Fuzzy Decision Support System Research Articles

A fuzzy analytic hierarchy processing decision support system to analyze occupational menace forecasting the spawning of shoulder and neck pain

This research provides an analytical tool, fuzzy decision support system (FDSS), to find the precedence of jeopardy in occupations spawning shoulder and neck pain (SNP), an important musculoskeletal disorder and the most ubiquitous pain complaint in an occupational environment. FDSS evaluates and prioritizes the relative importance of the imprecise, uncertain and vague nature of risk factors causing occupational SNP. The objective involves derivation of mechanical-, physical- and psychosocial-related risk categories using knowledge acquisition implemented by identifying the risk factors through literature analysis, conventional and concept mapping interviews with expert neurologists, orthopedists, psychologists and physiotherapists. Fuzzy analytic hierarchy process is applied as an evaluation tool to measure the significance of the risk factors in each occupation. The results indicate that the proposed system supplements SNP diagnosis experts with more precise key decision support information. This assists health care organizations to systematically identify appropriate occupations that grounds high risk for the occurrence of SNP and so the curative practices can be executed effectively.

Fuzzy logic as a decision-making support system for the indication of bariatric surgery based on an index (MAFOI) generated by the association between body fat and body mass index.

Abstract Background: A fuzzy obesity index (MAFOI) for use as an alternative to bariatric surgery indication (BSI) is presented. The search for a more accurate method to evaluate obesity and to indicate a better treatment is important in the world health context. BMI (body mass index) is considered the main criteria for obesity treatment and BSI. Nevertheless, the fat excess related to the percentage of Body Fat (%BF) is actually the principal harmful factor in obesity disease that is usually neglected. This paper presents a new fuzzy mechanism for evaluating obesity by associating BMI with %BF that yields a fuzzy obesity index for obesity evaluation and treatment and allows building up a Fuzzy Decision Support System (FDSS) for BSI. Methods: Seventy-two patients were evaluated for both BMI and %BF. These data are modified and treated as fuzzy sets. Afterwards, the BMI and %BF classes are aggregated yielding a new index (MAFOI) for input linguistic variable are considered the BMI and %BF, and as output linguistic variable is employed the MAFOI, an obesity classification with entirely new classes of obesity in the fuzzy context as well as is used for BSI. Results: There is gradual, smooth obesity classification and BSI when using the proposed fuzzy obesity index when compared with other traditional methods for dealing with obesity. Conclusion: The BMI is not adequate for surgical indication in all the conditions and fuzzy logic becomes an alternative for decision making in bariatric surgery indication based on the MAFOI.

A fuzzy AHP decision support system for selecting yard cranes in marine container terminals

One of the most important issues that planners and developers of ports have to address at the planning process of container terminals is how to effectively optimize truck turnaround times. The time that a truck spends at a container terminal for loading and/or unloading of cargo is a real cost scenario which affects not only the smooth operation of ports but also the overall cost of the container trade. The main objective of this article is to provide a decision support tool for selecting the best container yard gantry crane for loading/discharging operation of trucks at the landside of marine container terminals. Achieving this goal, this study aims at introducing and recommending the multiple attribute decision-making and the fuzzy analytical hierarchy process models that have been proven to provide a concrete basis for such a selection decision.

Decision support system based on hierarchical co-evolutionary fuzzy approach: A case study in detecting gamma ray signals

Decision support systems are powerful technologies for complex decision making and problem solving. However, constructing an accurate and interpretable decision support system (DSS) for any domain is a challenge. In this paper, a novel hierarchical co-evolutionary fuzzy system called HiCEFS is presented that can autonomously derive a fuzzy rule-based DSS from exemplar data. Most of the important components in HiCEFS, including irregular shaped membership functions (ISMFs) and fuzzy rules, are generated using a hierarchical co-evolutionary genetic algorithm that simultaneously co-evolves these components in separate genetic populations. Owing to its generic learning capability, the HiCEFS approach can be easily applied to produce DSSs for classification and regression tasks in various domains. As a case study, HiCEFS is employed to construct a DSS for detecting gamma ray signals. Experimental results show that the system is able to successfully discern the gamma rays from background hadrons, and performs superior to other established techniques.

Fuzzy logic as a decision-making support system for the indication of bariatric surgery based on an index (OBESINDEX) generated by the association between body fat and body mass index

Abstract Background: A Fuzzy Obesity Index (OBESINDEX) for use as an alternative in bariatric surgery indication (BSI) is presented. The search for a more accurate method to evaluate obesity and to indicate a better treatment is important in the world health context. BMI (body mass index) is considered the main criteria for obesity treatment and BSI. Nevertheless, the fat excess related to the percentage of Body Fat (%BF) is actually the principal harmful factor in obesity disease that is usually neglected. This paper presents a new fuzzy mechanism for evaluating obesity by associating BMI with %BF that yields a fuzzy obesity index for obesity evaluation and treatment and allows building up a Fuzzy Decision Support System (FDSS) for BSI. Methods: Seventy-two patients were evaluated for both BMI and %BF. These data are modified and treated as fuzzy sets. Afterwards, the BMI and %BF classes are aggregated yielding a new index (OBESINDEX) for input linguistic variable are considered the BMI and %BF, and as output linguistic variable is employed the OBESINDEX, an obesity classification with entirely new classes of obesity in the fuzzy context as well is used for BSI. Results: There is a gradual, smooth obesity classification and BSI when using the proposed fuzzy obesity index when compared with other traditional methods for dealing with obesity. Conclusion: The BMI is not adequate for surgical indication in all the conditions and fuzzy logic becomes an alternative for decision making in bariatric surgery indication based on the OBESINDEX.

A Fuzzy Decision Support System for Management of Breast Cancer

In the molecular era the management of cancer is no more a plan based on simple guidelines. Clinical findings, tumor characteristics, and molecular markers are integrated to identify different risk categories, based on which treatment is planned for each individual case. This paper aims at developing a fuzzy decision support system (DSS) to guide the doctors for the risk stratification of breast cancer, which is expected to have a great impact on treatment decision and to minimize individual variations in selecting the optimal treatment for a particular case. The developed system was based on clinical practice of Oncology Center Mansoura University (OCMU). This system has six input variables (Her2, hormone receptors, age, tumor grade, tumor size, and lymph node) and one output variable (risk status). The output variable is a value from 1 to 4; representing low risk status, intermediate risk status and high risk status. This system uses Mamdani inference method and simulation applied in MATLAB R2009b fuzzy logic toolbox.

Fuzzy Development of Multiple Response Optimization

This paper proposes a developed approach to Multiple Response Optimization (MRO) in two categories; responses without replicates and with some replicates based on fuzzy concepts. At first, the problem without any replicate in responses is investigated, and a fuzzy Decision Support System (DSS) is proposed based on Fuzzy Inference System (FIS) for MRO. The proposed methodology provides a fuzzy approach considering uncertainty in decision making environment. After calculating desirability of each response, total desirability of each experiment is measured by using values of each response desirability, applying membership function and fuzzy rules expressed by experts. Then Response Surface Methodology (RSM) is applied to fit a regression model between total desirability and controllable factors and optimize them. Next, a methodology is proposed for MRO with some replicates in responses which optimizes mean and variance simultaneously by applying fuzzy concepts. After introducing Deviation function based on robustness concept and using desirability function, a two objective problem is constituted. At last, a fuzzy programming is expressed to solve the problem applying degree of satisfaction from each objective. Then the problem is converted to a single objective model with the goals of increasing desirability and robustness simultaneously. The obtained optimum factor levels are fuzzy numbers so that a bigger satisfactory region could be provided. Finally, two numerical examples are expressed to illustrate the proposed methodologies for multiple responses without replicates and with some replicates.

Hybrid expert system aiding design of post-mining regions restoration

Reclamation as one of the stages in the life cycle of a mine is realized using different techniques and technology adapted to the unique characteristics for any given mining institution. Restoration of terrain from open-cast mining is influenced by many factors and processes; the results are open to interpretation and are not predictable. Most of the aforementioned factors have qualitative characteristics. The number and complex connections among these factors make the analysis of post-mining terrain restoration expensive and time-consuming. Therefore the automation of the decision making is desirable. In this paper, a fuzzy decision support system design is proposed for the restoration of post-mining regions. The system was applied to the testing of decision making concerning the direction of revitalization in an open-cast mining institution in Zator community, southern Poland. The considered gravel mine has worked since 1951 and belongs to the Cracow Aggregate Exploitation Company.

Domain experts’ knowledge-based intelligent decision support system in occupational shoulder and neck pain therapy

This research develops a fuzzy knowledge-based decision support system (FKBDSS) that measures and predicts the degree of severity of the work-related risk associated with shoulder and neck pain (SNP) that is a prevalent pain complaint in an occupational environment. Assessing the harshness of SNP is a dreary chore, since the risk factors are featured with imprecision, uncertainty and vagueness. Predicting SNP subjective risk level provides key decision support information to medical practitioners in diagnosis. The objective involves knowledge acquisition performed through literature analysis, traditional and concept mapping interviews with domain experts comprising neurologist, orthopaedist, psychologist and physiotherapist to identify risk factors that include mechanical, physical and psychosocial categories. The determination of ranking the relative factor importance has accomplished using analytic hierarchy processing (AHP) analysis. The linguistic variables that qualify risk levels are quantified using fuzzy set theory (FST) that provides linguistic and numeric value outputs to predict the hazard level of SNP.

AN INTELLIGENT INFORMATION SYSTEM FOR FUZZY ADDITIVE MODELLING (HYDROLOGICAL RISK APPLICATION)

In this paper we propose and construct Fuzzy Algebraic Additive Model, for the estimation of risk in various fields of human activities or nature’s behavior. Though the proposed model is useful in a wide range of scientific fields, it was designed for to torrential risk evaluation in the area of river Evros. Clearly the model’s performance improves when the number of parameters and the actual data increases. A Fuzzy Decision Support System was designed and implemented to incorporate the model’s risk estimation capacity and the risk estimation output of the system was compared with the output of other existing methods with very interesting results.

Power Distribution System Planning Evaluation by a Fuzzy Multi-Criteria Group Decision Support System

The evaluation of solutions is an important phase in power distribution system planning (PDSP) which allows issues such as quality of supply, cost, social service and environmental implications to be considered and usually involves the judgments of a group of experts. The planning problem is thus suitable for the multi-criteria group decision-making (MCGDM) method. The evaluation process and evaluation criteria often involve uncertainties incorporated in quantitative analysis with crisp values and qualitative judgments with linguistic terms; therefore, fuzzy sets techniques are applied in this study. This paper proposes a fuzzy multi-criteria group decision-making (FMCGDM) method for PDSP evaluation and applies a fuzzy multi-criteria group decision support system (FMCGDSS) to support the evaluation task. We introduce a PDSP evaluation model, which has evaluation criteria within three levels, based on the characteristics of a power distribution system. A case-based example is performed on a test distribution network and demonstrates how all the problems in a PDSP evaluation are addressed using FMCGDSS. The results are acceptable to expert evaluators.

Fuzzy MCDM approach for selecting the best environment-watershed plan

In the real word, the decision-making problems are very vague and uncertain in a number of ways. Most of the criteria have interdependent and interactive features, so they cannot be evaluated by conventional measure method. Such as the feasibility, thus, to approximate the human subjective evaluation process, it would be more suitable to apply a fuzzy method in the environment-watershed plan topic. This paper describes the design of a fuzzy decision support system in multi-criteria analysis approach for selecting the best plan alternatives or strategies in environment watershed. The fuzzy analytic hierarchy process (FAHP) method is used to determine the preference weightings of criteria for decision makers by subjective perception (natural language). A questionnaire was used to find out from three related groups comprising 15 experts, including 5 from the university of expert scholars (include Water Resources Engineering and Conservation, Landscape and Recreation, Urban Planning, Environment Engineering, Architectural Engineering, etc.), 5 from the government departments, and 5 from industry. Subjectivity and vagueness analysis is dealt with the criteria and alternatives for selection process and simulation results by using fuzzy numbers with linguistic terms. It incorporated the decision-makers’ attitude towards the preference; overall performance value of each alternative can be obtained based on the concept of fuzzy multiple-criteria decision-making (FMCDM). This research also gives an example of evaluation consisting of five alternatives, solicited from an environment-watershed plan work in Taiwan, is illustrated to demonstrate the effectiveness and usefulness of the proposed approach. The result is useful for destination planning and the sustainability of watershed tourism resources as well.

A perceptual measure of trade shows using fuzzy quality deployment development

In the past researches on the evaluation of trade shows (TSs), visitors were mostly assumed to conduct aggregating partial deliberations, which were analyzed with a single step procedure, yet unable to derive a whole picture of TSs. An organizer of trade TSs needs to know the framework of criteria used by potential visitors in their ex ante evaluation of trade shows. This paper comes to fill a gap in the published research, aiming to answer to research questions: which is the underlying structure of criteria? And which criteria are important for visitors in evaluating TSs to visit? A sample of retailers was interviewed and using fuzzy judgment to determine the vague cognition of visitors. Besides, fuzzy quality function deployment and entropy are used to determine the intensity of criteria in TSs to develop the fuzzy decision support system (FDSS). The resulting fuzzy values can be used to analyze the variance and the importance of criteria in TSs more effectively compared with the crisp values. The information will make it easier for the retailers to prioritize the criteria in TSs for marketing-oriented service.

Fuzzy decision support system for crisis management with a new structure for decision making

This paper introduces a fuzzy decision support system (FDSS) with a new decision making structure, which can be applied to manage the crisis conditions in any large scale systems with many parameters. After receiving both functional variables of the system and fault signals, the FDSS makes proper decisions to make up and repair the distorted situation and the affected elements of the network according to its data base established through experience gathered from expert managers and decision models properly developed. These decisions are expressed in the form of some scenarios with different desirability degrees, which are determined by some properly developed fuzzy multi-criteria decision making methods, helping the manager choose the best one according to his discretion.

Decider: A fuzzy multi-criteria group decision support system

Multi-criteria group decision making (MCGDM) aims to support preference-based decision over the available alternatives that are characterized by multiple criteria in a group. To increase the level of overall satisfaction for the final decision across the group and deal with uncertainty in decision process, a fuzzy MCGDM process (FMP) model is established in this study. This FMP model can also aggregate both subjective and objective information under multi-level hierarchies of criteria and evaluators. Based on the FMP model, a fuzzy MCGDM decision support system (called Decider) is developed, which can handle information expressed in linguistic terms, boolean values, as well as numeric values to assess and rank a set of alternatives within a group of decision makers. Real applications indicate that the presented FMP model and the Decider software are able to effectively handle fuzziness in both subjective and objective information and support group decision-making under multi-level criteria with a higher level of satisfaction by decision makers.

Development of a fuzzy decision support system for commodity acquisition using fuzzy analytic network process

Commodity acquisition is one of the most critical tasks in a firm especially for an accountancy department. Because of imprecise and uncertain product requirements, firm accountants have to make their best effort at this stage. Determining the most critical criteria for commodity acquisition process is a vital means for a firm to balance its limited budget. Therefore, firms have used different methods to cope with this time-consuming and mentally intensive process. This study develops a fuzzy analytic network process model which may help firm accountants in this process. Results derived from a data sample are presented to exemplify the established model. Briefly, this paper proposes an intelligent approach to vendor selection through a fuzzy ANP which takes into consideration quantitative and qualitative elements in evaluating vendor alternatives.

Developing a fuzzy decision support system to determine the location of a landfill site

This paper presents two case studies of municipal solid waste site location using a decision-support system based on fuzzy logic. This problem is very complex, as it requires the evaluation of different criteria, which involve environmental, social and economic data. Such data deal with a wide range of information that presents not only quantitative, but also qualitative knowledge. In order to deal with this characteristic, the developed system employs fuzzy rules due to its ability to treat linguistic variables and the human way of thinking. Conventional approaches tend to be less effective in dealing with the imprecise or vague nature of the linguistic assessment. A case study for selecting the location of a new municipal solid waste landfill for the city of Petropolis in Rio de Janeiro is presented. Testing of the proposed method was carried out using data from the municipal solid waste location for another municipality in Rio de Janeiro, Brazil.

Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard

An enterprise resource planning system (ERP) is the information backbone of a company that integrates and automates all business operations. It is a critical issue to select the suitable ERP system which meets all the business strategies and the goals of the company. This study presents an approach to select a suitable ERP system for textile industry. Textile companies have some difficulties to implement ERP systems such as variant structure of products, production variety and unqualified human resources. At first, the vision and the strategies of the organization are checked by using balanced scorecard. According to the company’s vision, strategies and KPIs, we can prepare a request for proposal. Then ERP packages that do not meet the requirements of the company are eliminated. After strategic management phase, the proposed methodology gives advice before ERP selection. The criteria were determined and then compared according to their importance. The rest ERP system solutions were selected to evaluate. An external evaluation team consisting of ERP consultants was assigned to select one of these solutions according to the predetermined criteria. In this study, the fuzzy analytic hierarchy process, a fuzzy extension of the multi-criteria decision-making technique AHP, was used to compare these ERP system solutions. The methodology was applied for a textile manufacturing company.

To rehabilitate or redevelop? A study of the decision criteria for urban regeneration projects

PurposeTo develop a multi‐criteria decision‐making framework for evaluating different schemes of urban regeneration project.Design/methodology/approachUrban decay in Hong Kong has long warranted community concern. To tackle the problem, there are generally two different approaches, namely building rehabilitation and redevelopment. In the past, urban regeneration was dominated by complete redevelopment. However, with the rise of the concept of sustainability, the choice of building rehabilitation is becoming increasingly popular. Nevertheless, with either option, difficulties are often encountered in balancing the diverse interests of the stakeholders, who have varied aims and ambitions for the achievements of a project. Therefore, we have developed a framework, which contains the factors to be considered when planning an urban renewal project. To obtain the relative importance of these factors in a reliable but reasonably inexpensive manner from the building‐related professionals, the Non‐structural Fuzzy Decision Support System was employed. In total, 34 building surveyors and 31 town planners were interviewed using structured questionnaires.FindingsThe set of perceived weightings of the decision criteria obtained from building surveyors was quite different from that from town planners. People of different backgrounds hold divergent views towards the relative importance of the decision criteria in an urban regeneration project.Research limitations/implicationsDue to the resource limitations, individuals from the professions of building surveying and town planning were studied only.Practical implicationsTo achieve a credible decision‐making process, it is therefore advisable to have a balanced mix of members in any urban regeneration project decision making panel. Also, the framework developed in this study can be used to facilitate the decision making process in the future project.Originality/valueThis paper is the first attempt to explore the relative importance of various criteria for the decision‐making process in urban regeneration projects perceived by different professionals.

Negotiation of on‐site buffer stocks

PurposeThe purpose of this paper is to examine the possibility of applying non‐structural fuzzy decision support theory in modelling the negotiation between various participants with conflicting objectives so as to establish the optimum buffer stocks for a construction project.Design/methodology/approachIn view of the divergence in objectives between various decision makers when determining the amount of materials to be delivered to site, the concept of integrating the non‐structural fuzzy decision support system (NFDSS) to multi‐attribute decision making is reviewed. With the help of a case study example, the process involved in the NFDSS and the methodology of evaluation are illustrated. Finally, the paper proposes the use of the Nash criterion to measure the utility of various decision makers so as to identify an equilibrium solution on the quantity of materials to be supplied.FindingsThe results indicate that the requisite number of on‐site stocks can be determined by referring to the utility of the parties involved in decision making.Research limitations/implicationsThe NFDSS systematically evaluates each scenario under different affected factors such as cost, schedule, quality, safety, etc. Finally, a scenario utility is computed to establish the preference of each party.Originality/valueThe paper is of value in showing how NFDSS can systematically analyse human judgements to generate relative weightings for the decision factors and elements. The NFDSS model can be applied to real‐world cases to determine the frequency of delivery and the amount of buffer stocks that would meet the interest of various project participants.