Selection　of　reverse　logistics　service　providers　is　a　typical　multiple　attributes　decision-making　problem.　We　establish　an　evaluation　index　system　for　the　reverse　logistics　service　provider　selection　and　propose　a　new　multiple　attributes　decision-making　method,　GI-TOPSIS,　based　on　combinational　weight　determination.　In　terms　of　weight　determination,　we　combine　ANP　with　entropy　method　to　consider　the　dependency　of　evaluation　indices　and　the　information　of　evaluation　data.　Furthermore,　we　introduce　the　degree　of　grey　incidence　into　TOPSIS　and　use　relative　similarity　degree　to　reflect　the　distance　differences　and　shape　changes,　which　is　more　complete　and　clear.　Finally,　an　empirical　study　on　the　reverse　logistics　service　provider　selection　in　an　electronic　manufacturing　enterprise　is　conducted,　and　the　results　demonstrate　the　feasibility　and　validity　of　the　evaluation　index　system　and　the　decision-making　method.