Data for the multi-depot vehicle routing problem with profit fairness

This data is generated in order to investigate the multi-depot vehicle routing problem with profit fairness (MDVRP-PF), a bi-objective optimization
problem that adds a fairness objective function to the classical cost minimization function. By studying the MDVRP-PF , we explore the effects of integrating fairness in
the optimization process.In order to perform the desired experiments, artifcial MDVRP-PF instances are generated. These instances represent different configurations that could be suitable for carrier coalitions,
especially with respect to customer locations and stand-alone revenue share of each carrier. In this sense, we differentiate between two types of customer locations (clustered vs. uniform)
and two types of initial revenue share distribution (balanced vs. unbalanced). In clustered instances customers are placed closer to the depots of the carriers, while being randomly
located in the uniform type. Both types represent possible realistic situations, where partners are located in different distant industrial/commercial regions or within the same urban area.
Regarding revenue share, in balanced instances, all carriers contribute a similar amount of revenue. Contrarily, in unbalanced instances, notable differences exist in the initial revenues
contributed by each carrier. For each pair of location-revenue share configurations (from now on coded as C B, C U, U B and U U), we generate a set of instances. Each set contains three instances of different sizes: 2 depots and 100 customers (2D 100C), three depots and 150 customers (3D 150C), and four depots and 200 customers (4D 200C). To keep simplicity of the experiments, all customers have the same demand (10) and the same revenue (100).

本数据集旨在探究多仓库车辆路径问题中的收益公平性（MDVRP-PF），该问题为经典的成本最小化函数增添了公平性目标函数，构成一种双目标优化问题。通过对MDVRP-PF的研究，我们探讨了在优化过程中融合公平性的影响。为了执行预期的实验，人工生成的MDVRP-PF实例被创建。这些实例代表了不同配置，尤其适用于承运联盟，特别是在客户位置和各承运人独立收益分配方面。在此意义上，我们区分了两种客户位置类型（集群型与均匀型）以及两种初始收益分配类型（平衡型与不平衡型）。在集群型实例中，客户被放置在承运人仓库附近，而在均匀型实例中，客户则被随机分布。这两种类型均代表了可能的现实情况，合作伙伴可能位于不同的遥远工业/商业区域，或位于同一城市区域内。至于收益分配，在平衡型实例中，所有承运人的贡献收益相似；相反，在不平衡型实例中，各承运人初始贡献的收益存在显著差异。对于每一对位置-收益分配配置（以下简称C B、C U、U B和U U），我们生成一系列实例。每个集合包含三种不同规模的实例：2个仓库和100个客户（2D 100C）、3个仓库和150个客户（3D 150C）以及4个仓库和200个客户（4D 200C）。为了保持实验的简洁性，所有客户的需求量（10）和收益（100）均相同。