In　open-pit　mining,　a　trade-off　between　determination　of　appropriate　sizes　of　mining　jobs　and　optimisation　of　allocating　and　sequencing　mining　equipment　units　at　each　operational　stage　is　one　of　critical　decisions　for　mining　practitioners.　To　simultaneously　optimise　the　above　data-driven　interplay　between　planning　and　scheduling　decisions　in　multi-stage　mine　production　timetabling,　we　introduce　a　novel　integrated-planning-scheduling　problem　for　considering　the　disturbances　and　variability　of　jobs＇　sizes　based　on　the　theory　of　parallel-machine　flow　shop　scheduling　with　lot　streaming.　This　new　problem　is　called　the　＂Multi-stage　Mine　Production　Timetabling　with　Optimising　the　Sizes　of　Mining　Operations　＂　and　abbreviated　as　the　MMPT-OSMO,　in　which　the　sizes　of　mining　jobs　(i.e.,　the　number　of　block　units　to　be　aggregated　on　different　working　benches)　are　considered　as　planning-type　variables　and　integrated　with　scheduling-type　variables　in　a　parallel-machine　flow　shop　scheduling　system.　Due　to　considerable　complexity,　an　innovative　math-heuristic　approach　embodied　as　a　hybridisation　of　decomposed　mixed　integer　programming　models　and　heuristic　algorithms　under　a　three-level　divide-&-conquer　scheme　is　devised　to　efficiently　solve　the　MMPT-OSMO.　By　integrating　both　planning　and　scheduling　decision　variables　in　such　a　solitary　problem,　the　MMPT-OSMO　intrinsically　characterises　the　potential　to　significantly　improve　mining　productivity,　which　is　validated　by　theoretical　analysis　and　extensive　computational　experiments.　In　real-world　implementation,　replacing　the　current　labour-intensive　manual　way,　the　proposed　MMPT-OSMO　methodology　provides　an　intelligent　decision-making　tool　to　mathematically　optimise　the　interactive　decisions　between　mine　planning　and　scheduling　engineers.　The　proposed　MMPT-OSMO　methodology　would　make　a　breakthrough　in　the　field　of　mining　optimisation,　as　it　contributes　to　extend　mathematical　modelling　boundary　by　applying　continuous-time　machine　scheduling　theory　to　operational-level　mining　optimisation　in　theory　and　to　help　mining　practitioners　improve　the　production　throughput　using　lot-streaming　techniques　in　practice.