Task Reallocating for Responding to Design Change in Complex Product Design

Abstract

Abstract In the real-world complex product design (CPD) process, task allocating is an ongoing reactive process where the presence of unexpected design change is usually inevitable. Therefore, reallocating is necessary to respond to design change positively as a procedure to repair the affected task plan. General reallocating literature addressed the reallocating versions with fixed executing time. In this paper, a multi-objective reallocation model is developed with a feasible assumption that the task executing time is controllable. To illustrate this idea, a compressing executing time strategy (CETS) is proposed in CPD process, where the executing time can be controlled with a non-linear compression cost. When design change occurs during the executing, task-resource reallocating is required to absorb the interference effects. Reallocating implies an increase in design cost and system instability; the proposed method CETS can address this issue effectively. CETS considers three objectives: completing time, stability, and change-adaptation cost. An adaptive multi-objective hybrid genetic algorithm and tabu search (AMOGATS) is developed to solve this mathematical method. The computational results of specific simulation examples verify the superiority. It shows that CETS is sensitive to design change, and the proposed algorithm AMOGATS can be effective to achieve the allocating by coordinating the objective consistency.