This　paper　studies　a　real-time　parking-sharing　program　with　which　owners　of　private　parking　spaces　can　lend　out　their　parking　spaces　to　other　drivers　to　park　when　these　are　not　in　use.　Compared　with　curbside　and　garage　parking　problems,　the　information　of　supplies　and　demands　is　randomly　announced　by　drivers　and　owners　respectively　via　a　parking-sharing　APP　installed　on　their　smartphones.　Besides,　the　parking　spaces　made　available　by　independent　owners　are　usually　heterogeneous　in　terms　of　their　locations　and　available　time　intervals.　Thus,　two　critical　issues　need　to　be　resolved:　(a)　appropriately　matching　demands　and　supplies　under　an　uncertain　setting;　and　(b)　efficiently　scheduling　the　demands　matched　to　avoid　potential　parking　conflicts.　We　propose　a　novel　real-time　reservation　approach　based　on　a　rolling-horizon　framework,　which　can　assign　multiple　drivers　to　a　single　parking　space　in　order　to　better　utilize　scarce　parking　resources.　For　each　period,　an　integrated　optimal　matching-and-scheduling　problem　is　formulated　as　a　mixed　integer　programming　model　and　proved　to　be　strongly　NP-hard.　To　fast　generate　a　near-optimal　solution　to　the　problem,　a　two-stage　heuristics　derived　from　the　minimum-cost　flow　problem　is　developed.　The　computational　results　validate　the　efficiency　and　effectiveness　of　the　proposed　approach.　Some　operational　insights　are　also　presented　and　discussed.　©　2020　Elsevier　Ltd