fuzzy set theory

This paper presents a multi-objective mathematical model for the reverse supply chain of hospital waste management in Iran during the COVID-19 pandemic, incorporating dimensions of sustainability. The objectives of the model are as follows: 1) Minimizing the costs associated with building facilities and waste treatment centers, vehicle fuel costs, and environmental costs due to pollutant emissions; 2) Maximizing the energy generated from the waste combustion process; 3) Minimizing the risk of virus transmission resulting from inadequate waste management; and 4) Maximizing the number of job opportunities in the established centers. It is important to note that existing uncertainties are addressed through the application of fuzzy set theory. Given the multi-objective nature of the model, two multi-objective algorithms, namely the Pareto archive-based Krill Herd Algorithm and Non-dominated Sorting Genetic Algorithm II (NSGA-II), are employed to solve the defined problem. The results indicate that the proposed Krill Herd Algorithm converges to a solution with higher quality and dispersion compared to NSGA-II. Additionally, through a comparison of the spacing index and running time of the two algorithms, it is observed that NSGA-II explores the solution space with higher uniformity and solves the model in less time.

In today's competitive world, production management practices based on the past method have lost their effectiveness. Since those methods have less integrity in their processesand companies need more integration in all their manufacturing processes. Stable supply chain can be considered as a practical tool that can deliver acceptable performance with a massive sustainability of business engagement to have properperformance for more sustainability of supply chain prescribes competitive priorities can be considered.
The present research aims to determine the competitive priorities in the sustainable supply chain.This research is quantitative based on objective and descriptive-analytic based on method.In this research, we first identify the dimensions of the supply chain which had a hierarchical structure, then by examining the literature, criteria and all the priorities and sub-criteria was identified on the set of companies that is active in the Pars Toosheh company. Then the datawas tested with a combination of multi-criteria decision-making techniques. The results of the research showed that the competitive priorities that the company is competing with in the market and willing to invest more in it are: cost, delivery and quality, andother priorities based on the value we value that we consider for V place in the next priorities.

In the current variable world, logistic planning and management is a way as to helping organizations for facing rapid changes, and identifying strengths and weaknesses and dominating weaknesses and threats that dangers the existence of organization. Future is not predictable but the organizations could prepare themselves for overcoming it, and this preparation causes competitive advantage for them. In this paper a model is proposed to developing and prioritizing the strategies in practical and scientific educational centers that fuzzy SWOT analysis is used in it. In this study, firstly, inter strengths and weaknesses and environmental opportunities and threats are identified, and they are initialized in IFE and EFE matrices in order for strategic place of the organization to be determined, and then the superior strategies are specified using SWOT and IEM matrices and finally they are prioritized using fuzzy QSPM.

Today's world is more competitive than ever, and industry increasingly looks to supply chain management as a competitive weapon. the objective of this research is to suggest an integrated approach for Closed Loop Supply Chain network including two phases. In the first phase, a framework for supplier selection criteria in RL is proposed. Beside a fuzzy method is utilized to evaluate suppliers based on quantitative and qualitative criteria. The output of this stage is the weight of each supplier according to each part.in the second phase, we propose a Multi Objctive mixd-integer linear programming model to determine weight of suppliers and amount of item for purching from them(strategic decisions), as well as weight of remanufacturing subcontractor and dedicate product to them (tactical decisions), and find out the optimal number of part and products in CLSC network(operational decisions). The objective functions maximize profit of selling, minimize factory cost as well as cost of unsatisfied demand and rest of them maximize weight of suppliers and allocate return product between recoverable centers in factory and remanufacturing subcontractors. the mathematical programming model is validated through numerical analysis.

Over the last few decades, reliability engineering has been successfully applied in many industries to improve the performance of equipment maintenance management. Many optimization models and researches are developed and widely used to achieve a high level of maintenance, i.e. the balance of performance, risk, resources, and cost to achieve an optimal solution. although, the application of these techniques to medical equipment is very new,One of the major challenges is how to improve the safety, security and reliability of medical devices. In this paper a FMEA based assessment model is proposed to improve human reliability in medical devices. Fuzzy linguistic variables are used to express the experts’ evaluation for risk factors of sub systems. The results indicate that the human reliability assessment of medical devices must be more focused on reducing soft failures and hidden risks. Also through the designing and developing processes of medical devices, attention must be paid to the communication between operators and designers.

Master production scheduling is a midterm phase in planning which translates the long term aggregate production planning to a plan which determines the scheduling and magnitude of different products production. This problem requires investigating a wide range of parameters about demand, manufacturing resource usage and costs. Uncertainty is an intrinsic characteristic of these parameters. In this paper, a model is developed for master production scheduling under uncertainty where demands are considered as stochastic variables, while cost and utilization parameters are expressed as fuzzy numbers. A hybrid approach is also proposed to solve the extended model. The application of the proposed method is examined in a numerical example.

Every project has many risks and as there are many complexities in projects today، recognizing the most important risks is essential for projects'' success and efficiency. In this research، we tried to determine most significant risk''s categories in the framework of risk breakdown structure of 4th edition of Project Management Body of Knowledge Guide that can be generalize to all projects in Iran. With considering dependencies and interactive relations between risks of project، we used DEMATEL method to determine the most significant project risk''s categories on the basis of risk breakdown structure of 4th edition of Project Management Body of Knowledge Guide. Also fuzzy set theory was applied to measure experts'' subjective judgments، experts who have rich expertise and knowledge in Iranian projects were selected to evaluate the influences. The results revealed that «External»، «Technical»، «Project Management» and «Organizational» risks are significant and in the most important risk''s category which is «External»، «Regulatory» risks and in «Technical»، «Project Management» and «Organizational» risks، «Technology»، «Estimating» and «Project Dependencies» are the most important risks respectively and should be paid more attention because they were in the first rank of importance.

The main purpose of supply chain management is to achieve customer satisfaction (competitiveness escalation). Therefore, every leading corporation of supply chains has to be able to discover its competitiveness situation (serving) by some models or techniques, and step forward in order to reach maximum customer satisfaction(maximizing competitiveness). At the time being, responding to the question "how the competitiveness (serving) of supply chains can be measured" is of great importance. The importance is due to the fact that by measuring the competitiveness of supply chains, while determining the current performance (weak points and strong points), it is possible to decide on the competitiveness situation (opportunities, and threats) of supply chains in comparison to each other, and this is an introduction to determining the performance ideals and the outset of improvement process. In this article, a new technique called competitiveness (serving) measurement technique of supply chains will be introduced. This technique is designed based on the guidelines and attributes of the supply chain operations reference model. The six steps of this technique are: identifying the effective attributes on the competitiveness of supply chains with the use of fuzzy screening technique, determining the relative importance (weight) of selected attributes in the first step with the use of fuzzy hierarchical analysis, determining the fuzzy quantity of each selected attribute in the first step in the supply chain of the industry under discussion, grading the supply chains under discussion with the use of fuzzy TOPSIS technique, confirming the fuzzy quantities in the from of four major components of competitiveness, and depicting the quantity of competitiveness of each supply chain on the four-dimensional graph.

As in other sectors of the economy, the insurance industry has experienced many changes in information technology over the years. Advances in hardware, software, and networks have offered benefits, such as reduced costs and time of data processing and increased potential for profit, as well as new challenges particularly in the area of increased competition. Technological innovations, such as data mining and data warehousing, have greatly reduced the cost of storing, accessing, and processing data. Data mining can be defined as the process of selecting, exploring, and modelling large amounts of data to uncover previously unknown patterns. In the insurance industry, data mining can help firms gain business advantage. For example, by applying data mining techniques, companies can fully exploit data about customer's buying patterns and behavior and achieve a greater understanding of customer motivations to help reduce fraud, anticipate resource demand, increase acquisition, and curb customer attrition. This paper designs expert system for intelligent fraud detection in e-insurance by fuzzy set theory with data mining approach.

Because of the increasing importance of suppliers to achieve competitive advantages, literature proposes different methods to help decision makers to face the complexity of the supplier selection process. However, these methods often face with this process as a single and one-dimensional problem. Also, these methods do not consider unreliability and ambiguity of supplier selection problem. But, supplier selection process needs a multi stage approach. This paper intends to propose a comprehensive multi stage approach to integrating supplier selection process. This approach tries to decrease ambiguity of this problem by using Fuzzy QFD approach and the concept of House of Quality. A Fuzzy algorithm is used to defining linguistic variables to express relative importance of inner variables (WHATs) and outer variables (HOWs) of house of quality and their interrelations. Finally, a case study is presented to illustrate this approach is applicable. In addition to ranking and selecting final supplier, the findings of study show human resource, infrastructures, and IT are the most important criteria to evaluating suppliers and technical and functional quality and after sale services of product are the most characteristics of product.

One of the most useful algorithms for inventory efficient control is the Fordyce Webster algorithm. This algorithm can solve problems with certainty parameters. In the real world, many parameters, like inflation, can change inventory costs. This article establishes an algorithm based on fuzzy inventory costs (e.g. holding costs, ordering costs, and purchase cost). These costs are fuzzy triangular numbers. A numerical example is explained. The results show that the proposed algorithm is very flexible.

In this paper a fuzzy-based approach for selecting a suitable organizational structure is introduced. The model is developed according to contingency approach. To do so, five stages are considered. In the first stage, a fuzzy system whose inputs are contextual dimensions is presented. These dimensions are technology, strategy, Environmental uncertainty, and Organization size. This system's outputs are the types of organizational structure. In the second stage, inputs and outputs are fuzzified. To do so, the triangular function is applied. Inference rules (inference engine) are developed in the third stage. The fourth stage is defuzzification stage. In the fifth stage the model is tested.

This paper introduces a fuzzy-based model for analysis of organizational structure. This model has three stages. In the first stage, it applies a fuzzy system to obtain the desired structural dimensions (system’s outputs) based on the contextual dimensions (system’s inputs). According to management literature the most significant structural dimensions consist of formalization, centralization and complexity and the major contextual dimensions are strategy, technology, environmental uncertainty and size of organization. In this stage, the model estimates the present structural dimensions too. In the second stage, the model presents a fuzzy system to determine the mechanistic and organic aspects of organizational structure (theoretical structure), which is estimated for both of the desired and present status. In this stage the system’s inputs are computed structural dimensions in first stage and its out put is theoretical structure. Finally after testing the model, a method is presented to analyze the gap existing between the desired and present structure in the third stage.