Fuzzy-based Methodology Research Articles

A Fuzzy-based Methodology for Landslide Susceptibility Mapping

The present paper proposes a new methodology to characterize the landslide susceptibility of Reggio Calabria territory. The values obtained were classified into five categories: very low, low, medium, high and very high, and were than exported into GIS environment to produce a landslide susceptibility map. The principal objective of the proposed study is to identify the sections of the road network exposed to landslide hazards starting from the susceptibility map. To this aim, a fuzzy system was designed with 2 input, 15 rules “if-then” and 1 output representing the “level of attention” for the considered transport infrastructure, being a higher value of attention level correspondent to a higher landslide hazard.

Evidential Reasoning–Based Condition Assessment Model for Offshore Gas Pipelines

Condition assessment of oil and gas pipelines is a significant component in pipeline operations and maintenance. Such assessments are used to ensure better decisions for repair and/or replacement to reduce pipelines’ failure possibilities. Therefore, it is essential to have an effective condition assessment model for pipelines as their failure incidents may lead to catastrophic, economical, and environmental consequences. Current practices of assessing gas pipelines condition can be considered simplified for the intended purpose. They mainly depend on experts’ opinions in interpreting inspection data, where the process is influenced by human subjectivity and reasoning uncertainty. In other words, they need detailed knowledge on the translation of raw inspection data into valuable information. This will surely lead to decisions lacking thorough and extensive review of the most influential aspects on pipelines’ conditions. To address the weaknesses of current practices, this research proposes a new fuzzy-based methodology that utilizes an integrated analytic network process (ANP) and hierarchical evidential reasoning (HER) to develop a meticulous condition assessment model for offshore gas pipelines. The proposed model is validated using historical inspection reports that are obtained from a local pipeline operator in Qatar. The model delivers satisfactory outcomes in assessing offshore gas pipelines’ conditions based on real field data.

A Fuzzy-based Methodology for an Aggregative Environmental Risk Assessment of Restored Soil

Environmental risks pertaining to contaminated soils have been well studied, while little attention has been paid to the risks of the soils after remediation. In this study, a concept model developed based on fuzzy set theory was applied to evaluate the uncertainties of three risk indicators, namely, plant growth, groundwater safety and human health, of a restored site that had been previously polluted by heavy metals. The concept model classified the grade and importance of risk factors by an 11-level ranking system and was able to yield a comprehensive risk result rather than multi-risk results for complex risk indicators. Modeling results showed that the risks to the three indicators were effectively reduced after the remediation. Moreover, great sensitivity of the risks was found related to the weight distribution among the three risk indicators. In general, the risks of both polluted and restored soils to the environment were in the order of groundwater safety > plant growth > human health. The model was proved to solve the problems of multi-risk results due to complex risk indicators that previously encountered by other researchers, which made it helpful in decision-making and management of restored soils.

A fuzzy analytical hierarchy methodology for the performance assessment of irrigation projects

Precise performance assessment of irrigation projects is a difficult task due to several uncertainties and ambiguity in some operation and maintenance parameters. This work proposes a fuzzy-based methodology to assess these projects using the major criteria of technical, managerial, environmental, social, and economic aspects. Twenty-one sub-criteria indicators are considered for the assessing process. The methodology is developed based on the well-known, multi-criteria decision method of Analytical Hierarchy Process (AHP) and fuzzy triangular numbers. The approach is applied to four irrigation projects, and the results demonstrate that the managerial criterion has the greatest impact on the assessment process. The technical, social, economical, and environmental criteria rank next in effectiveness, respectively. Hence, in order to improve irrigation system performance, all rehabilitation tasks should focus more on the managerial issues. The findings reveal that the overall score of irrigation projects is not sensitive to relative weight of an individual indicator and only large change in priority weights of several criteria has a significant effect on ranking of the irrigation projects. The most and least efficient performance values were 74.1 and 57.5%, respectively. The proposed methodology deals with uncertainty and vague data using triangular fuzzy numbers, and, moreover, it provides a comprehensive decision-making technique to assess the performance of irrigation projects.

Evaluating leakage potential in water distribution systems: a fuzzy-based methodology

Loss of water due to leakage is a common phenomenon observed in all water distribution systems. However, the water loss can be reduced to an acceptable limit by improved understanding of leakage mechanisms and pathways, and the influencing factors that lead to leakage. This study proposes a methodology to estimate leakage potential (LP) in a water distribution system (WDS). The study has identified various factors which are directly and/or indirectly associated with LP in a WDS. The research has defined a selected set of influencing factors as fuzzy variables and has developed a fuzzy rule-based (FRB) model using Mamdani inferencing algorithm. The impact of influencing factors varies under different operating pressure, therefore a generalized methodology has been proposed. The proposed methodology has been implemented for a real WDS in Bangkok (Thailand). To evaluate the impacts of uncertainties of influencing factors on LP, Monte Carlo simulation-based sensitivity analysis has been carried out. It has been concluded that the operating system pressure and age of the network are the two most important factors that contribute to LP of a WDS. The proposed model will help water utility managers to prioritize their network rehabilitation strategies and to establishing an efficient active leakage control program.

TRANSPORT ROUTE PLANNING MODELS BASED ON FUZZY APPROACH

Transport route planning is one of the most important and frequent activities in supply chain management. The design of information systems for route planning in real contexts faces two relevant challenges: the complexity of the planning and the lack of complete and precise information. The purpose of this paper is to nd methods for the development of transport route planning in uncertainty decision making contexts. The paper uses an approximation that integrates a speci c fuzzy-based methodology from Soft Computing. We present several fuzzy optimization models that address the imprecision and/or exibility of some of its components. These models allow transport route planning problems to be solve with the help of metaheuristics in a concise way. A simple numerical example is shown to illustrate this approach.

Leakage detection and location in water distribution systems using a fuzzy-based methodology

Loss of water due to leakage is a common phenomenon observed practically in all water distribution systems (WDS). However, the leakage volume can be reduced significantly if the occurrence of leakage is detected within minimal time after its occurrence. This paper proposes a novel methodology to detect and diagnose leakage in WDS. In the proposed methodology, a fuzzy-based algorithm has been employed that incorporates various uncertainties into different WDS parameters such as roughness, nodal demands, and water reservoir levels. Monitored pressure in different nodes and flow in different pipes have been used to estimate the degree of membership of leakage and its severity in terms of index of leakage propensity (ILP). Based on the degrees of leakage memberships and the ILPs, the location of the nearest leaky node or leaky pipe has been identified. To demonstrate the effectiveness of the proposed methodology, a small distribution network was investigated which showed very encouraging results. The proposed methodology has a significant potential to help water utility managers to detect and locate leakage in WDS within a minimal time after its occurrence and can help to prioritise leakage management strategies.

Computer-aided maintenance management systems selection based on a fuzzy AHP approach

Computerized maintenance management systems (CMMS) are common in today's industries. CMMS can bring a large number of benefits, which include increased productivity, reduced costs, and effective utilization of the assets in any manufacturing and service producer. The list of CMMS that are available in the market has grown very rapidly during the last years. When purchasing a system, one that suits the specific needs and objectives of the company's maintenance operations should be preferred. Several selection methods were proposed in literature. Up to now, no article has considered ambiguity and uncertainty factors when selecting effective CMMS. In addition, CMMS selection decisions involve the simultaneous consideration of multiple criteria, including tangible and intangible factors; prioritizing these factors can be a great challenge and a complex task. Therefore, no attempt has been made to incorporate fuzziness into multicriteria decision-making in the area of CMMS selection. This work proposes a fuzzy-based methodology for comparative evaluation of a number of CMMS alternatives. The proposal is based on the well-known multicriteria decision method called Analytical Hierarchy Process (AHP) with triangular numbers. An example is given to illustrate the proposed methodology. Finally, a software prototype for implementing this method was implemented. To illustrate and validate the proposed approach and the software prototype developed some details are presented and discussed.

Fuzzy-based methodology for performance assessment of emergency planning and its application

Emergency planning is the guidance in emergency responses and its performance can influence the whole rescue process. Hence aiming at improving the emergency planning performance and further promoting the rescue efficiency, there is an urgent requirement to appraise the performance of emergency planning. A mature emergency system is the premise to ensure emergency planning to be performed perfectly. Firstly, the maturity of emergency system is evaluated qualitatively by comparing the challenge caused by potential risk and emergency capability formed by emergency system. If the emergency capability can encounter the potential risk, the emergency system is viewed as mature. Secondly, functional attributes are analyzed and the performance assessment index system of emergency planning is set up by characterizing the attributes in detail. Thirdly, according to the hierarchical characteristics of the index system, the performance assessment method for emergency planning is put forward through the combination of Analytic Hierarchy Process (AHP) and fuzzy theories. In the method, defuzzification is introduced so as to achieve the integrated performance of emergency planning and performance taxis of all assessment indices. The taxis from priority to inferiority of performance indices can be used as references to improve the emergency planning. Finally, the application of the method in a petrochemical materials storage depot indicates that the disadvantages existing in the emergency system and emergency planning can be found out through the assessment results and the advices given to the depot improve the emergency system maturity and the emergency planning performance.

Fuzzy-based methodology for multi-objective scheduling in a robot-centered flexible manufacturing cell

A fuzzy logic based methodology for generating the sequence of part movements in a multi-product batch processing through a computerized machine cell is presented in this paper. A number of production objectives are taken into account. Two fuzzy based strategies: fuzzy-job and fuzzy-machine are proposed and their performance is compared to two well known dispatching rules such as SPT (Shortest Processing Time) and WEED (Weighted Earliest Due Date). The sequencing algorithm was implemented on a standard personnel computer and the scheduler was interfaced to a robot controller for implementing loading and unloading strategy within the cell. The proposed fuzzy-based methodologies especially fuzzy-job shows a superior performance compared to the traditional dispatching rules considered.

A Neural Network and Fuzzy-Based Methodology for the Prediction of Work Roll Surface Roughness in a Grinding Process

Grinding is a complex machining process with a lot of interactive parameters, which depend upon the grinding type and requirements of products. Surface roughness is a widely used index of product quality and in most cases it is considered as a technical requirement for mechanical product. In this paper, a neural network and fuzzy-based methodology are developed for predicting the surface roughness in a grinding process for work rolls used in cold rolling. This methodology predicts the most likely estimates of surface roughness along with lower and upper estimates using fuzzy numbers. The network model is trained using back-propagation algorithms. The initial training and testing dataset is identified on the basis of effect of a factor. The best possible network is chosen, with learning rate, number of neurons in hidden layer and error goals being decided automatically. A computer code was written in “C” language. The training and testing data are collected from carefully designed experiments. The validation of the methodology is carried out in a traverse cylindrical grinding machine in the Salem Steel Plant situated at Salem, India. The visual depiction of the training, testing and testing is presented. The validation of the methodology is carried out for grinding of alloy steel using a black carbide silicon grinding wheel. It is observed that the present methodology is able to make accurate predictions of surface roughness by utilizing small-sized training and testing datasets.

A fuzzy-based methodology for an aggregative environmental risk assessment: a case study of drilling waste

The objective of this study was to develop a methodology for estimating aggregative risk of various environmental activities, pollution sources and routes in a given process. The rate of risk is defined by a product of grade of risk ( r, magnitude) and grade of importance ( i, intensity). Both factors r and i are expressed by an 11-level qualitative scale which are defined by triangular fuzzy numbers to capture the vagueness in the linguistic variables. A three-stage hierarchical structure aggregative risk model was developed for grouping of risk items. For this grouping an analytical hierarchy process was used. A sensitivity analysis was also performed to verify the effect of weighting schemes on the assessment of a final aggregative risk. The developed methodology is applied to a case study of offshore drilling waste for evaluating various discharge scenarios.

A fuzzy-based methodology for the analysis of diabetic neuropathy

A new model for the fuzzy-based analysis of diabetic neuropathy is illustrated, whose pathogenesis so far is not well known. The underlying algebraic structure is a commutative l-monoid, whose support is a set of classifications based on the concept of linguistic variable introduced by Zadeh. The analysis is carried out by means of patient's anagraphical and clinical data, e.g. age, sex, duration of the disease, insulinic needs, severity of diabetes, possible presence of complications. The results obtained by us are identical with medical diagnoses. Moreover, analyzing suitable relevance factors one gets reasonable information about the etiology of the disease, our results agree with most credited clinical hypotheses.

Extension of a hybrid Genetic Algorithm for nonlinear programming problems with equality and inequality constraints

As an extension of the hybrid Genetic Algorithm-HGA proposed by Tang et al. (Comput. Math. Appl. 36 (1998) 11), this paper focuses on the critical techniques in the application of the GA to nonlinear programming (NLP) problems with equality and inequality constraints. Taking into account the equality constraints and embedding the information of infeasible points/chromosomes into the evaluation function, an extended fuzzy-based methodology and three new evaluation functions are proposed to formulate and evaluate the infeasible chromosomes. The extended version of concepts of dominated semi-feasible direction (DSFD), feasibility degree ( FD 1) of semi-feasible direction, feasibility degree ( FD 2) of infeasible points ‘belonging to’ feasible domain are introduced. Combining the new evaluation functions and weighted gradient direction search into the Genetic Algorithm, an extended hybrid Genetic Algorithm (EHGA) is developed to solve nonlinear programming (NLP) problems with equality and inequality constraints. Simulation shows that this new algorithm is efficient. Scope and purpose Non-linear Programming (NLP) problems with equality and inequality constraints is an important type of constrained optimization problems. Genetic Algorithm (GA) is one of the well known evolutionary computation techniques. In the application of GA to NLP problems, chromosomes randomly generated at the beginning and/or generated by genetic operators during the evolutionary process usually violate the constraints, resulting in infeasible chromosomes. Therefore, the handling of system constraints, particularly the nonlinear equation constraints, and the measurement and evaluation of infeasible chromosomes, are major concerns in GA. Penalty strategy in the construction of fitness function is commonly used to evaluate the infeasible chromosomes in some traditional AG methods. However, this approach essentially narrows down the search space by eliminating all infeasible chromosomes from the evolutionary process, and it may reduce the chances of finding better candidates for the global optimization. In particular, it absolutely ignores the information carried by the infeasible chromosomes itself. Therefore, formulating the infeasible chromosomes by embedding the relevant information into the evaluation function are important when applying GA to NLP. As an extension of the Hybrid Genetic Algorithm-HGA proposed by Tang et al. (1998), this paper focuses on the critical techniques in the application of GA to NLP problems with equality and inequality constraints. Taking into account the equality constraints and embedding the information of infeasible chromosomes into the evaluation function, an extended fuzzy-based methodology and three new evaluation functions are designed to formulate and evaluate the infeasible chromosomes. By introducing an extended version of the concepts of dominated semi-feasible direction (DSFD), feasibility degree ( FD 1) of semi-feasible direction, feasibility degree ( FD 2) of infeasible points ‘belonging to’ feasible domain, an extended hybrid Genetic Algorithm (EHGA) is developed for solving NLP problems with equality and inequality constraints.

Machine loading problem of FMS: A fuzzy-based heuristic approach

A fuzzy-based solution methodology has been formulated to address the machine-loading problem in a flexible manufacturing system. The objectives considered are minimization of system unbalance and maximization of throughput, whereas the systems technological constraints are posed by availability of machining time and tool slots. The job ordering/job sequence determination before loading is carried out by evaluating the membership contribution of each job to its characteristics such as batch size, essential operation processing time and optional operation processing time. The operation?machine allocation decisions are made based on the evaluation of membership contribution of operation machine allocation vector. The formulation of membership function is based on logical derivations and enjoys reasonable analytical support. The proposed heuristic is tested on 10 problems adopted from literatures and the results reveal substantial improvement in solution quality over, some of existing heuristic-based approaches.