A hybrid algorithm for task sequencing problems with iteration in product development
针对产品开发中因迭代导致项目延期的问题，提出一种结合层次聚类和遗传算法的混合算法，能在200个任务规模下以更短时间获得同等质量的排序方案。
Iteration is a major cause contributing to an increase in project duration and cost. By adopting the traditional models of design structure matrix and reward Markov chain, this paper proposes a hybrid algorithm for solving a task sequencing problem that aims for minimising the project duration. The proposed algorithm combines hierarchical clustering and genetic algorithms. This algorithmic strategy is intended to utilise the circuit concept to reduce the solution search space for GA. Our algorithm was compared to five other algorithms. Through numerical experiments, the proposed algorithm can solve large problems (number of tasks = 200), yield the same quality of solution results with shorter computational time, and deliver stable algorithmic performance.