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

Zhifeng Xiao, Jianing Zhi, Burcu B. Keskin

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number105897
JournalComputers and Operations Research
Volume146
DOIs
StatePublished - Oct 2022

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research

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