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A Multi-Task Evolutionary Algorithm for Series Product Design Change Plans [Research Frontier]
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DOI：10.1109/mci.2026.3680075.png)
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Abstract
En 中文
Due to factors such as changes in customer demand and supply disruption, design changes during product production are often inevitable. In many cases, a change to a shared key part (also known as component) has an impact on multiple related products owned by a company, requiring simultaneous design changes for these products. To simultaneously generate the optimal change plans for these affected products (i.e., series products), this paper proposes a multi-task multi-objective evolutionary generation algorithm based on part similarity. Firstly, a multi-task multi-objective optimization model is constructed on the basis of the multi-layer complex network model designed for series products. In this model, multiple indicators including the product service performance impact, carbon emissions, change costs and duration are considered. Subsequently, a multi-task multi-objective evolutionary algorithm based on part similarity is developed to solve the above model. Several new operators/strategies including the part similarity evaluation measure, the multi-task knowledge transfer strategy, and the multi-task constraint handling mechanisms are designed to enhance the algorithm’s performance. Finally, the effectiveness of both the proposed model and the proposed algorithm is validated on the design change problem of multiple TVs in Skyworth RGB Co., Ltd. Experimental results show that the proposed algorithm can generate highly competitive optimal change plans for multiple TVs simultaneously.
Keywords:
Multitasking
Supply and demand
Evolutionary computation
Product design
Pareto optimization
Product design
Product development
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11.2
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