Interval-parameter　nonlinear　programming　(INP)　is　an　extension　of　conventional　nonlinear　optimization　methods　for　handling　both　nonlinearities　and　uncertainties.　However,　challenges　exist　in　its　solution　method,　leading　to　difficulties　in　obtaining　a　global　optimum.　In　this　study,　a　0-1　piecewise　approximation　approach　is　provided　for　solving　the　INP,　through　integration　with　an　interactive　algorithm　for　interval-parameter　optimization　problems.　Thus,　the　INP　model　can　be　transformed　into　two　deterministic　submodels　that　correspond　to　the　lower　and　upper　bounds　of　the　objective-function　value.　By　solving　the　two　submodels,　interval　solutions　can　be　obtained,　which　are　used　for　generating　a　range　of　decision　options.　The　developed　method　is　applied　to　a　case　of　long-term　municipal　solid　waste　(MSW)　management　planning.　Not　only　uncertainties　expressed　as　interval　values　but　also　nonlinearities　in　the　objective　function　can　be　tackled.　Moreover,　economies　of　scale　(EOS)　effects　on　waste-management　cost　can　also　be　reflected.　The　results　obtained　can　be　used　for　generating　decision　alternatives　and　thus　help　waste　managers　to　identify　desired　policies　for　MSW　management　and　planning.　Compared　with　the　conventional　interval-parameter　linear　and　quadratic　programs,　the　developed　INP　can　better　reflect　system-cost　variations　and　generate　more　robust　solutions.