A DIRECTED MUTATION OPERATION FOR THE DIFFERENTIAL
EVOLUTION ALGORITHM
Hui-Yuan Fan
1, 2
, and Jouni Lampinen
1,*
1
Department of Information Technology
Lappeenranta University of Technology
P. O. Box 20, FIN-53851
Lappeenranta, Finland
E-mail: {fan, jlampine}@lut.fi
2
School of Energy and Power Engineering
Xi'an Jiaotong University
Xi'an, 710049, P R China
Email: huiyfan@xjtu.edu.cn
*
Corresponding author
A modification is proposed for the differential evolution algorithm which aims to improve its convergence performance.
The modification embeds an additional operation, directed mutation, into an original version of the differential evolution.
The aim of this operation is to increase the convergence velocity of the differential evolution and thereby to obtain an
acceptable solution with a lower number of objective function evaluations. Such an improvement can be useful in many
real-world problems where the evaluation of a candidate solution is a computationally expensive operation and
consequently finding the global optimum or a good sub-optimal solution with the algorithm is too time-consuming, or even
impossible within the time available. The modified version of the differential evolution was empirically examined with a
suite of six well-known test problems and compared with the original version of the differential evolution algorithm. The
obtained numerical simulation results suggested drawing a preliminary conclusion that the modified version statistically
outperforms the original one.
Keywords: Evolutionary algorithm, Differential evolution, Nonlinear optimization.
A SIMULATION BASED GENETIC ALGORITHM FOR RISK-BASED
PARTNER SELECTION IN NEW PRODUCT DEVELOPMENT
Hongyi Cao and Dingwei Wang
Collage of Information Science and Engineering
Northeastern University
Shenyang, Liaoning, People’s Republic of China
In this paper, we investigate the problem of partner selection in new product development. First, we give a formal
description of the problem and model it as a 0-1integer programming problem with non-linear objective function and
stochastic constraints. Because of the complexity of the constraints, a Monte Carlo method is used to measure the
probabilities of stochastic constraints satisfaction. Then, we develop a simulation based genetic algorithm approach to
find the optimal solution for partner selection. The approach is demonstrated by some numerical examples. The
results show that the suggested approach has high efficiency and the model has potential to practical applications.
Keywords:
development.
Genetic algorithm, Monte Carlo simulation, Partner selection, Risk analysis, New product
ADVANCED PROCESS PLANNING AND SCHEDULING WITH PRECEDENCE
CONSTRAINTS AND MACHINE SELECTION USING A GENETIC ALGORITHM
Chiung Moon and Young Hae Lee
Department of Industrial Engineering
Hanyang University, Ansan 425-791, Korea
Tel:+82-31-400-5268 Fax:+82-31-400-3843
cumoon@hanyang.ac.kr or cumoon@hanmail.net
ABSTRACT
This paper deals with integrated process planning and scheduling problems with minimizing makespan for a flexible
flow manufacturing where alternative operations sequences with precedence constraints and alternative machines. The
problem is formulated as a mathematical model which includes operation sequencing, machine selection, and operation
scheduling. The integrated planning of having more than one machine to perform the same operation and precedence
constraints for sequences increases the size of the solution space, and consequently, makes the problem even more complex.
We develop a new genetic algorithm approach using topological sort to solve the model efficiently. Schedules with
operations sequences and machine selections are currently decided by the proposed approach. Some experimental results
are presented for various problem sizes and parameter settings to describe the performance of the proposed approach.
KEYWORDS
Integrated planning and scheduling, topological sort, traveling salesman problem, genetic algorithms.
OPTIMIZING THE PRODUCTION SCHEDULING OF A PETROLEUM
REFINERY THROUGH GENETIC ALGORITHMS
Mayron Rodrigues de Almeida
Sílvio Hamacher
Marco Aurélio Cavalcanti Pacheco
Marley Maria B.R. Vellasco
Department of Industrial Engineering
Pontifícia Universidade Católica do Rio de Janeiro
Rua Marquês de São Vicente, 225, Gávea, Rio de Janeiro, RJ,
Brasil, 22453-900
{Mayron@ons.org.br ; Hamacher@rdc.puc-rio.br}
ICA – Laboratory of Applied Computational Intelligence
Department of Electrical Engineering
Pontifícia Universidade Católica do Rio de Janeiro
Rua Marquês de São Vicente, 225, Gávea, Rio de Janeiro, RJ,
Brasil, 22453-900
{Marco@ele.puc-rio.br ; Marley@ele.puc-rio.br}
This paper presents a method, based on Genetic Algorithms, to optimize the production scheduling of the fuel oil and
asphalt area in a petroleum refinery. The fuel oil and asphalt area is a multi-product plant, with two machine stages –
one mixer and a set of tanks – with no setup time and with resource constrains in continuous operation. Two genetic
algorithm models were developed to establish the sequence and size of all production shares. A special mutation
operator – Neighborhood Mutation - is proposed to minimize the number of changes in the production. A multiobjective fitness evaluation technique, based on an energy minimization method, was also incorporated to the Genetic
Algorithm models. The results obtained confirm that the proposed Genetic Algorithm models, associated with the
multi-objective energy minimization method, are able to solve the scheduling problem, optimizing the refinery
operational objectives.
Keywords: Production Scheduling, Petroleum Refinery, Genetic Algorithms.
A Genetic Algorithm Approach to Solving a Multiple-Inventory Loading Problem
J. Cole Smith*
Department of Systems and Industrial Engineering
University of Arizona
P.O. Box 210020
Tucson, AZ 85721
Email: cole@sie.arizona.edu
In this paper we consider a multiple-inventory loading problem involving a set of commodities that must be
transported from a distributor to a retailer. The vehicle carrying out this distribution is divided into several
compartments, in which only one type of commodity may be loaded. The problem becomes one of determining
optimal assignments of vehicle compartments to commodities in order to minimize a mix of transportation and
inventory costs. We first demonstrate the weakness of the underlying linear relaxation of a traditional mixed-integer
programming approach that must be solved in a branch-and-bound framework. Instead of pursuing the development
of an exact algorithm, we instead recommend the use of a genetic algorithm to quickly provide good quality
solutions. Next, we introduce an additional strategy for defeating symmetry complications arising in certain specially
structured problems. Finally, the effectiveness of each of the proposed techniques is demonstrated on a test bed of
problems.
Keywords: Genetic algorithms, Mathematical modeling, Inventory management, Logistics
*
Acknowledgement: This research was supported by University of Arizona Foundation and by the Office of the Vice
President for Research and Graduate Studies. The author also gratefully acknowledges the comments provided by two
anonymous referees, the Guest Editor, and by Mr. Ashwin Naik, which led to an improved presentation of this topic.
A NEURAL NETWORKS APPROACH FOR DUE-DATE ASSIGNMENT IN A
WAFER FABRICATION FACTORY
Pei-Chann Chang and Jih-Chang Hsieh
Department of Industrial Engineering
Yuan Ze University
Nei-Li, Taoyuan, Taiwan
Email: iepchang@saturn.yzu.edu.tw
The production processes in a wafer fabrication factory are very complicated and time-consuming. This presents a
challenging problem to the production planning and scheduling department for the due-date assignment of each
order. This research proposes a simulation model to mimic a real wafer fabrication factory and the flowtime of each
order is collected for the purpose of due-date assignment. Various influential variables related to the flowtime of
each order are identified through regression analysis. Accordingly, a neural network model is established to forecast
the due-date of each order. The system is very applicable in the real world and the experimental results show that
the proposed approach is very convincing when compared with the traditional approaches.
Keywords: Wafer fabrication factory, Due-date assignment, Backpropagation neural networks
JOINT MONITORING OF THE MEAN AND VARIANCE OF A PROCESS BY
USING AN ARTIFICIAL NEURAL NETWORK APPROACH
Chuen-Sheng Cheng and Shin-Jia Chen
Department of Industrial Engineering and Management
Yuan-Ze University
135 Yuan-Tung Road, Taoyuan,Taiwan, R.O.C.
In this paper we consider the joint control of process mean and variance using artificial neural network technology. The
performance of the neural network was evaluated by estimating the recognition accuracy. Extensive comparisons show that
the neural network appears to be a good control procedure for joint monitoring of the process mean and variance.
Keywords:
Shewhart Control Chart, Neural Network.
COMPARISON OF NEURAL AND STATISTICAL ALGORITHMS FOR
SUPERVISED CLASSIFICATION OF MULTI-DIMENSIONAL DATA
Te-Sheng Li1, Chang-You Chen2 and Chao-Ton Su2
1Department
of Industrial Engineering and Management
Minghsin Institute of Technology,
Hsinchu, 300 Taiwan
2Department
of Industrial Engineering and Management
National Chiao Tung University,
Hsinchu, 300 Taiwan
Various algorithms for supervised classification of multi-dimensional data have been implemented in the past decades.
Among these algorithms, neural and statistical classifiers are two major methodologies used in the literature. In this paper, a
comparison of different neural networks and statistical algorithms used for classification is presented. Three types of neural
classifiers are considered: Back-propagation (BP) network, Radial Basis Function (RBF), and Learning Vector Quantization
(LVQ). Meanwhile, the k-nearest neighbor (KNN) statistical classifier and linear discriminant analysis (LDA) are also
discussed in order to compare the accuracy of classification with those of using neural network models. This paper includes
an introduction to the theoretical background of the classifiers, their implementation procedures, and two case studies to
evaluate their performance in diagnosis of diseases and glass identification. Both neural networks and statistical models are
demonstrated to be efficient and effective methods for multi-dimensional data classification. For neural classifiers, the type
of neural network, the type of data, and the parameters of the neural network may have considerable impact on the
classification accuracy. For statistical models, the type of data distribution and the criteria used to determine the thresholds
are the two most important factors in classification. In the cases studied in this paper, the overall performance of neural
networks is better than that of statistical models. Finally, the comparison and discussion of these approaches are presented
in view of practical and theoretical considerations.
Keywords:
Back-propagation, Radial Basis Function, Learning Vector Quantization, k-nearest neighbor,
Discriminant analysis
THE PREDICTION OF AIRPLANE LANDING GRAVITY USING CASEBASED REASONING
Chaochang Chiu and Nan-Hsing Chiu
Department of Information Management
Yuan Ze University
Taoyuan, Taiwan, R. O. C.
Email:{C. C. Chiu, imchiu@saturn.yzu.edu.tw; N. H. Chiu, s887720@mail.yzu.edu.tw}
Most flight accidents occurring worldwide are due to the lack of an appropriate approach in the landing
phase. Recent data mining developments have provided aviation insights into the landing phase. Among
these methods, case-based reasoning is a potential approach that can be applied for predicting landing
gravity. This research proposes a novel model construction method that consists of non-linear similarity
functions and dynamic weighting mechanisms to optimize the prediction accuracy. We illustrate our
approach with the data obtained directly from flight data recorders of Boeing 747-400 airplanes. This
experiment also shows that non-linear similarity functions demonstrate better prediction accuracy over the
results from other approaches.
Keywords:
Case-based reasoning, Genetic algorithms, Feature weights, Similarity functions, Landing gravity.