The relative decision-making algorithm for ranking data. (30th June 2020)

• Record Type: Journal Article
• Authors: Chen, Yin-Ju; Lo, Jian-Ming
• Is Part Of: Data technologies and applications. Volume 55:Number 2(2021)
• Page Start: 177
• Page End: 191
• Publication Date: 2020-06-30
• Subjects: Decision-making -- Algorithm -- Ranking data -- Rough set -- Tourism -- Service; Information science -- Periodicals; Electronic information resources -- Periodicals; Knowledge management -- Periodicals; 020.5
• DOI: 10.1108/DTA-01-2019-0011
• Languages: English
• ISSNs: 2514-9288

Abstract

Abstract : Purpose: Decision-making is always an issue that managers have to deal with. Keenly observing to different preferences of the targets provides useful information for decision-makers who do not require too much information to make decisions. The main purpose is to avoid decision-makers in a dilemma because of too much or opaque information. Based on problem-oriented, this research aims to help decision-makers to develop a macro-vision strategy that fits the needs of different clusters of customers in terms of their favorite restaurants. This research also focuses on providing the rules to rank data sets for decision-makers to make choices for their favorite restaurant. Design/methodology/approach: When the decision-makers need to rethink a new strategic planning, they have to think about whether they want to retain or rebuild their relationship with the old consumers or continue to care for new customers. Furthermore, many of the lecturers show that the relative concept will be more effective than the absolute one. Therefore, based on rough set theory, this research proposes an algorithm of related concepts and sends questionnaires to verify the efficiency of the algorithm. Findings: By feeding the relative order of calculating the ranking rules, we find that it will be more efficient to deal with the faced problems. Originality/value: The algorithm proposed in this research is applied to the ranking data of food. This research proves that the algorithm is practical and has the potential to reveal important patterns in the data set.