[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2938":3,"related-2938":67},{"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":15,"doi":39,"paper":40,"created_at":66},2938,"TEKNO Method: Total Evaluation based on Knowledge-driven Normalized Optimization for Multi-Criteria Decision Making","https:\u002F\u002Fdoi.org\u002F10.67449\u002Fjodesma.v1i2.7","Multi-criteria decision making (MCDM) methods play an important role in supporting decisions involving multiple criteria with different characteristics and levels of importance. However, differences in normalization procedures, criterion treatment, and aggregation mechanisms can lead to variations in the resulting preference structures. This study proposes a new MCDM method, namely total evaluation based on knowledge-driven normalized optimization (TEKNO), which integrates normalization, relative evaluation, criterion weighting, optimization-based normalization, and total evaluation into a unified decision-making framework. The proposed method is designed to transform heterogeneous decision information into comparable evaluation values while preserving the relative contribution of each criterion. The applicability of TEKNO is evaluated through two decision-making case studies involving new store location selection and leasing customer selection. The evaluation framework includes ranking analysis, comparison with established MCDM methods, Spearman rank correlation analysis, and sensitivity analysis under variations in criterion weights. The results show that TEKNO achieves a Spearman rank correlation coefficient of 1.0000 for the new store location case and 0.9964 for the leasing customer selection case, indicating very strong agreement with the reference rankings. In addition, the ranking remains unchanged across the tested sensitivity scenarios, demonstrating the stability of TEKNO under variations in criterion weights. These findings indicate that TEKNO provides a transparent, systematic, and stable alternative for MCDM applications for practical decision support where reliable ranking, methodological transparency, and robustness across alternative evaluation conditions are required. Nevertheless, broader validation using diverse datasets, decision domains, weighting schemes, and statistical evaluation techniques is required to further establish its generalizability and comparative performance.","多准则决策（MCDM）方法在支持涉及多个具有不同特征和重要性水平的准则的决策中发挥着重要作用。然而，归一化程序、准则处理和聚合机制的差异可能导致所得偏好结构发生变化。本研究提出了一种新的MCDM方法，即基于知识驱动归一化优化的总体评价（TEKNO），该方法将归一化、相对评价、准则赋权、基于优化的归一化和总体评价整合到一个统一的决策框架中。所提出的方法旨在将异构决策信息转化为可比较的评价值，同时保留每个准则的相对贡献。TEKNO的适用性通过两个决策案例研究进行评估，分别涉及新店选址和租赁客户选择。评价框架包括排序分析、与已有MCDM方法的比较、Spearman秩相关分析以及准则权重变化下的敏感性分析。结果表明，TEKNO在新店选址案例中的Spearman秩相关系数为1.0000，在租赁客户选择案例中为0.9964，表明与参考排序具有极强的一致性。此外，在测试的敏感性情景下排序保持不变，表明TEKNO在准则权重变化下具有稳定性。这些发现表明，TEKNO为MCDM应用提供了一种透明、系统且稳定的替代方案，适用于需要可靠排序、方法透明性以及在不同评价条件下具有稳健性的实际决策支持。然而，仍需使用多样化的数据集、决策领域、赋权方案和统计评价技术进行更广泛的验证，以进一步确立其普适性和比较性能。",null,"OpenAlex","2026-09-17T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该论文为通用多准则决策方法研究，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":33,"tags":35},"I2",3,true,[],[34],"decision support",[],"农业相关性需要复核",[],"TEKNOMethod:TotalEvaluationbasedonKnowle-2938","10.67449\u002Fjodesma.v1i2.7",{"doi":39,"openalex_id":41,"authors":42,"venue":56,"cited_by_count":15,"oa_url":57,"card":58,"direction":64,"ingested_from":65},"W7213447260",[43,46,49,51,53],{"name":44,"orcid":45},"Yuri Rahmanto","https:\u002F\u002Forcid.org\u002F0000-0001-9673-4364",{"name":47,"orcid":48},"Ryan Randy Suryono","https:\u002F\u002Forcid.org\u002F0000-0001-9378-8148",{"name":50,"orcid":9},"Dedi Darwis",{"name":52,"orcid":9},"Abhishek R. Mehta",{"name":54,"orcid":55},"Auliya Rahman Isnain","https:\u002F\u002Forcid.org\u002F0000-0002-8468-6366","Journal of Decision Support Systems and Multi-Criteria Decision Making","https:\u002F\u002Fresearch.pilar.or.id\u002Findex.php\u002Fjodesma\u002Farticle\u002Fdownload\u002F7\u002F16",{"tldr":59,"method":60,"finding":61,"direction":62,"opportunity":63},"提出TEKNO多准则决策方法，统一归一化、加权与优化，实现稳定排序。","知识驱动归一化优化，结合Spearman相关与敏感性分析，用选址和客户选择案例验","TEKNO与参考排序高度一致（相关系数1.0000和0.9964），权重变化下排序稳定。","农业人工智能与决策模型","可将TEKNO用于农业多准则决策（如品种选择、灌溉方案），验证跨领域泛化性。","智慧农业 \u002F 农业物联网","openalex","2026-09-19T23:30:17.840172Z",{"total":15,"page":68,"page_size":68,"items":69},1,[]]