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The tutorial explains the Analytic Hierarchy Process (AHP) within MCDA: building a hierarchy, performing pair-wise comparisons, creating comparison matrices, normalizing, deriving criterion weights, and checking consistency via CI and CR.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/template/","Template",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/template/presentations/","Presentations",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/template/mcda_ahp_tutorial-multi-criteria-decision-analysis/240715/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/mcda_ahp_tutorial-multi-criteria-decision-analysis/240715.png","ImageObject",442,249,{"name":42,"@type":43},"นรินทร์","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-11",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What are the core steps in Multi Criteria Analysis (MCDA)?","Question",{"text":63,"@type":64},"The decision-makers formulate the problem, identify feasible alternatives, select evaluation criteria, and assign weights to each criterion to reflect preferences.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does AHP support MCDA decision-making?",{"text":68,"@type":64},"AHP analyzes decisions using a hierarchy and pair-wise comparison matrices to estimate the priority weights of criteria based on expert judgments.",{"name":70,"@type":61,"acceptedAnswer":71},"How are AHP criterion weights and consistency assessed?",{"text":72,"@type":64},"After filling and normalizing the pair-wise matrix, the average of matrix rows provides criterion weights. Consistency is checked using a consistency vector to compute lambda, the consistency index (CI), and the consistency ratio (CR) via RI.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},240715,1791724897,{"code":4,"msg":82,"data":83},"success",[84,88,93,98,103,108,113,118,123],{"id":85,"doc_module":22,"doc_module_name":25,"category_name":29,"show_sort_weight":86,"slug":87},11,90,"presentations",{"id":89,"doc_module":22,"doc_module_name":25,"category_name":90,"show_sort_weight":91,"slug":92},12,"Resumes",80,"resumes",{"id":94,"doc_module":22,"doc_module_name":25,"category_name":95,"show_sort_weight":96,"slug":97},14,"Invoices",70,"invoices",{"id":99,"doc_module":22,"doc_module_name":25,"category_name":100,"show_sort_weight":101,"slug":102},15,"Posters",60,"posters",{"id":104,"doc_module":22,"doc_module_name":25,"category_name":105,"show_sort_weight":106,"slug":107},16,"Social Media",50,"social-media",{"id":109,"doc_module":22,"doc_module_name":25,"category_name":110,"show_sort_weight":111,"slug":112},17,"Forms",40,"forms",{"id":114,"doc_module":22,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},18,"Letters",30,"letters",{"id":119,"doc_module":22,"doc_module_name":25,"category_name":120,"show_sort_weight":121,"slug":122},21,"Paper Templates",5,"papers-templates",{"id":124,"doc_module":22,"doc_module_name":25,"category_name":125,"show_sort_weight":4,"slug":126},158,"General","general-158",{"code":4,"msg":82,"data":128},{"doc_id":79,"user_id":129,"nickname":42,"user_avatar":130,"doc_module":22,"category_id":85,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":131,"file_id":132,"file_url":133,"file_type":134,"file_size":135,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":55,"language":136,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":12,"update_tm":140,"read_time":33},2336475104957,"https://ap-avatar.wpscdn.com/avatar/22000c4c6bd8a5076e1?x-image-process=image/resize,m_fixed,w_180,h_180&k=1787554080175789136","Tutorial  \nMulti-Criteria Decision Analysis  \nMulti-Criteria Decision Analysis, or MCDA, is a decision making tool that helps in simplifying complex decision-making which may involve many stakeholders/decision-makers, and to assess the diversity of possible outcomes.  \nIn general, a Multi Criteria Analysis seeks to identify the alternatives or options that are to be investigated and decided upon, based on a finite set of evaluation criteria by which to rank a finite number of decision options and to aggregate preferences. The final outcome is a preferred option or set of options that is based upon a priorities and preferences decided by the stakeholders/decision-makers.  \nThe following steps are carried out by the decision-makers in a Multi Criteria Analysis:  \n• To formulate problem  \n• Identify the feasible alternatives or preferred outcomes  \n• Identify the criteria by which to judge these outcomes  \n• Apply appropriate weights on each of the criteria which represents the preferences.  \nThe Analytic Hierarchy Process, one of the more widely used multi criteria methods, is used in SEFEA.  \nThe Analytic Hierarchy Process  \nAHP is MCDA method of analyzing decisions based upon a hierarchy of components of the decision, known as the Analytic Hierarchy Process (AHP). AHP method which can be used by the decision maker to calculate the priority weights for criteria using simple decision matrix.  \nThe AHP is based upon the construction of a series of ‘pair-wise comparison’matrices which compare criteria to one another. The  \ncomparison of criteria is based upon decision that is defined based on expert’s judgments.  \nThis is done to estimate a ranking or weighting of each of the criteria that describes the importance of each of these criteria in contributing to the overall objective.  \nSteps in SEFEA  \nStep 1. Select class of analysis  \nClass of analysis is based upon following classes:  \n• Priority based  \n• Distance based  \n• Attribute based  \n• Cluster based  \nStep 2. Select variables  \nSee the details on data notes  \nStep 3. Matrix calculation  \nA pair wise comparison of n criteria (C1…Cn) to reflect the importance or weighting of each criterion in influencing the overall goal, involves building an n by n matrix (M) which shows the comparison of the criteria in the left-hand side column with respect to each criterion in the top row.  \nStep A. Developing a hierarchical structure with a goal at the top level, the attributes/criteria at the second level and the alternatives at the third level.  \nExample  \nCreate a comparison matrix using AHP tool  \nFor our suitability model, assume we use three criteria Distance to train stations  \nDistance to high density areas  \nDistance to roads  \nDistance-based selection to convert land  \n\n| Attribute or Criteria | Distance to train stations | Distance to high density areas | Distance to roads |\n| --- | --- | --- | --- |\n| Convert land (High) | 8 | 6 | 10 |\n| Convert land (Medium) | 6 | NA | 6 |\n| Convert land (Low) | 4 | 10 | 2 |\n\nIn the above example each criterion to convert land has their own value of distance to train stations, distance to high density areas, distance to roads.  \nStep B) Determine the relative importance of different attributes or criteria with respect to the goal.  \nHaving the above example, how important is the distance to train station if we highly prioritize land conversion. Or what is the importance of distance to high density areas if we highly prioritize land conversion. This pair-wise comparison matrix is created with the help of scale of relative importance. Here is the scale of relative importance.  \nComparisons using selected intensities  \n1 = equal preference  \n3 = prefer  \n6 = strongly prefer  \nFor each paired choice below, tick the box to indicate which criteria is more important (or equal) for ranking  \n\n| Criteria | Strongly prefer | prefer | Equal | Prefer | strongly prefer | Criteria |\n| --- | --- | --- | --- | --- | --- | --- |\n| Distance to train station","cbCainbiVv6Fap4i","https://ap.wps.com/l/cbCainbiVv6Fap4i","pdf",312799,"English","# Multi-Criteria Decision Analysis (MCDA)\n## Steps in Multi Criteria Analysis\n# Analytic Hierarchy Process (AHP)\n## Developing a hierarchical structure\n## Pair-wise comparison matrices\n## Scale of relative importance\n## Normalization and weights\n## Consistency check (CI and CR)","[{\"question\":\"What are the core steps in Multi Criteria Analysis (MCDA)?\",\"answer\":\"The decision-makers formulate the problem, identify feasible alternatives, select evaluation criteria, and assign weights to each criterion to reflect preferences.\"},{\"question\":\"How does AHP support MCDA decision-making?\",\"answer\":\"AHP analyzes decisions using a hierarchy and pair-wise comparison matrices to estimate the priority weights of criteria based on expert judgments.\"},{\"question\":\"How are AHP criterion weights and consistency assessed?\",\"answer\":\"After filling and normalizing the pair-wise matrix, the average of matrix rows provides criterion weights. Consistency is checked using a consistency vector to compute lambda, the consistency index (CI), and the consistency ratio (CR) via RI.\"}]","MCDA_AHP_tutorial - Multi-Criteria Decision Analysis | PDF",1789164632]