Article
Computer Science, Artificial Intelligence
Decui Liang, Xin He, Zeshui Xu, Jiahong Li
Summary: This paper discusses a strict two-sided matching based on multi-attribute interval-valued preference ordinal information in decision problems. The study introduces a ranking method of probability degree to deal with information of various interval numbers, and proposes two methods for strict two-sided matching in the case of multiple attributes.
JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Junchang Qin, Sha Fan, Haiming Liang, Cong-Cong Li, Yucheng Dong
Summary: In this paper, an optimization method for solving the two-sided matching decision-making problem with incomplete weak preference ordering and heterogeneous fuzzy stable demand is proposed. The method calculates the expectation ordinal values, perceived difference matrices, and perceived value matrices to obtain stable alternatives. It takes into account the fuzzy expression of fuzzy stable demand and maximizes the perceived values of individuals in the two-sided matching party.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
(2022)
Article
Computer Science, Hardware & Architecture
Maha Kadadha, Hadi Otrok, Shakti Singh, Rabeb Mizouni, Anis Ouali
Summary: This paper proposes novel two-sided preferences task matching mechanisms for blockchain-based crowdsourcing, aiming to address the bias towards requesters in existing frameworks. By evaluating real datasets, the proposed mechanisms achieve higher performance in terms of worker satisfaction and confidence compared to the Nearest Neighbor Matching (NNM) mechanism.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2021)
Article
Management
Ali Aouad, Daniela Saban
Summary: Motivated by online labor markets, this paper focuses on the online assortment optimization problem faced by a two-sided matching platform. The study investigates how platforms should design online assortment algorithms to maximize the expected number of matches in such settings. The results show that a simple greedy algorithm is 1/2-competitive against an optimal clairvoyant algorithm. However, no randomized algorithm can achieve a better competitive ratio, even in asymptotic regimes. The study further explores structured settings and develops new preference-aware balancing algorithms to improve the competitive ratios. The findings highlight the importance of suppliers' choices in designing online assortment algorithms for two-sided matching platforms.
MANAGEMENT SCIENCE
(2023)
Article
Computer Science, Information Systems
Xinli You, Fujun Hou
Summary: The probabilistic linguistic preference relation is a useful tool for describing stakeholder preferences in group decision-making. Personalized feedback mechanisms, based on self-confidence and leadership, help update stakeholder preferences and reach consensus. Constructing trust relationships, assessing self-confidence levels, and identifying opinion leaders are key steps in this process.
INFORMATION SCIENCES
(2022)
Article
Automation & Control Systems
Feifei Jin, Shuyan Guo, Yuhang Cai, Jinpei Liu, Ligang Zhou
Summary: This paper proposes a 2-tuple linguistic decision-making method that incorporates a consistency adjustment algorithm and a 2-tuple linguistic data envelopment analysis model. It aims to retain the decision makers' initial preference information and improve the consistency and weight generation for alternatives with 2-TLPRs.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Zhen Zhang, Junliang Gao, Yuan Gao, Wenyu Yu
Summary: This paper presents an approach to utilize multi-granular hesitant fuzzy linguistic term sets in two-sided matching decision making. The method constructs optimization models to determine criteria weights for matching objects, aggregates hesitant fuzzy linguistic decision matrices to obtain collective assessments, and establishes an optimization model to maximize overall satisfaction degrees and determine the matching between objects. The proposed approach allows flexible linguistic assessments and can handle incomplete criteria weight information.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Zhen Zhang, Junliang Gao, Yuan Gao, Wenyu Yu
Summary: The paper presents an approach to two-sided matching decision making problems with multi-granular hesitant fuzzy linguistic information. The method constructs optimization models to determine standard weights, aggregate hesitant fuzzy linguistic decision matrices to obtain satisfaction degrees, and maximize overall satisfaction degrees for determining matching, while also being able to flexibly handle linguistic assessments and incomplete standard weight information provided by matching objects.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Zhen Zhang, Junliang Gao, Yuan Gao, Wenyu Yu
Summary: This paper investigates the issue of matching objects providing multi-granular linguistic information in two-sided matching decision making problems, proposing an approach based on multi-granular hesitant fuzzy linguistic term sets. The method aims to maximize matching objects' overall satisfaction degree by considering stable matching conditions, allowing flexible linguistic assessments and handling incomplete criteria weight information. A case study on the matching of green building technology supply and demand is provided to illustrate the characteristics of the proposed approach.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Zhen Zhang, Junliang Gao, Yuan Gao, Wenyu Yu
Summary: This paper introduces an approach for addressing two-sided matching decision making problems in real-world scenarios, which can handle multi-granular linguistic information provided by objects with different cultural and knowledge backgrounds. By constructing optimization models and aggregating evaluations, the satisfaction degrees of matching objects are determined to establish the final matchings. This method is flexible and capable of dealing with incomplete criteria weight information.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Guolin Wu, Wen Zhang, Zhibin Wu
Summary: This article focuses on analyzing the reliability of preference relations based on ordinal consistency and cardinal consistency. By defining a new ordinal consistency and designing two linear programming models to eliminate ordinal inconsistencies and control both ordinal and cardinal consistencies, the rationality of individual preference relations is ensured. The reliability of preferences is also analyzed in the consensus model, and the feasibility and effectiveness of these approaches are validated through classical examples.
INFORMATION FUSION
(2023)
Article
Computer Science, Artificial Intelligence
Xiang Jia, Liang Wang, Ying-Ming Wang, Hui-Hui Song
Summary: This paper proposes a two-sided matching decision-making (TSMDM) approach with probabilistic linguistic evaluations. By normalizing and aggregating the evaluations, satisfaction degrees are calculated and a multi-objective TSMDM model is built aiming to maximize the comprehensive satisfaction degree. Results from an illustrative example show that the proposed approach can avoid information loss and effectively integrate probabilistic linguistic term sets with correlative criteria.
INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
(2023)
Article
Computer Science, Artificial Intelligence
Xiang Jia, Xin-Fan Wang, Ying-Ming Wang, Lang Zhou
Summary: This paper proposes a two-sided matching decision-making approach using probabilistic linguistic evaluations. By considering the psychological behaviors of the subjects using prospect theory, the matching result is improved. The multi-objective two-sided matching decision-making model is built and transformed into a single-objective model to obtain the optimal matching result.
Article
Computer Science, Artificial Intelligence
Kwei-guu Liu, Kentaro Yahiro, Makoto Yokoo
Summary: In this study, a student-project-resource matching-allocation problem is examined. It is found that dividing the problem into two separate parts can lead to sub-optimal outcomes, and a whole integrated solution is needed. A new strategyproof mechanism called Sample and Deferred Acceptance (SDA) is developed to ensure fairness and efficiency. Experimental comparisons show that SDA strikes a good balance between fairness and efficiency when students are classified into different types based on their preferences.
ARTIFICIAL INTELLIGENCE
(2023)
Article
Management
Zhen Zhang, Xinyue Kou, Wenyu Yu, Yuan Gao
Summary: The paper introduces a decision-making method based on preference relations, utilizing the extended logarithmic least squares method and consistency improving algorithms to enhance the multiplicative consistency of inconsistent preference relations. The feasibility and effectiveness of the approach are demonstrated through an example involving the matching of knowledge suppliers and knowledge demanders.
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
(2021)
