The rise of e-commerce has increased the demands placed on pickup and delivery operations, as well as customer expectations regarding the quality of services provided by those operations. One strategy a logistics provider can employ for meeting these increases in demands and expectations is to complement and coordinate its fleet operations with those of for-hire, third-party logistics providers. Herein, we study an optimization problem for coordinating these operations: the time-dependent profitable pickup and delivery problem with time windows. In this problem, the logistics provider has the opportunity to use its fleet of capacitated vehicles to transport shipment requests, for a profit, from pickup to delivery locations. Owing to demographic and market trends, we focus on an urban setting, wherein road congestion is a factor. As a result, the problem explicitly recognizes that travel times may be time-dependent. The logistics provider seeks to maximize its profits from serving transportation requests, which we compute as the difference between the profits associated with transported requests and transportation costs. To solve this problem, we propose an adaptive large neighborhood search algorithm. The results of our extensive computational study show that the proposed algorithm can find high-quality solutions quickly on instances with up to 75 transportation requests. Furthermore, we study its impact on profits when explicitly recognizing traffic congestion during planning operations.

电子商务的兴起增加了对取货和送货业务的需求，以及客户对这些业务所提供服务质量的期望。物流供应商可以用来满足这些需求和期望增加的一种策略是，与租用的第三方物流供应商进行补充和协调。在本文中，我们研究了用于协调这些操作的优化问题：带有时间窗口的与时间有关的获利取货和交付问题。在这个问题中，物流供应商有机会利用其配备能力强大的车辆来运输装运请求，从获利地点到交货地点都可以获利。由于人口和市场趋势，我们将重点放在城市环境中，其中道路拥堵是一个因素。结果是，该问题明确认识到旅行时间可能与时间有关。物流提供商试图通过服务运输请求来最大化其利润，我们将其计算为与运输请求相关的利润与运输成本之间的差额。为了解决这个问题，我们提出了一种自适应的大邻域搜索算法。我们广泛的计算研究结果表明，该算法可以在多达75个运输请求的实例上快速找到高质量的解决方案。此外，当明确识别计划运营期间的交通拥堵时，我们将研究其对利润的影响。我们将其计算为与运输请求相关的利润与运输成本之间的差额。为了解决这个问题，我们提出了一种自适应的大邻域搜索算法。我们广泛的计算研究结果表明，该算法可以在多达75个运输请求的实例上快速找到高质量的解决方案。此外，我们在明确识别计划运营期间的交通拥堵时研究了其对利润的影响。我们将其计算为与运输请求相关的利润与运输成本之间的差额。为了解决这个问题，我们提出了一种自适应的大邻域搜索算法。我们广泛的计算研究结果表明，该算法可以在多达75个运输请求的实例上快速找到高质量的解决方案。此外，我们在明确识别计划运营期间的交通拥堵时研究了其对利润的影响。