جستجوی مقالات مرتبط با کلیدواژه « ranking » در نشریات گروه « ریاضی »

Supply Chain Management (SCM) is an integrated system of planning and control of materials and information, including suppliers, manufacturers, distributors, retailers, and customers. Chain performance measurement is an important issue in SCM. Also, given that the information plays a key role in improving supply chain performance, the kind and amount of information sharing should be investigated. In this paper, the effect of information sharing on supply chain performance will be evaluated. In this way, 17 different scenarios of information sharing are defined and ranked using the cross-efficiency method. Finally, values ​​for different scenarios using simulations and Rockwell Software Arena V5 are reported. The obtained results show that the proposed model is quite valid and efficient and can be easily applied to real-world cases.

Ranking fuzzy numbers have become of growing importance in recent years, especially as decision-making is increasingly performed under greater uncertainty. In this paper, we extend the concept of magnitude to rank fuzzy numbers to a more general definition to increase in flexibility and generality. More precisely, we propose a multidimensional approach to rank fuzzy numbers considering alternative magnitude definitions with three novel features: multidimensionality, normalization, and a ranking based on a parametric distance function. A multidimensional magnitude definition allows us to consider multiple attributes to represent and rank fuzzy numbers. Normalization prevents meaningless comparison among attributes due to scaling problems, and the use of the parametric Minkowski distance function becomes a more general and flexible ranking approach. The main contribution of our multidimensional approach is the representation of a fuzzy number as a point in a $n$-dimensional normalized space of attributes in which the distance to the origin is the magnitude value. We illustrate our methodology and provide further insights into different normalization approaches and parameters through several numerical examples. Finally, we describe an application of our ranking approach to a multicriteria decision-making problem within an economic context in which the main goal is to rank a set of credit applicants considering different financial ratios used as evaluation criteria.

Ranking fuzzy numbers(FNs) was a critical issue in fuzzy computing field. Generally, triangular FNs, trapezoidal FNs, and even interval-valued FNs(IVFNs) were often expressed in ranking. However, ranking intuitionistic FNs(IFNs) were less mentioned due to the complicated components in membership functions. Herein, we will develop fuzzy binary relation that is an extended fuzzy preference relation(EFPR) to express the preference degree of two IFNs, and then the relation is improved to be a relative preference relation(RPR) used to rank a set of IFNs. Since EFPR on IFNs is a total ordering relation, RPR will be also a total ordering relation. Based on belonging and non-belonging components of membership functions in IFNs, using EFPR being also fuzzy preference relation(FPR) is suitable to compare FNs on pairwise, but time complexity on fuzzy operation of comparison computing is complicated. Hence, RPR is developed to avoid comparing on pairwise. Through yielding RPR values for a set of IFNs, IFNs are effectively and efficiently ranked to utilize related decision-making problems.

In this paper, we use the Plackett-Luce model for detecting some referees judge arbitrariness with inaccuracy or without paying enough attention which is called semi-referees. We will investigate our method by simulation and sample of real data.

When we use the CCR model in the input-oriented with fuzzy data for ranking with the help of cross-efficiency, there is a possibility that the model will find a different optimal answer. This means that the ranking is not unique, that is, a decision-making unit may be assigned several ranks. Here, the judgment regarding the ranking faces a problem. To solve this problem, a secondary objective is determined for weight selection. According to that secondary objective, a suitable weight is selected from among the optimal solutions. In this article, the secondary goal of the concept of symmetrizing the weights plays a fundamental role in solving the mentioned problem. The model selects weights that are symmetrical, the act of choosing symmetrical weights causes many weights that are not useful to be removed from the set. The decision-making unit that selects symmetrical weights for all indicators, has a better performance than the decision-making unit that does not use symmetrical weights and covers its weak points with low weight and highlights its strong points with high weight. The model along with the mentioned secondary objective is used to evaluate decision-making units with fuzzy input and output, by choosing the optimal weight, a cross-efficiency table is formed. By using the cross-efficiency table, the efficiency of each unit is determined and ranked compared to other units. Units are done.

To face uncertainty in the real world, the two value logic has gradually replaced the fuzzy logic. In some real world problems, accurate determination of membership value is difficult and decision- making is associated with uncertainty and hesitation. This new perspective manages the uncertainty caused by hesitation. Generalized trapezoidal hesitant fuzzy numbers (GTHFN), whose membership degree is expressed by several trapezoidal fuzzy number, is best suited to solve the decision-making problem in real life than real numbers. In this paper, we refer to a new concept called 'generalized trapezoidal hesitant fuzzy numbers' and its combination with data envelopment analysis. Using this information, we consider the deviation and the score values as input and output of the data envelopment analysis model in two stages respectively; then we used the result to construct a paired comparison matrix and eventually we prioritize the receivers decision making units. To use some concepts in the proposed decision making method, we first present some definitions of concepts such as the score and deviation functions from the generalized trapezoidal hesitant fuzzy numbers. Finally, a numerical example is presented for the proposed method to confirm its applicability, and the ranking result is compared with the methods of AP, TOPSIS with GTHF number and the weighted geometric operator in [7].

Data Envelopment Analysis (DEA) is a broad range of mathematical models for measuring the relative efficiency of a set of homogeneous decision units with similar inputs and outputs. Multiple models of data envelopment analysis render a set of weights for input and output variables of each decision unit to calculate the relative efficiency of those units based on them. The calculation of different weights for the same indices in a set of homogeneous decision units is not realistic. Therefore, the Common Set of Weights (CSW) method was used to solve this problem and the Bootstrap method was used to determine which common set of weights would minimize the number of efficient units. The rank of a unit can provide useful information to decision-makers on the optimal activities of decision units. The priority order of units defines the superiority of a unit in terms of efficiency and effectiveness over others. Calculating unit efficiency for data envelopment analysis models can be a good criterion for ranking one unit. However, the main problem arises when several efficient units all rank first. This study aimed at proposing a model for ranking efficient units using the Bootstrap method to determine the common set of weights in data envelopment analysis by finding a possible confidence interval for the weights using the Bootstrap method. This led to the estimation of a set of possible common weights for the data envelopment analysis. Efficient units were then identified and ranked based on these weights..

Classic Data Envelopment Analysis assumes that the values of inputs and outputs are exactly determined. However, in most real-life problems, the exact values of some of the inputs and outputs are imprecise. One of such imprecise data is ordinal data. In this paper, a new model is presented for measuring the efficiency evaluation of decision making units with ordinal data. The general idea of this model is assigning real values to ordinal data with new approach. Furthermore, another new model for ranking efficient units is presented with the main idea of changes in controlled efficiency. Then, the results of proposed model are compared with Cooper model. Therefore, the efficiency scores obtained from proposed model are more realistic and reasonable than the results obtained from the Cooper's model.

The weights generated by the common weights approach provide a common criterion for ranking the decision-making units (DMUs) in data envelopment analysis (DEA). Existing common weights models in DEA are either very complicated or unable to produce a full ranking for DMUs. This paper proposes a new compromise solution model to seek a common set of weights for full ranking for DMUs. The maximum inefficiency scores calculated from the standard DEA model are regarded as the anti-ideal solution for the DMUs to avoid. A common set of weights that produces the vector of inefficiency scores for the DMUs furthest to the anti-ideal solution is sought. The discrimination power of the new model is tested using two numerical examples and its potential application for fully ranking DMUs is illustrated.

Ranking of fire stations is one of the most important issues in urban planning and crisis management. Because ranking increases the speed of service in crises. In the real world, the value of some attributes is non-controllable, so planners and decision makers can't change the values in the ranking process and it must be considered in the ranking. The aim of this study is ranking of fire stations candidates in district ten of Tehran municipality and for this, has used non-radial DEA model. The decision matrix consists of eleven alternatives and twelve attributes. The attributes are controllable (that the decision makers able to change values) and non-controllable (that the decision makers unable to change the values). The results show that station 5 is prioritized among the stations and has higher non-controllable attribute values in decision matrix than the others, and it validates the results.

Every year, many people are killed and wounded due to traffic accidents on the roads of Iran and lot of families suffer from physical, mental and financial problems. Today, the problem of traffic accidents has become an important issue in Iran but all provinces are not same condition in this issue. Transport authorities and experts tend to know position of each state based on different criteria. They should know that which province has the good condition and which one are in the worst position because for more exact planning, this issue has some influences on their decisions. In this paper, provinces based on various criteria, equipment related to intelligent transportation systems, are ranked. This method indicates that the numbers of dead and injured are not appropriate criteria for ranking of states and different criteria must be considered. This priority is performed by using of Analytical Hierarchy process (AHP). Based on the obtained results, the most important indicators for assessing Iranian provinces to improve road safety are a number of relief bases, type of damage, intelligent transportation equipment and road traffic rate. Extracting the results was done using Expert Choice software.

In today’s world, intrinsic complexity in numerous decision making conditions, decides the need of utilizing decision making methods more than previously. On the other hand, the present modern organization have turned out to be so widespread, which one doesn’t capable to oversee them. In this manner, this issue of multi attribute group decision making is considered as an organizational problem. In multi attribute group decision making problems, according to the different attributes in decision matrix, knowing the coefficient of importance or weight of each attribute in decision making is essential. As the weight of each attribute express its relative importance to the others and the conscious and correct selection of weights is a great help in achieving the desired goal. The purpose of this paper is to introduce a linear programming model to determine the weight of each attribute in multi attribute group decision making problems with intuitionistic fuzzy data. As good as, decision makers are prevented from making eventual mistakes in determining the weight of attributes. Finally, using the obtained weights, a new method for ranking the alternatives based on ELECTRE III method, is presented. A numerical applied example is provided to illustrate the details of the proposed method.

The purpose of this research is to present a method based on DEA-TOPSIS for ranking the DMUs in group decision making. In the proposed method, after creating an expert team and determining their weights, the DMUs, inputs and outputs are determined and ranked in three stages. In the first stage, by using Euclidean Distance, the matrix of each expert is normalized and consequently, the DEA models corresponding to each expert are solved. The DMUs are divided into two groups, the first group consists of DMUs where at least one experts’ opinion is inefficient and the second group consists of DMUs where the views of all experts are efficient. In the second stage, the rank of the DMUs of the first group is determined by calculating the weighted arithmetic average. In the third stage, the rank of the DMUs of the second group is determined using the TOPSIS method for each expert, consequently, the final rank of the DMUs is calculated using the Weighted Borda Scoring Method. Then the proposed approach was implemented to rank the six-paths of the Blue Ocean Strategy of the IRAFA industry and management group. The IRAFA team of experts, consisting of seven senior experts, was formed, and their weights were determined. It was also found that six paths to restructure the Blue Ocean Strategy (DMUs) are effective on IRAFA Value Innovation, and the DEA model has four outputs (utility, price, cost, and acceptance). The results of solving the DEA model of each expert showed that there are four paths in the first group and two paths in the second group. Applying the Weighted Borda Scoring Method to rank the DMUs of the second group shows that creating a functional-emotional orientation for the organization's customers is the top priority of IRAFA

The cross-efficiency method in data envelopment analysis (DEA) has widely been used as a suitable utility for ranking decision-making units (DMUs). In cross-efficiency, the average of n efficiency values for each DMU is considered as the overall efficiency score. Another method based on the concept of cross-efficiency for calculating the efficiency score for each DMU is to use the average comparable weights to calculate the efficiency, which is called the cross-weight evaluation. In the cross-weight method, as in the cross-efficiency method, there is the issue of multiple optimal solutions. In this paper, for overcoming this issue, we use the neutral strategy for cross-weight evaluation. Unlike the aggressive and benevolent formulations, the neutral way is only concerned with its own interests and is indifferent to other DMUs. This study proposes a new cross-weight evaluation by maximizing the minimum of the output weights while keeping the efficiency of the unit under evaluation unchanged. Numerical examples are provided to illustrate the potential application of this new model and its effectiveness in ranking DMUs.

One of the important issues discussed in DEA literature is ranking efficient units. This paper presents a new method based on slack based measure SBM with free variables in sign for ranking efficient units. The new method also can rank the efficient units non-extreme. It also does not have the problem of instability and infeasibility. The proposed model specifies the largest MPSS and after that other unit’s portraits to this unit. Then, using examples will be compared with the approaches.

Rail transportation plays an important role in one country's economic development. Therefore, many researchers have focused attention on the measurement of efficiency and ranking in the rail transportation industry. Evaluating organizational performance can produce significant effects on the organization’s activities. Data Envelopment Analysis (DEA) is a mathematical programming method, which measures the relative efficiency of the organizational units, which have multiple inputs and outputs. In this paper, we proposed a method for ranking all the Decision Making Units (DMUs) that is based on strong and weak supporting hyperplanes. As an applied project, we evaluate rail freight and passenger transportation in some Asian countries. The ranking is based on data from the International Union of Railways (UIC) in 2016.