Sustainable supply chains require close coordination between production and distribution operations, as these operations are interdependent; delays and thereby carbon footprints can be reduced if they are planned simultaneously. This study addresses the integrated batch scheduling of multi-factory production and distribution operations, accounting for product shelf life and vehicle capacity constraints. The objective is to minimize the maximum delivery time while ensuring that products are delivered to customers sufficiently early before the end of their shelf lives. Short-term production and distribution plans should be reviewed and updated regularly. In this context, the efficiency and effectiveness of the available solution algorithms determine their widespread adoption for solving the optimization problem. A novel integrated optimization framework that jointly considers batch scheduling, product shelf life constraints, and multi-factory routing decisions is developed, and it is supported by a customized Nearest Neighbor Search (NNS)-based heuristic specifically designed for this problem structure. K -means has been adapted as a highly efficient baseline. Extensive experiments on standard datasets were conducted to evaluate the quality of the developed algorithm, comparing it with the baseline. Results indicate that the NNS-based algorithm outperforms the K -means-based algorithm in the vast majority of cases. The findings provide a basis for further research into the use of clustering-based solution methods to tackle industry-scale scheduling problems.
Ying et al. (Thu,) studied this question.