TY - JOUR
T1 - Towards a machine learning-aided metaheuristic framework for a production/distribution system design problem
AU - Xiao, Zhifeng
AU - Zhi, Jianing
AU - Keskin, Burcu B.
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/10
Y1 - 2022/10
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85132231104&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85132231104&partnerID=8YFLogxK
U2 - 10.1016/j.cor.2022.105897
DO - 10.1016/j.cor.2022.105897
M3 - Article
AN - SCOPUS:85132231104
SN - 0305-0548
VL - 146
JO - Computers and Operations Research
JF - Computers and Operations Research
M1 - 105897
ER -