Multi-Criteria Decision Making (MCDM) with Preference Modeling

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Analysis".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 3837

Special Issue Editors

School of Business, Anhui University, Hefei 230601, China
Interests: business intelligence; group decision making; multi-criteria decision making; big data analysis

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Guest Editor
School of Internet, Anhui University, Hefei 230039, China
Interests: combination forecasting; big data analysis; information fusion
School of Big Data and Statistics, Anhui University, Hefei 230601, China
Interests: decision analysis; data envelopment analysis; management science; multicriteria analysis; mathematical programming optimization
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mathematical Sciences, Anhui University, Hefei 230601, China
Interests: group decision making; information fusion; combination forecasting, big data analysis

Special Issue Information

Dear Colleagues,

Multi-criteria decision making (MCDM) is a key branch of decision-making theory, which involves multiple evaluation criteria for all objects. The primary aim of MCDM is to acquire a complete ranking and choose the optimal object based on finite objects and evaluation values from multiple criteria.

There are different classifications of MCDM problems and methods: multi-criteria evaluation problems and multi-objective mathematical programming problems. Whether it is an evaluation problem or a design problem, DM preference information is required in order to differentiate between solutions. The solution methods for MCDM problems are commonly classified based on the timing of preference information obtained through DM.

Over the last few decades, MCDM has been successfully applied to complex decision-making problems in a wide range of fields, such as economics, finance, logistics, environmental restoration, and health or industrial organizations. This Special Issue aims to show the current progress in decision-making theory and also some applications of this theory.

You are invited to submit papers that are unpublished original works for this Special Issue. The topics include, but are not limited to:

  1. Interactive multi-objective optimization.
  2. Multi-criteria decision aiding.
  3. Preference modeling.
  4. Rank reversals in decision making.
  5. Superiority and inferiority ranking method.
  6. Multi-criteria choice, ranking, and sorting.
  7. Multi-objective continuous and combinatorial optimization.
  8. Evolutionary many-objective optimization.
  9. Multi-attribute utility theory.
  10. Outranking methods.
  11. Decision support model.
  12. Preference learning and ranking.
  13. Data envelopment analysis.
  14. Multi-objective metaheuristics.
  15. Fuzzy multi-criteria decision making.
  16. Data-driven and model-based multi-objective optimization.
  17. Dynamic multi-objective optimization.
  18. Applications of MCDM in business management.

We look forward to receiving your contributions. 

Dr. Peng Wu
Dr. Jiaming Zhu
Dr. Zhifu Tao
Prof. Dr. Li-Gang Zhou
Guest Editors

Manuscript Submission Information

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Keywords

  • multi-criteria decision making
  • preference learning
  • consensus
  • preference ranking
  • group decision making

Published Papers (3 papers)

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Research

18 pages, 957 KiB  
Article
Research on the Optimization of Pricing and the Replenishment Decision-Making Problem Based on LightGBM and Dynamic Programming
by Wenyue Tao, Chaoran Wu, Ting Wu and Fuyuan Chen
Axioms 2024, 13(4), 257; https://doi.org/10.3390/axioms13040257 - 13 Apr 2024
Viewed by 780
Abstract
Vegetables have a short period of freshness, and therefore, the purchase of vegetables has to be carefully matched with sales, especially in the “small production and big market” setting prevalent in China. Therefore, it is worthwhile to develop a systematic and comprehensive mathematical [...] Read more.
Vegetables have a short period of freshness, and therefore, the purchase of vegetables has to be carefully matched with sales, especially in the “small production and big market” setting prevalent in China. Therefore, it is worthwhile to develop a systematic and comprehensive mathematical model of replenishment plans and pricing strategies for each category of vegetables and individual products. In this paper, we analyze the following three questions: Question One: What is the distribution law and relationship between the sales volume of vegetable categories and single products? Question Two: What is the relationship between total sales volume and cost-plus pricing of vegetable categories? And is it possible to provide the daily total replenishment and pricing strategy of each vegetable category for the following week to maximize supermarket profit? Question Three: How can we incorporate the market demand for single vegetable products into a profit-maximizing program for supermarkets? Is it possible to further formulate the replenishment plan requirements for single products? To answer the first question, we created pivot tables to analyze occupancy. We found that mosaic leaves, peppers, and edible mushrooms accounted for a larger proportion of occupacy, while cauliflowers, aquatic rhizomes, and eggplants accounted for a smaller proportion. For the single items, lettuce, cabbage, green pepper, screw pepper, enoki mushroom, and shiitake mushroom accounted for a large proportion of their respective categories. We used the Pearson correlation coefficient and the Mfuzz package based on fuzzy c-means (FCM) algorithm to analyze the correlation between vegetable categories and single products. We found that there was a strong correlation between vegetable categories. Moreover, the sale of vegetable items belonging to the same category exhibited the same patterns of change over time. In order to address the second question, we established the LightGBM sales forecasting model. Combined with previous sales data, we forecasted and planned an efficient daily replenishment volume for each vegetable category in the coming week. In addition, we developed a pricing strategy for vegetable categories to maximize supermarket profits. For the third question, we built a dynamic programming model combining an optimal replenishment volume with a product pricing strategy for single items, which let the supermarket maximize its expected profits. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making (MCDM) with Preference Modeling)
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21 pages, 3043 KiB  
Article
Fuzzy-Set-Based Multi-Attribute Decision-Making, Its Computing Implementation, and Applications
by Mateus Alberto Dorna de Oliveira Ferreira, Laura Cozzi Ribeiro, Henrique Silva Schuffner, Matheus Pereira Libório and Petr Iakovlevitch Ekel
Axioms 2024, 13(3), 142; https://doi.org/10.3390/axioms13030142 - 23 Feb 2024
Viewed by 1064
Abstract
This paper reflects the results of research analyzing models of multi-attribute decision-making based on fuzzy preference relations. Questions of constructing the corresponding multi-attribute models to deal with quantitative information concomitantly with qualitative information based on experts’ knowledge are considered. Human preferences may be [...] Read more.
This paper reflects the results of research analyzing models of multi-attribute decision-making based on fuzzy preference relations. Questions of constructing the corresponding multi-attribute models to deal with quantitative information concomitantly with qualitative information based on experts’ knowledge are considered. Human preferences may be represented within the fuzzy preference relations and by applying diverse other preference formats. Considering this, so-called transformation functions reduce any preference format to fuzzy preference relations. This paper’s results can be applied independently or as part of a general approach to solving a wide class of problems with fuzzy coefficients, as well as within the framework of a general scheme of multi-criteria decision-making under conditions of uncertainty. The considered techniques for fuzzy preference modeling are directed at assessing, comparing, choosing, prioritizing, and/or ordering alternatives. These techniques have served to develop a computing system for multi-attribute decision-making. It has been implemented in the C# programming language, utilizing the “.NET” framework. The computing system allows one to represent decision-makers’ preferences in one of five preference formats. These formats and quantitative estimates are reduced to nonreciprocal fuzzy preference relations, providing homogeneous preference information for decision procedures. This paper’s results have a general character and were applied to analyze power engineering problems. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making (MCDM) with Preference Modeling)
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23 pages, 10265 KiB  
Article
A Hybrid DEMATEL and Bayesian Best–Worst Method Approach for Inland Port Development Evaluation
by Junchi Ma, Bart Wiegmans, Xifu Wang, Kai Yang and Lijun Jiang
Axioms 2023, 12(12), 1116; https://doi.org/10.3390/axioms12121116 - 13 Dec 2023
Viewed by 1130
Abstract
Inland ports are gaining more and more attention as important hubs for inland cities to promote foreign trade. However, studies on the evaluation of inland ports are lacking. In this work, we aim to construct an index system and propose a multi-criteria group [...] Read more.
Inland ports are gaining more and more attention as important hubs for inland cities to promote foreign trade. However, studies on the evaluation of inland ports are lacking. In this work, we aim to construct an index system and propose a multi-criteria group decision-making method to comprehensively evaluate the development of inland ports. Unlike previous studies, using pressure–state–response model as a reference, we built up a demand–risk–power–potential framework for the index system proposed in this study. To determine the different weights for each indicator, which is a typical multi-criteria decision-making problem, we innovatively combined the decision-making trial and evaluation laboratory (DEMATEL) and the Bayesian best–worst method (BBWM) based on their distinct advantages in dealing with data coupling and group decision-making. In addition, this work introduces a case study of inland ports in the Huaihai Economy Zone to validate the efficacy of the proposed evaluation model and method. After calculating and obtaining the comprehensive scores and rankings of each inland port in this case, we compared the evaluation results with those under the BBWM, TOPSIS, and CRITIC methodologies, and found that the results under the DEMATEL–BBWM methodology can provide better differentiation for inland port evaluation results. Moreover, based on the evaluation results, a performance–importance matrix is formulated to identify the areas requiring attention in the development process of each inland port. Subsequently, rational managerial insights are put forward to achieve the sustainable development of inland ports in the Huaihai Economy Zone. Full article
(This article belongs to the Special Issue Multi-Criteria Decision Making (MCDM) with Preference Modeling)
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