The　unprecedented　prosperity　of　the　industrial　Internet　of　Things　has　thoroughly　facilitated　the　transition　from　traditional　manufacturing　towards　intelligent　manufacturing.　In　industrial　environments,　resource-constrained　industrial　equipments　(IEs)　often　fail　to　meet　the　diverse　demands　of　numerous　compute-intensive　and　latency-sensitive　tasks.　Mobile　edge　computing　has　emerged　as　an　innovative　paradigm　for　lower　latency　and　energy　consumption　for　IEs.　However,　computational　offloading　and　coordinating　of　multiple　IEs　with　diverse　task　types　and　multiple　edge　nodes　in　industrial　environments　poses　challenges.　To　address　this　challenge,　we　propose　a　multi-task　approach　encompassing　scientific　and　concurrent　workflow　tasks　to　achieve　energy-efficient　and　latency-optimized　computation　offloading.　Furthermore,　this　work　designs　an　improved　Quantum　Multi-objective　Grey　wolf　optimizer　with　Manta　ray　foraging　and　Associative　learning　(QMGMA)　to　optimize　multi-task　computation　offloading.　Comprehensive　experiments　demonstrate　the　superior　efficiency　and　stability　of　QMAGA　compared　to　state-of-the-art　algorithms　in　balancing　latency　and　energy　consumption.　QMAGA　improves　average　inverse　generation　distance　and　average　spacing　by　37%　and　31%　on　average　than　multi-objective　grey　wolf　optimizer,　non-dominated　sorting　genetic　algorithm　II,　and　multi-objective　multi-verse　optimization,　proving　the　convergence　and　diversity　of　its　non-dominated　solutions.　©　2024　IEEE.