A Bi-objective AHP-MINLP-GA approach for Flexible Alternative Supplier Selection amid the COVID-19 Pandemic

A decision maker using the Analytic Hierarchy Process (AHP) could be consistent, and still provide no information in the resulting vector of priorities. An extreme example would be a pairwise comparison judgment matrix filled with 1s which is totally consistent under the various definitions of consistency, but has provided no information about the prioritization of alternatives resulting from the decision maker's judgments. In this paper, the quality of a consistent decision maker's judgments using the Analytic Hierarchy Process is placed in the context of the entropy of the resulting vector of priorities. Indeed, it is the purpose of this paper to provide a formal definition of this notion ofentropy of a priority vector, and to provide a framework for a quantitative measurement of the information content of consistent pairwise comparison judgment matrices of a decision maker who is using the Analytic Hierarchy Process. We will prove that the entropy of the vector of priorities for consistent matrices follows a normal distribution and discuss some general considerations of this result.

Owing to the needs for network interaction and teaching, most of the domestic universities develop and construct their own teaching platform. As far as Chinese language teaching is concerned, the learners our students will face in the future are mostly nonnative speakers of Chinese. Therefore, a lot of key issues are taken into consideration for this platform. Quality Function Deployment (QFD) is a technology that aims to satisfy customers' demands by product design. One of its characteristics is that the voice of customers will be designed into the product. An analytical hierarchy process (AHP) offers a methodology for ranking alternative courses of action based on the decision maker's judgments concerning the importance of the criteria. In this research, the quality concept was employed to develop a Chinese digital teaching platform in order to satisfy the students' needs and enhance their learning effect via QFD and AHP.

In order to transfuse the idea of sexual equalitarian to the students through the educational channels, some relative sexual equalitarian courses are provided in the university on the basis of requirements from ministry of education. Quality Function Deployment(QFD) is a technology that aims to satisfy customers' demands by product design. One of its characteristics is what the voice of customers will be designed into the product. Analytical Hierarchy Process(AHP) is usually used in the multiple criteria decision making. It offers a methodology for ranking alternative courses of action based on the decision maker's judgments concerning the importance of the criteria. In this research, QFD and AHP are employed to the course design of sexual equalitarian education in the general courses. A course design strategy was constructed on the basis of literature review, teachers' practical experiences, viewpoints from students' counselor and questionnaire. It must be helpful to achieve students' requirements and enhance the students' learning effect.

Recently, some researches have been carried out in the context of using data envelopment analysis (DEA) models to generate local weights of alternatives from pairwise comparison matrices used in the analytic hierarchy process (AHP). One of these models is the DEAHP. The main drawback of the DEAHP is that it generates counter-intuitive priority vectors for inconsistent pairwise comparison matrices. To overcome the drawbacks of the DEAHP, this paper proposes a new procedure entitled Revised DEAHP, and it will be shown that this procedure generates logical weights that are consistent with the decision maker's judgements and is sensitive to changes in data of the pairwise comparison matrices. Through a numerical example, it will be shown that the Revised DEAHP not only produces correct weights for inconsistent matrices but also does not suffer from rank reversal when an irrelevant alternative is added or removed.

An analytic hierarchy process based simulation model for entry mode decision regarding foreign direct investment

Recent recognition of the difficulties in measuring the true economic costs and benefits of major technological alternatives, as well as the much broader scope of analysis required for the full consideration of such alternatives, has highlighted the limitations of traditional capital investment analysis methods. This has led to an interest in the use of multicriteria methods of analysis in which financial returns are treated as one of several criteria. In this paper we argue that there should be no conflict between the objective of a multicriteria analysis and that of maximizing the wealth of the firm. We introduce a framework of analysis in which a decision maker's judgments concerning the intangible and difficult-to-quantify criteria are given economic value. The framework of analysis also incorporates consistency checks on the decision maker's judgments.

Integrated AHP & TOPSIS methodology using intuitionistic Z-numbers: An application on hydrogen storage technology selection

Consensus models for AHP group decision making under row geometric mean prioritization method

Material selection is a rapidly growing multi-criteria decision-making (MCDM) problem owing to the large number of factors affecting decision making. The right choice of available material is critical to the success and competitiveness of the manufacturing organisation. The analytical hierarchy process (AHP) is a tool designed to solve MCDM problems. The AHP was applied to select a material for a simple product (a key) utilising Expert Choice™ software. Confidence in the output is found to be high owing to the low inconsistency of the decision maker's judgement. Sensitivity analysis was performed to increase the confidence in the choice of material.

The analytic hierarchy process (AHP) introduced by T.L. Saaty is a well-known and popular analytic model of multi-criteria decision making.The final object of AHP is the priority ordering of the decision alternatives.However, because of a lot of uncertain factors and perturbations, the n column vectors in the matrix are absolutely not consistent.So, people have to look for the column vectors in the matrix is a " close" structure to compose a close consistent matrix.The " close" here means the "order clustering" that all column vectors of a judgment matrix have a same (or near same) rank ordering sequence.

The inconsistency of judgments in the fuzzy Analytic Hierarchy Process (AHP) is a crucial issue. To make the appropriate decision, the inconsistency in decision maker's (DM) judgments needs to be eliminated or reduced. This paper proposes two mathematical models to deal with inconsistency in fuzzy AHP. In the first model, the DM's judgments are modified where the preference order of the DM's judgments remained unchanged. The second model allows reversing the preference orders of judgments. The proposed models aim to eliminate or reduce the inconsistency of fuzzy AHP by changing judgments. The models cause fewer changes for the high certain judgments. Two examples solved by the proposed models are included for purposes of illustration.

Genetic algorithm optimised Hadamard product method for inconsistency judgement matrix adjustment in AHP and automatic analysis system development

Assessing venture capital investments with noncompensatory behavioral decision models

Inconsistency analysis by approximately specified priorities

Interval estimations of global weights in AHP by upper approximation