This　paper　proposes　a　linear　programming　method　for　generating　the　most　favorable　weights　(LP-GFW)　from　pairwise　comparison　matrices,　which　incorporates　the　variable　weight　concept　of　data　envelopment　analysis　(DEA)　into　the　priority　scheme　of　the　analytic　hierarchy　process　(AHP)　to　generate　the　most　favorable　weights　for　the　underlying　criteria　and　alternatives　on　the　basis　of　a　crisp　pairwise　comparison　matrix.　The　proposed　LP-GFW　method　can　generate　precise　weights　for　perfectly　consistent　pairwise　comparison　matrices　and　approximate　weights　for　inconsistent　pairwise　comparison　matrices,　which　are　not　too　far　from　Saaty＇s　principal　right　eigenvector　weights.　The　issue　of　aggregation　of　local　most　favorable　weights　and　rank　preservation　methods　is　also　discussed.　Four　numerical　examples　are　examined　using　the　LP-GFW　method　to　illustrate　its　potential　applications　and　significant　advantages　over　some　existing　priority　methods.　©　2007　Elsevier　Ltd.　All　rights　reserved.