Simulation Data

This dataset contains the complete simulation results from a study evaluating adaptive heuristics for batch initiation timing in same-day delivery (SDD) warehouse operations. The data was generated using a discrete-event simulation model built in Python (SimPy), modeling an 8-hour operational day in a manual picker-to-part warehouse with a rectangular layout and single front I/O point.

A full factorial experimental design was employed, combining 8 trigger methods × 6 incoming order rates × 12 picker configurations × 2 batching methods × 3 cart capacities × 2 routing policies = 6,912 unique scenarios, each replicated 30 times, yielding 207,360 simulation runs in total.

The dataset supports the analysis of how adaptive, state-dependent batch initiation timing strategies compare against traditional static methods (Fixed Time Window Batching and Variable Time Window Batching) across diverse operational conditions. Key findings include a 27.04% average tardiness reduction achieved by the Modified Priority Based Heuristic (MPBH) over static benchmarks.

本数据集收录了一项针对当日送达（Same-Day Delivery, SDD）仓库运营中批次启动时机自适应启发式算法的评估研究的完整仿真结果。该数据基于Python构建的离散事件仿真模型（SimPy）生成，模拟了一个采用矩形布局、仅设单个前端输入输出点、采用人工拣选员到货品作业模式的仓库的8小时运营时长。
本研究采用全因子实验设计，组合了8种触发方式×6种入站订单速率×12种拣选员配置×2种批次处理方式×3种手推车载重能力×2种路径规划策略，共计6912种独特场景，每个场景重复仿真30次，总仿真运行次数达207360次。
本数据集可用于分析在多样化运营场景下，自适应状态依赖型批次启动时机策略相较于传统静态方法（固定时间窗口批次处理与可变时间窗口批次处理）的性能差异。核心研究结果显示，改进型基于优先级启发式算法（MPBH）相较于静态基准方法，平均拖期率降低了27.04%。