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2. Consistency and Consensus Driven for Hesitant Fuzzy Linguistic Decision Making with Pairwise Comparisons

Author

Ren, Peijia, Liu, Zixu, Zhang, Wei-Guo, and Wu, Xilan

Subjects
Computer Science - Artificial Intelligence, Computer Science - Information Theory, 90B50
Abstract

Hesitant fuzzy linguistic preference relation (HFLPR) is of interest because it provides an efficient way for opinion expression under uncertainty. For enhancing the theory of decision making with HFLPR, the paper introduces an algorithm for group decision making with HFLPRs based on the acceptable consistency and consensus measurements, which involves (1) defining a hesitant fuzzy linguistic geometric consistency index (HFLGCI) and proposing a procedure for consistency checking and inconsistency improving for HFLPR; (2) measuring the group consensus based on the similarity between the original individual HFLPRs and the overall perfect HFLPR, then establishing a procedure for consensus ensuring including the determination of decision-makers weights. The convergence and monotonicity of the proposed two procedures have been proved. Some experiments are furtherly performed to investigate the critical values of the defined HFLGCI, and comparative analyses are conducted to show the effectiveness of the proposed algorithm. A case concerning the performance evaluation of venture capital guiding funds is given to illustrate the availability of the proposed algorithm. As an application of our work, an online decision-making portal is finally provided for decision-makers to utilize the proposed algorithms to solve decision-making problems., Comment: Pulished by Expert Systems with Applications (ISSN: 0957-4174)

Published
2021

14. Introduction

Author

Ren, Peijia, Xu, Zeshui, Kacprzyk, Janusz, Series Editor, Ren, Peijia, and Xu, Zeshui

Published
2021
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40. Online choice decision support for consumers: Data-driven analytic hierarchy process based on reviews and feedback.

Author

Ren, Peijia, Zhu, Bin, Ren, Long, and Ding, Ning

Subjects
ANALYTIC hierarchy process, CONSUMERS, INFORMATION overload, CONSUMER preferences, REVISION (Writing process)
Abstract

As online shopping flourished, consumers in their shopping can refer to rich product descriptions and a large amount of review information. For the scenario of consumer online choice decision among candidate products characterized by limited attributes, we refer to it as an online multi-attribute decision-making problem. To address the challenge of online choice decision support for consumers, we propose a data-driven analytic hierarchy process (AHP). The data-driven AHP includes extracting attributes of candidate products, calculating attribute values, attribute-weight learning, interaction-based preference revision process, and product ranking. In particular, we develop an Exp-strategy for attribute-weight learning, which helps learn the attribute weights of consumers who provide reviews as a reference for an end consumer. This learning method can handle dynamic online reviews without the problem of information overload. In addition, we design the interaction-based preference revision process to help the end consumer identify his attribute weights and make a choice decision. [ABSTRACT FROM AUTHOR]

Published
2023
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41. Online Information Serves Offline Sales: Knowledge Graph-Based Attribute Preference Learning

Author

Zhu, Bin, Xu, Panpan, and Ren, Peijia

Abstract

The combination of online and offline shopping is becoming more common. We define the scenario where online information serves offline sales as online–offline scenario. Online and offline shopping each has its own advantages and challenges, making it necessary to integrate the strengths of both online and offline channels. Online platforms can discover consumers' attribute preferences through their online generated data. Since consumers maintain coherent attribute preferences over a period of time in both online and offline, offline salesmen can use the attribute preferences transferred from online platforms to create personalized marketing plans. In this online–offline scenario, it is crucial to learn consumer attribute preferences. We propose a knowledge graph-based multiattribute preference learning method (KG-APL), which integrates knowledge graph (KG) and multiattribute decision-making (MADM) theory. Based on MADM theory, KG-APL can learn multilevel attribute preferences in a data-driven way and provide an explanatory analysis for attribute preferences. The explanations rely on both the MADM theory and rich side information about product contained in the KG. Specifically, MADM describes the consumer's decision-making process and KG provides a hierarchical structure from products to attributes and subattributes. To verify its effectiveness and robustness, we use randomly generated data for experiments and real-life data for simulated decision making. Our article provides insight into the way of achieving integration between online and offline channels and offers theoretical and methodological support in enhance online–offline purchase services.

Published
2024
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46. Heterogeneous group decision making with thermodynamical parameters

Author

Ren, Peijia, Xu, Zeshui, Verma, Mohit, Zeng, Xiao-Jun, Liao, Huchang, Wang, Xinxin, Ren, Peijia, Xu, Zeshui, Verma, Mohit, Zeng, Xiao-Jun, Liao, Huchang, and Wang, Xinxin

Abstract

There often exist different types of information due to the subjective and objective criteria in practical decision-making problems, thus it is necessary to develop some efficient frameworks to deal with the decision-making problems with heterogeneous information. The paper proposes a framework for group decisionmaking problems with heterogeneous information with thermodynamical parameters consisting of three parts to achieving this goal. The first part builds the rectifications of criteria weights according to decision makers’ confidence in evaluations. The second part adopts thermodynamical parameters to measure the numerical values and the data distribution of heterogeneous information to characterize the heterogeneous information fully. The last part applies the TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision Making) to aggregate the decision-making results based on the characterized heterogeneous information without transforming it into a unified form. By depicting decision makers’ different sensitive attitudes towards uncertainty by several mathematical expressions, experiments are performed to assess the sensitive attitudes’ impacts on decisionmaking results with the proposed framework. Finally, a case study on the selection of a green supplier under the low-carbon economy is provided to illustrate the flexibility and feasibility of the proposed framework.

Published
2022

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