A Modified TOPSIS Method Based on D

Multicriteria decision-making (MCDM) is an important branch of operations research which composes multiple-criteria to make decision. TOPSIS is an effective method in handling MCDM problem, while there still exist some shortcomings about it. Upon facing the MCDM problem, various types of uncertainty are inevitable such as incompleteness, fuzziness, and imprecision result from the powerlessness of human beings subjective judgment. However, the TOPSIS method cannot adequately deal with these types of uncertainties. In this paper, a D-TOPSIS method is proposed for MCDM problem based on a new effective and feasible representation of uncertain information, called D numbers. The D-TOPSIS method is an extension of the classical TOPSIS method. Within the proposed method, D numbers theory denotes the decision matrix given by experts considering the interrelation of multicriteria. An application about human resources selection, which essentially is a multicriteria decision-making problem, is conducted to demonstrate the effectiveness of the proposed D-TOPSIS method

A study of multiple attributes decision making methods facing uncertain attributes

Master of ScienceDepartment of Industrial & Manufacturing Systems EngineeringShing I. ChangMany decision-making methods have been developed to help decision makers (DMs) make efficient decisions. One decision making method involves selecting the best choice among alternatives based on a set of criteria. Multiple Attribute Decision-Making (MADM) methods allow opportunities to determine the optimal alternative based on multiple attributes. This research aims to overcome two concerns in current MADM methods: uncertainty of attributes and sensitivity of ranking results. Based on availability of information for attributes, a DM maybe certain or uncertain on his judgment on alternatives. Researchers have introduced the use of linguistic terms or uncertain intervals to tackle the uncertainty problems. This study provides an integrated approach to model uncertainty in one of the most popular MADM methods: TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). Current MADM methods also provide a final ranking of alternatives under consideration and, the final solution is based on a calculated number assigned to each alternative. Results have shown that the final value of alternatives may be close to each other uncertain attributes, but current methods rank alternatives according to the final scores. It exhibits a sensitivity issue related to formation of the ranking list. The proposed method solves this problem by simulating random numbers within uncertain intervals in the decision matrix. The proposed outcome is a ranking distribution for alternatives. The proposed method is based on TOPSIS, which defines the best and the worst solution for each attribute and defines the best alternative as closest to best and farthest from the worst solution. Random number distributions were studied under the proposed simulation solution approach. Result showed that triangular random number distribution provides better ranking results than uniform distribution. A case study of building design selection considering resiliency and sustainability attributes was presented to demonstrate use of the proposed method. The study demonstrated that proposed method can provide better decision option for designers due to the ability to consider uncertain attributes. In addition using the proposed method, a DM can observe the final ranking distribution resulted from uncertain attribute values

An intelligent group decision-support system and its application for project performance evaluation

Purpose: In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects affect the achievement of these goals. The purpose of this paper is to develop a fuzzy multiple attribute-based group decision-support system (FMAGDSS) to evaluate projects' performance in promoting the organization's goals utilizing simple additive weighting (SAW) algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. The proposed FMAGDSS deals with choosing the most appropriate fuzzy ranking algorithm for solving a given fuzzy multi attribute decision making (FMADM) problem with both qualitative and quantitative criteria (attributes), and uncertain judgments of decision makers. Design/methodology/approach: In this paper, a FMAGDSS model is designed to determine scores and ranks of every project in promoting the organization's goals. In the first step of FMAGDSS model, all projects are assessed by experts based on evaluation criteria and the organization's goals. The proposed FMAGDSS model will then choose the most appropriate fuzzy ranking method to solve the given FMADM problem. Finally, a sensitivity analysis system is developed to assess the reliability of the decision-making process and provide an opportunity to analyze the impacts of "criteria weights" and "projects" performance' on evaluating projects in achieving the organizations' goals, and to assess the reliability of the decision-making process. In addition, a software prototype has been developed on the basis of FMAGDSS model that can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes. Findings: The result of this study simplifies and accelerates the evaluation process. The proposed system not only helps organizations to choose the most efficient projects for sustainable development, but also helps them to assess the reliability of the decision-making process, and decrease the uncertainty in final decision caused by uncertain judgment of decision makers. Research limitations/implications: Future studies are suggested to expand this system to evaluate and rank the project proposals. To achieve this goal, the efficiency of the projects in line with organization's goals, should be predicted.Originality/value: This study contributes to the relevant literature by proposing a FMAGDSS model to evaluate projects in promoting organization's goals. The proposed FMAGDSS has ability to choose the most appropriate fuzzy ranking algorithm to solve a given FMADM problem based on the type and the number of attributes and alternatives, considering the least computation and time consumption for ranking alternatives. © Emerald Group Publishing Limited

A modified AHP algorithm for network selection

This paper addresses the concept of ranking networks to the multiple criteria of customers to find the best and alternative networks. The use of modified AHP algorithm has been shown to provide better network ranking for reasonable customer objectives than the traditional AHP method. Both the traditional method and the proposed method produced results subjective to the customer requirements. However, the proposed method is more intuitive to the customers through direct capture of their exact requirements rather than an interpretation of their requirements through pair-wise comparison alone. Also, the proposed method is less time-consuming and results are of higher quality

Marking as judgment

An aspect of assessment which has received little attention compared with perennial concerns, such as standards or reliability, is the role of judgment in marking. This paper explores marking as an act of judgment, paying particular attention to the nature of judgment and the processes involved. It brings together studies which have explored marking from a psychological perspective for the purpose of critical discussion of the light they shed on each other and on the practice of marking. Later stages speculate on recent developments in psychology and neuroscience which may cast further light on educational assessment

Mixed Signals: Why investors may misjudge first time high technology founders

This paper seeks to explain an unexpected result of a previous quantitative study which suggested sub-optimal evaluation by investors of the human capital of first time high tech venture founders. A literature review revealed two possible reasons for this finding: biases/heuristics and signaling. Six investors across three countries (one venture capitalist and one business angel each from the US, UK and Israel) with experience in investing in early stage high technology ventures were interviewed using an identical semi-structured interview protocol. This research design is appropriate for research that seeks to reflect back unexpected findings of previous quantitative research on the subjects of research. Interviewees were first asked to state their own investment criteria, and then presented with the results of the quantitative study and asked for their views. Previous research suggesting a gap between in-use and espoused criteria, and extensive use of gut feeling in decision-making, was supported. Interviewees focused on harvest potential and de-emphasised measures of founder technology capability that predicted early survival and growth in the earlier study. The paper concludes by suggesting how investors might improve funding decisions in high tech ventures led by first-time entrepreneurs, noting the study's limitations and making recommendations for further research