In　the　literature　of　mining　optimisation,　most　classical　mine　block　sequencing　or　open-pit　mine　production　scheduling　problems　are　applied　at　strategic　and　tactical　levels,　because　their　objectives　are　to　determine　the　optimal　sequence　of　mineral　blocks　across　a　certain　number　of　fixed　time-length　periods　(measured　in　weeks,　months　or　years)　such　that　the　total　revenue　is　maximised.　The　application　of　continuous-time　machine　scheduling　theory　to　mine　equipment　timetabling　is　challenging　and　rare　in　the　current　mining　literature.　In　this　study,　inspired　by　microscopic　scheduling　requirements　(measured　in　hours,　minutes　and　seconds)　in　the　iron　ore　open-pit　mining　industry,　a　new　short-term　Mine　Excavators　Timetabling　(MET)　problem　is　introduced　and　defined.　The　MET　simultaneously　assigns　mine　blocks　to　excavators　and　decides　the　sequencing　of　assigned　blocks　for　each　excavator　so　that　the　detailed　scheduling　of　starting　and　completion　time　for　excavation　of　each　block　is　determined.　Different　from　discrete　periods　in　the　classical　mining　optimisation　problems,　timing　factors　in　the　proposed　MET　are　continuous　and　precise.　The　proposed　MET　aims　at　minimising　the　total　weighted　tardiness　(delay　cost)　and　the　total　weighted　movement　time　(relocation　cost)　at　the　operational　level.　By　analysing　the　key　characteristics　of　excavating　operations,　the　MET　problem　is　transformed　into　a　specific　type　of　continuous-time　machine　scheduling　problem,　which　is　an　innovative　application　to　operational-level　mining　equipment　management.　Based　on　extensive　computational　experiments　by　mixed　integer　programming,　a　dominance-based　constructive　algorithm　and　a　hybrid　Tabu-Search　Threshold-Accepting　metaheuristic　algorithm,　theoretical　insights　and　practical　benefits　are　discussed　in　depth　for　real-world　implementations.　In　comparison　to　the　exact　MIP　solver　(ILOG-CPLEX),　computational　experiments　indicate　that　the　proposed　hybrid　metaheuristic　can　obtain　the　same　optimal　solutions　of　small-size　instances　but　with　over　240　times　less　computational　time.　For　solving　medium-size　instances,　the　proposed　hybrid　metaheuristic　also　outperforms　ILOG-CPLEX　in　both　solution　quality　and　computational　times　(10.94　times　better　solution　with　38.36　times　less　computational　time　on　average).　A　sensitivity　analysis　under　different　scenarios　is　conducted　to　determine　the　saturation　number　of　excavators　with　the　best　cost-effectiveness　ratio　in　a　demand-responsive　scheduling　horizon.　The　result　analyses　validate　that　the　implementation　of　the　optimised　extraction　timetable　can　significantly　reduce　the　total　relocation　cost　of　excavators　for　large-size　instances.