Supply chains (SCs), complex networks spanning from raw material acquisition to product delivery, with enterprises as interconnected nodes, play a pivotal role in organizational success. However, optimizing SCs remains challenging, particularly in partner selection, a key bottleneck shaped by competitive and cooperative dynamics. This challenge constitutes a multi-objective dynamic game requiring a synergistic integration of Multi-Criteria Decision-Making and Game Theory. Traditional approaches, grounded in mathematical simplifications and managerial heuristics, fail to capture real-world intricacies and risk introducing subjective biases. Multi-agent simulation offers promise, but prior research has largely relied on fixed, uniform agent logic, limiting practical applicability. Recent advances in LLMs create opportunities to represent complex SC requirements and hybrid game logic. However, challenges persist in modeling dynamic SC relationships, ensuring interpretability, and balancing agent autonomy with expert control. We present SCSimulator, a visual analytics framework that integrates LLM-driven MAS with human-in-the-loop collaboration for SC partner selection. It simulates SC evolution via adaptive network structures and enterprise behaviors, which are visualized via interpretable interfaces. By combining CoT reasoning with XAI techniques, it generates multi-faceted, transparent explanations of decision trade-offs. Users can iteratively adjust simulation settings to explore outcomes aligned with their expectations and strategic priorities. Developed through iterative co-design with SC experts and industry managers, SCSimulator serves as a proof-of-concept, offering methodological contributions and practical insights for future research on SC decision-making and interactive AI-driven analytics. Usage scenarios and a user study demonstrate the system's effectiveness and usability.

翻译：供应链作为从原材料获取到产品交付的复杂网络，以企业为相互连接的节点，在组织成功中发挥着关键作用。然而，供应链优化仍面临挑战，尤其在伙伴选择这一受竞争与合作动态影响的关键瓶颈环节。该挑战构成了一个需要多准则决策与博弈论协同整合的多目标动态博弈问题。传统方法基于数学简化与管理启发式规则，难以捕捉现实世界的复杂性，且易引入主观偏差。多智能体仿真展现出潜力，但现有研究多依赖固定、统一的智能体逻辑，限制了实际应用性。大语言模型的最新进展为表征复杂供应链需求与混合博弈逻辑创造了机遇。然而，在动态供应链关系建模、确保可解释性以及平衡智能体自主性与专家控制方面仍存在挑战。本文提出SCSimulator——一个集成大语言模型驱动多智能体仿真与人机协同的供应链伙伴选择可视分析框架。该框架通过自适应网络结构与企业行为模拟供应链演化过程，并借助可解释界面进行可视化呈现。通过将思维链推理与可解释人工智能技术相结合，系统生成多维度、透明的决策权衡解释。用户可迭代调整仿真设置，以探索符合其预期与战略优先级的结果。通过与供应链专家及行业管理者进行迭代式协同设计开发，SCSimulator作为概念验证系统，为未来供应链决策与交互式人工智能驱动分析研究提供了方法论贡献与实践启示。应用场景与用户研究验证了系统的有效性与可用性。