A new fuzzy methodology-based structured framework for RAM and risk analysis

PurposeThe purpose of this research is to calculate and enhance the cheese cheddar manufacturing plant efficiency under actual workplace conditions by measuring reliability, availability and maintainability (RAM) indices. The authors highlight how RAM analysis is important in determining periodic maintenance and in scheduling and managing the appropriate maintenance policy.Design/methodology/approachThe current work is conducted using statistical approaches to evaluate failure and repair statistics. The RAM estimation was calculated on the basis of quantitative data obtained over a span of 32 months. Descriptive statistics, Pareto analysis, as well as the presumption of independence were ensured through trend and serial correlation tests. In addition, the reliability and maintainability of the cheddar cheese processing plant and its machines were calculated at various mission periods.FindingsThe primary goal of the implementation approach is to understand the fault patterns and the accurate quantitative assessment of the reliability and maintainability of the cheddar production plant. The findings revealed the essential aspects of the line, which need improvement by an appropriate maintenance program.Originality/valueThis study is intended to serve to highlight the RAM assessment and its impact on the performance of the real-time system. The benefit of the technique is the continual control of the manufacturing process by means of acceptable indexes, whose use corresponds to a continuous improvement process.

PurposeThe purpose of this paper is to provide results for a complete reliability, availability, and maintainability (RAM) analysis utilizing data sets from a production system in a wine packaging line. Through the illustrated case study, the author demonstrates how RAM analysis is very useful for deciding maintenance intervals, and for planning and organizing the adequate maintenance strategy.Design/methodology/approachRAM analysis has been done for each machine by using failures data. The parameters of some common probability distributions, such as Weibull, exponential, lognormal, and normal distributions, have been estimated by using the Minitab software package. An investigation to determine which of these distributions provide the best fit for characterizing the failure pattern at machine and line level has been made. Reliability and maintainability of both wine packaging and its machines has been estimated at different mission times with their best fit distribution. High maintainability issues and potential factors with their potential failure modes were presented, through failure mode and effect analysis process.FindingsAnalysis of the total downtime, breakdown frequency, reliability, and maintainability characteristics of different machines shows that: first, the availability for the wine packaging line was 91.80 percent, and for the remaining 8.2 percent the line is under repair. Second, about two failures per shift are displayed on the line, whereas for the mean time-to-repair (TTR) a failure is 24 minutes. Third, there is no correlation between the time-between-failures and the TTRs for the wine packaging line. Fourth, the main three factors affecting the maintainability process in the production line are: resources availability, manpower management, and maintenance planning procedures.Originality/valueThis study is anticipated to serve as an illuminating effort in conducting a complete RAM analysis in the much advertised field of wine packaging production line which on the other hand so little has been published on operational availability and equipment effectiveness. It can also be useful to serve as a valid data source for winery product manufacturers, who wish to improve the design and operation of their production lines.

Performance modeling in critical engineering systems using RAM analysis

The state-of-the-art related to reliability, availability and maintainability (RAM), provides indices that allow to effectively measuring different attributes on single complex assets. However, those methodologies usually present limitations when the aim is to analyse grouping maintenance or the remaining useful life in fleets of assets. The purpose of this contribution is to suggest the advantages of measures based on the notion of Shannon entropy, applied to fleet RAM analysis. Particularly, this research presents a derivation from well-known concepts such as the Gini, Hoover, and Theil indices.

Integrated framework to optimize RAM and cost decisions in a process plant

The current paper presents a comprehensive methodology for supplier selection. In the first stage, the linguistic values expressed as trapezoidal fuzzy numbers are used to assess the weights of the criteria. The Axiomatic Fuzzy Set clustering (AFS) method, which handles ambiguity and fuzziness in the supplier selection problem effectively, is applied to cluster the suppliers and evaluate each potential supplier that aims at obtaining initial supplier ranking. In the second stage, the Fuzzy Analytic Hierarchy Process (FAHP) model is constructed to determine the weight of various quantitative and qualitative criteria. To address multiple decision criteria in supplier ranking, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to select the final suppliers. A numerical example composed of 30 suppliers and 6 criteria is studied, and the experimental results show that the proposed evaluation framework is suitable for supplier selection decisions even with the dependent criteria/attributes.

PurposeThe purpose of this paper is to develop an agility evaluation approach to determine the most suitable agile system for implementing mass customization (MC) strategies. Evaluating the alternatives and comparing across them, the best practices of the efficient organization can be identified and transferred to different organizations.Design/methodology/approachGrey relation approach is a simple mathematical technique useful in situations where the information is not known precisely. Grey relation approach has been applied to measure the agility of various organizations based on agile entities and accordingly the organizations are ranked. The ranking so obtained is compared with the ranking obtained by a popular multi‐attribute decision making (MADM) process known as Fuzzy TOPSIS (technique for order preference by similarity to ideal solution) to test the robustness of the proposed method. It is to be noted that grey theory considers the condition of the fuzziness and can deal flexibly with the fuzziness situation.FindingsIt is demonstrated that the grey approach is an appropriate method for solving MADM problems in an uncertain situation with less computational efforts. The alternatives can easily be benchmarked and the best agile system can be selected. As the ranking obtained through grey relation approach closely agree with the ranking found from Fuzzy TOPSIS method, the robustness of the proposed approach is validated. Both the methods lead to choosing a suitable agile system related to mass customization.Research limitations/implicationsIn this paper, the proposed approach has been compared with Fuzzy TOPSIS method to test the robustness of the method. Other MADM approaches may be used for comparison purpose to gain insight into the methodology of the proposed approach.Originality/valueAn alternative approach for MADM is proposed to obtain good decisions in an uncertain environment and used for agility evaluation in selected organizations. As agile manufacturing is relatively a new concept, certain and complete information on systems are not available. In such situations, the proposed method can deal with the issue conveniently and results in workable solutions.

The attributes influencing the decision-making process in planning transportation of goods from selected facilities locations in disaster zones are considered. Experts evaluate each candidate for humanitarian aid distribution centers (HADCs) (service centers) against each uncertainty factor in q-rung orthopair fuzzy sets (q-ROFS). For representation of experts’ knowledge in the input data for planning emergency service facilities locations a q-rung orthopair fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach is developed. Based on the offered fuzzy TOPSIS aggregation a new innovative objective function is introduced which maximizes a candidate HADC’s selection index and reduces HADCs opening risks in disaster zones. The HADCs location and goods transportation problem is reduced to the bi-criteria problem of partitioning the set of customers by the set of service centers: 1) Minimization of opened HADCs and goods transportation total costs; 2) Maximization of HADCs selection index. Partitioning type transportation constraints are also constructed. Our approach for solving the constructed bi-criteria partitioning problem consists of two phases. In the first phase, based on the covering’s matrix, we generate a new matrix with columns allowing to find all possible partitioning of the demand points with the opened HADCs. In the second phase, using the generated matrix and our exact algorithm we find the partitioning –allocations of the HADCs to the centers corresponded to the Pareto-optimal solutions. The constructed model is illustrated with a numerical example.

This research aims to improve project quality at PT ABC by integrating the Fuzzy Analytical Hierarchy Process (F-AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) within the Lean Six Sigma framework. This method assesses and selects quality improvement projects by considering uncertainty and complexity in decision-making. The Fuzzy AHP approach is used to flexibly assign criteria weights, while TOPSIS is used to rank projects based on performance scores relative to the ideal solution. This research aims to select the most suitable Lean Six Sigma project using the MCDM fuzzy AHP and TOPSIS approaches at PT ABC. There are three alternative quality improvement projects and three assessment criteria. The data processing results using Fuzzy AHP and TOPSIS showed that the most influential criterion was the impact criterion, with a value of 0.665. Overall, the order of influencing criteria is cost (0.260) and time (0.075). Based on a review of all criteria, the project to increase the drive plate production line (A) capacity was ranked first with a value of 0.772. Second place is the project to increase capacity and consolidate rear axle assemblies A and B with a value of 0.5529. Finally, the third is the process innovation and integrated manufacturing project to strengthen the business with a value of 0.4053.

In this paper, first, the criteria that make logistics service providers more “green” are determined as: cooperation with customer’s company and its customer, green government regulations, environmental management system, green process design, reduction in energy consumption and green network design. The criteria weights are determined by fuzzy AHP, based on expert opinions. Then, a new method is proposed, which is the combination of fuzzy TOPSIS and GRA, and used to evaluate green 3PLs based on different separation measures, as an extension, using trapezoidal fuzzy numbers. Five Turkish 3PLs serve in Istanbul are selected in order to apply a case study to show the applicability of the proposed method. Finally, the proposed method is verified with respect to different resolving coefficient values and separation measures and also compared with fuzzy TOPSIS and fuzzy VIKOR method results. Different multi-criteria decision-making methods can be applied and compared to check validity of our results for future studies. The proposed method can also be implemented to 3PLs in other countries.

Abstract. Six sigma method widely applied in production and service businesses is known as a project-oriented method. In six sigma method, selection of the prior project among others can be considered as a multi -criteria decision making problem. The conducted literature review has revealed that there is a large number of methods to select six sigma projects. It is more appropriate to use fuzzy multi-criteria decision making methods in project selection since evaluation criteria of six sigma projects include uncertainties. The aim of this study is to select the most appropriate project as a result of evaluating the projects by Fuzzy VIKOR, Fuzzy TOPSIS and Fuzzy COPRAS as methods of fuzzy multicriteria decision-making and integrating the ranking scores obtained from each method by Copeland method. The proposed method has been implemented in a large scale production company, operating in Aydin ASTIM Organized Industrial Zone. Keywords. Six Sigma Projects, Fuzzy VIKOR, Fuzzy TOPSIS, Fuzzy COPRAS, Fuzzy AHP, Copeland Method. JEL. M11, C44, L20, C02, D70, O22.

Failure mode and effects analysis (FMEA) has been widely applied to many industries to identify potential deteriorated problems for product design or process fabrication. Generally, the conventional FMEA prioritizes specific failure modes based on a so-called risk priority number (i.e., RPN=O*D*S), which is simply a mathematical product of three risk factors: occurrence (O), detection (D), and severity (S). Obviously, there exist several flaws for the conventional FMEA: (1) the relative importance among three risk factors is neglected, and (2) failure modes caused by different combinations of three risk factors are possible to generate the same RPN and hence the identification of their root causes becomes quite difficult in practice. As a result, a fuzzy multi-criteria decision making (MCDM) based framework that incorporates fuzzy entropy and fuzzy TOPSIS (technique for order preference by similarity to ideal solution) is presented to improve the conventional FMEA. An industrial example regarding the manufacturing process of PCB (printed circuit board) is demonstrated in this study.

Streamlining apartment provider evaluation: A spherical fuzzy multi-criteria decision-making model

In this Chapter an evaluation of the possibilistic quantification of operational risk is performed. The assessment is achieved through fuzzy analytic hierarchy process (FAHP) and fuzzy extension of the technique for order preference by similarity to ideal solution (FTOPSIS) alongwith life cycle assessment (LCA) study. The fuzzy versions of analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS) take care of the inherent uncertainty and vagueness in the operational risk data. The trapezoidal fuzzy numbers are used to model the fuzzy variables. The evaluation process of the possibilistic quantification of operational risk thus becomes more dynamic through integration of different fuzzy approaches.

The purpose of this paper is to develop a new two-stage method for fuzzy multi-objective linear program and apply to engineering project portfolio selection. In the fuzzy multi-objective linear program, all the objective coefficients, technological coefficients and resources are trapezoidal fuzzy numbers (TrFNs). An order relationship for TrFNs is introduced by using the interval expectation of TrFNs. In the first stage, the fuzzy multi-objective linear program with TrFNs is transformed into an interval multi-objective linear program according to the order relationship of TrFNs. Combining the ranking order relation between intervals with the satisfactory crisp equivalent forms of interval inequality relations, the interval multi-objective linear program is further transformed into a crisp multi-objective linear program. In the second stage, the positive and negative ideal solutions are calculated as well as the closeness degrees from the positive ideal solution to all objectives on the basis of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Then, using the closeness degrees, we convert the crisp multi-objective linear program into mono-objective program to solve. The proposed method is not only mathematically rigorous, but also can adequately consider the acceptance degree of decision maker that the fuzzy constraints may be violated. The other possible cases of the fuzzy multi-objective linear program are also discussed. The proposed method is illustrated by means of a project portfolio selection problem.