Nowadays, in the pharmaceutical industry, a growing concern with sustainability has become a strict consideration during the COVID-19 pandemic. There is a lack of good mathematical models in the field. In this research, a production–distribution–inventory–allocation–location problem in the sustainable medical supply chain network is designed to fill this gap. Also, the distribution of medicines related to COVID-19 patients and the periods of production and delivery of medicine according to the perishability of some medicines are considered. In the model, a multi-objective, multi-level, multi-product, and multi-period problem for a sustainable medical supply chain network is designed. Three hybrid meta-heuristic algorithms, namely, ant colony optimization, fish swarm algorithm, and firefly algorithm are suggested, hybridized with variable neighborhood search to solve the sustainable medical supply chain network model. Response surface method is used to tune the parameters since meta-heuristic algorithms are sensitive to input parameters. Six assessment metrics were used to assess the quality of the obtained Pareto frontier by the meta-heuristic algorithms on the considered problems. A real case study is used and empirical results indicate the superiority of the hybrid fish swarm algorithm with variable neighborhood search.

如今，在制药行业，对可持续性的日益关注已成为 COVID-19 大流行期间的严格考虑因素。该领域缺乏良好的数学模型。在这项研究中，可持续医疗供应链网络中的生产-分销-库存-分配-定位问题旨在填补这一空白。此外，还考虑了与 COVID-19 患者相关的药物分配，以及根据某些药物的易腐烂性考虑药物的生产和交付周期。在该模型中，为可持续医疗供应链网络设计了一个多目标、多层次、多产品和多时期的问题。提出了三种混合元启发式算法，即蚁群优化、鱼群算法和萤火虫算法，与可变邻域搜索混合解决可持续医疗供应链网络模型。由于元启发式算法对输入参数敏感，因此使用响应面法来调整参数。六个评估指标用于评估元启发式算法在所考虑问题上获得的帕累托边界的质量。使用了一个真实的案例研究，经验结果表明了具有可变邻域搜索的混合鱼群算法的优越性。