Sample PDF
Predicting Credit Ratings with a GA-MLP Hybrid
Chapter 12
Music and Neural Networks  (pages 239-264)
Giuseppe Buzzanca
This chapter pertains to the research in the field of music and artificial neural networks. The first attempts to codify human musical cognition... Sample PDF
Music and Neural Networks
Chapter 13
Alfonso Iglesias, Bernardino Arcay, José M. Cotos
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Connectionist Systems for Fishing Prediction
Chapter 14
Kun-Chang Lee, Tae-Young Paik
Cost managers working in manufacturing firms have suffered from the difficulty of determining an optimal cost control strategy. Though the concept... Sample PDF
A Neural Network Approach to Cost Minimizatin in a Production Scheduling Setting
Chapter 15
Tarun Bhaskar, Narasimha Kamath B.
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Intrusion Detection Using Modern Techniques: Integration of Genetic Algorithms and Rough Set with Neural Networks
Chapter 16
Alejandra Rodriguez, Carlos Dafonte, Bernardino Arcay, Iciar Carricajo, Minia Manteiga
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