园区货位与运输车辆协同分配数据集

以“货位-车辆”协同为核心目标，构建“因素-算法-协同”三级框架：第一步，梳理关键因素（货物类型/重量、货位容量/位置、车辆到港时间/装卸需求）；第二步，用贪心算法以“搬运距离最短+车辆等待时间最少”为目标，计算最优货位分配方案；第三步，建立货位与车辆调度协同规则，将货位分配结果同步至车辆调度系统，明确车辆到港后的装卸泊位与货位位置，形成“货位-车辆”协同闭环。

Taking "slot-vehicle" collaboration as the core objective, a three-level "Factor-Algorithm-Collaboration" framework is constructed. The implementation includes three sequential steps: First, sort out the key influencing factors, including cargo type/weight, slot capacity/location, vehicle arrival time/loading and unloading requirements; Second, adopt the greedy algorithm with the dual goals of minimizing handling distance and minimizing vehicle waiting time to calculate the optimal slot allocation scheme; Third, establish collaborative rules for slot and vehicle scheduling, synchronize the slot allocation results to the vehicle scheduling system, clarify the loading/unloading berth and slot position after the vehicle arrives at the port, and finally form a closed-loop of "slot-vehicle" collaboration.

搜集汇总

背景与挑战

背景概述

该数据集专注于园区仓储环境中货位与运输车辆的协同分配问题，数据规模为1.58GB，每日更新。它通过分析货物特征、货位信息和车辆计划，采用贪心算法优化货位分配与车辆调度，旨在减少等待时间、提升周转效率，实现仓储装卸环节的降本增效。