DEVELOPMENT OF A FUZZY EXPERT SYSTEM TO PRIORITIZE TRAFFIC CALMING PROJECTS

2016 ◽  
Vol 78 (2) ◽  
Author(s):  
Amir Falamarzi ◽  
Muhamad Nazri Borhan ◽  
Riza Atiq O. K. Rahmat ◽  
Samira Cheraghi ◽  
Hamid Haj Seyyed Javadi
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Expert Systems ◽  
Traffic Safety ◽  
Effective Parameters ◽  
Traffic Calming ◽  
Inference Engines ◽  
Efficiency And Effectiveness ◽  
Decision Making Processes

Nowadays, due to the constraints of budget and time, the prioritization of traffic calming projects before installation of traffic calming measures is vital for transportation engineers and urban planners. The purpose of this study is to develop an expert system for prioritizing streets that are affected by problems associated with traffic safety using Fuzzy Logic. Expert systems have been used widely and globally for facilitating decision-making processes in various fields of engineering. Due to the uncertainty and vagueness in traffic and transportation related problems, the use of fuzzy logic in the inference engines and decision-making processes of expert systems, is effective. In the proposed expert system, effective parameters in prioritizing traffic calming projects in residential streets including traffic volume, residential density, differential speed and number of accidents are investigated. The Fuzzy Logic toolbox, which is embedded in MATLAB (R2010b), is employed to design and simulate this expert system on the basis of Fuzzy Logic. A specific GUI was developed for this purpose. By developing this system, engineers and decision-makers will be able to rank projects according to their importance. This expert system was tested through prioritizing a number of residential streets in the city of Tehran. The output of the tests showed that the proposed system is helpful in prioritizing different traffic calming projects. Finally, the evaluation of the system was conducted. According to the assessment, most evaluators acknowledged the efficiency and effectiveness of the system. 

A Generalization of the Theory of Expertons

2018 ◽  
Vol 26 (Suppl. 1) ◽  
pp. 121-139 ◽  
Author(s):  
Joan Carles Ferrer-Comalat ◽  
Salvador Linares-Mustarós ◽  
Dolors Corominas-Coll
Keyword(s):  
Social Sciences ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Data Fusion ◽  
Expert Systems ◽  
Data Gathering ◽  
New Approach ◽  
The Social ◽  
Support Element ◽  
Decision Making Processes

With the advent of fuzzy logic applications in the field of economics and in the context of expert systems we are witnessing a new approach to data-gathering methods as the aggregation of data provided by various experts brings with it new data fusion techniques. In 1987, the exploration of these techniques gave rise to the experton concept as an integrating element that allows the collection of all information expressed by a group of experts relating to the level or degree of truth of a statement or the degree of fulfilment of a certain vague or imprecise characteristic. Over the thirty years since its formulation, the experton concept has been applied as a support element in decision-making processes in many areas of the social sciences. The aim of this article is to present a generalization of the experton concept for both the discrete and continuous cases, which respects known properties and has the potential to be practically applied in various situations where there is a need to perform a simulation of various opinion scenarios relating to a characteristic or statement, and thus explore new approaches to decision-making models.

Rxpert: A Prototype Expert System for Formulary Decision Making

1992 ◽  
Vol 26 (2) ◽  
pp. 244-250 ◽  
Author(s):  
Marianne L. Greer
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Engineering ◽  
Drug Product ◽  
Computer Assisted ◽  
Health Related ◽  
Human Decision ◽  
Decision Making Processes ◽  
Complex Decision Making

OBJECTIVE: RXPERT, a prototype, computer-based, expert system that models the decision-making processes for an ambulatory (non-hospital) formulary, is described as an example of how expert systems may be used to support pharmacy decision making. Basic information about expert-system technology is provided through this example. BACKGROUND: Computer-assisted decision making is becoming an important and accepted aspect of complex, health-related decisions. Because expert-system support may become an integral component of future, complex, pharmacy decision making, it is important for pharmacists to become familiar with this technology and its possibilities for supporting pharmacy decisions. METHOD: Expert systems offer the potential advantages of making the human decision-making process explicit, more consistent, easily duplicated in many locations simultaneously, and easy to update and document. Although an expert system is seldom intended to replace human decision makers, it can provide valuable support for complex, multivariable decisions. Typical knowledge-acquisition and knowledge-engineering techniques, as well as the characteristics and structure of expert systems, are described, relative to the development of the RXPERT prototype. CONCLUSIONS: Although RXPERT is not yet in use, the process for using an expert system to support an individual committee member's personal assessment of a drug product is described. Decision-support expert systems are potentially useful to pharmacists in complex decision-making tasks.

scholarly journals The Performance of Stock Portfolios formed using Fuzzy Logic

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Amit K. Sinha 1 ◽  
Andrew J. Jacob 2
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Expert System ◽  
Expert Systems ◽  
Fuzzy Membership ◽  
Thought Process ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Stock Portfolios ◽  
Financial Securities

Expert systems, a type of artificial intelligence that replicate how experts think, can aide unskilled users in making decisions or apply an expert’s thought process to a sample much larger than could be examined by a human expert. In this paper, an expert system that ranks financial securities using fuzzy membership functions is developed and applied to form portfolios. Our results indicate that this approach to form stock portfolios can result in superior returns than the market as measured by the return on the S&P 500. These portfolios may also provide superior risk-adjusted returns when compared to the market.

WASPAS based MULTIMOORA method under IVIF environment for the selection of hub location

2020 ◽  
Vol 33 (5) ◽  
pp. 1233-1256
Author(s):  
Nezir Aydin ◽  
Sukran Seker
Keyword(s):  
Decision Making ◽  
Airline Industry ◽  
Low Cost ◽  
Evaluation Criteria ◽  
Hub Location ◽  
Content Type ◽  
Efficiency And Effectiveness ◽  
Decision Making Processes ◽  
Product Assessment ◽  
The City

PurposeLow cost carriers (LCCs) have become one of the most significantly growing parts of the airline servicing market. This new player has redesigned the whole airline industry, which was previously led by the national/international full-service airline companies. Considering such advancements, the hub locations of LCCs became an important issue than ever before. Within this concept, a guiding framework is developed for an LCC company, which is in search of a new hub airport location within Turkey to satisfy the demand and attract new passengers.Design/methodology/approachAn interval-valued intuitionistic fuzzy (IVIF) sets based weighted aggregated sum product assessment (WASPAS) and multi-objective optimization by ratio analysis (MULTIMOORA) methods are developed for decision-making processes.FindingsFive airport locations are evaluated using the developed method. Results showed that in determining hub locations for LCCs, potential number of passengers of the city, airport quality and the number of hotels within the city are obtained as the three most important criteria among 12 evaluation criteria. The best location for the LCC company is determined as Antalya Airport.Research limitations/implicationsTo apply the proposed method to a different set of alternatives, data gathered on comparing location of alternatives from experts should be updated.Originality/valueProposed hybrid framework is presented as the first time in the literature as a decision-making tool. In order to validate framework's applicability, efficiency and effectiveness, a comparison and a sensitivity analysis are conducted at the end of the study.

Decision-Making Methods in MIS Outsourcing

2011 ◽  
pp. 134-152
Author(s):  
Jeanette Nasem Morgan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Fuzzy Logic ◽  
Case Studies ◽  
Cost Benefit ◽  
Information Services ◽  
Data Warehouses ◽  
Government Decision ◽  
Trade Offs ◽  
Decision Making Processes

This chapter commences with a discussion of corporate and government decision-making processes and the management sciences that support development of decisions. Special decision-making considerations, trade-offs analyses, and cost-benefit studies all figure into decisions that result in outsourcing. Technologies that support different methods of decision-making include data warehouses and data mining, rules-based logic, heuristical processes, fuzzy logic, and expert-based reasoning are presented. The chapter presents case studies and current and evolving technologies. The following sections will address the decision-making methods that are used in considering, executing and monitoring outsourced MIS projects or in service lines related to provision of information services in the organization.

The Influence of Familiarity and Security on Decision Making Processes in E-Commerce

2011 ◽  
pp. 343-353
Author(s):  
Fahim Akhter ◽  
Wendy Hui
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Negative Impact ◽  
Security Technology ◽  
Trust Level ◽  
Fuzzy Logic Approach ◽  
Decision Making Processes ◽  
Online Business ◽  
Logic Approach ◽  
Perception Of Security

E-commerce can enhance its acceptance among users through fostering online trust, which is vital for decision-making process. The perception and computation of trust is crucial for vendors and users for the success of e-commerce. The calculation and measurement of trust antecedent involves complex aspect such as presence of security controls and familiarity within the website. Most companies are acquiring ‘security technology’ because everybody else is doing the same, but not because there has been a proper assessment of its association with trust. The purpose of this paper is to analyze the role of trust antecedents such as security, and familiarity when they are used collectively to do online transactions. Trust, in general, is an important factor in conducting e-transaction, which revolve around uncertainty and ambiguity. The Fuzzy logic approach provides a means for coping with this uncertainty and vagueness that are present in e-commerce. Therefore, the fuzzy logic approach is been deployed to develop scales to measure the effects of users’ familiarity and perception of security in an online business-to-consumer (B2C) context. This research provides guidelines to vendors on how they could ascertain the trust level of their business and ways of mitigate the negative impact on the trust level.

Application of Fuzzy Logic in Investment-Intensive Decision Making

2020 ◽  
pp. 386-404
Author(s):  
Prateek Pandey ◽  
Shishir Kumar ◽  
Sandeep Shrivastava
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
New Product Development ◽  
Investment Decision ◽  
High Stake ◽  
Investment Decision Making ◽  
High Investment ◽  
Decision Making Processes ◽  
Robust System ◽  
Critical Process

Fuzzy logic has been serving the industry for decades by resolving the ambiguities that appear as a result of imprecise environment. High-stake decision-making processes require inputs from various stakeholders to incorporate. If the risk is high, as in the case of high investment decision making, a robust system of incorporating opinions from multiple stakeholders must be set in place in order to avoid any inconsistency or bad decisions. Fuzzy matrices and arithmetic can play a rescuer in such situations. In this chapter, the authors demonstrate a decision-making framework incorporating the use of fuzzy numbers and arithmetic to make critical decisions in strategic marketing and new product development. Forecasting in the domains of new products is an utmost complex and critical process because no relevant history is available owing to the product's ‘one-of-its-kind' nature. In such cases, computation via analogy is an interesting paradigm, which is also discussed in the chapter.

scholarly journals Diagnose Expert System Computer Malfunction Certainty Factor Method

2020 ◽  
Vol 2 (1) ◽  
pp. 63-71
Author(s):  
Djouking Kiray ◽  
Fricles Ariwisanto Sianturi
Keyword(s):  
Decision Making ◽  
Expert System ◽  
Expert Systems ◽  
Knowledge Base ◽  
Base System ◽  
Certainty Factor ◽  
Factor Method ◽  
Knowledge Base System ◽  
Two Factors ◽  
Computer Based

An expert system is a knowledge base system that solves problems using an expert's knowledge that is entered into a computer, thereby increasing productivity, Because an expert can work faster than a human lay works like an expert. Expert systems Also solve problems by imitating the ways in the which an expert expert offer section with problems in his field, one of the which is in the field of computer repair, the problem of computer damage Becomes a fairly complicated problem, this problem is Generally experienced by individuals and institutions. One of them is in school institutions that have computer laboratories. to diagnose computer use can damage the certainty factor method that helps identify damage to the computer and find the cause of damage to the computer based on the symptoms that occur and the solution to repair it. Certainty Factor is one of the techniques used to deal with uncertainty in decision making. In dealing with a problem, answers are Often found that do not have full certainty. This uncertainty is influenced by two factors items, namely the uncertain rules and user uncertain answers. Uncertain rules are rules of symptoms that are determined for a damage.

scholarly journals Knowledge Discovery in Data in Construction Projects

2016 ◽  
Vol 62 (2) ◽  
pp. 217-228 ◽  
Author(s):  
J. Szelka ◽  
Z. Wrona
Keyword(s):  
Decision Making ◽  
Expert Systems ◽  
Knowledge Acquisition ◽  
Knowledge Discovery ◽  
Construction Projects ◽  
Domain Expert ◽  
Knowledge Engineer ◽  
Computer Aided ◽  
Structured Problems ◽  
Decision Making Processes

Abstract Decision-making processes, including the ones related to ill-structured problems, are of considerable significance in the area of construction projects. Computer-aided inference under such conditions requires the employment of specific methods and tools (non-algorithmic ones), the best recognized and successfully used in practice represented by expert systems. The knowledge indispensable for such systems to perform inference is most frequently acquired directly from experts (through a dialogue: a domain expert - a knowledge engineer) and from various source documents. Little is known, however, about the possibility of automating knowledge acquisition in this area and as a result, in practice it is scarcely ever used. It has to be noted that in numerous areas of management more and more attention is paid to the issue of acquiring knowledge from available data. What is known and successfully employed in the practice of aiding the decision-making is the different methods and tools. The paper attempts to select methods for knowledge discovery in data and presents possible ways of representing the acquired knowledge as well as sample tools (including programming ones), allowing for the use of this knowledge in the area under consideration.

scholarly journals Optimization Model of Fuzzy Rule Based Expert System Using Max-Min Composition and Schema Mapping Translation

INSIST ◽  
2017 ◽  
Vol 2 (1) ◽  
pp. 30 ◽  
Author(s):  
Hartono Hartono ◽  
Tiarma Simanihuruk
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Expert System ◽  
Membership Function ◽  
Fuzzy Rule ◽  
Fuzzy Decision Making ◽  
Schema Mapping ◽  
Fuzzy Decision ◽  
Rule Based ◽  
Knowledge Based

Abstract— Fuzzy Decision Making involves a process of selecting one or more alternatives or solutions from a finite set of alternatives which suits a set of constraints. In the rule-based expert system, the terms following in the decision making is using knowledge based and the IF Statements of the rule are called the premises, while the THEN part of the rule is called conclusion. Membership function and knowledge based determines the performance of fuzzy rule based expert system. Membership function determines the performance of fuzzy logic as it relates to represent fuzzy set in a computer. Knowledge Based in the other side relates to capturing human cognitive and judgemental processes, such as thinking and reasoning. In this paper, we have proposed a method by using Max-Min Composition combined with Genetic Algorithm for determining membership function of Fuzzy Logic and Schema Mapping Translation for the rules assignment.Keywords— Fuzzy Decision Making, Rule-Based Expert System, Membership Function, Knowledge Based, Max-Min Composition, Schema Mapping Translation

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