Papers
21
Total Citations
246
H-Index
8
About
Xiwang Guo is a prominent researcher specializing in sustainable manufacturing, remanufacturing optimization, and intelligent human-robot collaboration systems. His work centers on one of modern industry's most pressing challenges: efficiently disassembling end-of-life products to minimize environmental pollution and resource waste. Guo has made substantial contributions to disassembly line balancing problems (DLBPs), developing sophisticated multi-objective optimization algorithms tailored to real-world manufacturing complexity. Among his most impactful contributions is his pioneering research into human-robot collaborative disassembly systems, recognizing that neither purely manual nor fully automated approaches are optimal. His 2023 paper on stochastic operation times using a Multi-Objective Shuffled Frog Leaping Algorithm has already garnered 69 citations, reflecting its significant influence on the field. Guo has consistently pushed boundaries by addressing increasingly complex scenarios, including multi-product lines, U-shaped configurations, task failure uncertainty, and resource sharing across parallel workstations. His methodological toolkit is impressively diverse, encompassing artificial bee colony algorithms, Q-learning, brainstorming optimizers, and multiverse optimization frameworks. With over 212 cumulative citations across his published works, Guo's research provides both theoretical foundations and practical solutions that meaningfully advance sustainable manufacturing, circular economy principles, and intelligent industrial automation.
Research Focus
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