扰动下通用拓扑供应链与制造网络的最优反应式运行
Optimal Reactive Operation of General Topology Supply Chain and Manufacturing Networks under Disruptions
AI总结：
针对化工供应链制造网络易受扰动的问题，提出整合多环节的多周期混合整数线性规划模型，适配任意拓扑与多扰动场景，案例验证其可扩展性并探究相关因素影响，提供易适配的响应式运营规划框架。
摘要：
化工行业的供应与制造网络涉及不同地点的多样加工步骤，使其运行易受非计划事件带来的扰动影响。最优响应需考虑产品分配、延迟发货、价格重新谈判等诸多因素。在此背景下，我们提出一种多周期混合整数线性规划模型，该模型整合生产、调度、运输与订单管理，以最小化此类扰动造成的财务影响。该模型适配任意供应链拓扑，并纳入多种扰动场景，可适应现实世界的复杂性。一项来自化工行业的案例研究验证了该模型在更精细时间离散化下的可扩展性，并探究了扰动类型与订单管理成本对最优调度的影响。该方法为不确定性下的供应链与制造网络制定响应式运营计划提供了一个易处理、适应性强的框架。
英文摘要：
Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, and price renegotiation , among other factors. In such context, we propose a multiperiod mixed-integer linear programming model that integrates production, scheduling, shipping, and order management to minimize the financial impact of such disruptions. The model accommodates arbitrary supply chain topologies and incorporates various disruption scenarios, offering adaptability to real-world complexities. A case study from the chemical industry demonstrates the scalability of the model under finer time discretization and explores the influence of disruption types and order management costs on optimal schedules. This approach provides a tractable, adaptable framework for developing responsive operational plans in supply chain and manufacturing networks under uncertainty.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
