Applied Artificial Intelligence

Applied Artificial Intelligence Proceedings of the 7th International FUNS Conference

Edited by

Da Ruan Pierre D'hondt Paolo F. Fantoni Martine De Cock Mike Nachtegael Etienne E. Kerre

flpplied Artificial Intelligence

Published Volumes Fuzzy Logic and Intelligent Technologies in Nuclear Science Eds. D. Ruan, P. D'hondt, P. Govaerts, and E. E. Kerre ISBN 981-02-2003-0 (1994) Intelligent Systems and Soft Computing for Nuclear Science and Industry Eds. D. Ruan, P. D'hondt, P. Govaerts, and E. E. Kerre ISBN 981-02-2738-8 (1996) Fuzzy Logic and Intelligent Technologies for Nuclear Science and Industry Eds. D. Ruan, H. Ait Abderrahim, P. D'hondt, and E. E. Kerre ISBN 981-02-3532-1 (1998) Intelligent Techniques and Soft Computing in Nuclear Science and Engineering Eds. D. Ruan, H. Ait Abderrahim, P. D'hondt, and E. E. Kerre ISBN 981-02-4356-1 (2000) Computational Intelligent Systems for Applied Research Eds. D. Ruan, P. D'hondt, and E. E. Kerre ISBN 981-238-066-3 (2002) Applied Computational Intelligence Eds. D. Ruan, P. D'hondt, M. De Cock, M. Nachtegael, and E. E. Kerre ISBN 981-238-873-7 (2004)

Applied Artificial Intelligence Proceedings of the 7th International FUNS Conference Genova, Italy

29 - 31 August 2006

Edited by

Da Ruan Pierre D'hondt Belgian Nuclear Research Centre (SCK'CEN), Belgium

Paolo F. Fantoni Institute for Energy Technology, Norway

Martine De Cock Mike Nachtegael Etienne E. Kerre Ghent University, Belgium

YJ> World Scientific NEW JERSEY • LONDON

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APPLIED ARTIFICIAL INTELLIGENCE Proceedings of the 7th International FLINS Conference Copyright © 2006 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher.

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FOREWORD FLINS, an acronym for Fuzzy Logic and Intelligent technologies in Nuclear Science, is a well-established international research forum to advance the theory and applications of computational intelligence for applied research in general and for nuclear science and engineering in particular. FLINS2006 is the seventh in a series of conferences on Applied Artificial Intelligence. It follows the successful FLINS'94 in Mol, FLINS'96 in Mol, FLINS'98 in Antwerp, FLINS2000 in Bruges, FLINS2002 in Gent, and FLINS2004 in Blankenberge, Belgium. FLINS2006 in Genova, Italy, for the first time being held outside of Belgium, once again aims at covering state-of-the-art research and development in all aspects related to Applied AI. The principal missions of FLINS are: (1) conducting research on applied AI systems for solving intricate problems pertaining to nuclear/power research and related complex systems; (2) bridging the gap between machine intelligence and complex systems via joint research with Belgian, European, and international research institutes and universities; and (3) encouraging interdisciplinary research and bringing multi-discipline researchers together via the international FLINS conferences on applied AI. FLINS2006, co-organized by the Belgian Nuclear Research Centre (SCK-CEN), Ghent University (UGent) in Belgium, IFE-OECD Halden Reactor Project in Norway, Softeco and Convention Bureau Genova in Italy, offers a unique international forum to present and discuss techniques that are new and promising for applied AI and to launch international co-operations. The FLINS2006 proceedings consist of a series of invited lectures by distinguished professors and individual presentations, in a total of 131 papers selected out of 207 regular submissions and 6 invited papers from 25 countries. The volume begins with the list of the invited lectures: (a) Computation with information described in natural language by Lotfi Zadeh (University of California, Berkeley, USA), (b) Learning techniques in service robotic environment by Zenn Bien (Korea Advanced Institute of Science and Technology, South Korea), (d) Foundations of many-valued reasoning by Daniele Mundici (University of Florence, Italy), (e) Integrated operations in arctic environments by Fridtjov 0wre (Institute for Energy Technology/Halden Reactor Project, Norway), (f) Can the semantic web be designed without using fuzzy logic? by Elie Sanchez (University of the Mediterranean, Marseille, France), and (g) The role of soft computing in applied sciences by Paul Wang

v

VI

(Duke University, Durham, USA). The volume is followed by three contributed parts: (1) Foundations and recent developments, (2) Decision making and knowledge discovery, and (3) Applied research and nuclear applications. At the previous FLINS2004 conference, we presented a FLINS gold medal to Lotfi Zadeh, Hans Zimmermann, Ronald Yager, Paul Wang, Madan Gupta, Javier Montero, Guoqing Chen, and Yang Xu for their long support to FLINS conferences. At FLINS2006, we present a FLINS gold medal to Zenn Bien, Daniele Mundici, Fridtjov 0wre, and Elie Sanchez for their support to FLINS conferences. We also present one more FLINS gold medal to our long time nuclear and AI professor Marzio Marseguerra on the occasion of his retirement. Our 2006 FLINS Outstanding Service Award goes to Cengiz Kahraman for his tremendous efforts to attract many Turkish researchers to FLINS2006. Special thanks are due to all contributors, referees, regular and invited sessions' chairs, and program committee members of FLINS2006 for their kind co-operation and enthusiasm for FLINS2006; to Pierre D'hondt and Etienne Kerre for their roles as FLINS advisors and program co-chairs of FLINS2006; to Martine De Cock and Mike Nachtegael (FLINS2006 conference co-managers) and Paolo Fantoni (the local organization chair of FLINS2006 in Genova) for their great efforts to make FLINS2006 a success, and to Chelsea Chin (Editor, World Scientific) for her kind advice and help to publish this volume.

Da Ruan, FLINS2006 chair Mol & Gent, May 2006

CONTENTS Foreword D. Ruan

v

Invited Lectures

1

Computation with Information Described in Natural Language — The Concept of Generalized-Constraint-based Computation L.A. Zadeh

3

Learning Techniques in Service Robotic Environment Z.Z. Bien, H.E. Lee, S.W. Lee, andK.H. Park

5

Foundations of Many-Valued Reasoning D. Mundici

8

Integrated Operations in Arctic Environments F. 0wre

11

Can the Semantic Web be Designed without Using Fuzzy Logic? E. Sanchez

13

The Role of Soft Computing in Applied Sciences P.P. Wang

16

PART 1: FOUNDATIONS AND RECENT DEVELOPMENTS

17

A Functional Tool for Fuzzy First Order Logic Evaluation V. Lopez, J.M. Cleva, and J. Montero

19

Field Theory and Computing with Words G. Resconi and M Nikravesh

27

New Operators for Context Adaptation of Mamdani Fuzzy Systems A. Botta, B. Lazzerini, and F. Marcelloni

35

vn

Vlll

Using Parametric Functions to Solve Systems of Linear Fuzzy Equations — An Improved Algorithm 43 A. Vroman, G. Deschrijver, and E.E. Kerre Numerical Implementation Strategies of the Fuzzy Finite Element Method for Application in Structural Dynamics D. Moens andD. Vandepitte

51

Environmental/Economic Dispatch Using Genetic Algorithm and Fuzzy Number Ranking Method G. Zhang, G. Zhang, J. Lu, andH. Lu

59

Minimizing the Number of Affected Concepts in Handling Inconsistent Knowledge E. Gregoire

67

A Knowledge Management based Fuzzy Model for Intelligent Information Disposal X. Liang, Z. Zhang, D. Zhu, andB. Tang

75

A Semantical Assistant Method for Grammar Parsing Y Wang, G. Gan, Z. Wu, andF. Li

81

Lukasiewicz Algebra Model of Linguistic Values of Truth and Their Reasoning L. Yi, Z. Pei, and Y. Xu

87

Propositional Logic L6P(X) based on Six Linguistic Term Lattice Implication Algebra W. Wang, Y. Xu, andL. Zou

95

Weighting Qualitative Fuzzy First-Order Logic and its Resolution Method L. Zou, B. Li, W. Wang, and Y. Xu

103

Annihilator and Alpha-Subset X.Q. Long, Y. Xu, andL.Z. Yi

111

IX

Multi-Fold Fuzzy Implicative Filter of Residuated Lattice Implication Algebras H. Zhu, J. Zhao, Y. Xu, andL. Yi

116

PD-Algebras Y. Liu and Y. Xu

122

Li-Yorke Chaos in a Spatiotemporal Chaotic System P. Li, Z.Li, W.A. Halang, and G Chen

130

On the Probability and Random Variables on IF Events B. Riecan

138

Another Approach to Test the Reliability of a Model for Calculating Fuzzy Probabilities C. Huang and D. Jia

146

A Novel Gaussian Processes Model for Regression and Prediction Y. Zhou, T. Zhang, and Z. Lu

154

On PCA Error of Subject Classification L.H. Feng, F.S. Hu, andL. Wan

162

Optimized Algorithm of Discovering Functional Dependencies with Degrees of Satisfaction Q. Wei and G. Chen

169

From Analogy Reasoning to Instances based Learning W. Pan and T. Li

177

A Kind of Weak Ratio Rules for Forecasting Upper Bound Q. Wei, B. Jiang, K. Wu, and W. Wang

185

Combining Validity Indexes and Multi-Objective Optimization based Clustering T. Ozyer andR. Alhajj

193

A Method for Reducing Linguistic Terms in Sensory Evaluation Using Principle of Rough Set Theory X. Liu, X. Zeng, L. Koehl, and Y. Xu The Specificity of Neural Networks in Extracting Rules from Data M. Holeha

201

209

Stable Neural Architecture of Dynamic Neural Units with Adaptive Time Delays /. Bukovsky, J. Bila, and MM. Gupta

215

Evaluation Characteristics for Multilayer Perceptrons and Takagi Sugeno Models W. Kaestner, T. Foerster, C. Lintow, R. Hampel

223

Research on Improved Multi-Objective Particle Swarm Optimization Algorithms D. Zhao and W. Jin

231

PART 2: DECISION MAKING AND KNOWLEDGE DISCOVERY

239

Knowledge Discovery for Customer Classification on the Principle of Maximum Profit C. Zeng, YXu, and W. Xie

241

An Integrated Analysis Method for Bank Customer Classification J. Zhang, J. Lu, G. Zhang, andX. Yan

247

Two Stage Fuzzy Clustering based on Knowledge Discovery and its Application Y. Qian

253

Application of Support Vector Machines to the Modelling and Forecasting of Inflation M. Marcek and D. Marcek

259

XI

Assessing the Reliability of Complex Networks: Empirical Models based on Machine Learning CM. Rocco andM. Muse Hi

267

Fuzzy Time Series Modelling by SCL Learning M. Marcek andD. Marcek

275

Investment Analysis Using Grey and Fuzzy Logic C. Kahraman andZ. Ulukan

283

An Extended Branch-And-Bound Algorithm for Fuzzy Linear Bilevel Programming G. Zhang, J. Lu, and T. Dillon

291

Fuzzy Multi-Objective Interactive Goal Programming Approach to Aggregate Production Planning T. Ertay

299

Fuzzy Linear Programming Model for Multiattribute Group Decision Making to Evaluate Knowledge Management Performance Y.E. Albayrak and Y.C. Erensal

307

Product-Mix Decision with Compromise LP Having Fuzzy Objective Function Coefficients (CLPFOFC) S. Susanto, P. Vasant, A. Bhattacharya, and C. Kahraman

315

Modeling the Supply Chain: A Fuzzy Linear Optimization Approach N.Y. Ates and S. Cevik

321

A Fuzzy Multi-Objective Evaluation Model in Supply Chain Management X. Liang, X Liu, D. Zhu, B. Tang, and H. Zhuang

329

Evaluating Radio Frequency Identification Investments Using Fuzzy Cognitive Maps A. Ustundag and M. Tanyas Analysing Success Criteria for ICT Projects K. Milis andK. Van hoof

335

343

Xll

Multi-Attribute Comparison of Ergonomics Mobile Phone Design based on Information Axiom 351 G. Yucel and E. Aktas Facility Location Selection Using a Fuzzy Outranking Method /. Kaya andD. Cinar

359

Evaluation of the Suppliers' Environmental Management Performances by a Fuzzy Compromise Ranking Technique G. Biiyiikdzkan and O. Feyzioglu

367

A Fuzzy Multiattribute Decision Making Model to Evaluate Knowledge based Human Resource Flexibility Problem M.E. Genevois and Y.E. Albayrak

375

Fuzzy Evaluation of on the Job Training Alternatives in Industrial Companies G. Kayakutlu, G. Biiyiikdzkan, B.C. Metin, andS. Ercan

383

A Study of Fuzzy Analytic Hierarchy Process: An Application in Media Sector M. Ozyol and Y.E. Albayrak

389

Prioritization of Relational Capital Measurement Indicators Using Fuzzy AHP A. Beskese and F.T. Bozbura

395

Multicriteria Map Overlay in Geospatial Information System via Intuitionistic Fuzzy AHP Method T. Silavi, M.R. Malek, and M.R. Delavar

401

A Consensus Model for Group Decision Making in Heterogeneous Contexts L. Martinez, F. Mata, andE. Herrera-Viedma

409

A Linguistic 360-Degree Performance Appraisal Evaluation Model R. de Andres, J.L. Garcia-Lapresta, andL. Martinez

417

Xlll

An Interactive Support System to Aid Experts to Express Consistent Preferences S. Alonso, E. Herrera-Viedma, F. Herrera, F.J. Cabrerizo, and F.Chiclana

425

A Model of Decision-Making with Linguistic Information based on Lattice-Valued Logic J. Ma, S. Chen, and Y. Xu

433

Information Integration based Team Situation Assessment in an Uncertain Environment J. Lu and G. Zhang

441

Scheduling a Flowshop Problem with Fuzzy Processing Times Using Ant Colony Optimization S. Kilic and C. Kahraman

449

Time Dependent Vehicle Routing Problem with Fuzzy Traveling Times under Different Traffic Conditions T. Demirel andN.C. Demirel

457

A Programming Model for Vehicle Schedule Problem with Accident C. Zeng, Y. Xu, and W. Xie

465

A Web Data Extraction Model based on XML and its Improvement W. Xie and C. Zeng

471

Evaluation of E-Service Providers Using a Fuzzy Multi-Attribute Group Decision-Making Method C. Kahraman and G. Biiyukozkan

477

A Case based Research on the Directive Function of Website Intelligence to Human Flow Z Lu, Z. Deng, and Y. Wang

485

Genetic Algorithm for Interval Optimization and its Application in the Web Advertising Income Control Q. Liao andX. Li

493

XIV

Design and Implementation of an E-Commerce Online Game for Education and Training P. Zhang, M. Fang, Y. Zeng, and J. Yu

499

Selection Model of Semantic Web Services X. Wang, Y. Zhao, and W.A. Halang

505

A Trust Assertion Maker Tool P. Ceravolo, E. Damiani, M. Viviani, A. Curcio, and M. Pinelli

511

Web Access Log Mining with Soft Sequential Patterns C. Fiot, A. Laurent, andM. Teisseire

519

An Improved ECC Digital Signature Algorithm and Application in E-Commerce X.P. Xu

525

An Immune Symmetrical Network-based Service Model in Peer-to-Peer Network Environment X. Zhang, L. Ren, and Y. Ding

533

Machine Learning and Soft-Computing in Bioinformatics — A Short Journey F.-M. Schleif, T. Villmann, T. Elssner, J. Decker, and M. Kostrzewa Full-Length HPLC Signal Clustering and Biomarker Identification in Tomato Plants M. Striekert, T. Czauderna, S. Peterek, A. Matros, H.-P. Mock, and U. Seiffert Feature Scoring by Mutual Information for Classification of Mass Spectra C. Krier, D. Francois, V. Wertz, and M. Verleysen Peak Intensity Prediction for PMF Mass Spectra Using Support Vector Regression W. Timm, S. Bocker, T. Twellmann, and T. W. Nattkemper

541

549

557

565

XV

Learning Comprehensible Classification Rules from Gene Expression Data Using Genetic Programming and Biological Ontologies 573 B. Goertzel, L. Coelho, C. Pennachin, I. Goertzel, M. Queiroz, F. Prosdocimi, andF. Lobo Protein Secondary Structure Prediction: How to Improve Accuracy by Integration L. Palopoli, S.E. Rombo, G. Terracina, G. Tradigo, and P. Veltri

579

The Stabilization Effect of the Triplex Vaccine F. Pappalardo, S. Motta, E. Mastriani, M. Pennisi, and P.-L. Lollini

587

Learning Classifiers for High-Dimensional Micro-Array Data A. Bosin, N. Dessi, andB. Pes

593

Prediction of Residue Exposure and Contact Number for Simplified HP Lattice Model Proteins Using Learning Classifier Systems M. Stout, J. Bacardit, J.D. Hirst, J. Blazewicz, and N. Krasnogor

601

A Study on the Effect of Using Physico-Chemical Features in Protein Secondary Structure Prediction G.L.J. Rama, M. Palaniswami, D. Lai, and M. W. Parker

609

Gene Expression Data Analysis in the Membership Embedding Space: A Constructive Approach M. Filippone, F. Masulli, and S. Rovetta

617

BICA and Random Subspace Ensembles for DNA Microarray-Based Diagnosis B. Apolloni, G. Valentini, and A. Brega

625

Prediction of Sclerotinia Sclerotiorum (Lib) De Baey Disease on Winter Rapeseed (B. Napus) based on Grey GM(1,1) Model G. Liao and F. Xiao

633

XVI

PART 3: APPLIED RESEARCH AND NUCLEAR APPLICATIONS

641

Identification of Seismic Activities through Visualization and Scale-Space Filtering C. Qin, Y. Leung, and J. Zhang

643

Fuzzy Approximation Network Perturbation Systems and its Application to Risk Analysis in Transportation Capacity K. Zou

651

Application of Artificial Neural Networks in the Flood Forecast L. Feng and J. Lu

659

Integrated Management Pattern of Marine Security Synthesis Risk Y. Wang, X.H. Ren, Y.S. Ding, andC.Y. Yu

665

Risk Analysis and Management of Urban Rainstorm Water Logging in Tianjin S.Han, Y.Xie, andD.Li Study on Environmental Risk Influence Factor of Tongliao X.H. Ren, Y.H. Li, H.X. Tian, and Y. Wang

671

678

Practical Research of the Flood Risk based on Information Diffusion Theory X. Zhang and L. Feng

686

Risk Analysis for Agricultural Drought based on Neural Network Optimized by Chaos Algorithm L. Qiu, X. Chen, C. Duan, and Q. Huang

692

A Computer Simulation Method for Harmony among Departments for Emergency Management F. Yang and C. Huang

698

An Approach of Mobile Robot Environment Modeling based on Ultrasonic Sensors Array Principal Components 704 Y.Q. Zhang, F. Li, H.M. Wang, Z.G. Hou, M. Tan, MM Gupta, and P.N. Nikiforuk

XV11

Slam with Corner Features from a Novel Curvature-based Local Map Representation R. Vazquez-Martin, P. Nunez, J.C. Del Toro, A. Bandera, andF. Sandoval Obstacle Avoidance Learning for Biomimetic Robot Fish Z Shen, M. Tan, Z Cao, S. Wang, and Z Hou

711

719

Snake-Like Behaviors Using Macroevolutionary Algorithms and Modulation based Architectures J. A. Becerra, F. Bellas, R.J. Duro, and J. de Lope

725

Decision Tree and Lie Algebra Method in the Singularity Analysis of Parallel Manipulators K. Hao and Y. Ding

731

Combining AdaBoost with a Hill-Climbing Evolutionary Feature Search for Efficient Training of Performant Visual Object Detectors Y. Abramson, F. Moutarde, B. Stanciulescu, andB. Steux

737

Intelligent System Supporting Non-Destructive Evaluation of SCC Using Eddy Current Test S. Kanemoto, W. Cheng, I. Komura, M. Shiwa, and S. Tsunoyama

745

The Continuous-Sentential KSSL Recognition and Representation System Using Data Glove and Motion Tracking based on the Post Wearable PC J.H. Kim andK.S. Hong

753

On the Intuitionistic Denazification of Digital Images for Contrast Enhancement I.K. Vlachos and G.D. Sergiadis

759

A Heuristic Approach to Intuitionistic Fuzzification of Color Images I.K. Vlachos and G.D. Sergiadis Intuitionistic Fuzzy Feature Extraction for Query Image Retrieval from Colour Images K.S. Babu andR.S. Kumar

767

775

XV1U

Classification with Intuitionistic Fuzzy Region in Geospatial Information System M.R. Malek, J. Karami, and S. Aliabady

783

On-line Training Evaluation in Virtual Reality Simulators Using Fuzzy Bayes Rule R.M. de Moraes andL.S. Machado

791

Assessement of Gynecological Procedures in a Simulator based on Virtual Reality L.S. Machado, M.C. de Oliveira Valdek, andR.M. de Moraes

799

Screaming Racers: Competitive Autonomous Drivers for Racing Games F. Gallego, F. Llorens, and R. Satorre

805

Urban Signal Control Using Intelligent Agents M.A. Alipour and S. Jalili

811

Considerations on Uncertain Spatio-Temporal Reasoning in Smart Home Systems J. Liu, J.C. Augusto, andH. Wang

817

Neuro-Fuzzy Modeling for Fault Diagnosis in Rotating Machinery E. Zio and G. Gola

825

FLC Design for Electric Power Steering Automation J.E. Naranjo, C. Gonzalez, R. Garcia, and T. de Pedro

833

Studying on Acceleration Sensor's Fault-Tolerance Technology of Tilting Trains J. Lin, Y. Zhang, Y. Gao, and T. Li

839

A Risk-Risk Analysis based Abstraction Approach to Societal Problem-Solving in Nuclear Systems S.Rao

845

A Fuzzy Logic Methodology for Open Source Information Synthesis in a Non-Proliferation Framework I. Maschio

851

XIX

A Financial-Option Methodology for Determining a Fuzzy Discount Rate in Radioactive Waste Management P.L. Kunsch

859

Application of Intelligent Decision System to Nuclear Waste Depository Option Analysis D.L. Xu, J.B. Yang, B. Carle, F. Hardeman, andD. Ruan

867

Model of Fuzzy Expert System for the Calculation of Performance and Safety Indicator of Nuclear Power Plants K. C. Souto and R. Schirru

875

Artificial Intelligence Applied to Simulation of Radiation Damage in Ferritic Alloys 883 R.P. Domingos, G.M. Cerchiara, F. Djurabekova, andL. Malerba Particle Swarm Optimization Applied to the Combinatorial Problem in order to Solve the Nuclear Reactor Fuel Reloading Problem 891 A. Meneses andR. Schirru Use of Genetic Algorithm to Optimize Similar Pressurizer Experiments D. Botelho, P. de Sampaio, C. Lapa, C. Pereira, M. Moreira, and A. Barroso

899

Particle Swarm Optimization Applied to the Nuclear Core Reload Problem 907 M. Waintraub, R.P. Baptista, R. Schirru, and C. Pereira Parallel Evolutionary Methods Applied to a PWR Core Reload Pattern Optimization R. Schirru, A. de Lima, and M.D. Machado

915

Robust Distance Measures for On-Line Monitoring: Why Use Euclidean? D.R. Garvey and J. W. Hines

922

Multiple Objective Evolutionary Optimisation for Robust Design D.E. Salazar A., CM. Rocco S., andE. Zio

930

XX

Feature Selection for Transients Classification by a Niched Pareto Genetic Algorithm E. Zio, P. Baraldi, andN. Pedroni

938

Optimized Direct Fuzzy Model Reference Adaptive Control Applied to Nuclear Reactor Dynamics F. Cadini and E. Zio

946

A Fuzzy-Logic-Based Methodology for Signal Trend Identification E. Zio and I.C. Popescu

954

Identification of Transients in Nuclear Systems by a Supervised Evolutionary Possibilistic Clustering Approach 962 E. Zio, P. Baraldi, and D. Mercurio Signal Grouping Algorithm for an Improved on-line Calibration Monitoring System 970 M. Hoffmann Intelligent Transient Normalization for Improved Empirical Diagnosis D. Roverso

977

User Interface for Validation of Power Control Algorithms in a Triga Reactor J.S. Benitez-Read, C.L. Ramirez-Chavez, andD. Ruan

985

Author Index

993

PRODUCT-MIX DECISION WITH COMPROMISE LP HAVING FUZZY OBJECTIVE FUNCTION COEFFICIENTS (CLPFOFC) SANI SUSANTO* Senior Lecturer, Department of Industrial Engineering, Faculty of Industrial Technology, Parahyangan Catholic University, Jin. Ciumbuleuit 94, Bandung - 40141, Indonesia. PANDIAN VASANT* Research Lecturer, EEE Program, Universiti Teknologi Petronas, 31750 Tronoh.BSI, PerakDR, Malaysia. ARIJIT BHATTACHARYA § Examiner of Patents & Designs, The Patent Office, Bouddhik Sampada Bhawan, CP-2, Sector V, Salt Lake, Kolkata 700 091, West Bengal, India. CENGIZ KAHRAMAN** Professor, Department of Industrial Engineering, Istanbul Technical University, 34367 Macka Besiktas, Istanbul, Turkey. This paper outlines, first, a compromise linear programming (LP) having fuzzy objective function coefficients (CLPFOFC) and thereafter, a real-world industrial problem for product-mix selection involving 29 constraints and 8 variables is solved using CLPFOFC. This problem occurs in production planning management in which a decision-maker (DM) plays a pivotal role in making decision under a highly fuzzy environment. Authors have tried to find a solution that is flexible as well as robust for the DM to make an eclectic decision under real-time fuzzy environment.

1. Introduction The theory of fuzzy LP was developed to tackle imprecise or vague problems using the fundamental concept of artificial intelligence, especially in reasoning and modelling linguistic terms. Conventional mathematical programming techniques fail to solve fuzzy programming problems (Kolman and Beck, 1995). Thus, the CLPFOFC approach is best suited to solve some of real-life problems. E-mail: [email protected] [email protected] [email protected] ' [email protected]

315

316 Some previous attempts to set fuzzy intervals, where coefficients of the criteria are given by intervals, were reported by Bitran (1980), Jiuping (2000) and Sengupta, Pal and Chakroborty (2001). Wang (1997) used triangular MF for LP modelling. A real-life industrial problem for optimal product-mix selection involving 29 constraints and 8 variables has been delineated in this paper. 2. Product-Mix Problem of Chocoman, Inc. The firm Chocoman, Inc. manufactures 8 different kinds of chocolate products. There are 8 raw materials to be mixed in different proportions and 9 processes (facilities) to be utilized having limitations in resources of raw materials. Constraints, viz., product-mix requirement, main product line requirement and lower and upper limit of demand for each product, are imposed by the marketing department. All the above requirements and conditions are fuzzy. The objective is to obtain maximum profit (z) with certain degree of LOS of the DM. 2.1. Fuzzy Objective Coefficients and Non-Fuzzy Constraints The two sets of non-fuzzy constraints are raw material availability, and facility capacity constraints. These constraints are inevitable for each material and facility, based on material consumption, facility usage and resource availability. The decision variables for the product-mix problem are: X] to x8 (viz., milk chocolate of 250g to be produced (in '000); milk chocolate of lOOg (in '000); crunchy chocolate of 250g (in '000); crunchy chocolate of 1 OOg (in '000); chocolate with nuts of 250g (in '000); chocolate with nuts of lOOg (in '000); chocolate candy (in '000 packs); chocolate wafer (in '000 packs)). The following constraints are established by the sales department of Chocoman, Inc.: Product mix requirements: Large-sized products (250g) of each type should not exceed 60% (non fuzzy value) of the small-sized product (lOOg) such that: Constraint-1: xj < 0.6 x2, Constraint-2: X3 < 0.6 X4, and Constraint-3: x5 < 0.6 x6 Main product line requirement: The total sales from candy and wafer products should not exceed 15% (non fuzzy value) of the total revenues from the chocolate bar products, such that: Constraint-4: 400x7 + 150x8 < 0.15(375x, + 150x2 + 400x3 + 160x4 + 420x5 + 175x6) 3. Rest of the identified 29 constraints, i.e., material requirement and facility usages, are given below: Constraint-5 (cocoa usage): 87.5x, + 35x2 + 75x3 + 30x4 + 50x5 + 20x6 + 60x7 + 12x8 < 100000 Constraint-6 (Milk usage): 62.5x, + 25x2 + 50x3 + 20x4 + 50x5 + 20x6 + 30x7 + 12x8 < 120000

Constraint-! (nuts usage): Ox, +0x 2 +37.5x 3 +15x 4 +75x 5 +30x 6 +0x 7 + Ox8 < 60000 Constraint-8 (confectionary sugar usage): lOOx, +40x2 +87.5x3+35x4 + 75x5 +30x6 + 210x7 + 24x8 a

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00

(20)

4. Results, Discussions and Conclusion The WinQSB® software have been used to obtain the results (Table 1). Solving eqs. (9) to (11), the following values are obtained respectively: \xz\(x) = 0.5030, Hz2(x) = 0.5030 and (J.z3(x) = 0.5039. By solving the definition of a from eq. (13) one gets a = min{ n Z| (x),|x Z2 (x),n Zj (x)} = 0.5030 The value of a corresponds to the two MF nzi(x) and u^x). From these two MFs, the interpretation of the value of a can be obtained through the following steps: (i) From the definitions given in Step 5 for the optimal solution in Table 1, the obtained values are: z, = 33404.033, z2 = 133899.404, and z3 = 33504.823; (ii) Secondly, from the definition one gets: uzi(x) = 0.5030. This value represents the LOS of the DM achieved by the optimal solution in Table 1. The highest and the lowest LOS are achieved when the difference between the value of ex and c" x is 0 and 67215.600, respectively. By linear interpolation, (c-c")x = Zi= 33404.033 which corresponds to the LOS of DM at |jzl(x) = 0.5030. (iii) Thirdly, from the definition nz2(x) = 0.5030. The highest and the lowest LOS are achieved when the value of ex is 0 and 266184.900 respectively. Using linear interpolation, cx=z2= 133 899.404 which corresponds to the LOS of DM at uz2(x) = 0.5030. Table 1. Optimal combination of products from WinQSB'1' software Quantity to produce per 1000 units Product MC250 MC100 CC250 CC 100

46.037 76.728 360.000

CN250 CN 100

600.000 0.000 0.000

CANDY WAFER

100.790 0.0000

320 The solution using the developed fuzzified multi objective compromise LP algorithm considers imprecision of the given information. The non-fuzzy solution of Tabucanon (1996) results in optimal value of z as 266,157 (assuming LOS of the DM always remains constant at 100%) while fuzzy solution of Vasant (2003) using modified S-curve MF results in optimal z = 318,000 (for 100% LOS of the DM which is an ideal assumption) and z = 254,400 (at 50% LOS with a pre-determined vagueness value of 13.8). The CLPFOFC model uses triangular MF with an optimal profit z = 266,184 (at 100% LOS of the DM) and z = 133,899 (at 50.30% of LOS of the DM). The model presented by Tabucanon (1996) was not flexible enough to incorporate DM's LOS. Inter alia, Vasant (2003) didn't use such a fuzzified compromise model. Further extension of the present model using a suitably designed smooth logistic MF (which is of course a more realistic assumption) may increase profit of the Chocoman, Inc. trading off suitably among decision variables and other constraints in making product-mix decision. References 1. Bitran, G.R., 1980, Linear multiple objective problems with interval coefficients. Management Science 26, 694-706. 2. Jiuping, X., 2000, A kind of fuzzy linear programming problems based on interval-valued fuzzy sets. A journal of Chinese universities 15(1), 65-72. 3. Kolman, B. and Beck, R.E., 1995, Elementary linear programming with applications. Academic Press, USA. 4. Sengupta, A., Pal, T.K. and Chakraborty, D., 2001, Interpretation of inequality constraints involving interval coefficients and a solution to interval linear programming. Fuzzy Sets and Systems 119, 129-138. 5. Tabucanon, M.T., 1996, Multi objective programming for industrial engineers. Mathematical programming for industrial engineers. Marcel Dekker, Inc., New York, 487-542. 6. Vasant, P., 2003, Application of fuzzy linear programming in production planning. Fuzzy Optimization and Decision Making, 2(3), 229-241. 7. Wang, L.-X., 1997, A Course in Fuzzy Systems and Control, Prentice-Hall Int., London.

AUTHOR INDEX Abramson Y. Aktas E. Albayrak Y.E. Alhajj Aliabady S. Alipour M. A. Alonso S. Apolloni B. Ates N.Y. Augusto J.C

737 351 307, 375, 389 193 783 811 425 625 321 817

Babu K.S. Bacardit J. Bandera A. Baptista R.P. Baraldi P. Barroso A. Becerra J.A. Bellas F. Benftez-Read J.S. Beskese A. Bhattacharya A. Bien Z. Bila J. Blazewicz J. Bocker S. Bosin A. Botelho D. Botta A. Bozbura F.T. Brega A. Bukovsky I. Biiytikozkan G.

775 601 711 907 938, 962 899 725 725 985 395 315 5 215 601 565 593 899 35 395 625 215 367, 383, 477

Cabrerizo F.J. Cadini F. CaoZ. Carle B.

425 946 719 867

Ceravolo P. Cerchiara G.M. Cevik S. Chen G. Chen S. Chen X. Cheng W. Chiclana F. Cinar D. Cleva J.M. Coelho L. Curcio A. Czauderna S.T.

511 883 321 130, 169 433 692 745 425 359 19 573 511 549

Damiani E. de Andres R. de Lima A. De Lope J. de Moraes R.M. de Oliveira Valdek M.C. de Pedro T. de Sampaio P. Decker J. Del Toro J.C. Delavar M.R. Demirel N.C. Demirel T. Deng Z. Deschrijver G. Dessi N. Dillon T. Ding Y. Djurabekova F. Domingos R.P. Duan C. Duro R.J.

511 417 915 725 791,799 799 833 899 541 711 401 457 457 485 43 593 291 533,665,731 883 883 692 725

Elssner T.

541

993

994 Ercan S. Erensal Y.C. Ertay T.

383 307 299

FangM. Feng L. Feyzioglu O. Filippone M. Fiot C. Foerster T. Francois D.

499 162,659,686 367 617 519 223 557

Gallego F. GanG. GaoY. Garcia R. Garcia-Lapresta J.L. Garvey D.R. Genevois M.E. Goertzel B. Goertzel I. Gola G. Gonzalez C. Gregoire E. Gupta M.M.

805 81 839 833 417 922 375 573 573 825 833 67 215,704

Halang W.A. Hampel R. HanS. HaoK. Hardeman F. Herrera F. Herrera-Viedma E. Hines J.W. Hirst J.D. Hoffmann M. Holena M. Hong K.S. HouZ. Hu F.S. Huang C. Huang Q.

130,505 223 671 731 867 425 409, 425 922 601 970 209 753 704,719 162 146, 698 692

JinW. Jalili S. JiaD. Jiang B.

231 811 146 185

Kaestner W. Kahraman C. Kanemoto S. Karami J. Kaya I. Kayakutlu G. Kerre E.E. Kilic S. KimJ.H. Koehl L. Komura I. Kostrzewa M. Krasnogor N. Krier C. Kumar R.S. Kunsch P.L

223 283,315,449, 477 745 783 359 383 43 449 753 201 745 541 601 557 775 859

LaiD. Lapa C. Laurent A. Lazzerini B. Lee H.E. Lee S.W. Leung Y. LiB. LiD. LiF. Li P. LiT. LiX. Li Y. LiZ. Liang X. Liao G. Liao Q. Lin J.

609 899 519 35 5 5 643 103 671 81,704 130 177, 839 493 678 130 75, 329 633 493 839

Lintow C. Liu J. LiuX. LiuY. Llorens F. Lobo F. Lollini P.-L. LongX. Lopez V. LuH. LuJ. LuZ.

223 817 201, 329 122 805 573 587 111 19 59 59,247,291, 441, 659 154,485

Ma J. Machado L.S. Machado M.D. Malek M.R. Malerba L. Marcek D. Marcek M. Marcelloni F. Martinez L. Maschio I. Mastriani E. Masulli F. Mata F. Matros H.-P. Meneses A. Mercurio D. Metin B.C. Milis K. Mock U. Moens D. Montero J. Moreira M. Motta S. Moutarde F. Mundici D. Muselli M. Naranjo J.E. Nattkemper T.W.

433 791, 799 915 401, 783 883 259, 275 259, 275 35 409,417 851 587 617 409 549 891 962 383 343 549 51 19 899 587 737 8 267 833 565

Nikiforuk P.N. Nikravesh M. Nunez P.

704 27 711

0wre F. Ozyer T. Ozyol M.

11 193 389

Palaniswami M. Palopoli L. PanW. Pappalardo F. Park K.H. Parker M.W. Pedroni N. PeiZ. Pennachin C. Pennisi M. Pereira C. PesB. Peterek A. Pinelli M. Popescu I.C. Prosdocimi F.

609 579 177 587 5 609 938 87 573 587 899, 907 593 549 511 954 573

Qian Y. QinC. QiuL. Queiroz M.

253 643 692 573

Rama G.L.J. Ramirez-Chavez C.L. RaoS. RenL. Ren X.H. Resconi G. Riecan B. Rocco C. M. Rombo S.E. Roverso D. Rovetta S. Ruan D.

609 985 845 533 665, 678 27 138 267, 930 579 977 617 867,985

996 Salazar A. D.E. Sanchez E. Sandoval F. Satorre R. Schirru R. SchleifF.-M. Seiffert Sergiadis G.D. Shen Z. Shiwa M. Silavi T. Souto K.C. Stanciulescu B. Steux B. Stout M. Strickert M. Susanto S.

930 13 711 805 875, 891, 907, 915 541 549 759, 767 719 745 401 875 737 737 601 549 315

TanM. Tang B. Tanyas M. Teisseire M. Terracina G. Tian H.X. Timm W. Tradigo Tsunoyama S. Twellmann T.

704, 719 75, 329 335 519 579 678 565 565 745 565

Ulukan Z. Ustundag A. Valentini G. Vandepitte D. Vanhoof K. Vasant P. Vazquez-Martin R. Verleysen Veltri P. Villmann T. Viviani M. Vlachos I.K.

283 335 625 51 343 315 711 557 579 541 511 759, 767

Vroman A.

43

Waintraub M. WanL. Wang H. Wang P.P. Wang S. Wang W. WangX. Wang Y.

907 162 704, 817 16 719 95, 103, 185 505 81,485,665, 678 169, 185 557 185 81

WeiQ. Wertz V. WuK. WuZ. Xiao F. XieW. XieY. Xu D.L. Xu X.P. XuY.

633 241,465,471 671 867 525 87, 95, 103, 111, 116, 122, 201,241,433, 465

YanX. Yang F. Yang J.B. YiL. Yu C.Y. YuJ. Yucel G.

247 698 867 87, 111, 116 665 499 351

Zadeh L.A. Zeng C. Zeng X. Zeng Y. Zhang G.

3 241,465,471 201 499 59,247,291, 441 247, 643 499

Zhang J. Zhang P.

997 Zhang T. Zhang X. Zhang Y. Zhang Z. ZhaoD. Zhao J. ZhaoY. ZhouY.

154 533,686 704,839 75 231 116 505 154

ZhuD. ZhuH. ZhuangH. Zio E. ZouK. Zou L.

75,329 116 329 825,930,938, 946,954,962 651 95,103

FUNS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is now extended to Applied Artificial Intelligence for Applied Research. The contributions to the seventh in the series of FUNS conferences contained in this volume cover state-of-the-art research and development in applied artificial intelligence for applied research in general and for power/nuclear engineering in particular.

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