Using TODIM to evaluate green supply chain practices under uncertainty

This study approaches the problem of exploring the importance and performance levels in green supply chain practices (GSCP) under conditions of uncertainty. This contribution creates a mechanism that assists in the process of analyzing and selecting the alternatives aligned with the proposed criteria on both the qualitative and quantitative scales. This analysis uses the acronym from the interactive and multi-criteria decision-making method (known as TODIM in Portuguese), a discrete multi-criteria method based on prospect theory. However, the criteria weights and alternatives are described as linguistic preferences and involve quantitative data. The qualitative preferences transformed into crisp values and the quantitative data converts into comparable scale. The results indicate the discrepancies between the importance and performance levels of the GSCP. This study identified set of principal criteria that can influence the recommendations for strategic direction in the most systematic and wide-ranging manner.     

A slacks-based measure of super-efficiency in data envelopment analysis

In most models of Data Envelopment Analysis (DEA), the best performers have the full efficient status denoted by unity (or 100%), and, from experience, we know that usually plural Decision Making Units (DMUs) have this “efficient status”. To discriminate between these efficient DMUs is an interesting subject. This paper addresses this “super-efficiency” issue by using the slacks-based measure (SBM) of efficiency, which the author proposed in his previous paper [European Journal of Operational Research 130 (2001) 498]. The method differs from the traditional one based on the radial measure, e.g. Andersen and Petersen model, in that the former deals directly with slacks in inputs/outputs, while the latter does not take account of the existence of slacks. We will demonstrate the rationality of our approach by comparing it with the radial measure of super-efficiency. The proposed method will be particularly useful when the number of DMUs are small compared with the number of criteria employed for evaluation.     

A dynamic benchmarking system for assessing the recovery of inpatients: Evidence from the neurorehabilitation process

The shortage of medical resources (mainly beds) is a critical and increasingly prevalent problem affecting hospitals. Of the factors that contribute to these shortages, the ambiguity and insufficiency of the criteria used to identify whether an inpatient should be discharged are among the most detrimental. To address this issue, this study applies data envelopment analysis (DEA) on existing inpatient data from the Neurorehabilitation Center at Toronto’s Bridgepoint Hospital to create a dynamic benchmarking system to evaluate the health stage of an inpatient ready to be discharged. Unlike the more traditional parametric techniques, DEA provides non-subjective benchmarking that does not require any prior specification of the production function making it a more desirable choice for this application. The dynamic model categorizes the inpatient’s discharge status as rejected, under observation, or approved. This new approach not only allows managers to gain insight into the potential causes of medical resource shortages, but also allows clinicians to treat inpatients more effectively based on their discharge categories. For validation, the results of the dynamic model were compared with actual inpatient discharge assessments provided by the Bridgepoint Hospital.     

A Personnel Selection Problem in Healthcare System Using Fuzzy-TOPSIS Approach

The methods of multiple criteria decision-making (MCDM) are increasingly becoming the most desired tools for making daily decisions in various fields of human endeavors. Staff employment in any sector requires a thorough evaluation of the applicant before selection to ensure effective and efficient service delivery. Besides, healthcare is one of the most complicated organizations dealing with human lives. This paper has developed a staff selection model considering a fuzzy environment by using the technique for order preference similar to the ideal solution (TOPSIS) method. For the delivery and promotion of quality healthcare systems, medical staff selection is crucial to the system. Therefore, the study evaluates medical staff by using the expert''s linguistic judgement under the criteria of skill, experience and ability to respond to a problem. The expert''s vagueness in judgments has been represented by using fuzzy triangular numbers. The study determines the closeness coefficient, the measures of separation and the ideal solutions of the TOPSIS method. The most appropriate medical staff are ranked and selected based on the available criteria. The Fuzzy-TOPSIS method is simple and can help other organizations achieve proper ranking, evaluation and selection of qualified candidates, as it takes imprecise information into account.     

Prospective and current secondary mathematics teachers’ criteria for evaluating mathematical cognitive technologies

As technology becomes more ubiquitous in the mathematics classroom, teachers are being asked to incorporate it into their lessons more than ever before. The amount of resources available online is staggering and teachers need to be able to analyse and identify resources that would be most appropriate and effective with their students. This study examines the criteria prospective and current secondary mathematics teachers use and value most when evaluating mathematical cognitive technologies (MCTs). Results indicate all groups of participants developed criteria focused on how well an MCT represents the mathematics, student interaction and engagement with the MCT, and whether the MCT was user-friendly. However, none of their criteria focused on how well an MCT would reflect students’ solution strategies or illuminate their thinking. In addition, there were some differences between the criteria created by participants with and without teaching experience, specifically the types of supports available in an MCT. Implications for mathematics teacher educators are discussed.     

An integrated FCM and fuzzy soft set for supplier selection problem based on risk evaluation

Supplier selection problem, considered as a multi-criteria decision-making (MCDM) problem, is one of the most important issues for firms. Lots of literatures about it have been emitted since 1960s. However, research on supplier selection under operational risks is limited. What’s more, the criteria used by most of them are independent, which usually does not correspond with the real world. Although the analytic network process (ANP) has been proposed to deal with the problems above, several problems make the method impractical. This study first integrates the fuzzy cognitive map (FCM) and fuzzy soft set model for solving the supplier selection problem. This method not only considers the dependent and feedback effect among criteria, but also considers the uncertainties on decision making process. Finally, a case study of supplier selection considering risk factors is given to demonstrate the proposed method’s effectiveness.     

Intuitionistic fuzzy cognitive map approach for the evaluation of supply chain configuration criteria

Because of the growing global competence and effectiveness concepts, supply chain becomes more important for organizations. Therefore, managers object to find best supply chain configuration for their firms. This study proposes a comprehensive configuration for supply chain management process, and it enables to understand relationships among supply chain integration, supply chain strategies, supply chain risk factors, and performance criteria. By reviewing the literature and using experts' knowledge, supply chain configuration criteria are determined. Intuitionistic fuzzy cognitive map methodology is employed to consider the interrelations between criteria. Intuitionistic fuzzy cognitive map methodology is a suitable tool due to the presence of causalities and relationships among criteria and the difficulty of expressing the interrelations with crisp numbers. It also deals with uncertain and vague data and allows representing hesitation. The application is conducted in an automobile factory, which is one of the largest manufacturers in Turkey. The results show that selection of proper supplier is the most significant supply chain configuration criteria. Thus, the importance of supplier selection criteria is also analyzed as the second phase of the study.     

Cloister and society: Analyzing the public benefit of monastic and mendicant institutions

It has long been thought that religious orders, by their very existence (apart from any direct service such as teaching or nursing), enhance the secular social welfare. This paper reports theoretical predictions, derived from the postulates of comparison theory, concerning the existence of this public benefit of the cloister and the determinants of its magnitude, including the effects of economic inequality and of the societal valued goods. The basic predictions suggest further implications for the rates of vocations and defections, for state sympathies toward religious orders, and for the rise and development of monastic and mendicant institutions.     

Comparison of Criteria to Select Working Correlation Matrix in Generalized Estimating Equations

In this paper, we compare two modified Gaussian pseudolikelihood criteria (GPCs) with existing Gaussian pseudolikelihood criterion and empirical likelihood based criteria to choose the working correlation matrix in generalized estimating equations approach. Rich simulation studies are conducted to investigate the performance of these criteria under a range of model settings. The results show that the modified criteria outperform the original GPC and empirical likelihood based criteria in most cases in terms of selection accuracy. Empirical likelihood based criteria perform better to identify exchangeable structure in data with binary response. In the end, these criteria are applied to epilepsy seizure and Madras longitudinal schizophrenia study clinical data sets analysis.     

Mathematical modeling of church growth

The possibility of using mathematics to model church growth is investigated using ideas from population modeling. It is proposed that a major mechanism of growth is through contact between religious enthusiasts and unbelievers, where the enthusiasts are only enthusiastic for a limited period. After that period they remain church members but less effective in recruitment. This leads to the general epidemic model which is applied to a variety of church growth situations. Results show that even a simple model like this can help understand the way in which churches grow, particularly in times of religious revival.     

A survey of recent results on continuous-time Markov decision processes

This paper is a survey of recent results on continuous-time Markov decision processes (MDPs) withunbounded transition rates, and reward rates that may beunbounded from above and from below. These results pertain to discounted and average reward optimality criteria, which are the most commonly used criteria, and also to more selective concepts, such as bias optimality and sensitive discount criteria. For concreteness, we consider only MDPs with a countable state space, but we indicate how the results can be extended to more general MDPs or to Markov games. Research partially supported by grants NSFC, DRFP and NCET. Research partially supported by CONACyT (Mexico) Grant 45693-F.     

Zero—one programming with multiple criteria

The study of multiple criteria problems is not new, but during the last seventeen years more and more study has been done in this area. The study of 0–1 multiple criteria problems started much later than the study of continuous multiple criteria problems. Some of the earliest works on 0–1 problems with multiple criteria was first presented in 1973. Quite a few solution methods have been developed since then, especially during the last seven years. In this paper most of these methods and some applications will be treated.     

A hierarchical interactive method for ranking alternatives with multiple qualitative criteria

In this study we deal with the problem of finding the most preferred composite ranking of a set of alternatives evaluated using a large number of criteria having a hierarchical structure. The criteria may be qualitative or quantitative. The decision maker evaluates alternatives using each criterion at the lowest (basic) level. That information is then used to construct the generalized correlation matrix to describe interdependencies between the criteria. The correlation matrix and the criterion hierarchy are the basic information used in the approach. Our interactive approach is designed to help the decision maker find the most preferred aggregation of the kth level criteria, which produces the criteria at the (k + 1)st level. As the final result of the aggregation we obtain the strength of the preference matrix for the criterion at the highest level. By means of that matrix, we produce the final ranking of the alternatives using the Bowman and Colantoni (1973) model. The approach is easy to implement and convenient to use. We have implemented an experimental version of it on an Apple III microcomputer. The graphical colour display is used as an aid in finding the most preferred aggregation. An illustrative example is provided.     

Heuristics for scheduling in a flow shop with multiple processors

This study investigates the performance of scheduling heuristics in a flow shop with multiple processors. We investigated five better performing flow shop heuristics for their performances of makespan and mean flow time criteria in a flow shop with multiple processors. The study examined the effects of problem characteristics (number of jobs, number of machine stages and number of parallel processors at each stage) and the performance of heuristics using regression analysis. We found that although structural characteristics explain most of the variation in performance, heuristics also had an effect. The experimental results showed that flow shop heuristics developed by Nawaz, Enscore, and Ham and that of Ho were comparable in performance in a flow shop with multiple processors. However, the former was slightly more consistent in results for both criteria.     

Management of multicriteria inventory classification

The most common method that materials managers use for classifying inventory items for planning and control purposes is the annual-dollar-usage ranking method (ABC classification). Recently, it has been suggested that multiple criteria ABC classification can provide a more comprehensive managerial approach, allowing consideration of other criteria such as lead time and criticality. This paper proposes the use of the Analytical Hierarchy Process (AHP) to reduce these multiple criteria to a univariate and consistent measure to consider multiple inventory management objectives.     

Weight determination for consistently ranking alternatives in multiple criteria decision analysis

One of the most difficult tasks in multiple criteria decision analysis (MCDA) is determining the weights of individual criteria so that all alternatives can be compared based on the aggregate performance of all criteria. This problem can be transformed into the compromise programming of seeking alternatives with a shorter distance to the ideal or a longer distance to the anti-ideal despite the rankings based on the two distance measures possibly not being the same. In order to obtain consistent rankings, this paper proposes a measure of relative distance, which involves the calculation of the relative position of an alternative between the anti-ideal and the ideal for ranking. In this case, minimizing the distance to the ideal is equivalent to maximizing the distance to the anti-ideal, so the rankings obtained from the two criteria are the same. An example is used to discuss the advantages and disadvantages of the proposed method, and the results are compared with those obtained from the TOPSIS method.     

A method of fuzzy preference relation to authorize the worldwide interoperability for microwave access license in Taiwan

This study proposes a preference relation based evaluation framework to help the National Communication Commission (NCC) in Taiwan authorize a worldwide interoperability for microwave access (WiMAX) license under a fuzzy environment where the uncertainty, subjectivity and vagueness are dealt with linguistic variables parameterized by triangular fuzzy numbers. This study applies the fuzzy multi-criteria decision making approach to determine the importance weights of evaluation criteria and consolidate the performance ratings of possible alternatives. Aggregated the evaluators’ opinions toward the criteria and alternatives, the fuzzy preference relation approach is utilized to obtain the non-dominated degree of each alternative for the decision makers to make a final decision. Simultaneously, an empirical case involving sixteen quantitative and fifteen qualitative evaluation criteria, thirteen telecommunication applicants assessed by twelve specialists from various fields of telecommunication industry in Taiwan is solicited to demonstrate the proposed approach.     

A Comparative Investigation on Model Selection in Independent Factor Analysis

With uncorrelated Gaussian factors extended to mutually independent factors beyond Gaussian, the conventional factor analysis is extended to what is recently called independent factor analysis. Typically, it is called binary factor analysis (BFA) when the factors are binary and called non-Gaussian factor analysis (NFA) when the factors are from real non-Gaussian distributions. A crucial issue in both BFA and NFA is the determination of the number of factors. In the literature of statistics, there are a number of model selection criteria that can be used for this purpose. Also, the Bayesian Ying-Yang (BYY) harmony learning provides a new principle for this purpose. This paper further investigates BYY harmony learning in comparison with existing typical criteria, including Akaik’s information criterion (AIC), the consistent Akaike’s information criterion (CAIC), the Bayesian inference criterion (BIC), and the cross-validation (CV) criterion on selection of the number of factors. This comparative study is made via experiments on the data sets with different sample sizes, data space dimensions, noise variances, and hidden factors numbers. Experiments have shown that for both BFA and NFA, in most cases BIC outperforms AIC, CAIC, and CV while the BYY criterion is either comparable with or better than BIC. In consideration of the fact that the selection by these criteria has to be implemented at the second stage based on a set of candidate models which have to be obtained at the first stage of parameter learning, while BYY harmony learning can provide not only a new class of criteria implemented in a similar way but also a new family of algorithms that perform parameter learning at the first stage with automated model selection, BYY harmony learning is more preferred since computing costs can be saved significantly.     

Value efficiency analysis of academic research

We propose a systematic approach to analyzing academic research performance at universities and research institutes. The analysis is based on identifying a set of decision-relevant (abstract) criteria. The scales for these criteria are defined by means of concrete indicators, all which are, however, not necessarily quantitative. Qualitative information is quantified using appropriate analytical tools. Once the criteria and indicators have been agreed upon and quantified, data on the research units is collected and a value efficiency analysis is performed. The efficiency of research units is defined in the spirit of data envelopment analysis (DEA), complemented with decision maker's (DM's) (rector in the European university system) preference information. This information is obtained by asking the DM to locate a point on the efficient frontier having the most preferred combination of input and output values. Our approach and the accompanying decision support system enables a university to allocate resources more efficiently than previously to its research units. Using data from the Helsinki school of economics, we illustrate how the approach works.     

Performance in primary schools

Data for this paper was collected from the OFSTED database on Hampshire primary schools. The schools in Southampton and Porstmouth were used in order to assess the factors that influence their productive efficiency. The data set included 19 variables on 176 schools and was analysed by means of Data Envelopment Analysis. Contextual variables, not included in the efficiency analysis, were used to explain the sources of inefficiency. It was found that religious orientation, parental influence and level of exclusions all impacted on the ability of a school to deliver the best possible results in standard assessment tests. This study is set within local and national priorities in education.