运筹与管理 ›› 2025, Vol. 34 ›› Issue (11): 65-73.DOI: 10.12005/orms.2025.0344

• 理论分析与方法探讨 • 上一篇    下一篇

基于改进WASPAS的q阶orthopair不确定语言决策方法

王浩伦1, 冯良清1, 张发明2   

  1. 1.南昌航空大学 经济管理学院,江西 南昌 330036;
    2.桂林电子科技大学 商学院,广西 桂林 541004
  • 收稿日期:2022-12-28 出版日期:2025-11-25 发布日期:2026-03-30
  • 通讯作者: 王浩伦(1981-),男,浙江金华人,博士,副教授,硕士生导师,研究方向:决策理论与方法。Email: hlwang71162@nchu.edu.cn。
  • 基金资助:
    国家自然科学基金资助项目(72561006,72461025,72361026,72161006);教育部人文社会科学研究规划基金项目(19YJC630164);广西自然科学基金重点项目(2023JJD110010);江西省高校人文社会科学研究项目(GL23104)

q-Rung Orthopair Uncertain Linguistic Multi-criteria Decision-makingMethod Based on Improved WASPAS

WANG Haolun1, FENG Liangqing1, ZHANG Faming2   

  1. 1. School of Economics & Management, Nanchang Hangkong University, Nanchang 330036, China;
    2. School of Business, Guilin University of Electronic Technology, Guilin 541004, China
  • Received:2022-12-28 Online:2025-11-25 Published:2026-03-30

摘要: 针对传统WASPAS方法忽视决策变量间关联性和无法处理q阶orthopair不确定语言(q-ROUL)信息的多准则决策问题,提出一种基于改进WASPAS的q-ROUL决策方法。首先提出q-ROUL变量(q-ROULVs)的Acezl-Asina(AA)运算规则并开发q-ROUL AA Hamy和对偶Hamy平均算子(q-ROULAAHM和q-ROULAADHM)及其加权形式。其次,在q-ROUL汉明距离测度上构建最大偏差模型来确定准则的权重向量。再次,在q-ROUL多准则决策模型中利用上述算子和距离测度对WASPAS方法进行改进。最后,算例计算、灵敏度和方法对比分析结果表明所提方法的可行性、有效性和合理性。

关键词: q-ROUL集, Acezl-Asina t-norms, WASPAS方法, Hamy均值算子, 多准则决策

Abstract: Due to the rapid development of economy and society, decision-making problems have become more and more complex. The evaluation of alternatives to different criteria contains more vague and uncertain information. Complex practical decision-making problems contain a lot of uncertain information. Decision-makers can hardly rely on accurate data to depict uncertainty. Linguistic evaluation is favored by decision-makers. q-Rung orthopair uncertain linguistic set is composed of uncertain linguistic part and q-rung orthopair fuzzy part. It is an effective and novel expression tool for uncertain and vague information. WASPAS method is a simple decision-making technique which combines the weighted sum model (WSM) with the weighted product model (WPM) and makes the alternatives rank accurately and the results stable.
   Although this method has been extended and applied in various decision-making environments, such as intuitionistic fuzzy sets, Pythagorean fuzzy sets, picture fuzzy sets, rough sets and so on, there are still rooms for extension and improvement. Therefore, there are some issues that need to be solved: (1)The existing WASPAS methods have not been extended in the q-ROUL settings. (2)In many methods, the WSM and WPM often use basic algebraic operation rules, but rarely can flexibly adjust the decision results to the decision scenarios. (3)Most of the existing studies on the WASPAS method ignore the interrelationship between any two criteria, but do not consider the interrelationship among multiple criteria. (4)The score function or expectation function is a de-fuzzification method often used by the WASPAS method, but the decision result may be inaccurate due to the loss of some decision information.
   To solve these problems, a novel q-ROUL multi-criteria decision-making method based on the improved WASPAS is proposed in this paper. The Acezl-Asina (AA) operational laws of q-ROUL numbers are introduced based on the concepts of q-ROUL set and AA t-norm and s-norm. The q-ROUL Acezl-Asina Hamy mean (q-ROULAAHM) and q-ROUL Acezl-Asina dual Hamy mean (q-ROULAADHM) operators and their weighted forms (i.e., q-ROULAAWHM, q-ROULAAWDHM) are developed. Then, the maximum deviation model is constructed on the q-ROUL Hamming distance measure to determine the weight vector of criteria. Thirdly, the above aggregation operators and Hamming distance are employed to improve the WASPAS method, so that the decision-making process of this method is more flexible, this method can capture the interrelationship among multiple criteria and the information of decision results is complete and accurate. Finally, the effectiveness and rationality of the improved WASPAS method are verified by a numerical example.

Key words: q-ROULS, Acezl-Asina t-norms, WASPAS method, Hamy mean, multi-criteria decision-making

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