In　multi-attribute　decision-making　problems,　vague　decision　information　is　well-represented　by　intuitionistic　fuzzy　sets.　However,　many　of　the　scoring　functions　of　existing　methods　cannot　always　obtain　a　ranking　for　the　alternatives.　In　this　paper,　a　TOPSIS　-based　decision-making　method　is　proposed　for　multi-attribute　decision-making　problems　in　which　the　attribute　weights　are　unknown　and　the　decision　information　is　in　the　form　of　intuitionistic　fuzzy　numbers.　First,　a　revised　definition　of　the　scoring　function　is　introduced　and　used　to　solve　the　intuitionistic　fuzzy　entropy,　which　is　then　used　to　objectively　determine　the　attribute　weights.　Second,　intuitionistic　fuzzy-weighted　geometric　operators　are　used　to　integrate　the　information.　The　positive　and　negative　ideal　solutions　of　the　comprehensive　attribute　values　are　determined,　and　the　similarities　between　each　alternative　and　the　positive　and　negative　ideal　solutions　are　calculated.　Finally,　the　alternatives　set　is　ranked　by　comparing　the　relative　closeness　of　the　alternatives.　This　proposed　method　increases　the　range　of　applications　of　the　traditional　entropy-weighted　method.　Moreover,　it　does　not　require　the　decision-maker　to　specify　the　attribute　weights　in　advance.　The　results　hence　tend　to　be　more　objective.　Examples　comparing　this　method　with　existing　TOPSIS-based　methods　illustrate　its　practicality.