Towards a machine learning-aided metaheuristic framework for a production/distribution system design problem

Recent advances have seen vast success in the application of metaheuristics in NP-hard combinatorial problems. A generic metaheuristic design usually consists of three core elements that jointly determine the algorithm performance, including an initial candidate solution, a guided search procedure, and a fitness function that approximates the objective value. This paper proposes a data-driven metaheuristic (DDMH) framework that leverages the predictive power of machine learning models, which exploit location information and mine structural knowledge of a supply chain network for intelligent decision making. Specifically, the proposed framework offers three performance boosters, including an initial solution heuristic, a narrowed search space, and an efficient learning-based fitness function. The framework can be readily integrated into existing MHs. As a case study, we apply DDMH to a production/distribution network design problem. Experimental results show that the DDMH outperforms the traditional MHs with better solution quality and comparable running time, especially for hard problems.