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Quality function deployment is a well-known customer-oriented design procedure for translating the voice of customers into a final production. This is a way that higher customer satisfaction is achieved while the other goals of company may also be met. This method, at the first stage, attempts to determine the best fulfillment levels of design requirements which are emanated by customer needs. In real-world applications, product design processes are performed in an uncertain and imprecise environment, more than one objective should be considered to identify the target levels of design requirements, and the values of design requirements are often discrete. Regarding these issues, a fuzzy mixed-integer linear goal programming model with a flexible goal hierarchy is proposed to achieve the optimized compromise solution from a given number of design requirement alternatives .To determine relative importance of customer needs, as an important input data, we apply the well-known fuzzy AHP method. Inspired by a numerical problem, the efficiency of our proposed approach is demonstrated by several experiments. Notably, the approach can easily and efficiently be matched with QFD problems.

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Abstract This author introduces the concept of Stepwise Strategy Approach (SSA) for dealing with a number of problems arises in the current age of technology. This new idea is combined with the knowledge of Grey Theory for adding flexibility to decision making process. Grey theory is useful for grasping the ambiguity exists in the utilized information and the fuzziness appears in the human judgments and preferences. This article is a very useful source of information for Fuzzy Grey and decision making using more than one decision makers in fuzzy environment. A case study on system selection comprised of 12 attributes and 4 alternatives is constructed and solved by the proposed method and the results are analyzed. For the validation of the results obtained by the Grey theory, the fuzzy VIKOR and Fuzzy TOPSIS were employed for computational purposes. The results of these three approaches on the proposed case study are closely related. Due to the fact that this author proposes the “Stepwise Strategy” approach for implementing a new technology in industries, where already the management of an older compatible type of technology is in existence, along with the grey theory concept and data whitenization approach, its contribution to the literature of operations research is highly recognizable.

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Risk assessment of urban network using traffic indicators determines vulnerable links with high danger of traffic incidents. Thus Determination of an appropriate methodology remains a big challenge to achieve this objective. This paper proposed a methodology based on data fusion concept using Fuzzy-AHP and TOPSIS to achieve this aim. The proposed methodology tries to overcome two main problems, first of all using Fuzzy AHP for weight estimation of risk indicator, overcomes the problem of some famous weighting method such as AHP that uses limited scale of Saaty (1-9) for weight estimation. Because in risk assessment decision maker prefer to compare criteria with a range instead of using exact number such as Saaty scale As a result fuzzy triangular number was proposed in our methodology. What’s more using TOPSIS method is proposed for risk score estimation respecting estimated weight, because all input risk data are numeric furthermore risk evaluation would be done using distance from ideal solution.To test the proposed methodology an urban network in North of Washington was selected as pilot area. In the next step input criteria such as annual average daily traffic (AADT index), accident severity (IR index), average slope and closeness to critical place (that need traffic controlling such as school) were determined as risk indicators using Iranian traffic organization expert’s idea then nonlinear-Fuzzy-AHP was used to estimate weight of input criteria. Estimated weight entered to TOPSIS method to determine vulnerable links that are in high danger of traffic incidents.

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The definition of ordered weighted averaging (OWA) operators and their applications in decision making are reviewed. Also, some generalizations of OWA operators are studied and then, the notion of 2-symmetric OWA operators is introduced. These generalizations are illustrated by some examples.

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 We are concerned with solving Fuzzy Flexible Linear Programming (FFLP) problems. Even though, this model is very practical and is useful for many applications, but there are only a few methods for its situation. In most approaches proposed in the literature, the solution process needs at least, two phases where each phase needs to solve a linear programming problem. Here, we propose a method to solve the given problem in just one phase using only one problem. Furthermore, using our approach, sensitivity analysis of Fuzzy Flexible Linear Programming (FFLP) problem is simpler. For an illustration of our method, some numerical examples given. In particular, a practical problem is formulated and is solved by our method and several other methods and the obtained results are compared.

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One important step to achieve a sustainable transportation system is to control the impact and evaluate the effect of various influencing factors toward three dimensions of sustainability. Within this context, diverse analytical approaches have been developed to assess the sustainability level of various transportation systems, however, sustainability evaluation based on fuzzy multiple criteria decision-making approaches are still limited. In current research activity, an integrated quantitative evaluation technique is proposed to narrow the identified gap. The developed decision-making approach is consisted of two main phases. Firstly, fuzzy analytic hierarchy process is utilized to weigh the sustainability dimensions resulting in the incorporation of the experts’ knowledge along with the evaluation process. Then, a proposed fuzzy inference mechanism is proposed to provide an indication on the performance of an evaluated road transportation system. The developed approach is applied on a real-world case study. Finally, future works are presented together with some concluding remarks.

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Cost efficiency models evaluate the ability of decision-making units (DMUs) to produce current
outputs at minimal cost. In real applications, the observed values of the input-output data and
their corresponding input prices are imprecise and vague. This paper employs a fuzzy data
envelopment analysis (Fuzzy DEA) method to study cost efficiency of DMUs. In previous studies
on the cost efficiency, no attention has been paid to the issue of ranking problem in fuzzy
environment. In addition, adequate accuracy is ignored in regards to appropriate range of fuzzy
cost efficiency scores. In this study, the proposed method is applied to assess fuzzy cost efficiency
in accordance with the  -level based approach. In this method, data information is considered
as triangular fuzzy numbers. The main idea is to convert the fuzzy DEA model into a family of
parametric crisp models to estimate the lower and upper bounds of the a-cut of the membership
functions of the cost efficiency measures. Moreover, the problem of ranking DMUs is investigated
based on the fuzzy cost efficiency, using a new method. Finally, the proposed method is illustrated
applying a numerical example, and then comparisons between the proposed method and previous
approaches are carried out.
 

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New concepts of -feasibility and -efficiency of solutions for fuzzy mathematical programming problems are used, where  is a vector of distinct satisfaction degrees. Recently, a special kind of fuzzy mathematical programming entitled Fuzzy Flexible Linear programming (FFLP) is attracted many interests. Using the mentioned concepts, we propose a two-phase approach to solve FFLP. In the first phase, the original FFLP problem converts it to a Multi-Parametric Linear Programing (MPLP) problem, and then in phase II using the convenient optimal solution with the higher feasibility degree is concluded. Using this concept, we have solved the problem of the animal diet. In the process of milk production, the highest cost relates to animal feed. Based on reports provided by the experts, around seventy percent of dairy livestock costs included feed costs. In order to minimize the total price of livestock feed, according to the limits of feed sources in each region or season, and also the transportation and maintenance costs and ultimately milk price reduction, optimization of the livestock nutrition program is an essential issue. Because of the uncertainty and lack of precision in the optimal food ration done with existing methods based on linear programming, there is a need to use appropriate methods to meet this purpose. Therefore, in this study formulation of completely mixed nutrient diets of dairy cows is done by using a fuzzy linear programming in early lactation. Application of fuzzy optimization method and floating price make it possible to formulate and change the completely mixed diets with adequate safety margins. Therefore, applications of fuzzy methods in feed rations of dairy cattle are recommended to optimize the diets. Obviously, it would be useful to design suitable software, which provides the possibility of using floating prices to set feed rations by the use of fuzzy optimization method.

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Hubs are facilities that can decrease the cost of many-to-many distribution systems by acting as an interconnector between the demand and supply nodes. This type of facility can reduce the number of direct links needed in a logistics network. Hub location problems (HLP) have been discussed by many authors for more than four decades, and different approaches have been developed for modeling and solving this problem. We propose a fuzzy type I and II programming approach for a new model presented in the literature, i.e., the single allocation ordered median problem. The level of flow among the nodes will be considered as a fuzzy parameter. In the fuzzy type I approach, a linear programming problem with fuzzy parameters is used, while for the fuzzy type II approach, the rules of interval arithmetic are developed to simplify the problem to the fuzzy type I case. Finally, we apply our method on Kalleh Dairy Co. data of transportation as a case study and compare crisp and fuzzy situations. We show that the results of the fuzzy approach could be 2% better than the crisp approach and also discuss the pros and cons of fuzzy type I and type II approaches.

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Data envelopment analysis (DEA), as a well-established nonparametric method, is used to meet efficiency evaluation purposes in many businesses, organizations, and decision units. This paper aims to present a novel integrated approach to fuzzy interpretive structural modeling (FISM) and dynamic network data envelopment analysis (DNDEA) for the selection and ranking of sustainable suppliers. First, suppliers' efficiency values in a supply chain are determined, using the dynamic network data envelopment analysis (DNDEA) model developed for this purpose. Then, a heuristic method is presented based on the fuzzy interpretive structural modeling (FISM) to find a common set of weights (CSWs) for the variables involved. Depending on the data conditions, two approaches, viz. centralized and decentralized, are proposed for efficiency measurement. To illustrate the model's capability, the proposed methodology is further applied to the real data of a company, named Nirou Moharekeh Industries (NMI). The results of a study on 12 suppliers within the DNDEA model accordingly reveal that one unit (i.e. KARAN) obtains an efficient value, but an inefficient score is observed in 11 units, whose technical efficiency value is in the range of 0.6409 to 0.9983. After utilizing the weights gained from the heuristic method, the efficiency value of the most inefficient supplier (that is, SIRINS.N.) dwindles from 0.6409 to 0.6319.

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This research was carried out with the aim of providing a comprehensive model for redesigning the organizational structure that fulfills the requirements of activity in the era of the fourth industrial revolution with the approach of developing smart business. The research was of a sequential mixed type of quantitative and qualitative type. In the quantitative part, which was conducted using a descriptive survey method, in the first stage, organization pathology and organizational design models were examined and identified through a systematic review of the research literature, and Burton's multi-contingency model, which simultaneously includes pathology and organization design, was selected. In the second stage, the researcher developed a questionnaire based on different editions of Burton's multi-contingency model, and the validity and reliability of the questionnaire (content validity using the Laushe method, face validity and construct validity using the confirmatory factor analysis method with Smart PLS software and reliability It was evaluated by the methods of Cronbach's alpha, composite reliability and divergent and convergent reliability of Fornell and Lockerre), the results of which indicate the high internal validity of the research and the validity of the tool designed in the society. Finally, a 90-item questionnaire was developed to measure 14 dimensions and 28 components, which was arranged in the form of a Likert scale and distributed among 263 managers and experts of Esfahan Steel Company, who were selected by stratified random method. After data collection and analysis, it was found that the different dimensions of the organizational structure of Esfahan Steel Company based on Burton's multi-contingency model are not located in one area and are scattered in four different areas of the diagram, and the dimensions of the structure are not proportional and aligned. In the future, in order to redesign the appropriate structure, the research was continued using the qualitative method. In this section, with the purposeful sampling technique dependent on the criterion among 30 academic and steel industry experts related to the subject, the most important dimensions of the organizational structure (using the fuzzy Delphi method and in two rounds) and the components of business intelligence (using the fuzzy Delphi method) and during three rounds) were determined. Then, by using the fuzzy Delphi technique during four rounds, which stopped at the Schmidt agreement criterion, a comprehensive model of organizational structure redesign with the approach of developing smart business was obtained. The findings showed that the most effective components of business intelligence include commercial intelligence, artificial intelligence, strategic intelligence, and competitive intelligence, which can provide suitable platforms and facilitators to achieve a suitable and intelligent organizational structure. The results show that the use of business intelligence factors consisting of strategic, competitive, commercial and artificial intelligence models are effective platforms for making appropriate decisions in order to make changes and redesign the organizational structure (organization plan) and create appropriateness and alignment. It is between dimensions with the aim of developing smart business.

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One of the most useful tools in Operations Research (OR) which is essentially applied to evaluate the performance of treated Decision-Making Units (DMUs) is Data Envelopment Analysis (DEA). Because of in the current decades, DEA models have been used and extended in many disciplines and hence attracted much interests. The traditional DEA treats DMUs as black boxes and calculates their efficiencies by considering their initial inputs and their final outputs. Since, in the real situations, input data are included some uncertainties, hence in this study we consider a DEA with fuzzy stochastic data and suggest a three-stage DEA model by taking into account undesirable output. To achieve this aim, an extended probability approach is applied to the reform of three-stage DEA models. Finally, we give an illustrative example by considering a case study on the banking industry.

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The most convenient models of Solid Transportation (ST) problems have been justly considered a kind of uncertainty in their parameters such as fuzzy, grey, stochastic, etc. and usually, they suggest solving the main problems by solving some crisp equivalent model/models based on their proposed approach such as using ranking functions, embedding problems etc. Furthermore, there exist some shortcomings in formulating the main model for the realistic situations, since it omitted the flexibility conditions in their studies. Hence, to overcome these shortages, we formulate these conditions for the mentioned these problems with fuzzy flexible constraints, where there are no exact predictions for the values of the resources. In particluar, numerical investigation shows that each increasing for the values of the supply and demand is not effective for improving the objective function.  The value of the objective function is sensitive when supply and demand change, so we conduct a new study to diversify the value of the objective function, due to changes in resource and demand levels simultaneously.

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Due to the importance of vehicle routing for delivering a large number of orders with different restrictions in the world, various optimization methods have been studied in past researches. In this article, a number of researches of recent years have been discussed, then the proposed model is described in 3 phases with the penalty index. This model has the ability to assign orders, route vehicles and determine the number of active vehicles dynamically with the aim of minimizing the total cost of distribution. By examining valid metaheuristic models and using their strengths and weaknesses, and considering multiple limitations, a new model of "dynamic 3-phase optimization" has been designed. The main application of the proposed model is for vehicle routing problems with capacity constraints of fleet number and capacity constraints (maximum and minimum number of orders). Finally, with simulation, the outputs of the model have been analyzed in different conditions . Although the limitation of maximum and minimum capacity is added to the problem, by dynamically considering the number of vehicles and using star clustering (initiative of this research), three social, environmental and economic dimensions were improved. The time for orders to reach customers decreased by 19.3%, fuel consumption and air pollution by 14.9%, and logistics costs by 8.7%. To calculate the final value of system stability, a unique 3D fuzzy model has been used. With the sensitivity analysis, we came to the conclusion that the 3-phase dynamic optimization model has led to a 14.58% improvement in system stability.

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In third world countries, organizational leaders rarely have figured out to consider happiness and joy of work as a part of the system they are managing. Usually, happiness in organizations is not considered as a management style. Gradually, it became obvious that joy and fun at the workplace will decrease the health care costs, enhances the customers’ loyalty, and increases productivity and profits as a result. Most research on this subject matter relied upon very specific case studies. No research exits dealing with the risks and benefits of Joyful organization. The objectives of this paper are twofold: (1) to utilize hierarchical fuzzy technique for order preference by similarity to ideal solution (TOPSIS) to determine the most suitable type of Joyful Organization (JO), and (2) to list key risks and benefits of Joyful Organization. This researcher explains the importance of selection criteria for evaluation of Joyful organizations. It provides key elements on JO, Quantitative strategic planning matrix (QSPM), and fuzzy hierarchical TOPSIS methodology. Since QSPM is used with SWOT by many practitioners and researchers in various fields of study, it was selected as a tool for validation purposes. A case study is taken under consideration and results are explained for both approaches.  The finding of this research points to the suitability of semi conventional organization strategy which means implementing about 50% of the rules of main cultural organizations. A sensitivity analysis was performed on TOPSIS using the weights generated by the hierarchical fuzzy TOPSIS approach, Shannon entropy weight, and TOPSIS approach. The ranking results obtained are identical for all these three cases.