基于2-Opt的小型电商仓库拣选路径优化与物流工程应用

2-Opt-Based Order Picking Route Optimization and Logistics Engineering Applications in Small E-Commerce Warehouses

摘要: 针对小型电商仓库人工拣选中路径依赖经验、重复行走和局部折返的问题，从物流工程作业改善与工业工程方法研究的角度，提出基于最近邻与2-opt局部搜索的单订单路径优化方法。在可沿行列方向连续通行的理想网格假设下，将仓库抽象为带权图，以曼哈顿距离构建旅行商问题模型，并以固定行列顺序为基准、枚举最优解为质量参照开展仿真。以6 × 10储位布局、30种商品和100条加权随机订单为算例，固定行列顺序、最近邻及最近邻-2-opt的总距离分别为2132、2026和1920个网格距离单位，后两者相对基准分别降低4.97%和9.94%；最近邻-2-opt与枚举最优总距离1910相比相差0.52%。不同订单规模与10组随机种子分析支持其在所设情景下的稳定性。进一步给出算法伪代码、Excel VBA与Python原型实现方案，以及数据准备、人员分工、试运行和绩效验收步骤。该方法可为小型仓库的拣选路径标准化与物流系统工程改进提供参考，但实际通道适配、工时收益及部署成本仍需现场验证。

Abstract: To address experience-dependent routing, redundant walking, and local backtracking in small e-commerce warehouses, this study develops a single-order routing method combining the nearest-neighbor heuristic with 2-opt local search from the perspective of logistics engineering and industrial engineering method improvement. Under an ideal grid assumption allowing continuous row-wise and column-wise travel, the warehouse is represented as a weighted graph and formulated as a traveling salesman problem using Manhattan distances. A fixed row-column sequence serves as the baseline, and complete enumeration provides the optimal benchmark. For a 6 × 10 layout with 30 products and 100 weighted random orders, the total distances of the baseline, nearest-neighbor, and nearest-neighbor-2-opt methods are 2132, 2026, and 1920 grid-distance units, respectively. The latter two methods reduce distance by 4.97% and 9.94%, while the nearest-neighbor-2-opt result is 0.52% above the enumerated optimum of 1910. Analyses of different order scales and ten random seeds support stability within the simulated scenarios. Algorithm pseudocode, prototype implementation proposals using Excel VBA and Python, and practical steps for data preparation, staffing, pilot operation, and performance acceptance are also provided. The method offers a reference for standardized picking routes and logistics systems improvement, while adaptation to actual aisles, labor-time benefits, and deployment costs require field validation.