Given the input/output constraints and cross couplings of supply chain (SC) nodes, model predictive control (MPC) is efficient to seek the optimal solutions to the problems posed by interacting nodes to satisfy customer demands. In supply chain applications, due to the growing spatial distribution and interactions between the supply network elements, the information flow management becomes a challenging yet significant task. To reduce numerical complexity while maintaining implementability, a distributed MPC strategy is proposed. The scheme aims at finding the Nash equilibrium where the controller of each subsystem communicates with other ones in the presence of noncooperative interaction and strong coupled inputs due to the ordering decisions. Extensive numerical simulations verify that the strategy outperforms conventional policies in terms of substantially reduced SC operating cost.

中文翻译：

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牛鞭式库存管理策略的分布式模型预测控制策略


考虑到供应链（SC）节点的输入/输出约束和交叉耦合，模型预测控制（MPC）可以有效地寻求交互节点所带来的问题的最佳解决方案，以满足客户需求。在供应链应用中，由于供应网络元素之间的空间分布和交互不断增长，信息流管理成为一项具有挑战性但又重要的任务。为了在保持可实现性的同时降低数值复杂度，提出了分布式 MPC 策略。该方案旨在找到纳什均衡，其中每个子系统的控制器在存在非合作交互和由于排序决策而产生的强耦合输入的情况下与其他子系统进行通信。大量的数值模拟验证了该策略在大幅降低 SC 运营成本方面优于传统策略。