Machine Learning-Based Vehicle Assignment Model for Dispatch Operations

Code 1 develops and validates the Random Forest model for vehicle assignment, including data preparation, model training, performance metrics, and professional visualizations comparing the AS IS and TO BE dispatch times. Code 2 builds the supervisor interface, integrating ERP/WMS inputs, RFID/IoT traceability, vehicle availability, priority queue management, feasible transport alternatives, cost validation, and decision history to support faster, traceable, and data-driven dispatch decisions.代码1开发并验证了面向车辆分配任务的随机森林(Random Forest)模型，涵盖数据预处理、模型训练、性能指标计算，以及用于对比现状(AS IS)与未来状态(TO BE)调度时长的专业可视化分析。代码2搭建了调度主管端界面，集成了企业资源计划(ERP)/仓库管理系统(WMS)输入数据、射频识别(RFID)/物联网(IoT)溯源功能、车辆可用状态、优先级队列管理模块、可行运输备选方案、成本校验机制与决策历史记录，以支撑更高效、可溯源且数据驱动的调度决策。