This　paper　investigates　a　real-time　dynamic　job-shop　scheduling　problem　in　a　robotic　cell,　in　which　multiple　jobs　enter　into　the　cell　with　unexpected　arriving　rates.　Different　from　classical　flow-shop　and　job-shop　scheduling　problems,　the　jobs’　transportation　handled　by　a　robot　must　be　considered.　Another　characteristic　is　that　the　jobs’　processing　times　are　not　constant　values　but　confined　in　time-window　constraints.　To　efficiently　solve　this　problem　in　real　time,　the　original　schedule　is　restricted　to　zero　changes.　The　problem　is　formulated　as　a　sophisticated　Mixed　Integer　Programming　(MIP)　model　in　which　the　new　jobs’　processing　and　transportation　operations　are　inserted　into　the　available　time　intervals　of　the　original　schedule.　To　strengthen　the　MIP　model,　speed-up　constraints　are　added　by　taking　advantage　of　specific　relationships　between　the　available　time　intervals　arranged　for　a　job＇s　processing　and　transportation　operations.　Furthermore,　an　exact　iterative　algorithm　is　proposed,　which　starts　with　a　relaxed　solution　of　the　MIP　model　and　iteratively　adds　essential　robot　handling　capacity　constraints　back　to　the　relaxed　MIP　model　until　an　optimal　solution　is　found.　Computational　results　validate　effectiveness　and　efficiency　of　the　strengthened　MIP　model　and　the　iterative　algorithm.　©　2018