Permutation　flow-shop　scheduling　problems　with　many　objectives　have　wide　applications　in　the　modern　manufacturing　domain　such　as　the　printed　circuit　board　(PCB)　industry.　In　this　paper,　an　upgraded　method　called　reinforcement　cumulative　prospect　theory　is　proposed　for　solving　many-objective　permutation　flow-shop　scheduling　problems.　Reinforcement　cumulative　prospect　theory　is　determined　by　two　reference　points,　the　improved　prospect　value　function,　and　the　entropy-based　decision　weight　function.　A　novel　many-objective　optimization　algorithm,　namely,　an　optimal　foraging　algorithm　based　on　the　reinforcement　cumulative　prospect　theory　(OFA/RCPT),　is　presented.　The　comprehensive　prospect　value　is　used　as　the　fitness　strategy　of　the　OFA/RCPT　algorithm　to　guide　the　optimization　process.　The　performance　of　the　proposed　algorithm　is　assessed　by　a　comparison　with　nine　state-of-the-art　algorithms.　Three　classification　experiments　are　carried　out　on　six　Walking-Fish-Group　test　cases,　seven　permutation　flow-shop　scheduling　benchmark　instances,　and　a　practical　permutation　flow-shop　scheduling　problem　in　low-volume　PCB　manufacturing.　For　the　experiment,　four　performance　metrics　are　adopted,　and　the　commercial　software　Quest　is　used　to　simulate　a　PCB　production　line.　The　simulation　results　show　that　the　proposed　algorithm　has　better　performance　than　the　other　algorithms.