论文标题
偶然受限的拨号问题以及公用事业最大化需求和多个定价结构
A chance-constrained dial-a-ride problem with utility-maximizing demand and multiple pricing structures
论文作者
论文摘要
经典的拨号问题（DARP）旨在设计最低成本的路由，该路由在运营计划级别上适应一组用户请求，在此级别上，用户的偏好和收入管理经常被忽略。在本文中，我们提出了一种基于替代运输模式的代表性实用程序，在需求响应式运输（DRT）上下文中接受/拒绝用户请求的机制。我们考虑实用最大化用户，并为机会限制的DARP（CC-DARP）提出一种混合成员编程公式，该公式从长远来看通过Logit模型捕获了用户的偏好。我们进一步介绍了基于班级的用户组，并考虑用于DRT服务的各种定价结构。开发了一种定制的基于本地搜索的启发式方法来解决拟议的CC-DARP。我们报告了DARP基准测试实例和基于纽约市黄色出租车数据的现实案例研究的数值结果。使用拟议的局部搜索启发式方法，对105个基准测试实例进行的计算实验的平均最佳差距为2.69％。在现实的案例研究中获得的结果表明，在优化收入和乘客方面，区域票价结构是最佳策略。拟议的CC-DARP配方提供了一种新的决策支持工具，可在战略计划级别为DRT系统提供收入和车队管理。
The classic Dial-A-Ride Problem (DARP) aims at designing the minimum-cost routing that accommodates a set of user requests under constraints at an operations planning level, where users' preferences and revenue management are often overlooked. In this paper, we present a mechanism for accepting/rejecting user requests in a Demand Responsive Transportation (DRT) context based on the representative utilities of alternative transportation modes. We consider utility-maximizing users and propose a mixed-integer programming formulation for a Chance Constrained DARP (CC-DARP), that captures users' preferences in the long run via a Logit model. We further introduce class-based user groups and consider various pricing structures for DRT services. A customised local search based heuristic is developed to solve the proposed CC-DARP. We report numerical results for both DARP benchmarking instances and a realistic case study based on New York City yellow taxi trip data. Computational experiments performed on 105 benchmarking instances with up to 96 nodes yield an average optimality gap of 2.69% using the proposed local search heuristic. The results obtained on the realistic case study reveal that a zonal fare structure is the best strategy in terms of optimising revenue and ridership. The proposed CC-DARP formulation provides a new decision-support tool to inform on revenue and fleet management for DRT systems at a strategic planning level.
