Preference　voting　and　aggregation　require　the　determination　of　the　weights　associated　with　different　ranking　places.　This　paper　proposes　three　new　models　to　assess　the　weights.　Two　of　them　are　linear　programming　(　LP)　models　which　determine　a　common　set　of　weights　for　all　the　candidates　considered　and　the　other　is　a　nonlinear　programming　(NLP)　model　that　determines　the　most　favourable　weights　for　each　candidate.　The　proposed　models　are　examined　with　two　numerical　examples　and　it　is　shown　that　the　proposed　models　cannot　only　choose　a　winner,　but　also　give　a　full　ranking　of　all　the　candidates.