Due to the increasing price pressure in the less-than-truckload (LTL) market, horizontal cooperation is an effective and efficient way for small- and medium-sized LTL carriers to enhance their profits by exchanging requests. For this purpose, a decentralized auction-based collaboration framework has proven to provide a good approach. In this paper, such a collaboration framework is adopted and extended by applying it to a practical-oriented routing problem, the Pickup and Delivery Problem with Time Windows and Heterogeneous Vehicle Fleets. Particularly, we analyse the bundle selection process made by the auctioneers, which is a stochastic problem and specify which requests are supposed to be offered together in a bundle to the cooperating carriers. For the purpose of solving the selection problem properly, we implement a new procedure required due to the characteristics of the considered routing problem: a scenario-based bundle selection approach. In order to make this approach applicable, two pre-selection techniques (cluster- and neural network-based) are developed. Our approach is evaluated on 240 collaboration network instances created from well-known pickup and delivery research data sets generated by Li and Lim (2001). The collaboration framework results are compared with respect to the profit to individual transportation planning of each carrier (lower threshold) as well as centralized transportation planning of all carriers (upper threshold). It can be shown that the auction-based collaboration approach is up to 43.49% better than the individual planning as well as exhausts at least 53.5% of the centralized transportation planning potential on average.

由于零担市场的价格压力越来越大，横向合作是中小型零担运输公司通过交换需求提高利润的有效途径。为此，基于拍卖的分散式协作框架已被证明是一种很好的方法。在本文中，通过将这种协作框架应用于面向实际的路由问题，即具有时间窗和异构车队的取货和交付问题，采用并扩展了这种协作框架。特别是，我们分析了拍卖商进行的捆绑选择过程，这是一个随机问题，并指定哪些请求应该以捆绑的形式一起提供给合作运营商。为了正确解决选择问题，由于所考虑的路由问题的特点，我们实施了一个新的程序：基于场景的捆绑选择方法。为了使这种方法适用，开发了两种预选技术（基于集群和基于神经网络）。我们的方法在 240 个协作网络实例上进行了评估，这些实例由 Li 和 Lim (2001) 生成的知名取货和交付研究数据集创建。将协作框架结果与每个承运人的单独运输计划（下限）以及所有承运人的集中运输计划（上限）进行比较。可以看出，基于拍卖的协作方法比个人计划好高达 43.49%，并且消耗至少 53。