面向客户协同的产品创新任务分组模型及方法

Task Grouping Model and Approach for Customer Collaboration in Product Innovation

客户协同产品创新中,针对产品创新任务之间相互关联,导致创新主体交互、协同频率与复杂度增加,降低协同创新效率的问题,论文分析了由客户与企业专业人员构成的创新团队结构及其特点,提出了模糊数字化设计结构矩阵,从敏感度、可变度以及任务要求相似度三个方面度量任务间的关联程度。在此基础上,以任务组内聚度最大化,任务组间耦合度最小化为目标,任务组可执行性等为约束,建立任务分组模型,采用双种群自适应遗传算法求解。最后通过实例分析说明模型和方法的可行性和有效性。

Large interdependent product innovation tasks often lead to increase the interaction frequency and complexity of customers and professionals,which may decrease product innovation efficiency.For addressing this problem,this study analyzes the structure of innovative teams composed of customers and professionals.To measure the correlation between tasks,fuzzy numerical design structure matrix is applied and three indexes of sensibility,changeability and similarity of task requirements are proposed.Moreover,we take the maximum of the cohesion degree in a task group and minimum of the coupling degree between task groups as targets,the executability of task groups as constraint to construct task grouping model,and double-population adaptive genetic algorithm is used to solve this model.The effectiveness and feasibility of the model and approaches are then demonstrated by a case study.