Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule

In this paper, a likelihood based evidence acquisition approach is proposed to acquire evidence from experts'assessments as recorded in historical datasets. Then a data-driven evidential reasoning rule based model is introduced to R&D project selection process by combining multiple pieces of evidence with different weights and reliabilities. As a result, the total belief degrees and the overall performance can be generated for ranking and selecting projects. Finally, a case study on the R&D project selection for the National Science Foundation of China is conducted to show the effectiveness of the proposed model. The data-driven evidential reasoning rule based model for project evaluation and selection (1) utilizes experimental data to represent experts' assessments by using belief distributions over the set of final funding outcomes, and through this historic statistics it helps experts and applicants to understand the funding probability to a given assessment grade, (2) implies the mapping relationships between the evaluation grades and the final funding outcomes by using historical data, and (3) provides a way to make fair decisions by taking experts' reliabilities into account. In the data-driven evidential reasoning rule based model, experts play different roles in accordance with their reliabilities which are determined by their previous review track records, and the selection process is made interpretable and fairer. The newly proposed model reduces the time-consuming panel review work for both managers and experts, and significantly improves the efficiency and quality of project selection process. Although the model is demonstrated for project selection in the NSFC, it can be generalized to other funding agencies or industries.Comment: 20 pages, forthcoming in International Journal of Project Management (2019

Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

Evaluation of Corporate Sustainability

As a consequence of an increasing demand in sustainable development for business organizations, the evaluation of corporate sustainability has become a topic intensively focused by academic researchers and business practitioners. Several techniques in the context of multiple criteria decision analysis (MCDA) have been suggested to facilitate the evaluation and the analysis of sustainability performance. However, due to the complexity of evaluation, such as a compilation of quantitative and qualitative measures, interrelationships among various sustainability criteria, the assessor’s hesitation in scoring, or incomplete information, simple techniques may not be able to generate reliable results which can reflect the overall sustainability performance of a company. This paper proposes a series of mathematical formulations based upon the evidential reasoning (ER) approach which can be used to aggregate results from qualitative judgments with quantitative measurements under various types of complex and uncertain situations. The evaluation of corporate sustainability through the ER model is demonstrated using actual data generated from three sugar manufacturing companies in Thailand. The proposed model facilitates managers in analysing the performance and identifying improvement plans and goals. It also simplifies decision making related to sustainable development initiatives. The model can be generalized to a wider area of performance assessment, as well as to any cases of multiple criteria analysis

Fuzzy multi criteria evaluation for performance of bus companies

A multi criteria decision making in ranking the bus companies using fuzzy rule is proposed. The proposed method uses the application of fuzzy sets and approximate reasoning in deciding the ranking of the performance of several bus companies. The proposed method introduces data normalization using similarity function which dampens extreme values that exist in the data. The use of the model is suitable in evaluating situation that involves subjectivity, vagueness and imprecise information. Experimental results are comparable to several previous methods

Influences on classification accuracy of exam sets: an example from vocational education and training

Classification accuracy of single exams is well studied in the educational measurement literature. However, when making important decisions, such as certification decisions, one usually uses several exams: an exam set. This chapter elaborates on classification accuracy of exam sets. This is influenced by the shape of the ability distribution, the height of the standards, and the possibility for compensation. This is studied using an example from vocational education and training (VET). The classification accuracy for an exam set is computed using item response theory (IRT) simulation. Classification accuracy is high when all exams from an exam set have equal and standardized ability distributions. Furthermore, exams where few or no students pass or fail increase classification accuracy. Finally, allowing compensation increases classification accurac

Fuzzy subjective evaluation of Asia Pacific airport services

This paper presents a fuzzy decision-making model to determine the ranking of fourteen Asia Pacific airports based on the services provided to passengers. Airport services were represented by six attributes namely comfort, processing time, convenience, courtesy of staff, information visibility and security. Data for the attributes given by travel experts are in the triangular fuzzy number form. Based on fuzzy set and approximate reasoning, the model allows decision makers to make the best choice in accordance with human thinking and reasoning processes.The use of fuzzy rules which are extracted directly from the input data in making evaluation, contributes to a better decision and is less dependent on experts.Experimental results show that the proposed model is comparable to previous studies.The model is suitable for various fuzzy environments

The Principles Of Developing A Management Decision Support System For Scientific Employees

Employees engaged in mental work have become the most valuable assets of any organization in the 21st century. The satisfaction of those involved in mental work requires the provision of objectivity and transparency in their decision-making. This, in turn, entails the development of scientifically motivated decision making mechanisms and scientific-methodological approaches to evaluate their performance based on innovative technologies.The main goal of this article is in development of the scientific and methodological framework for the establishment of a decision support system to manage the employees engaged in mental work and operating in uncertainty. In this regard, initially, the question of evaluating the activities of scientific workers is examined, its characteristic features are determined, and the fuzzy relation model is proposed as a multi-criterion issue formed in uncertainty. Taking into consideration the hierarchical structure of the criteria that allows evaluating the activities of scientific workers, a phased solution method based on an additive aggregation method is proposed. In accordance with the methodology, a functional scheme of the decision support system to manage the scientific personnel is developed. The working principle of each block and the interaction of the blocks are described. The rules for the employees\u27 management decisions are shown by referring to the knowledge production model.Based on the proposed methodological approach, the implementation phases of the decision support system for the management of the scientific workers of the Institute of Information Technology of ANAS are described. To evaluate the employees\u27 performance, the tools to collect initial information, evaluate the system of criteria, define their importance coefficients and mathematical descriptions are provided. Some results of the system software are presented. The opportunities of the system based on the proposed methodology to support enterprise mangers to make scientifically justified decisions are provided