Multi-objective teaching–learning-based optimization algorithm for carbon-efficient integrated scheduling of distributed production and distribution considering shared transportation resource

Being “carbon efficient” is always one of the missions for suppliers to stay competitive. Production and distribution are the core sections of the supply chain, and integrated scheduling of production and distribution has received increasing research interest because of its great potential to enhance operational performance. Distributed production has gained popularity in recent years. However, distribution strategies compatible with distributed production have not been considered. In this paper, we investigated carbon-efficient integrated scheduling of distributed production and distribution considering shared transportation resource. Particularly, the shared transportation resource strategy, which allows vehicles to serve customers from various depots, enables a more flexible distribution than the traditional method. A bi-objective model is constructed to minimize total carbon emission and completion time simultaneously. To address the computational challenge, an enhanced multi-objective teaching–learning-based optimization (EMTLBO) algorithm is proposed. In EMTLBO, several heuristic rules are introduced to obtain high-quality initial solutions and neighborhood structures are designed for efficient neighborhood search. The comprehensive experiments have demonstrated that (1) the proposed enhancement strategies are effective, (2) the overall performance of EMTLBO is superior to seven well-known algorithms in solving this problem, and (3) the shared transportation resource strategy considerably reduces carbon emission during distribution stage, leading to average decreasing of 41.0 %, 70.6 %, and 41.5% for the instance sets. This work presents significance in promoting a clean and efficient modern supply chain.