Bijan Fazlollahi

Fuzzy-genetic approach to aggregate production-distribution planning in supply chain management

Aggregate production-distribution planning (APDP) is one of the most important activities in supply chain management (SCM). When solving the problem of APDP, we are usually faced with uncertain market demands and capacities in production environment, imprecise process times, and other factors introducing inherent uncertainty to the solution. Using deterministic and stochastic models in such conditions may not lead to fully satisfactory results. Using fuzzy models allows us to remove this drawback. It also facilitates the inclusion of expert knowledge. However, the majority of existing fuzzy models deal only with separate aggregate production planning without taking into account the interrelated nature of production and distribution systems. This limited approach often leads to inadequate results. An integration of the two interconnected processes within a single production-distribution model would allow better planning and management. Due to the need for a joint general strategic plan for production and distribution and vague planning data, in this paper we develop a fuzzy integrated multi-period and multi-product production and distribution model in supply chain. The model is formulated in terms of fuzzy programming and the solution is provided by genetic optimization (genetic algorithm). The use of the interactive aggregate production-distribution planning procedure developed on the basis of the proposed fuzzy integrated model with fuzzy objective function and soft constraints allows sound trade-off between the maximization of profit and fillrate. The experimental results demonstrate high efficiency of the proposed method.

The Effectiveness of Decisional Guidance: An Empirical Evaluation

Decisional guidance is defined as how a decision support system (DSS) influences its users as they structure and execute the decision-making process. It is assumed that decisional guidance has profound effects on decision making, but these effects are understudied and empirically unproven. This paper describes an empirical, laboratory-experiment-based evaluation of the effectiveness of deliberate decisional guidance and its four types. We developed and used a comprehensive model consisting of four evaluation criteria: decision quality, user satisfaction, user learning, and decision-making efficiency. On these criteria, we compared decisional guidance versus no guidance, informative versus suggestive decisional guidance, and predefined versus dynamic decisional guidance. We found that deliberate decisional guidance was more effective on all four criteria; suggestive guidance was more effective in improving decision quality and user satisfaction, and informative guidance was more effective in user learning about the problem domain, whereas dynamic guidance was more effective than predefined guidance in improving decision quality and user learning; and both suggestive guidance and dynamic guidance reduced the decision time.

Fuzzy logic-based generalized decision theory with imperfect information

The existing decision models have been successfully applied to solving many decision problems in management, business, economics and other fields, but nowadays arises a need to develop more realistic decision models. The main drawback of the existing utility theories starting from von Neumann-Moregnstern expected utility to the advanced non-expected models is that they are designed for laboratory examples with simple, well-defined gambles which do not adequately enough reflect real decision situations. In real-life decision making problems preferences are vague and decision-relevant information is imperfect as described in natural language (NL). Vagueness of preferences and imperfect decision relevant information require using suitable utility model which would be fundamentally different to the existing precise utility models. Precise utility models cannot reflect vagueness of preferences, vagueness of objective conditions and outcomes, imprecise beliefs, etc. The time has come for a new generation of decision theories. In this study, we propose a decision theory, which is capable to deal with vague preferences and imperfect information. The theory discussed here is based on a fuzzy-valued non-expected utility model representing linguistic preference relations and imprecise beliefs.

Soft Computing and its Applications in Business and Economics

1 Introduction to Soft Computing.- 1.1 Basic Concepts of Soft Computing.- 2.2 Combination of Constituents of Soft Computing.- References.- 2. Constituent Methodologies of Soft Computing.- 2.1 Elements of Fuzzy Sets Theory.- 2.1.1 Fuzzy Sets and Operations Over Them.- 2.2.2 Mathematics of Fuzzy Computing.- 2.1.3 Fuzzy Logic and Approximate Reasoning.- 2.1.4 Probability and Fuzziness.- 2.1.5 Fuzzy Sets and Possibility Theory.- 2.2 Foundations of Neurocomputing.- 2.2.1 Basic Types and Architectures of Neural Networks.- 2.2.2 Learning Algorithms of Neural Networks.- 2.3 Probabilistic Computing.- 2.3.1 Bayesian Approach.- 2.3.2 Dempster-Shafer Theory of Belief.- 2.4 Evolutionary Computing.- 2.4.1 Evolution Programming and Genetic Algorithms.- 2.4.2 Computation with Genetic Algorithms.- 2.5 Chaotic Computing.- 2.5.1 Elements of Chaotic Computing.- 2.5.2 Non-Linear Dynamics and Chaotic Analysis.- 2.5.3 Empirical Chaotic Analysis.- References.- 3. Emerging Combined Soft Computing Technologies.- 3.1 Neuro-Fuzzy Technology.- 3.2 Neuro-Genetic Approach.- 3.3 Fuzzy Genetic Paradigm.- 3.4 Genetic Algorithms with Fuzzy Logic.- 3.5 Neuro-Fuzzy-Genetic Paradigm.- 3.6 Multi-Agent Distributed Intelligent Systems Paradigm.- 3.7 Computing with Words Technology.- References.- 4. Soft Computing Technologies in Business and Economic Forecasting.- 4.1 Neuro-Computing and Forecasting.- 4.2 Fuzzy Time Series Based Forecasting.- 4.3 Fuzzy Delphi Method.- 4.4 Soft Computing Based Forecasting Complex Time Series.- 4.5 Soft Computing Based Prediction Ensemble for Forecasting in Chaotic Time Series.- References.- 5 Soft Computing Based Decision Making and DSS.- 5.1 Fuzzy Linear Programming.- 5.2 Evolutionary Algorithm Based Fuzzy Linear Programming.- 5.3 Fuzzy Chaos Approach to Fuzzy Linear Programming Problem.- 5.4 Fuzzy-Probabilistic Scheduling for Oil Refinery.- 5.5 Fuzzy Decision Making.- 5.6 Multi-Agent Distributed Intelligent System Based on Fuzzy Decision Making.- 5.7 Soft Computing and Data Mining.- 5.8 Soft Computing Based Multi-Agent Marketing DSS.- 5.9 Hybrid DSS Based on Simulation and Genetic Algorithms.- 5.10 Soft Computing Based Alternatives Generations by Decision Support Systems.- References.- 6 Soft Computing in Marketing.- 6.1 Marketing Analysis of a Customers Purchasing Behavior.- 6.2 Customer Credit Evaluation.- 6.3 Soft Computing Based Fraud Detection.- 6.4 Fuzzy Evaluation of Service Quality.- 6.5 Application of Fuzzy Programming to Hospitals Service Performance Evaluating.- References.- 7 Soft Computing Applications in Operations Management.- 7.1 Application of Fuzzy Logic in Transportation Logistics.- 7.2 Scheduling Fuzzy Expert Systems with Probabilistic Reasoning for Oil Refineries.- 7.3 Detection and Withdrawal of Defect Parts in the Computer Aided Manufacturing of Evaporators.- 7.4 Genetic Algorithms Based Fuzzy Regression Analysis and Its Applications for Quality Evaluation.- 7.5 An Intelligent System for Diagnosis of the Oil-Refinery Plant.- 7.6 Neuro-Fuzzy Pattern Recognition in Manufacturing.- 7.7 Soft Computing Based Inventory Control.- 7.8 Fuzzy Project Scheduling.- 7.9 CW Based Decision Analysis on Risk Assessment of an Engineering Project.- References.- 8 Soft Computing in Finance.- 8.1 Soft Computing Based Stock Market Predicting System.- 8.2 Fuzzy Nonlinear Programming Approach to Portfolio Selection.- 8.3 Neuro-Fuzzy Approach to Modeling of Credit Risk in Trading Portfolios.- 8.4 A Fuzzy Approach to the Credit Portfolio Constructing.- 8.5 Soft Computing Based TDSS Multi-Agent Systems in Finance.- 8.6 Neural Nonlinear Modeling for Risk Management in Banking.- 8.7 Neuro-Fuzzy Loan Assessment System.- References.- 9 Soft Computing in Electronic Business.- 9.1 A Multi-Agent System for E-Commerce Decisions.- 9.2 Soft Computing and Personalization of Electronic Commerce.- 9.3 Risk Analysis in Electronic Commerce Using Fuzzy Weighted Average.- References.

Evolutionary algorithm-based learning of fuzzy neural networks. Part 2: Recurrent fuzzy neural networks

Fuzzy neural networks (FNN) as opposed to neuro-fuzzy systems, whose main task is to process numerical relationships, can process both numerical (measurement based) information and perception based information. In spite of great importance of fuzzy feed-forward and recurrent neural networks for solving wide range of real-world problems, today there are no effective training algorithm for them. Currently there are two approaches for training of FNN. First approach is based on application of the level-sets of fuzzy numbers and the back-propagation (BP) algorithm. The second approach involves using evolutionary algorithms to minimize error function and determine the fuzzy connection weights and biases. The method based on the second approach was proposed by the authors and published in Part 1 of this paper [R.A. Aliev, B. Fazlollahi, R. Vahidov, Genetic algorithm-based learning of fuzzy neural networks. Part 1: feed-forward fuzzy neural networks, Fuzzy Sets and Systems 118 (2001) 351-358]. In contrast to the BP and other supervised learning algorithms, evolutionary algorithms do not require nor use information about differentials, and hence, they are most effective in case where the derivative is very difficult to obtain or even unavailable. However, the main deficiency of the existing FNN based on the feed-forward architecture is its adherence to static problems. In case of dynamic or temporal problems there is a need for recurrent fuzzy neural networks (RFNN). Designing efficient training algorithms for RFNN has recently become an active research direction. In this paper we propose an effective differential evolution optimization (DEO) based learning algorithm for RFNN with fuzzy inputs, fuzzy weights and biases, and fuzzy outputs. The effectiveness of the proposed method is illustrated through simulation of benchmark forecasting and identification problems and comparisons with the existing methods. The suggested approach has also been used for real applications in an oil refinery plant for petrol production forecasting.

Pluralistic multi-agent decision support system: a framework and an empirical test

Recent research in decision support systems (DSSs) has focused on building active cooperative intelligent systems. Research in agent-based decision support is a promising stream in this direction. This paper proposes a framework for a pluralistic multi-agent decision support system (MADSS). The distinguishing feature of the proposed approach is its organization around human decision making process. The framework builds upon the decision support pyramid with agents organized into groups according to the phases of the problem solving model. We outline the design principles and develop architecture for MADSS. The framework is illustrated through an investment MADSS prototype. The results of the empirical test are presented.

Adaptive decision support systems

Abstract The effectiveness of decision support systems (DSS) is enhanced through dynamic adaptation of support to the needs of the decision maker, to the problem, and to the decision context. We define this enhanced DSS as adaptive decision support systems (ADSS) and propose its architecture. In an ADSS, the decision maker controls the decision process. However, the system monitors the process to match support to the needs. The proposed architecture evolves from the traditional DSS models and includes an additional intelligent‘Adaptation’ component. The ‘Adaptation’ component workd with the data, model, and interface components to provide adaptive support. The architecture also integrates enhancements proposed in the past research. In this paper, we have illustrated the proposed architecture with two examples, a prototype system, and results from a preliminary empirical investigations

Genetic algorithm-based learning of fuzzy neural networks. Part 1: feed-forward fuzzy neural networks

In spite of great importance of fuzzy feed-forward and recurrent neural networks (FNN) for solving wide range of real-world problems, today there is no effective learning algorithm for FNN. In this paper we propose an effective genetic-based learning mechanism for FNN with fuzzy inputs, fuzzy weights expressed as LR-fuzzy numbers, and fuzzy outputs. The effectiveness of the proposed method is illustrated through simulation of fuzzy regression for quality evaluation and comparison with the widely used learning method based on α-cuts and fuzzy arithmetic. Finally, we demonstrate the use of the proposed learning procedure for calculating fuzzy-valued profit in an oligopolistic environment.

Selecting a requirement determination methodology-contingency approach revisited

Abstract Previous research proposed a contingency approach to selecting a particular requirement determination methodology because of the uncertainty that exists in defining application/ organizational information requirements. Recent research has shown that organizations use information to reduce uncertainty, to resolve equivocality, or both. Since the nature of information used to reduce uncertainty is different from that used to resolve equivocality, it is necessary to decide which is needed before starting to design the future system. This paper presents a three step approach to selecting a requirement determination methodology. The first step calls for assessment of the degree of uncertainty and equivocality present in the application. Based on this, an appropriate information acquisition strategy is identified. The third step calls for the selection of RD methodologies that are effective in acquiring the needed information. This approach is illustrated with a set of examples.

MCDM Approach for Generating and Evaluating Alternatives in Requirement Analysis

Determining user requirements and generating alternative system solutions to meet these requirements are two critical steps in the requirement analysis phase of the system development life cycle. Much of the MIS research in the requirement analysis phase has been devoted to the topic of requirement determination and its verification. Alternative generation and evaluation is left, to a significant degree, to the judgment and expertise of an analyst. This paper proposes a multiple criteria decision making MCDM approach for generating and evaluating alternatives when the user requirements are expressed in terms of certain operational criteria such as time, cost, risk, etc. These alternatives form the basis for the user to make the necessary trade-offs.