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About

Yuanjun Laili is a prominent researcher whose work sits at the intersection of intelligent robotics, remanufacturing, and cyber-physical systems. His scholarship centers on three interconnected domains: robotic disassembly optimization, evolutionary computation, and industrial automation — areas where he has made substantial and lasting contributions to both theory and practice. Laili's most recognized work tackles the complex challenge of disassembly line balancing and sequence planning for end-of-life product remanufacturing. His 2018 paper on evolutionary many-objective optimization for mixed-model disassembly lines (99 citations) and his 2019 work on robotic disassembly re-planning using a super-fast bees algorithm (81 citations) demonstrate his ability to develop sophisticated computational solutions for real-world industrial problems. Recognizing that disassembly environments are inherently uncertain — plagued by rust, corrosion, and missing components — he pioneered self-evolving planning systems and backup action strategies that make robotic remanufacturing more resilient and adaptive. Beyond remanufacturing, Laili has expanded his research into industrial cyber-physical systems, contributing innovative approaches to robotic grasping detection and multi-granularity service composition. His 2023 work on big data in construction (88 citations) signals a broadening interdisciplinary reach. Across his portfolio, Laili's research addresses urgent sustainability challenges while advancing the frontiers of intelligent manufacturing automation.