[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3664":3,"related-3664":68},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":21,"classification":22,"search_phrases":37,"slug":38,"view_count":39,"doi":40,"paper":41,"created_at":67},3664,"Development of the Range of Value (ROV) Method Based on Dynamic Weighting for Decision Support Systems","https:\u002F\u002Fdoi.org\u002F10.67449\u002Fjodesma.v1i3.12","Decision-making in complex environments requires an evaluation framework capable of integrating multiple criteria with different levels of importance and performance. This study proposes Dynamic-ROV, a modified Rank Order Value approach designed to evaluate alternatives through data-driven criterion weighting and utility-based assessment. The proposed procedure consists of decision matrix normalization, standard deviation-based criterion weighting, best and worst utility evaluation, final utility calculation, and ranking. The approach was applied to the selection of nine manager candidates using seven evaluation criteria. Its robustness was assessed through 28 sensitivity scenarios involving positive and negative changes in individual criterion weights, while its ranking consistency was further examined against SAW, MOORA, GRA, and SMART. The results show that Candidate M-02 achieves the highest final utility value of 0.3862 and obtains the first rank, whereas Candidate M-08 records the lowest utility value of 0.1179 and ranks ninth. The sensitivity analysis indicates that the principal ranking structure remains stable across all tested scenarios, with M-02 consistently occupying the first position and M-08 remaining in the last position. Comparative evaluation also demonstrates that Dynamic-ROV produces ranking patterns that are broadly consistent with the established MCDM methods. These findings indicate that Dynamic-ROV provides a reliable and stable framework for multi-criteria evaluation while offering a data-oriented mechanism for determining criterion importance and assessing alternative performance. The proposed approach can therefore serve as an alternative decision-support method for selection problems in organizational settings.","复杂环境中的决策需要一个能够整合多个具有不同重要性和表现水平的准则的评价框架。本研究提出Dynamic-ROV，一种改进的排序值（Rank Order Value）方法，旨在通过数据驱动的准则赋权和基于效用的评估来评价备选方案。所提出的流程包括决策矩阵归一化、基于标准差（standard deviation）的准则赋权、最优与最劣效用评估、最终效用计算以及排序。该方法被应用于使用七个评价准则对九名经理候选人进行选拔。其稳健性通过28个敏感性情景进行评估，这些情景涉及单个准则权重的正向和负向变化，同时其排序一致性进一步与SAW、MOORA、GRA和SMART进行了比较检验。结果表明，候选人M-02取得最高最终效用值0.3862并获得第一名，而候选人M-08录得最低效用值0.1179并排名第九。敏感性分析表明，主要排序结构在所有测试情景中保持稳定，M-02始终占据第一位，M-08始终位于最后一位。比较评估还表明，Dynamic-ROV产生的排序模式与已有MCDM方法大体一致。这些发现表明，Dynamic-ROV为多准则评价提供了一个可靠且稳定的框架，同时为确定准则重要性和评估备选方案表现提供了一种面向数据的机制。因此，所提出的方法可作为组织环境中选拔问题的替代性决策支持方法。",null,"Journal of Decision Support Systems and Multi-Criteria Decision Making","2026-09-25T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"多准则决策方法论文，与三农、农业信息化、智慧农业等主题无关，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":36},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":35},"I2",8,true,[33],"decision support",[],[],"农业相关性需要复核",[],"DevelopmentoftheRangeofValue(ROV)MethodB-3664",2,"10.67449\u002Fjodesma.v1i3.12",{"doi":40,"openalex_id":42,"authors":43,"venue":10,"cited_by_count":15,"oa_url":58,"card":59,"direction":65,"ingested_from":66},"W7214325612",[44,47,50,53,56],{"name":45,"orcid":46},"Erlin Windia Ambarsari","https:\u002F\u002Forcid.org\u002F0000-0002-3489-5821",{"name":48,"orcid":49},"Muhammad Waqas Arshad","https:\u002F\u002Forcid.org\u002F0000-0003-3262-5833",{"name":51,"orcid":52},"Gustian Rama Putra","https:\u002F\u002Forcid.org\u002F0000-0003-1051-4454",{"name":54,"orcid":55},"Yuwan Jumaryadi","https:\u002F\u002Forcid.org\u002F0000-0002-5757-6321",{"name":57,"orcid":9},"Febryo Ponco Sulistyo","https:\u002F\u002Fresearch.pilar.or.id\u002Findex.php\u002Fjodesma\u002Farticle\u002Fdownload\u002F12\u002F25",{"tldr":60,"method":61,"finding":62,"direction":63,"opportunity":64},"提出动态加权ROV多准则决策方法，用于候选人或方案排序评估。","基于标准差赋权与效用评估，用9名候选人7项指标及28种敏感性场景验证。","Dynamic-ROV排序稳定，与SAW、MOORA、GRA、SMART结果一致，M-02最优。","农业人工智能与决策模型","可将Dynamic-ROV迁移至农业技术采纳、农机选型或智慧农业项目优先序评估等场景。","智慧农业 \u002F 农业物联网","openalex","2026-09-28T23:30:11.646063Z",{"total":15,"page":69,"page_size":69,"items":70},1,[]]