The　flexible　job-shop　scheduling　problem　surpasses　the　limitations　of　conventional　workshop　scheduling　problems　that　reduce　machine　constraints,　increase　uncertainty,　and　belong　to　the　NP-hard　problem.　An　adaptive　simulated　annealing　genetic　algorithm　based　on　reinforcement　learning　is　put　forward　to　overcome　the　constraints　of　complex　parameter　determination　and　poor　local　search　capabilities　that　standard　genetic　algorithms　face　when　dealing　with　flexible　job　shop　scheduling.　The　approach　uses　a　multi-parent　POX　crossover　operation　and　introduces　a　simulated　annealing　algorithm　into　the　mutation　operation　to　enhance　the　method＇s　capability　for　both　global　and　local　optimization　in　the　evolution　process.　The　tournament　method　is　combined　with　the　optimal　strategy　to　ensure　the　algorithm＇s　convergence.　The　crossover　and　mutation　parameters　are　dynamically　adjusted　and　optimized　using　the　reinforcement　learning　algorithm　in　conjunction　with　the　improved　simulated　annealing　genetic　algorithm　so　that　the　parameters　of　the　algorithm　can　adapt　to　the　evolution　process　according　to　experience,　and　the　searchability　and　computational　efficiency　of　the　algorithm　are　improved.　By　testing　and　examining　the　common　examples,　the　proposed　algorithm＇s　rationality　and　superiority　are　ultimately　demonstrated.　©　2023　SPIE.　All　rights　reserved.