The　concept　of　green　manufacturing　emphasizes　the　importance　of　product　disassembly　in　achieving　energyefficient　recycling　and　remanufacturing　operations.　Disassembly　line　balancing　(DLB)　is　a　critical　component　of　the　product　disassembly　process,　wherein　a　set　of　tasks　must　be　allocated　to　workstations　for　disassembly.　This　study　proposes　a　multi-objective　DLB　model　that　aims　to　minimize　multiple　conflicting　objectives　simultaneously　including　idle　rate,　smoothness,　labor　cost,　and　energy　consumption.　A　key　innovation　of　this　study　involves　the　creation　of　a　tailored　adaptive　large　neighborhood　search　(ALNS)　algorithm　which　is　one　of　the　first　studies　in　the　literature　on　product　disassembly　algorithms.　The　developed　ALNS　employs　efficient　construction　and　destruction　heuristics　to　solve　the　proposed　multi-objective　DLB　problem.　The　ALNS　algorithm　aims　to　destroy　and　repair　solutions　effectively,　while　a　local　search　procedure　helps　it　escape　from　local　optimum　solutions.　The　provided　DLB　model　is　effectively　solved　by　applying　the　proposed　ALNS　algorithm　to　the　disassembly　process　of　a　turbine　reducer.　The　obtained　results　from　this　application　serve　as　a　compelling　demonstration　of　the　efficiency　and　effectiveness　of　the　proposed　approach.　Furthermore,　comparisons　conducted　with　various　state-ofthe-art　algorithms　using　small　and　large　instance　sets　consistently　highlight　the　superiority　of　the　proposed　ALNS　approach.