Integrating　energy　savings　into　production　efficiency　is　considered　as　one　essential　factor　in　modern　industrial　practice.　A　lot　of　research　dealing　with　energy　efficiency　problems　in　the　manufacturing　process　focuses　solely　on　building　a　mathematical　model　within　a　static　scenario.　However,　in　the　physical　world　shop　scheduling　problems　are　dynamic　where　unexpected　events　may　lead　to　changes　in　the　original　schedule　after　the　start　time.　This　paper　makes　an　investigation　into　minimizing　the　total　tardiness,　the　total　energy　cost　and　the　disruption　to　the　original　schedule　in　the　job　shop　with　new　urgent　arrival　jobs.　Because　of　the　NP　hardness　of　this　problem,　a　dual　heterogeneous　island　parallel　genetic　algorithm　with　the　event　driven　strategy　is　developed.　To　reach　a　quick　response　in　the　dynamic　scenario,　the　method　we　propose　is　made　with　a　two-level　parallelization　where　the　lower　level　is　appropriate　for　concurrent　execution　within　GPUs　or　a　multi-core　CPU　while　codes　from　the　two　sides　can　be　executed　simultaneously　at　the　upper　level.　In　the　end,　numerical　tests　are　implemented　and　display　that　the　proposed　approach　can　solve　the　problem　efficiently.　Meanwhile,　the　average　results　have　been　improved　with　a　significant　execution　time　decrease.　(C)　2020　Elsevier　B.V.　All　rights　reserved.