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The increase in the amount and variety of evaluations provided by the users of different websites regarding the products displayed is becoming an increasingly familiar scenario. That is, decision makers (DMs) constantly receive linguistic evaluations (LEs) from unknown evaluators when considering different choice alternatives. The imprecision of the LEs and the fact that the evaluators may have biased interests when describing a product must be considered by the DMs when computing their expected utilities. We define a Bayesian-updated probability (BUP) function that accounts for the fuzziness inherent in the LEs and the reputation of the evaluator to represent the beliefs of DMs. The proposed BUP process allows the DMs to subjectively adjust the probability mass that is shifted across evaluation intervals when updating their beliefs and computing their corresponding expected utilities. We illustrate the behavior of the BUP function numerically and describe potential decision support applications. [ABSTRACT FROM AUTHOR]

The current paper analyzes the formation of social networks determined by the preferences of their users, who are endowed with incomplete information regarding the characteristics of other users from who they receive friendship requests. The acceptance or rejection decision is determined by the limited information available when receiving the requests, the expectations of the users regarding the remaining characteristics of the requesters and the resulting improvement in network capacity derived from accepting the friendship requests. We illustrate how the similarity in preferences among users leads to more concentrated clusters within the incomplete information scenario analyzed. At the same time, the emergence of disutility costs derived from a suboptimal decision when accepting to interact with other users increments the dispersion between clusters. In this regard, the inclusion of requesters endowed with average preferences relative to those of the standard users composing the network acts as a connectivity-enhancing mechanism designed to reduce the dispersion and differences existing between clusters. [ABSTRACT FROM AUTHOR]

The Analytical Hierarchy Process (AHP) is a reliable, rigorous, and robust method for eliciting and quantifying subjective judgments in multi-criteria decision-making (MCDM). Despite the many benefits, the complications of the pairwise comparison process and the limitations of consistency in AHP are challenges that have been the subject of extensive research. AHP revolutionized how we resolve complex decision problems and has evolved substantially over three decades. We recap this evolution by introducing five new hybrid methods that combine AHP with popular weighting methods in MCDM. The proposed methods are described and evaluated systematically by implementing a widely used example in the AHP literature. We show that (i) the hybrid methods proposed in this study require fewer expert judgments than AHP but deliver the same ranking, (ii) a higher degree of involvement in the hybrid voting AHP methods leads to higher acceptability of the results when experts are also the decision-makers, and (iii) experts are more motivated and attentive in methods requiring fewer pairwise comparisons and less interaction, resulting in a more efficient process and higher acceptability. [ABSTRACT FROM AUTHOR]

Information retrieval behavior of the patient, satisficing and impatient decision makers (DMs) through the initial nodes of a binary decision tree. [Display omitted] • We study the effect of user impatience and search rankings on click-through rates (CTR). • We design a stochastic information retrieval algorithm to mimic real-life CTR behavior. • Two versions of the algorithm are defined to incorporate the degree of user impatience. • The CTRs of the top three ranked alternatives remain stable as users grow impatient. • CTR differences widen as growingly impatient users proceed halfway through the ranking. This study analyzes the effects that the position of the alternatives ranked by a search engine and the relative impatience of users have on their information retrieval behavior. We design a stochastic information retrieval algorithm calibrated to mimic the click-through rates (CTRs) of users observed in real-life environments. We introduce two versions of the mimicking algorithm designed to demonstrate the importance of impatience as a determinant of CTRs conditioned by the alternatives' ranking position. The first version assumes that users proceed sequentially through the ranking until they find an alternative satisficing their expectations. Once they find a satisficing alternative, they continue retrieving information until they observe an alternative that violates their expectations. The second version increases users' impatience, who stop retrieving information as soon as an alternative does not satisfy their expectations – even if it is the top-ranked one. All three algorithmic structures are sufficiently malleable to incorporate any potential modification to users' beliefs and preferences. We simulate sets of 1,000,000 queries to illustrate how the CTRs of the top three ranked alternatives remain stable as users grow impatient, with differences widening as growingly impatient users proceed halfway through the ranking. [ABSTRACT FROM AUTHOR]

Abstract: We consider a rational utility maximizer decision maker (DM) who must gather two pieces of information from a set of multidimensional products before making a choice. We analyze the resulting sequential information acquisition process where the DM tries to find the best possible product subject to his information acquisition constraint. In addition, we introduce publicly observable signals that allow the DM to update his expected utility functions following a standard Bayesian learning rule. Even though it seems intuitively plausible to assume that the transmission of positive and credible information may lead DMs to accept any product signaled more eagerly, this paper illustrates how transmitting credible positive information is not sufficient to decrease the rejection probability faced by the information sender on its set of products. A significant difference in product rejection probabilities arises depending on the characteristic on which signals are issued, as will be illustrated numerically for both risk-neutral and risk-averse DMs. [Copyright &y& Elsevier]

• A decision maker (DM) observes the numerical value assigned by raters to an alternative. • The DM defines an uncertain interval within which the evaluation observed is contained. • Its width depends on the subjective perception differences between the DM and the raters. • The DM may improve upon an uncertain evaluation by retrieving more uncertain information. • Several scenarios are built based on the capacity of the DM to fully assimilate uncertainty. We formalize the information acquisition and choice structure of a decision maker (DM) when the main characteristics defining the alternatives are not directly observed but numerical evaluations of unknown quality are provided by external raters. The DM observes the overall numerical value assigned by the raters to an alternative and defines an uncertain interval within which the evaluation observed is contained. The width of the interval is determined by the subjective perception and evaluation differences existing between the DM and the raters transmitting the information. We analyze the incentives of the DM to improve upon an evaluation contained within an uncertain interval by retrieving further information from the raters of other alternatives. Different scenarios will be developed based on the ability of the DM to fully assimilate uncertainty and the introduction of heuristic approximations to account for the potential frictions arising from uncertainty. One of the main qualities of the current framework is its capacity to formalize interactions among alternatives determined by interval width differences across their characteristics, providing an analytical advantage over the operational complexity involved in the use of fuzzy and intuitionistic fuzzy sets. The same remark applies to the formalization of the interactions across attributes that must be considered when defining sequential decision processes or dynamical systems while dealing with multiple sources of uncertainty. Numerical simulations are provided to compare the different scenarios developed and describe the main consequences derived from ignoring the uncertainty inherent to the evaluations received. In particular, we illustrate the ranking consequences derived from increasing the spread of the evaluation uncertainty, an effect that can be easily combined with the risk attitude exhibited by DMs. The inclusion of both these features bridges the gap between economics, psychology and multiple criteria decision making, whose techniques do not generally account for these differences among DMs. [ABSTRACT FROM AUTHOR]

This paper studies a linear programming (LP) and a multiple attribute decision-making (MADM) model with hesitant fuzzy numbers (HFNs) for solving group decision-making problems under uncertainty. We introduce a new type of HFN whose flexibility allows for its direct application to standard LP and MADM methods while facing credibility considerations or strategic reports from the experts evaluating the alternatives. These problems are initially formulated as models with uncertain, crisp data. Decision makers and experts are then invited to evaluate the data and propose HFNs representing its credibility. As a result, the crisp LP and MADM techniques are transformed into hesitant fuzzy LP (HFLP) and MADM (HF-MADM) models. The popular simplex method and the steps defining the corresponding MADM model, namely, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), are extended to implement the new HFNs. The strategic environment introduced allows for the definition of selection strategies and equilibria determined by the credibility profiles of experts providing evaluations. Developing a probabilistic framework applicable to these models would require defining convolution functions that become increasingly complex as further experts are added to the analysis. We present several numerical comparisons between the proposed hesitant fuzzy simplex and MADM results and those derived from the initial crisp models. The strategic importance of data credibility as a fundamental determinant of the optimality of results obtained will also be highlighted. [ABSTRACT FROM AUTHOR]

Shortest path (SP) optimization problems arise in a wide range of applications such as telecommunications and transportation industries. The main purpose of these problems is to find a path between two predetermined nodes within a network as cheaply or quickly as possible. Conventional SP problems generally assume that the arc weights are defined by crisp variables, though imprecise data have been lately incorporated into the analysis. The present study formulates the SP problem in a directed interval-valued triangular fuzzy network. The resulting interval-valued fuzzy SP (IVFSP) problem is converted into a multi objective linear programming (MOLP) problem. Then, a lexicographic optimization structure is used to obtain the efficient solution of the resulting MOLP problem. The optimization process confirms that the optimum interval-valued fuzzy shortest path weight preserves the form of an interval-valued triangular fuzzy number. The applicability of the proposed approach is illustrated through an example dealing with wireless sensor networks. [ABSTRACT FROM AUTHOR]

In this paper, a method based on network data envelopment analysis (DEA) is proposed to measure the efficiency and effectiveness of decision making units (DMUs). In this regard, a version of the Malmquist productivity index is designed to accommodate network DEA structures. In this type of environments, sub-DMUs are considered when assessing the efficiency of the main DMU, which helps evaluating the internal structure of the DMUs. The proposed method is applied to measure the productivity of several Iranian oil refineries. After identifying the main factors determining the productivity of these refineries, the operation of nine of them is analyzed using data from the 2015–2016 period. The results show that the management of resource utilization, particularly capital and energy, is inappropriate and investment insufficient. In particular, investment does not aim at upgrading the technology level, despite the fact that the depreciation rate of capital facilities is particularly high in this industry. This particular feature highlights the need to increase the rate of investment in order to replace the depreciated capital. [ABSTRACT FROM AUTHOR]

• We fuse Data Envelopment Analysis (DEA) and Multi-Attribute Decision-Making (MADM). • We compensate for the weaknesses of DEA with the strengths of MADM and vice versa. • We propose and evaluate several new and promising hybrid MADM-DEA methods. • We develop a new technique to evaluate similarity among multiple rankings in MADM. • This new rank absolute deviation is based on the mean absolute deviation method. Analytic hierarchy process (AHP) and data envelopment analysis (DEA) are two popular decision science methods with many business, science, and engineering applications. AHP is a Multi-Attribute Decision-Making (MADM) method for prioritizing alternatives, and DEA is a non-parametric method for estimating production frontiers. Each method has known strengths and weaknesses, and the strengths in one method can overcome the weaknesses in the other. We study several strategies to diminish the weaknesses of DEA with the strengths of pairwise comparisons in AHP. We tackle the low discrimination power inherent to conventional DEA methods. AHP, with its pairwise comparison capability, has been consistently used to increase the discrimination power and accuracy in DEA. We propose and evaluate several new hybrid MADM-DEA models of different computational complexity and consistency, including combinations of the best-worst method (BWM) and its variants with DEA as well as a novel method composed of Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) and DEA. We further develop a new technique for evaluating the similarity among multiple ranking results in MADM. The new simple but powerful technique is called Rank Absolute Deviation (RAD) and is inspired by the mean absolute deviation method. Several numerical examples and a real-world problem are used to demonstrate the applicability and efficacy of the new BWM-DEA, MACBETH-DEA, and RAD methods proposed in this study. We illustrate how less computationally demanding MADM-DEA techniques provide rankings that are highly correlated with the benchmark DEA-AHP and different consensus ranking models. [ABSTRACT FROM AUTHOR]

User acceptance of technology is essential to determine its success. The current paper incorporates the main properties of the technology acceptance models (TAMs) developed by management scholars into a pre-commitment signaling duopolistic framework, where two competing firms must decide the level of technological improvement of the products being introduced. As a result, the corresponding equilibria of the duopolistic technological games will be determined by demand-based factors, providing a novel approach and complementing the current supply-based economic and operational research models developed in the literature. The proposed model will be simulated numerically to illustrate the strategic optimality of the update process of smartphone and tablet characteristics defined by Apple and Samsung as the market developed. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is generally used to evaluate past performance and multi objective linear programming (MOLP) is often used to plan for future performance goals. In this study, we establish an equivalence relationship between MOLP problems and combined-oriented DEA models using a direction distance function designed to account for desirable and undesirable inputs and outputs together with uncontrollable variables. This equivalence model can be effectively used to support interactive processes and performance measures designed to establish future performance goals while taking into account the preferences of decision makers (DMs). In particular, it allows DMs to consider different efficiency improvement strategies when subject to budgetary restrictions. The applicability of the proposed method and the efficacy of the procedures and algorithms are demonstrated using a case study where the performance of high schools in the City of Philadelphia is evaluated. [ABSTRACT FROM AUTHOR]

We introduce a novel method called subdividing labeling genetic algorithm (SLGA) to solve optimization problems involving n – dimensional continuous nonlinear functions. SLGA is based on the mutation and crossover operators of genetic algorithms, which are applied on a subdivided search space where an integer label is defined on a polytope built on the n – dimensional space. The SLGA method approaches a global optimal solution by reducing the feasible search region in each iteration. One of its main advantages is that it does not require computing the derivatives of the objective function to guarantee convergence. We apply the SLGA method to solve optimization problems involving complex combinatorial and large-scale systems and illustrate numerically how it outperforms several other competing algorithms such as Differential Evolution even when considering problems with a large number of elements. [ABSTRACT FROM AUTHOR]

Cross-docking is a logistics strategy for direct distribution of products from a supplier or manufacturing plant to a customer or retail outlet with little or no handling and storage time. The classical cross-docking models are used to find the optimal inbound/outbound truck schedule that minimizes the total operational time. We propose a new multi-period cross-docking model with multiple products, due dates, variable truck capacities, and temporary warehouse. The problem is formulated as mixed-integer programming and an evolutionary computation approach based on a genetic algorithm (GA) is designed to solve it. The structure of the chromosomes, the operators, and the constraint handling strategy are specifically designed for multi-period problems. Several test instances have been generated to compare the performance of the proposed GA with that of a branch and bound solution procedure. Moreover, a comprehensive statistical analysis is conducted to illustrate the performance efficacy of the proposed GA relative to the branch and bound algorithm. This analysis reveals that the GA provides a substantial decrease in the computational burden when compared to the branch and bound algorithm. [ABSTRACT FROM AUTHOR]

Circular supply chain (CSC) networks improve sustainability and create socially responsible enterprises through recycling, harvesting, and refurbishing. This study develops a Lagrangian relaxation (LR) algorithm for solving location-inventory-routing (LIR) problems with heterogeneous vehicles in multi-period and multi-product sustainable CSC networks. The proposed Artificial Internet of Things (AIoT) enabled sustainable CSC is designed to increase network performance and create a secure and traceable environment. For the first time, an LR algorithm is proposed to solve the LIR problems in an AIoT-enabled CSC network with storage, backorder shortage, split-delivery, and time window potentials. Sixteen small- and medium-size simulated problems were produced to assess the performance of the proposed algorithm relative to the GAMS software. The results show the proposed algorithm can solve the small- and medium-size problems as effectively as GAMS software but faster and more efficiently. In addition, eight large-size simulation problems were produced and solved by the algorithm. While the GAMS software failed to solve the large-size problems, the LR algorithm solved them efficiently and successfully. [ABSTRACT FROM AUTHOR]

We propose a new bi-objective multi-product combined cross-docking truck-scheduling model with direct drone shipping and multiple fleets.The proposed model considers two conflicting objective functions (scheduling cost and time) within a multi-objective mixed integer mathematical programming problem. Several constraint sets are also considered for both allocation and scheduling phenomena. An efficient multi-objective epsilon-constraint method is adapted to solve the proposed model. Several numerical examples and metrics are provided to demonstrate the applicability of the proposed model and exhibit the efficacy of the solution procedures and algorithms. The efficient frontiers of the numerical examples are estimated by generating non-dominated solutions. The effects that modifications in the costs associated with the direct shipping of products have on the corresponding Pareto frontiers are analyzed. Finally, sensitivity analysis is used to assess the robustness of the results of the model in the presence of uncertainty. [ABSTRACT FROM AUTHOR]

Project selection and resource allocation are critical issues in project-based organizations. These organizations are required to plan, evaluate, and control their projects in accordance with the organizational mission and objectives. In this study, we propose a three-stage hybrid method for selecting an optimal combination of projects. We obtain the maximum fitness between the final selection and the project initial rankings while considering various organizational objectives. The proposed model is comprised of three stages and each stage is composed of several steps and procedures. We use Data Envelopment Analysis (DEA) for the initial screening, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking the projects, and linear Integer Programming (IP) for selecting the most suitable project portfolio in a fuzzy environment according to organizational objectives. Finally, a case study is used to demonstrate the applicability of the proposed method and exhibit the efficacy of the algorithms and procedures. [ABSTRACT FROM AUTHOR]

Earthquakes occurring in urban areas constitute an important concern for emergency management and rescue services. Emergency service location problems may be formulated in discrete space or by restricting the potential location(s) to a specified finite set of points in continuous space. We propose a Multicriteria Spatial Decision Support System to identify shelters and emergency service locations in urban evacuation planning. The proposed system has emerged as an integration of the geographical information systems (GIS) and the multicriteria Decision-Making method of Preference Ranking Organization Method for Enrichment Evaluation IV (PROMETHEE IV). This system incorporates multiple and often conflicting criteria and decision-makers’ preferences into a spatial decision model. We consider three standard structural attributes (i.e., durability density, population density, and oldness density) in the form of spatial maps to determine the zones most vulnerable to an earthquake. The information on these spatial maps is then entered into the ArcGIS software to define the relevant scores for each point with regards to the aforementioned attributes. These scores will be used to compute the preference functions in PROMETHEE IV, whose net flow outranking for each alternative will be inputted in ArcGIS to determine the zones that are most vulnerable to an earthquake. The final scores obtained are integrated into a mathematical programming model designed to find the most suitable locations for the construction of emergency service stations. We demonstrate the applicability of the proposed method and the efficacy of the procedures and algorithms in an earthquake emergency service station planning case study in the city of Tehran. [ABSTRACT FROM AUTHOR]

Highlights: [•] Information acquisition is essential for sustaining a firm’s competitive advantage. [•] We analyze the strategic side of the information acquisition process. [•] Decision makers (DMs) are allowed to exhibit any type of risk attitude. [•] Managers can estimate our model using linguistic evaluations obtained from DMs. [•] Our model can be implemented within several multicriteria decision making methods. [Copyright &y& Elsevier]

Developing sustainable municipal waste management systems requires an in-depth analysis and synthesis of economic, environmental, and social sustainable development indicators. However, despite its profound impact on organizational performance, social sustainability has received little attention in previous studies compared to economic and environmental sustainability. Although a few studies have been conducted to analyze and measure the impact of social sustainability indicators, most of these endeavors fail to consider many indicators that must be evaluated under uncertain and incomplete information. This study proposes a new decision model that implements Interval-Valued Neutrosophic Sets (IVNS) within a multi-distance measure defined with respect to an ideal reference solution. IVNS allows decision-makers to reliably express their opinions using truth, indeterminacy, and falsity membership functions. A linguistic framework is developed to categorize indicators based on their performance and suggest potential solutions when detecting indicators with relatively weak performances. Social sustainability failures in the municipal waste management system of Istanbul are investigated to show the applicability and efficacy of the proposed approach. We identify salary satisfaction and health insurance as the most significant social indicators determining the success of the system, while freedom of association and citizen participation are categorized as the worst-performing ones. The stability of the proposed methodology is illustrated by performing a comparative analysis with Single-Valued Neutrosophic Sets and Interval-Valued Fuzzy Pythagorean Sets. [Display omitted] • Municipal waste management systems build on economic, environmental, and social pillars. • Social sustainability (SS) indicators often rely on uncertain and incomplete information. • We define a model based on interval-valued neutrosophic sets and multi-distance measures. • A linguistic framework is developed to categorize indicators based on their performance. • We analyze the main SS failures in the municipal waste management system of Istanbul. [ABSTRACT FROM AUTHOR]

Abstract: We study an endogenous growth model of technology assimilation through an adaptive learning process defining the accumulation of technological knowledge among both workers and industrial clusters. The assimilation of new technology and the arrival rate of innovations are both based on the distance from the technological frontier to the current technological development level of the country. We illustrate how, even if technological innovations become immediately available to all countries, and absent educational and institutional frictions among countries, differences in technological development levels allow for the existence of poverty traps leading to the economic stagnation of technological laggards. [Copyright &y& Elsevier]

Several methods have been proposed in data envelopment analysis (DEA) for measuring efficiency in problems with interval or ordinal data. In this study, we review the weaknesses and drawbacks of these methods and show how converting ordinal or interval data into precise data can lead to violations of established DEA axioms. One of the axioms violated by these conversion processes is the inclusion of observations axiom, which requires a consistent definition of the production possibility set. We describe the special properties of ordinal and interval data together with their effect on the DEA-based rankings using a theorem and an example. We also propose a new algorithm and apply random dataset generation to overcome the problems arising from violations of the inclusion of observations axiom in DEA settings with ordinal or internal data. Several numerical examples are presented to demonstrate the applicability and exhibit the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Conventional data envelopment analysis (DEA) models treat the decision-making units (DMUs) as black-boxes: inputs enter the system and outputs exit the system, with no consideration for the intermediate steps characterizing the DMUs. As a result, intermediate measures are lost in the process of changing the inputs to outputs and it becomes difficult, if not impossible, to provide individual DMU managers with specific information on what part of a DMU is responsible for the overall inefficiency. This study defines a two-stage DEA model, where each DMU is composed of two sub-DMUs in series, the intermediate products by the sub-DMU in Stage 1 are partly consumed by the sub-DMU in Stage 2, and the initial inputs of the DMU can be freely allocated in both stages. Also, there are additional inputs directly consumed in Stage 2 while part of the outputs of Stage 1 are final outputs. We develop four new linear models to determine the upper and lower bounds of the efficiencies of the two sub-DMUs in a non-cooperative setting and a linear model to calculate the overall efficiency of DMU in a cooperative setting. That is, the overall efficiency of a DMU is modelled in a cooperative setting via upper and lower bounds obtained in the non-cooperative one. The proposed two-stage DEA method allows for important applications to several management areas. A case study in the banking industry is presented to demonstrate the applicability and exhibit the efficacy of the proposed models. [ABSTRACT FROM AUTHOR]

Supplier evaluation and selection constitutes a central issue in supply chain management (SCM). However, the data on which to base the corresponding choices in real life problems are often imprecise or vague, which has led to the introduction of fuzzy approaches. Predictive intelligent-based techniques, such as Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS), have been recently applied in different research fields to model fuzzy multi-criteria decision processes where the understanding and learning of the relationships between the input and output data are the key to select suitable solutions. In this paper, a hybrid ANFIS-ANN model is proposed to assist managers in their supplier evaluation process. After aggregating the data set through the Analytical Hierarchy Process (AHP), the most influential criteria on the suppliers’ performance are determined by ANFIS. Then, Multi-Layer Perceptron (MLP) is used to predict and rank the suppliers’ performance based on the most effective criteria. A case study is presented to illustrate the main steps of the model and show its accuracy in prediction. A battery of parametric tests and sensitivity analyses has been implemented to evaluate the overall performance of several models based on different effective criteria combinations. [ABSTRACT FROM AUTHOR]

In this paper, we model a multi-mode time–cost–quality trade-off project scheduling problem under generalized precedence relations using mixed-integer mathematical programming. Several solution procedures, including the classical epsilon-constraint, the efficient epsilon-constraint method, dynamic self-adaptive multi-objective particle swarm optimization (DSAMOPSO), and the multi-start partial bound enumeration algorithm, are provided to solve the proposed model. Several test problems are simulated and solved with the four methods and the performance of the methods are compared according to a set of accuracy and diversity comparison metrics. Additional analyses and tests are performed on the generated Pareto fronts of the solution procedures. Computational experiments are conducted to determine the validity and the efficiency of the DSAMOPSO method. The results show that this method outperforms the other three methods. We also carry out a sensitivity analysis of the DSAMOPSO algorithm to study the effects of parameter changes on the CPU time. [ABSTRACT FROM AUTHOR]

The quality of its players is one of the most significant features determining the failure or success of a sports team. The wide array of factors contributing to the performance of the players together with the inherent financial limitations of the clubs have transformed the selection of players into a complex problem. The current paper presents an integrated approach that combines multiple‐criteria decision‐making analysis and mathematical programming to support the decision maker through the building process of a soccer team. First, the fuzzy analytic network process is applied to evaluate the significance of the different performance criteria for each position in the field. The score attained by the different players in each potential position is computed using PROMETHEE II. A biobjective integer programming model has been designed to evaluate the transfer status of the players. Finally, data envelopment analysis is used to identify the most efficient Pareto solution determining the status of each player. In order to demonstrate the applicability of the proposed approach, the position in the field and transfer status of 60 players being considered by a real soccer team have been determined. [ABSTRACT FROM AUTHOR]