具有乘客服务质量偏好的第一英里共乘问题的数学启发式和精确算法

Ping He , Jian Gang Jin , Martin Trépanier , Frederik Schulte
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引用次数: 0

摘要

随着人们对高质量交通服务的需求不断增长,交通服务提供商需要提供不仅能满足乘客基本出行需求,还能确保服务质量具有吸引力的交通服务。在上下班高峰期,车队规模往往不足以满足所有乘客对服务质量的偏好,如乘车时间和共乘人数,从而导致部分乘客牺牲服务质量。受这些做法的启发,我们研究了一个包含乘客服务质量偏好的第一英里共乘问题。该问题涉及请求与车辆之间的匹配、车辆路由和路线计划等复杂决策。为了解决这个问题,我们首先为这个问题开发了一个基于弧的混合整数线性规划(MILP)模型。为了在实际计算时间要求内获得接近最优的解决方案,我们将 MILP 模型重新表述为基于行程的集合划分模型,并提出了一种数学启发式算法。该算法建立在列生成算法的基础上,并采用新颖的优势规则定制了双向标注算法。此外,我们还引入了一个命题来确定每条共享乘车路线的最佳时间表。为了获得大规模实例的最优解,我们引入了分支加价格精确算法。基于真实世界道路网络和随机生成实例的计算实验证实了所提方法的有效性和效率,证明所提的数学启发式能在 40 秒内为所有实例找到接近最优的解决方案。实验结果还表明,所提出的方法显著提高了优先乘客的第一英里服务质量,即使在车队普遍不足的情况下,满足请求的比率也提高了 23%。
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A math-heuristic and exact algorithm for first-mile ridesharing problem with passenger service quality preferences

With the growing demand for high-quality mobility services, transportation service providers need to offer transit services that not only fulfill passengers’ basic travel needs but also ensure an appealing quality of service. During rush hours, fleet sizes are often insufficient to cater to all passenger preferences on service quality, such as ride time and number of co-riders, leading to the sacrifice of service quality for some passengers. Motivated by these practices, we investigate a first-mile ridesharing problem incorporating passenger service quality preferences. This problem involves intricate decisions about the match between requests and vehicles, vehicle routing, and route schedules. To solve this problem, we first develop an arc-based mixed-integer linear programming (MILP) model for this problem. For obtaining near-optimal solutions within practical computation time requirements, we reformulate the MILP model as a trip-based set-partitioning model and propose a math-heuristic algorithm. This algorithm builds upon the column-generation algorithm and tailored bidirectional labeling algorithms with novel dominance rules. Additionally, we introduce a proposition to determine the best schedule for each ridesharing route. To obtain the optimal solution for large-scale instances, we introduce a branch-and-price exact algorithm. Computational experiments based on real-world road networks and randomly generated instances confirm the effectiveness and efficiency of the proposed approaches, demonstrating that the proposed matheuristic finds near-optimal solutions within 40 s for all instances. The results also show that the presented approach significantly improves the quality of first-mile services for prioritized riders, with the ratio of satisfied requests increasing by 23% even when the fleet is generally insufficient.

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来源期刊
CiteScore
16.20
自引率
16.00%
发文量
285
审稿时长
62 days
期刊介绍: Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management. Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.
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