Supply chain network design (SCND) is a complicated constrained optimization problem that plays a significant role in the business management. This article extends the SCND model to a large-scale SCND with uncertainties (LUSCND), which is more practical but also more challenging. However, it is difficult for traditional approaches to obtain the feasible solutions in the large-scale search space within the limited time. This article proposes a cooperative coevolutionary bare-bones particle swarm optimization (CCBBPSO) with function independent decomposition (FID), called CCBBPSO-FID, for a multiperiod three-echelon LUSCND problem. For the large-scale issue, binary encoding of the original model is converted to integer encoding for dimensionality reduction, and a novel FID is designed to efficiently decompose the problem. For obtaining the feasible solutions, two repair methods are designed to repair the infeasible solutions that appear frequently in the LUSCND problem. A step translation method is proposed to deal with the variables out of bounds, and a labeled reposition operator with adaptive probabilities is designed to repair the infeasible solutions that violate the constraints. Experiments are conducted on 405 instances with three different scales. The results show that CCBBPSO-FID has an evident superiority over contestant algorithms.
Bibliographical noteThis work was supported in part by the Outstanding Youth Science Foundation under Grant 61822602, in part by the National Natural Science Foundation of China under Grant 61772207 and Grant 61873097, in part by the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003, in part by the Guangdong–Hong Kong Joint Innovation Platform under Grant 2018B050502006, and in part by the Hong Kong GRF-RGC General Research Fund 9042489 under Grant CityU 11206317.
- Bare-bones particle swarm optimization (BBPSO)
- cooperative coevolution (CC)
- large-scale supply chain network design under uncertainties (LUSCND)