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Supplier selection in electronic marketplaces using satisficing and fuzzy AHP

EXPERT SYSTEMS WITH APPLICATIONS 2010年第1期37卷 490-498页

作者： Chamodrakas, I. Batis, D. Martakos, D. Natl & Kapodistrian Univ Athens Dept Informat & Telecommun Athens 11528 Greece

Supplier selection is a critical and demanding task for companies that participate in electronic marketplaces to find suppliers and to execute electronically their transactions. This paper is aimed to Suggest a fresh approach for decision support enabling effective supplier selection processes in electronic marketplaces. We introduce an evaluation method with two stages: initial screening of the suppliers through the enforcement of hard constraints on the selection criteria and final supplier evaluation through the application of a modified variant of the Fuzz Preference programming (FPP) method. The proposed method alleviates the information overload effect that is inherent in the environment of electronic marketplaces, facilitates an easier elicitation of user preferences through the reduction of necessary user input (i.e. pairwise comparisons) and reduces computational complexity, in terms of the number of linear programs to be solved, in comparison with the original FPP method. The FPP method is adopted and modified accordingly in order to tackle the issue of inconsistency/uncertainty of human preference models. Our approach is demonstrated with the example of a hypothetical metal manufacturing company that finds and selects Suppliers in the environment of an electronic marketplace. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Supplier selection fuzzy AHP Electronic marketplaces Satisficing fuzzy programming MCDM

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An interval-based possibilistic programming method for waste management with cost minimization and environmental-impact abatement under uncertainty

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SCIENCE OF THE TOTAL ENVIRONMENT 2010年第20期408卷 4296-4308页

作者： Li, Y. P. Huang, G. H. N China Elect Power Univ Res Acad Energy & Environm Studies Beijing 102206 Peoples R China

Considerable public concerns have been raised in the past decades since a large amount of pollutant emissions from municipal solid waste (MSW) disposal of processes pose risks on surrounding environment and human health. Moreover, in MSW management, various uncertainties exist in the related costs, impact factors and objectives, which can affect the optimization processes and the decision schemes generated. In this study, an interval-based possibilistic programming (IBPP) method is developed for planning the MSW management with minimized system cost and environmental impact under uncertainty. The developed method can deal with uncertainties expressed as interval values and fuzzy sets in the left- and right-hand sides of constraints and objective function. An interactive algorithm is provided for solving the IBPP problem, which does not lead to more complicated intermediate submodels and has a relatively low computational requirement. The developed model is applied to a case study of planning a MSW management system, where mixed integer linear programming (MILP) technique is introduced into the IBPP framework to facilitate dynamic analysis for decisions of timing, sizing and siting in terms of capacity expansion for waste-management facilities. Three cases based on different waste-management policies are examined. The results obtained indicate that inclusion of environmental impacts in the optimization model can change the traditional waste-allocation pattern merely based on the economic-oriented planning approach. The results obtained can help identify desired alternatives for managing MSW, which has advantages in providing compromised schemes under an integrated consideration of economic efficiency and environmental impact under uncertainty. (c) 2010 Elsevier B.V. All rights reserved.

关键词： Decision Environmental impact fuzzy programming Interval analysis Optimization Pollution control Solid waste Uncertainty

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A new data envelopment analysis model with fuzzy random inputs and outputs

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JOURNAL OF APPLIED MATHEMATICS AND COMPUTING 2010年第1-2期33卷 327-356页

作者： Qin, Rui Liu, Yan-Kui Hebei Univ Coll Math & Comp Sci Baoding 071002 Hebei Peoples R China

This paper first presents several formulas for mean chance distributions of triangular fuzzy random variables and their functions, then develops a new class of fuzzy random data envelopment analysis (FRDEA) models with mean chance constraints, in which the inputs and outputs are assumed to be characterized by fuzzy random variables with known possibility and probability distributions. According to the established formulas for the mean chance distributions, we can turn the mean chance constraints into their equivalent stochastic ones. On the other hand, since the objective in the FRDEA model is the expectation about the ratio of the weighted sum of outputs and the weighted sum of inputs for a target decision-making unite (DMU), for general fuzzy random inputs and outputs, we suggest an approximation method to evaluate the objective;and for triangular fuzzy random inputs and outputs, we propose a method to reduce the objective to its equivalent stochastic one. As a consequence, under the assumption that the inputs and the outputs are triangular fuzzy random vectors, the proposed FRDEA model can be reduced to its equivalent stochastic programming one, in which the constraints contain the standard normal distribution function, and the objective is the expectation for a function of the normal random variable. To solve the equivalent stochastic programming model, we design a hybrid algorithm by integrating stochastic simulation and genetic algorithm (GA). Finally, one numerical example is presented to demonstrate the proposed FRDEA modeling idea and the effectiveness of the designed hybrid algorithm.

关键词： fuzzy programming fuzzy random variable Mean chance Data envelopment analysis Hybrid algorithm

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A credibility-based fuzzy location model with Hurwicz criteria for the design of distribution systems in B2C e-commerce

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COMPUTERS & INDUSTRIAL ENGINEERING 2010年第4期59卷 873-886页

作者： Lau, H. C. W. Jiang, Zhong-Zhong Ip, W. H. Wang, Dingwei Northeastern Univ Sch Business Adm Dept Management Sci & Engn Shenyang 110004 Peoples R China Univ Western Sydney Sch Management Sydney NSW Australia Hong Kong Polytech Univ Dept Ind & Syst Engn Hong Kong Hong Kong Peoples R China Northeastern Univ Sch Informat Sci & Engn Dept Syst Engn Shenyang 110004 Peoples R China

Facility location problem is one of the most critical elements in the design of distribution systems, and numerous studies have focused on this issue. However, facility location theory and guidelines for N2C firms are sparse. In this paper, with regard to the customer characteristics peculiar to B2C e-commerce and the turbulence of the competitive market, a new fuzzy location model is proposed to optimize the distribution system design in B2C e-commerce. The model adopts a hierarchical agglomerative clustering method to classify customers and estimate the fuzzy delivery cost. At the same time, due to the turbulence of competitive market, both market supply and customer demand are treated as fuzzy variables in the model. Afterward, the credibility measure and Hurwicz criterion are introduced to convert the model into a crisp one which has NP-hard complexity. In order to solve the crisp model, an improved genetic algorithm with particle swarm optimization is developed. Finally, the computational results of some numerical examples are used to illustrate the application and performance of the proposed model and algorithm. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Logistics B2C e-commerce fuzzy location model fuzzy programming Credibility measure Meta-heuristics

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Dynamic pricing model and algorithm for perishable products with fuzzy demand

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APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY 2010年第6期26卷 758-774页

作者： Xiong, Yu Li, Gendao Fernandes, Kiran Jude Jilin Univ Sch Management Changchun 130023 Peoples R China Queens Univ Sch Management Belfast BT7 1NN Antrim North Ireland Univ York York Management Sch York YO10 5DD N Yorkshire England

This paper studies the dynamic pricing problem of selling fixed stock of perishable items over a finite horizon, where the decision maker does not have the necessary historic data to estimate the distribution of uncertain demand, but has imprecise information about the quantity demand. We model this uncertainty using fuzzy variables. The dynamic pricing problem based on credibility theory is formulated using three fuzzy programming models, viz.: the fuzzy expected revenue maximization model, alpha-optimistic revenue maximization model, and credibility maximization model. fuzzy simulations for functions with fuzzy parameters are given and embedded into a genetic algorithm to design a hybrid intelligent algorithm to solve these three models. Finally, a real-world example is presented to highlight the effectiveness of the developed model and algorithm. Copyright (C) 2009 John Wiley & Sons, Ltd.

关键词： dynamic pricing revenue management credibility theory fuzzy programming fuzzy simulation genetic algorithm

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Multiple criteria optimization-based data mining methods and applications: a systematic survey

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KNOWLEDGE AND INFORMATION SYSTEMS 2010年第3期24卷 369-391页

作者： Shi, Yong Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100080 Peoples R China

Support Vector Machine, an optimization technique, is well known in the data mining community. In fact, many other optimization techniques have been effectively used in dealing with data separation and analysis. For the last 10 years, the author and his colleagues have proposed and extended a series of optimization-based classification models via Multiple Criteria Linear programming (MCLP) and Multiple Criteria Quadratic programming (MCQP). These methods are different from statistics, decision tree induction, and neural networks. The purpose of this paper is to review the basic concepts and frameworks of these methods and promote the research interests in the data mining community. According to the evolution of multiple criteria programming, the paper starts with the bases of MCLP. Then, it further discusses penalized MCLP, MCQP, Multiple Criteria fuzzy Linear programming (MCFLP), Multi-Class Multiple Criteria programming (MCMCP), and the kernel-based Multiple Criteria Linear Program, as well as MCLP-based regression. This paper also outlines several applications of Multiple Criteria optimization-based data mining methods, such as Credit Card Risk Analysis, Classification of HIV-1 Mediated Neuronal Dendritic and Synaptic Damage, Network Intrusion Detection, Firm Bankruptcy Prediction, and VIP E-Mail Behavior Analysis.

关键词： Data mining Classification Multi-criteria programming fuzzy programming Regression Credit scoring Bioinformatics Network intrusion detection Bankruptcy prediction

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Mean-variance-skewness model for portfolio selection with fuzzy returns

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第1期202卷 239-247页

作者： Li, Xiang Qin, Zhongfeng Kar, Samarjit Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Tsinghua Univ Dept Math Sci Beijing 100084 Peoples R China Natl Inst Technol Dept Math Durgapur 713209 India

Numerous empirical studies show that portfolio returns are generally asymmetric. and investors would prefer a portfolio return with larger degree of asymmetry when the mean Value and variance are same. In order to measure the asymmetry of fuzzy portfolio return, a concept of skewness is defined as the third central moment in this paper, and its mathematical properties are studied. As an extension of the fuzzy mean-variance model, a mean-variance-skewness model is presented and the corresponding variations are also considered. In order to solve the proposed models, a genetic algorithm integrating fuzzy simulation is designed. Finally, several numerical examples are given to illustrate the modelling idea and the effectiveness of the proposed algorithm. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

关键词： Portfolio selection fuzzy variable Mean-variance-skewness model fuzzy programming Credibility measure

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Logistics network design for product recovery in fuzzy environment

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第2期202卷 479-490页

作者： Qin, Zhongfeng Ji, Xiaoyu Renmin Univ China Sch Business Beijing 100872 Peoples R China Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China

The design of product recovery network is one of the important and challenging problems in the field of reverse logistics. Some models have been formatted by researchers under deterministic environment. However, uncertainty is inherent during the process of the practical product recovery. In order to deal with uncertainty, this paper employs a fuzzy programming tool to design the product recovery network. Based on different criteria, three types of optimization models are proposed and some properties of them are investigated. To solve the proposed models, we design a hybrid intelligent algorithm which integrates fuzzy simulation and genetic algorithm. Finally, several numerical examples are presented to illustrate the effectiveness of the proposed models and algorithm. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Credibility measure Product recovery Reverse logistics fuzzy programming Genetic algorithm fuzzy simulation

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Domain-Driven Classification Based on Multiple Criteria and Multiple Constraint-Level programming for Intelligent Credit Scoring

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2010年第6期22卷 826-838页

作者： He, Jing Zhang, Yanchun Shi, Yong Huang, Guangyan Victoria Univ Sch Sci & Engn Melbourne Vic Australia Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China

Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless, and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of multiple criteria and multiple constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various data sets validated the effectiveness and efficiency of the proposed methods.

关键词： Credit scoring domain-driven classification mathematical programming multiple criteria and multiple constraint-level programming fuzzy programming satisfying solution

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A dual-interval vertex analysis method and its application to environmental decision making under uncertainty

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第2期200卷 536-550页

作者： Li, Y. P. Huang, G. H. Guo, P. Yang, Z. F. Nie, S. L. Peking Univ Coll Urban & Environm Sci Beijing 100871 Peoples R China Beijing Normal Univ Chinese Res Acad Environm Sci Beijing 100012 Peoples R China Univ Regina Fac Engn Regina SK S4S 0A2 Canada Beijing Normal Univ Sch Environm State Key Lab Water Environm Simulat Beijing 100875 Peoples R China Beijing Univ Technol Coll Mech Engn & Appl Elect Technol Beijing 100022 Peoples R China

In this study, a dual-interval vertex analysis (DIVA) method is developed, through incorporating the vertex method within an interval-parameter programming framework. The developed DIVA method can tackle uncertainties presented as dual intervals that exist in the objective function and the left- and right-hand sides of the modeling constraints. An interactive algorithm and a vertex analysis approach are proposed for solving the DIVA model. Solutions under an associated a-cut level can be generated by solving a series of deterministic submodels. They can help quantify relationships between the objective function value and the membership grade, which is meaningful for supporting in-depth analyses of tradeoffs between environmental and economic objectives as well as those between system optimality and reliability. A management problem in terms of regional air pollution control is studied to illustrate applicability of the proposed approach. The results indicate that useful solutions for planning the air quality management practices have been generated. They can help decision makers to identify desired pollution-abatement strategies with minimized costs and maximized environmental efficiencies. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Air quality Decision making Dual interval Environment fuzzy programming Optimization Vertex analysis Uncertainty

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