End-of-life product recovery operations require performance improvement to be viable in an industrial environment. A genetic algorithm (GA) is proposed to optimize end-of-life partial disassembly decisions based on disassembly costs, revenues, and environmental impacts. Facilitating disassembly optimization with costs, revenues, and environmental impacts is necessary to enhance sustainable manufacturing through value recovery. End-of-life products may not warrant disassembly past a unique disassembly stage due to limited recovered component market demand and minimal material recovery value. Remanufacturing is introduced into disassembly sequence optimization in the proposed GA as an alternative to recycling, reuse, and disposal. The proposed GA’s performance is first verified through optimizing partial disassembly sequences considering costs and environmental impacts independently. Extension to a multi-objective case concerning costs, revenues, and impacts is achieved by specifying a new set of multi-objective crossover probabilities from independent crossover probabilities.

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