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This paper aims to develop a multiple-criteria model for the assessment of human resource management (HRM), focusing on groups of organizations with respect to industry.

The approach presented in this paper follows the framework procedure for multiple-criteria decision-making based on the Quantified Dialectical Systems Theory. It considers the factor analysis results in structuring the problem. By considering several experts’ judgments already when measuring the importance of criteria, it enables respondents to omit those sets of criteria for which they are neither experts nor responsible.

The paper shows that the factor analysis results can also be used in structuring the multi-dimensional concept in multiple-criteria model for assessing HRM – a step forward to multi-methodology. The obtained aggregate values show human resource managers the key success and failure factors to adopt an integrated/requisitely holistic and innovated strategy related to HRM in organizations.

The approach presented in this paper helps managers in developing and implementing a requisitely holistic model of HRM, adapted to several groups of organizations, such as with respect to their industry, in any country.

This paper provides recommendations for HRM in organizations.

This paper fills the gap in the research on multiple-criteria HRM assessment in organizations with respect to their industry by developing a multiple-criteria model for the assessment of HRM in groups of organizations, with application based on their industries.

In this paper, the four popular multiple-criteria decision-making (MCDM) methods in fuzzy environment are utilized to reflect the vagueness and uncertainty on the judgments of decision-makers (DMs), because the crisp pairwise comparison in these conventional MCDM methods seems to be insufficient and imprecise to capture the right judgments of DMs. Of these methods, as Fuzzy analytic hierarchy process (F-AHP) is used to calculate criteria weights, the other methods; Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS), Fuzzy Grey relational analysis (F-GRA) and Fuzzy Preference Ranking Organization METhod for Enrichment of Evaluations (F- PROMETHEE II) are used to rank alternatives in the three different ways for a comparative study.

The demand for green products has dramatically increased because the importance and public awareness of the preservation of natural environment was taken into consideration much more in the last two decades. As a result of this, especially manufacturing companies have been forced to design more green products, resulting in a problem of how they incorporate environmental issues into their design and evaluate concept options. The need for the practical decision-making tools to address this problem is rapidly evolving since the problem turns into an MCDM problem in the presence of a set of green concept alternatives and criteria.

The incorporation of fuzzy set theory into these methods is discussed on a real-life case study, and a comparative analysis is done by using its numerical results in which the three fuzzy-based methods reveal the same outcomes (or rankings), while F-GRA requires less computational steps. Moreover, more detailed analyses on the numerical results of the case study are completed on the normalization methods, distance metrics, aggregation functions, defuzzification methods and other issues.

The designing and manufacturing environmental-friendly products in a product design process has been a vital issue for many companies which take care of reflecting environmental issues into their product design and meeting standards of recent green guidelines. These companies have utilized these guidelines by following special procedures at the design phase. Along the design process consisting of various steps, the environmental issues have been considered an important factor in the end-of-life of products since it can reduce the impact on the nature. In the stage of developing a new product with the aim of environmental-friendly design, the green thinking should be incorporated as early as possible in the process.

The case study was inspired from the previous work of the author, which was realized in a hot runner systems manufacturer, used in injection molding systems in a Canada. In a new product development process, the back- and front-ends of development efforts mainly determine the following criteria: cost, risk, quality and green used in this paper. The case study showed that the three fuzzy MCDM methods come to the same ranking outcomes. F-GRA has a better time complexity compared to the other two methods and uses a smaller number of computational steps. Moreover, a comparative analysis of the three F-MCDM methods; F-PROMETHEE II, F-TOPSIS and F-GRA used in ranking for green concept alternatives using the numerical results of the case study. For the case study; as seen in table 20, the three F-MCDM methods produced the numerical results on the rankings of the green concept alternatives as follows; {Concept A-Concept C–Concept B–Concept D}.

Inclusion of environmental-related criteria into concept selection problem has been gaining increasing importance in the last decade. Therefore, to facilitate necessary calculations in applying each method especially with its fuzzy extension, it can be developed a knowledge-based (KB) or an expert system (ES) to help the DMs make the required calculations of each method, and interpret its results with detailed analysis.

The objective of the research was to propose a F-AHP based F-MCDM approach to green concept selection problem through F-PROMETHEE II, F-TOPSIS and F-GRA methods. As the F-AHP is used to weight evaluation criteria, the other methods are respectively used for ranking the concept alternatives and determine the best concept alternative.

Environmental decision making involving multiple stakeholders can benefit from the use of a formal process to structure stakeholder interactions, leading to more successful outcomes than traditional discursive decision processes. There are many tools available to handle complex decision making. Here we illustrate the use of a multicriteria decision analysis (MCDA) outranking tool (PROMETHEE) to facilitate decision making at the watershed scale, involving multiple stakeholders, multiple criteria, and multiple objectives. We compare various MCDA methods and their theoretical underpinnings, examining methods that most realistically model complex decision problems in ways that are understandable and transparent to stakeholders.

The multiple objective decision making problem arises when two or more non‐comparable objective functions are to be simultaneously optimised. There is a definite trend towards utilising interactive techniques for solving the multiple objective decision making problem. Interactive techniques allow the involvement of the DM throughout the decision process. In this paper we first provide a brief overview of multiple objective decision making, and then give a survey of literature dealing with interactive multiple objective decision making from 1965 to 1988.

Attempts to develop a model of maintenance decision making using the analytic hierarchy (AHP). Describes problems in maintenance arising from not having clear criteria and not having robust decisions with which to maintain failing equipment. The objective being to develop a dynamic and adaptable maintenance system that utilises existing data and supports decisions accordingly. Proposes a three‐stage system that can handle multiple criteria decision analysis, conflicting objectives, and subjective judgements. Moreover, the methodology facilitates and supports a group decision‐making process. This systematic, and adaptable, approach will determine what specific actions to perform given current working conditions. The first stage involves identifying the criteria upon which engineering personnel wish to formulate a maintenance decision, or action. The second stage is to prioritise the different criteria by implementing a multiple‐criteria evaluation method. Finally, based on different criteria, machines are ranked according to criticality. This is followed by an analysis of failures in a graphical and a hierarchical format.

The purpose of this paper is to define and describe Pugh matrix analysis as a method for decision making when there are multiple criteria that must be factored into a decision.

Using theory and example, the author relates the use of Pugh matrix analysis to the successful selection of prioritized criteria in digital library projects.

Pugh matrix analysis (PMA) is useful as a method for determining a course of action as well as gaining consensus with a project team. Because it is based on multiple criteria decision analysis techniques, PMA can help a team understand the relationship of multiple issues within a project as well as the individual perspectives project team members and the user community bring to the issues at hand.

This paper fills a gap in the digital library project management literature by providing an overview of a useful tool to prioritize the issues, factors, and courses of action within a project that has multiple criteria decision points.

Selecting the ‘best’ main contractor is a complex decision process for construction clients. It requires a large number of criteria to be simultaneously measured and evaluated. Many of these criteria are related to one another in a complex way and therefore, they very often conflict insofar as improvement in one often results in decline of another(s). Furthermore, as contractors' attributes are expressed in both quantitative and qualitative terms, decision‐makers have to base their judgements on both quantitative data and experiential subjective assessments. In this paper, the evidential reasoning (ER) approach (which is capable of processing both quantitative and qualitative measures) is applied as a means of solving the contractor selection problem (CSP). The process of building a multiple criteria decision model of a hierarchical structure is presented, in which both quantitative and qualitative information is represented in a unified manner. The CSP is then fully investigated using the ER approach. Both the advantages of applying this model in practice and the analysis process itself are discussed.

Technical education plays an important role in the development of a country in this age of knowledge economy. Indian technical education system is facing many opportunities and challenges, one of which is how to assess the performance of technical institutions based on multiple criteria. The purpose of this paper is to describe and illustrate an application of a structured approach to determine relative efficiency and ranking of a set of private engineering colleges under multi-criteria environment.

To cater to the increasing need of technical manpower, a very large number of private engineering colleges have been established in the state of West Bengal of eastern India within a very short period. Uniform and acceptable quality of the graduates from many of these private engineering colleges is a concern today and therefore the need for performance evaluation and ranking of these colleges is paramount. For the proposed framework a comparatively new multiple criteria decision-making tool, multiple objective optimization on the basis of simple ratio analysis (MOOSRA) is applied for performance evaluation of eight private engineering colleges taking into account some selected criteria. The subjective weights of the criteria are determined using fuzzy analytic hierarchy process (AHP).

For the analysis, the required data have been provided by the management of the colleges for the academic year of 2011-2012. Based on request of the management identities of these institutes are not disclosed. The institutes are considered as anonymous institute and coded as A, B, C, D, E, F, G and H, respectively. The result of the study reveals that E is the best and the ranking the authors get is in the order of E > F > A > H > D > C > G > B. The result shows that composite performance scores of institutions A, E and F are above the mean performance score value. Therefore these three institutions can be considered as the benchmark or peer group for the remaining five institutions which lie below the mean line of the performance score value.

This paper provides a comprehensive yet detailed methodology for performance evaluation of academic institutions. The novelty in the approach is that fuzzy AHP and MOOSRA are being used as a benchmarking technique in a simple methodology which is generic in nature. It is one of the few studies that evaluate the performance of technical institutions in India.

To provide decision makers (DMs) an option for addressing problems involving finite alternative sets and multiple criteria, where criterion weighting is difficult or impossible.

The multicriteria decision problem is described, and a typically descriptive (rather than prescriptive) tool, data envelopment analysis (DEA), is summarized, along with a hypothetical but typical example of a multicriteria decision (vendor selection). The DEA approach is modified to incorporate weight constraints and is used to rank the available vendors. Results are compared with those from the use of a popular multicriteria decision tool (SMART) and a naïve averaging approach.

The modified DEA approach yields results very similar to those produced using SMART; these results are quite satisfactory in spite of the fact that DEA requires less involvement on the part of the DM. In addition, non‐dominant optima (a possible anomaly with DEA) are avoided, and often a single alternative, rather than a non‐dominated set, will result, thus providing a unique optimum.

Results are based on the analysis of a single data set. Future investigation should examine the performance of the DEA approach when other data sets involving more like as well as more unlike alternatives are involved.

With DEA the burden on the DM is reduced, as the need for eliciting criterion weights is obviated. DEA should thus provide an acceptable alternative to prescriptive modeling tools when multiple DMs are involved and/or criterion weight determination is unfeasible.

This paper demonstrates how DEA, a tool used more typically in post hoc evaluations, can be used also, with some modifications, as a prescriptive decision support tool.

Prioritising items for management attention has been advocated in operations management for a long time, normally using ABC analysis (inventory control). This focuses attention on the “A” category items to maximise managerial effectiveness. Empirical evidence shows that this is a reasonable rule for allocating scarce resource‐management time but presents difficulties when the manager has to take more than one important dimension of a situation into account. A joint criteria matrix is put forward within the ABC framework and an industrial application given. The joint criteria matrix has practical utility provided ranking on some scale of measurement is realistic. The appropriate number of categories must be defined by the user. Combining criteria will probably require different analytical approaches, e.g. goal programming or heuristic approaches. Utilisation of the matrix by managers can provide an explicit method for taking a range of criteria into account in the development of inventory policies.