Edge　nodes　(ENs)　in　mobile　edge　computing　can　support　current　delay-sensitive　applications　of　the　Industrial　Internet　of　Things.　ENs　are　deployed　in　the　network　edge　and　can　execute　computational　tasks　offloaded　from　users＇　mobile　devices　(MDs)　in　a　timely　way.　However,　their　computing　and　communication　resources　are　limited　and　cannot　execute　all　offloaded　tasks.　Thus,　a　cloud　data　center　(CDC)　is　highly　needed　and　hybrid　cloud-edge　systems　emerge　to　provide　low-delay　services.　This　work　investigates　a　joint　optimization　problem　of　task　offloading,　task　partitioning,　and　user　association　to　minimize　the　total　cost　of　the　system.　This　work　focuses　on　applications　that　can　be　split　into　multiple　dependent　subtasks,　each　of　which　can　be　completed　in　MDs,　ENs,　and　CDC.　Specifically,　a　mixed　integer　nonlinear　program　is　formulated　to　minimize　the　total　cost.　Then,　a　hybrid　algorithm　named　Genetic　Simulated-annealing-based　Particle　Swarm　Optimizer　(GSPSO)　is　designed　to　solve　it.　GSPSO　yields　a　close-to-optimal　strategy　to　jointly　optimize　connections　among　MDs　and　ENs,　and　allocation　ratios　among　MDs,　ENs,　and　CDC.　Experimental　results　demonstrate　that　compared　with　benchmark　methods,　GSPSO　decreases　the　total　cost　while　fully　meeting　the　completion　time　requirements　of　user　tasks.