Balancing optimality and efficiency in solving flexible process planning: A parameter-free two-stage algorithm

Flexible process planning (FPP) involves developing production plans that translate design specifications into manufacturable steps while satisfying technical constraints. Existing FPP methods struggle to provide effective solutions due to the complexities arising from processing, sequencing, and operation flexibility. This paper addresses these challenges by decomposing the FPP problem into three subproblems based on the types of flexibility and proposing a parameter-free two-stage algorithm. In the first stage, a metaheuristic–variable neighbourhood search–is improved to tackle the NP-hard operation sequencing problem. In the second stage, the alternative operation selection and manufacturing resource allocation problems are transformed into a shortest path problem, which can be optimally solved in polynomial time. This two-stage algorithm effectively balances optimality and efficiency. Comparative experiments with six state-of-the-art methods on real-world and large-scale cases demonstrate that the proposed algorithm ranks first in 84.7% of overall performance metrics. Additionally, integrating the second-stage algorithm into existing metaheuristics results in an average performance improvement of 80.4%. These findings highlight the robustness, scalability, and effectiveness of the proposed algorithm, making it highly practical for real-world process planning.