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A simulation optimization framework for shipment planning at RDC considering time and quantity consolidation with uncertain demands

机译：考虑需求的时间和数量整合，RDC发货规划的仿真优化框架

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Shipment planning (SP) at regional distribution center (RDC) involves order consolidation and vehicle routing decisions under uncertain demands, which is generally very hard to be solved by traditional analytical methods such as mathematical programs. To cope with the complexity of this important problem existing in logistics systems, a general-purpose simulation optimization framework is proposed. Discrete-event simulation (DES) is employed to model the complicated shipping processes and capture the system's dynamics and uncertainties. A new policy (ID-policy) considering time and quantity consolidation is developed to improve consolidation effectiveness. The consolidated orders and system's performance obtained by simulation are then transformed as input into a genetic algorithm designed to optimize the vehicle routes via evolutionary computation. Experiment results show that the ID-policy outperforms traditional consolidation policies such as T-policy, Q-policy and D-policy under different conditions. The proposed simulation optimization framework is also validated by the exemplary case.
机译：区域配送中心（RDC）的装运规划（SP）涉及不确定的需求下的订单整合和车辆路径决策，这通常是通过数学计划等传统分析方法的难以解决。为了应对物流系统中存在这个重要问题的复杂性，提出了一种通用的模拟优化框架。采用离散事件仿真（DES）来模拟复杂的运输过程并捕获系统的动态和不确定性。考虑时间和数量整合的新政策（ID-Policy）是制定的，以提高整合效率。然后将通过模拟获得的综合订单和系统的性能转换为输入遗传算法，该遗传算法通过进化计算优化车辆路线。实验结果表明，ID-Policy在不同条件下优于T-Policy，Q-Policy和D-Policy等传统的整合策略。所提出的仿真优化框架也由示例性情况验证。

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