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.