Industrial information integration plays a crucial role in modern supply chains by ensuring the smooth flow of data across all stages, including recovery, recycling, and disposal, which is essential for the successful implementation of a closed-loop supply chain (CLSC) model. Building on this, our paper addresses a global CLSC problem by incorporating International Commercial Terms (Incoterms) and international transportation modes, bridging global supply chain operations with sustainability criteria. This innovative approach advances the development of a globally sustainable CLSC by focusing on the integration of economic, environmental, and social factors, i.e., the triple bottom line of sustainability. Specifically, we address environmental concerns through the introduction of carbon taxation and enhance social sustainability by exploring the impact of advertising on customer satisfaction. To further refine this model, we classify customers based on their sustainability engagement and apply a fuzzy programming approach to account for uncertainty in customer demand influenced by advertising. To solve this complex global CLSC model, we conduct a thorough analysis of constraints and develop a robust Lagrangian relaxation reformulation. While the initial solution may result in infeasibility, we propose a heuristic algorithm that ensures feasible solutions. Our efficient Lagrangian-based heuristic, incorporating an adaptive strategy, is capable of solving large-scale networks with an approximate 10 % optimality gap. Ultimately, this research provides both a comprehensive framework for practitioners to improve the environmental performance and global operations of their supply chains, as well as significant theoretical contributions to the field of industrial information systems.

中文翻译：

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考虑国际贸易术语解释通则规则和广告影响的全球可持续闭环供应链网络


工业信息集成在现代供应链中发挥着至关重要的作用，它确保数据在所有阶段（包括回收、循环和处置）的顺畅流动，这对于成功实施闭环供应链 （CLSC） 模型至关重要。在此基础上，我们的论文通过结合国际商业术语 （Incoterms） 和国际运输方式来解决全球 CLSC 问题，将全球供应链运营与可持续性标准联系起来。这种创新方法通过专注于经济、环境和社会因素的整合，即可持续性的三重底线，推动了全球可持续发展的 CLSC 的发展。具体来说，我们通过引入碳税来解决环境问题，并通过探索广告对客户满意度的影响来提高社会可持续性。为了进一步完善此模型，我们根据顾客的可持续性参与度对顾客进行分类，并应用模糊编程方法来解释受广告影响的顾客需求的不确定性。为了解决这个复杂的全局 CLSC 模型，我们对约束进行了彻底的分析，并开发了一个稳健的拉格朗日松弛重新公式。虽然初始解决方案可能会导致不可行性，但我们提出了一种启发式算法来确保可行的解决方案。我们高效的基于拉格朗日的启发式方法，结合自适应策略，能够解决具有大约 10% 最优性差距的大规模网络。 最终，这项研究为从业者提供了一个全面的框架，以改善其供应链的环境绩效和全球运营，并为工业信息系统领域做出了重大的理论贡献。