Cloud decision support framework for treatment technology selection of health-care waste

With the attention of people to environmental and health issues, health-care waste (HCW) management has become one of the focus of researchers. The selection of appropriate HCW treatment technology is vital to the survival and development of human beings. In the assessment process of HCW disposal alternative, the evaluation information given by decision makers (DMs) often has uncertainty and ambiguity. The expression, transformation and integration of this information need to be further studied. We develop an applicable decision support framework of HCW treatment technology to provide reference for relevant staff. Firstly, the evaluation information of DMs is represented by interval 2-tuple linguistic term sets (ITLTs). To effectively express qualitative information, the cloud model theory is used to process the linguistic information, a novel concept of interval 2-tuple linguistic integrated cloud (ITLIC) is proposed, and the relevant operations, distance measure and possibility degree of ITLICs are defined. Moreover, a weighted Heronian mean (HM) operator based ITLIC is presented to fuse cloud information. Secondly, the HCW treatment technology decision support model based on the BWM and PROMETHEE is established. Finally, the proposed model is demonstrated through an empirical example, and the effectiveness and feasibility of the model is verified by comparison with extant methods.

A Study of Supplier Selection Method Based on SVM for Weighting Expert Evaluation

How to choose suppliers scientifically is an important part of strategic decision-making management of enterprises. Expert evaluation is subjective and uncontrollable; sometimes, there exists biased evaluation, which will lead to controversial or unfair results in supplier selection. To tackle this problem, this paper proposes a novel method that employs machine learning to learn the credibility of expert from historical data, which is converted to weights in evaluation process. We first use the Support Vector Machine (SVM) classifier to classify the historical evaluation data of experts and calculate the experts’ evaluation credibility, then determine the weights of the evaluation experts, finally assemble the weighted evaluation results, and get a preference order of choosing suppliers. The main contribution of this method is that it overcomes the shortcomings of multiple conversions and large loss on evaluation information, maintains the initial evaluation information to the maximum extent, and improves the credibility of evaluation results and the fairness and scientificity of supplier selection. The results show that it is feasible to classify the past evaluation data of the evaluation experts by the SVM classification model, and the expert weights determined on the basis of the evaluation credibility of experts are adjustable.

PENGEMBANGAN SISTEM INFORMASI EVALUASI LAYANAN AKADEMIK UNTUK INSTITUT TEKNOLOGI KALIMANTAN

Higher Education can exercise management autonomy, that is to evaluate using an information system independently. The case study taken in this study is the Kalimantan Institute of Technology (ITK). So far, ITK has used a survey information system to evaluate ITK, but the information system is static so that it is not following the current needs of ITK. Based on that, this research is developing an evaluation information system at ITK so that this information system is expected to be able to monitor and evaluate the process of activities at ITK. The method used is the waterfall model. Based on the waterfall method, the methodology used in this research is start from the identification of the problem, then study the literature and enter the system building stage, namely the analysis, design, implementation and testing stages, as well as conclusions and suggestions. The results obtained are in the form of an information system for evaluating academic activities and services that have been approved by ITK on the http://evaluasi.itk.ac.id page. Keywords: evaluation; information system; ITK Abstrak: Perguruan Tinggi memiliki kemampuan untuk melaksanakan otonomi pengelolaan yaitu dievaluasi secara mandiri menggunakan sistem informasi. Studi kasus yang diambil pada penelitian ini adalah Institut Teknologi Kalimantan (ITK). Selama ini ITK telah menggunakan sistem informasi survey untuk mengevaluasi ITK, namun sistem informasi tersebut bersifat statis sehingga telah tidak sesuai dengan kebutuhan ITK saat ini. Berdasarkan hal itu, penelitian ini adalah mengembangkan sistem informasi evaluasi di ITK sehingga diharapkan sistem informasi ini dapat memantau dan mengevaluasi proses kegiatan di ITK. Metode yang digunakan adalah waterfall model. Berdasarkan metode waterfall tersebut, maka metodologi yang digunakan pada penelitian ini dimulai dari identifikasi masalah, kemudian studi literatur dan memasuki tahap membangun sistem yaitu tahap analisis, desain, implementasi dan pengujian, serta kesimpulan dan saran. Hasil penelitian yang diperoleh adalah berupa sistem informasi evaluasi kegiatan akademik dan layanan yang telah disetujui ITK di halaman http://evaluasi.itk.ac.id. Kata kunci: evaluasi; sistem informasi; ITK

Vague multi-attribute group decision-making method based on evidence theory with a new aspect to solve weights

In order to solve the problems of weight solving and information aggregation in the Vague multi-attribute group decision-making, this paper first solves the weight of Vague evaluation value, and then fuses the information of Vague sets through evidence theory, and obtains an information aggregation algorithm for Vague multi-attribute group decision-making. Firstly, The algorithm draws on the idea of solving the weight of evidence in the improved evidence theory algorithm, and calculates the weight of Vague evaluation value, and revises the original evaluation information after obtaining the weight of each Vague evaluation value. Secondly, this algorithm analyzes the mathematical relationship between the Vague sets and the evidence theory, and uses the evidence theory to fuse the evaluation information to obtain the final Vague evaluation value of each alternative. Finally, this algorithm uses a score function to calculate the score of each alternative to determine the best alternative. The algorithm given in the paper enables decision-makers to make rational decisions in uncertain environments, and then select the best alternative.

The optimal emergency decision-making method with incomplete probabilistic information

Emergencies often occur irregularly, such as infectious diseases, earthquakes, wars, floods, the diffusion and leakage of chemically toxic and harmful substances, etc. These emergencies can bring huge disasters to people, even worse, the time left for people to make critical decisions is usually very limited. When an emergency occurs, the most important thing for people is to make reasonable decisions as soon as possible to deal with the current problems, otherwise, the situation may deteriorate further. The paper proposes an emergency decision-making algorithm under the constraints of the limited time and incomplete information, the research is mainly carried out from the following aspects, firstly, we use the data structure of the hesitant fuzzy probabilistic linguistic set to collect the basic data after careful comparison, which has three advantages, (1) considering the hesitation in the decision-making process, each evaluation information is allowed to contain multiple values instead of just one value; (2) each evaluation value is followed by a probability value, which further describes the details of the evaluation information; (3) the data structure allows some probability information to be unknown, which effectively expands the application scope of the algorithm. Secondly, the maximization gap model is proposed to calculate unknown parameters, the model can distinguish alternatives with small differences. Thirdly, all the evaluation information will be aggregated by the dynamic hesitant probability fuzzy weighted arithmetic operator. Subsequently, an instance is given to illustrate the effectiveness and the accuracy of the algorithm proposed in the paper. Finally, the advantages of the proposed algorithm are further demonstrated by comparing it with other outstanding algorithms. The main contribution of the paper is that we propose the maximization gap model to obtain the unknown parameters, which can effectively and accurately distinguish alternatives with small differences.

Comprehensive Evaluation of Talent Growth Factor Index System Based on Hesitant Fuzzy Language

An effective evaluation of the index system of talent growth factors can lay a solid foundation to build the talent pool, as well as select and establish talents. In this study, based on the theory of “Man-Machine-Environment” system engineering (MMESE), the MMESE talent growth factor index system is proposed and verified for its effectiveness by comparing it with the traditional system. Based on the text-free grammar and transformation function, expert judgments of talent growth factor indexes were transformed into hesitation fuzzy language terms, namely, pertinence, systematicness, practicability, foresight, and dynamics, which were then used to create a dataset to describe the comprehensive evaluation of the index system. The entropy of hesitation fuzzy language terms adopts the algorithm, which calculates the index weights according to the relative entropy values and adjusts the expert weights with the expert group consensus model. The expert evaluation information was weighed and transformed into the corresponding probability language combination which was calculated as the comprehensive evaluation result of the talent growth factor index system.

Cross-Efficiency Evaluation under Probabilistic Hesitant Fuzzy Environment and Its Application to the Portfolio Selection Process

In the case of insufficient quantitative data, qualitative evaluation information is very important for investment decision-making. However, if it is completely based on qualitative evaluation information, the results may be subjective. In response to this problem, this paper proposes a method, namely, probabilistic hesitant fuzzy cross-efficiency evaluation (PHFCEE) method, based on probabilistic hesitant fuzzy theory, cross-efficiency data envelopment analysis (CEDEA). This method uses probabilistic hesitant fuzzy sets to collect qualitative evaluation information and then uses the cross-efficiency DEA model to fuse quantitative data and qualitative information. And finally, an investment portfolio is built based on the cross-efficiency value and its variance. In addition, this article gives the specific operating steps of the PHFCEE method and uses the construction of a portfolio of 10 stocks in the China CSI 300 Index as an example to illustrate the effectiveness of this method.

Behavioral Risky Multiple Attribute Decision Making with Interval Type-2 Fuzzy Ranking Method and TOPSIS Method

Considering the decision maker’s psychological state will influence their evaluation result in the risky multi-attribute decision-making problem, and the uncertainty of evaluation information. In this paper, we will propose a behavioral risky multiple attribute decision making with interval type-2 fuzzy ranking method and TOPSIS method. The interval type-2 fuzzy set is used to express the uncertainty of evaluation information, the prospect theory is applied to describe people’s psychological state in the processing of risk decision making. First, we define a new ranking method for interval type-2 fuzzy set to compare the interval type-2 fuzzy evaluation information and the expectation. Second, we give a relative distance for interval type-2 fuzzy set to get the distance between the interval type-2 fuzzy evaluation information and expectation. Third, we use the prospect theory, the new defined ranking method and the new defined distance formula to obtain the comprehensive prospect value. Fourth, we use the improved TOPSIS method and the comprehensive prospect value to rank the alternatives. Based on the above-mentioned steps, we give the solution for risky interval type-2 fuzzy multiple attribute decision-making problem, which named as the behavioral risky multiple attribute decision making with interval type-2 fuzzy ranking method and TOPSIS method. Finally, we use an example to show the rationality of this method.

A Consensus-Reaching Approach to the Evaluation of Product Design Alternatives with Multiple Preference Structures

With interdisciplinarity being an important characteristic of contemporary product design, the evaluation of design alternatives also involves multiple disciplines, and the evaluator group usually consists of evaluators from different fields and with obvious heterogeneous characteristics. To effectively satisfy the heterogeneous needs of evaluators and improve the credibility of evaluation results, the paper introduces a consensus-reaching approach that incorporates multiple preferences to the evaluation of product design alternatives. First, in order to obtain individual preference information, each evaluator is asked to evaluate all the design alternatives using a preference structure that he/she is familiar with. Second, we use a transfer function to uniform the evaluation information obtained from various preference structures into a complementary judgment matrix. Then, we use the Hybrid Weighted Averaging (HWA) operator weight determination model to aggregate the preference information and obtain the group preference information. Then, we measure the consensus degree between individual evaluators and the group using a consensus measurement method. After that, we use the feedback mechanism to instruct individual evaluators to modify their preferences until a consensus is achieved. We explain the application steps and the feasibility of this approach through the evaluation of the design alternatives of multichannel fluorescence immunochromatography analyzers (MFIAs).