Online car-sharing problem with variable booking times

IF 0.9 4区 数学 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Haodong Liu, Kelin Luo, Yinfeng Xu, Huili Zhang
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Abstract

In this paper, we address the problem of online car-sharing with variable booking times (CSV for short). In this scenario, customers submit ride requests, each specifying two important time parameters: the booking time and the pick-up time (start time), as well as two location parameters—the pick-up location and the drop-off location within a graph. For each request, it’s important to note that it must be booked before its scheduled start time. The booking time can fall within a specific interval prior to the request’s starting time. Additionally, each car is capable of serving only one request at any given time. The primary objective of the scheduler is to optimize the utilization of k cars to serve as many requests as possible. As requests arrive at their booking times, the scheduler faces an immediate decision: whether to accept or decline the request. This decision must be made promptly upon request submission, precisely at the booking time. We prove that no deterministic online algorithm can achieve a competitive ratio smaller than \(L+1\) even on a special case of a path (where L denotes the ratio between the largest and the smallest request travel time). For general graphs, we give a Greedy Algorithm that achieves \((3L+1)\)-competitive ratio for CSV. We also give a Parted Greedy Algorithm with competitive ratio \((\frac{5}{2}L+10)\) when the number of cars k is no less than \(\frac{5}{4}L+20\); for CSV on a special case of a path, the competitive ratio of Parted Greedy Algorithm is \((2L+10)\) when \(k\ge L+20\).

预订时间不固定的在线汽车共享问题
在本文中,我们探讨了预订时间可变的在线汽车共享(简称 CSV)问题。在这种情况下,用户提交乘车请求,每个请求都指定了两个重要的时间参数:预订时间和取车时间(开始时间),以及两个位置参数--图中的取车位置和下车位置。对于每个请求,必须注意的是必须在预定的开始时间之前预订。预订时间可以是在请求开始时间之前的一个特定时间间隔内。此外,每辆车在任何时候都只能为一个请求提供服务。调度员的主要目标是优化 k 辆车的利用率,为尽可能多的请求提供服务。当请求在预订时间到达时,调度员需要立即做出决定:是接受请求还是拒绝请求。这个决定必须在请求提交后立即做出,准确地说是在预订时间做出。我们证明,即使是在路径的特殊情况下(其中 L 表示最大和最小请求旅行时间之比),也没有一种确定性在线算法能达到小于 \(L+1\)的竞争比。对于一般图,我们给出了一种贪婪算法,它可以实现 CSV 的 \((3L+1)\) 竞争比。我们还给出了一种分部贪婪算法,当汽车数量k不小于\(\frac{5}{4}L+20\)时,其竞争比为\((\frac{5}{2}L+10)\);对于路径特例上的CSV,当\(k\ge L+20\)时,分部贪婪算法的竞争比为\((2L+10)\)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Combinatorial Optimization
Journal of Combinatorial Optimization 数学-计算机:跨学科应用
CiteScore
2.00
自引率
10.00%
发文量
83
审稿时长
6 months
期刊介绍: The objective of Journal of Combinatorial Optimization is to advance and promote the theory and applications of combinatorial optimization, which is an area of research at the intersection of applied mathematics, computer science, and operations research and which overlaps with many other areas such as computation complexity, computational biology, VLSI design, communication networks, and management science. It includes complexity analysis and algorithm design for combinatorial optimization problems, numerical experiments and problem discovery with applications in science and engineering. The Journal of Combinatorial Optimization publishes refereed papers dealing with all theoretical, computational and applied aspects of combinatorial optimization. It also publishes reviews of appropriate books and special issues of journals.
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