Customer-oriented multi-objective optimization on a novel collaborative multi-heterogeneous-depot electric vehicle routing problem with mixed time windows

Through combination of four types of customer size, three types of depot quantity and three types of battery swapping station quantity, 36 benchmark instances were generated. Similar to the traditional benchmark instances, one depot was randomly generated near position (50, 50) in a 100 × 100 grid, and the other depots were randomly generated near positions (25, 50), (75, 50), (50, 75) or (50, 25). Five types of products were generated that can be stored in multiple heterogeneous depots. Three of the five types of products were selected randomly and stored in each depot. The sizes of customers were set as 40, 80, 120 or 160 in these instances and the locations of customers were randomly scattered. The numbers of depots were set as 3, 4, or 5. The demand type for each customer was randomly selected from one of the five product types, and the demand quantity was randomly selected from 5, 10 or 15. The time window types were randomly selected from the hard or soft time window. The numbers of battery swapping stations were set as 2, 4 or 6. The locations of the battery swapping stations were randomly generated near the positions (25, 25), (75, 75), (25, 75) or (75, 25). Each instance is named in the form of “number of customers_number of depots_number of battery swapping stations”. For example, the instance “40_4_6” implies that it involves 40 customers, 4 depots and 6 battery swapping stations.

通过四类客户规模、三类仓库数量与三类换电站数量的组合，共生成36个基准测试实例。与传统基准测试实例一致，1个仓库随机生成于100×100网格的(50, 50)位置附近，其余仓库则随机分布于(25, 50)、(75, 50)、(50, 75)或(50, 25)位置周边。本次数据集生成了可存储于多异构仓库的五类产品，每个仓库随机选取其中三类产品进行存储。该类实例中客户规模设定为40、80、120或160，客户位置随机散布于网格内。仓库数量设定为3、4或5三类。每个客户的需求类型从五类产品中随机选取，需求量则从5、10或15中随机抽取。时间窗口类型分为硬时间窗与软时间窗两类，随机选取其一。换电站数量设定为2、4或6三类，其位置随机生成于(25, 25)、(75, 75)、(25, 75)或(75, 25)位置附近。所有实例均采用"客户数量_仓库数量_换电站数量"的格式命名，例如实例"40_4_6"即代表该实例包含40个客户、4个仓库与6个换电站。