在线多容量拼车的竞争比率
Competitive Ratios for Online Multi-capacity Ridesharing
摘要：
在多容量共乘中，多个请求（例如，客户、食品、包裹）在一个资源中旅行。近年来，在线多容量拼车服务（即，在那里的任务是在网上进行的），如Uber-pool，foodpanda和按需穿梭车已经在运输，食品配送，物流和其他领域非常受欢迎。这是因为多容量共乘服务使所有参与方（客户（由于较低的成本）、司机（由于较高的收入）和匹配平台（由于每辆车/资源的较高收入））受益。最重要的是，这些服务还有助于减少碳排放（由于道路上的车辆减少）。在线多容量拼车极具挑战性，因为底层匹配图不再是二分图（如单位容量情况下），而是具有资源的三分图（例如，出租车、汽车）、请求和请求组（可以一起旅行的请求的组合）。资源和请求组之间的期望匹配受到该三方图中请求和请求组之间的边的约束（即，请求可以是最终分配中至多一个请求组的一部分）。虽然已经有近视启发式方法用于解决在线多容量拼车问题，但它们不提供任何解决方案质量的保证。为此，本文提出了第一种方法，该方法限制了在线多容量乘车的竞争比（当资源在服务一组请求后重新加入系统时）。
英文摘要：
In multi-capacity ridesharing, multiple requests (e.g., customers, food items, parcels) with different origin and destination pairs travel in one resource. In recent years, online multi-capacity ridesharing services (i.e., where assignments are made online) like Uber-pool, foodpanda, and on-demand shuttles have become hugely popular in transportation, food delivery, logistics and other domains. This is because multi-capacity ridesharing services benefit all parties involved { the customers (due to lower costs), the drivers (due to higher revenues) and the matching platforms (due to higher revenues per vehicle/resource). Most importantly these services can also help reduce carbon emissions (due to fewer vehicles on roads). Online multi-capacity ridesharing is extremely challenging as the underlying matching graph is no longer bipartite (as in the unit-capacity case) but a tripartite graph with resources (e.g., taxis, cars), requests and request groups (combinations of requests that can travel together). The desired matching between resources and request groups is constrained by the edges between requests and request groups in this tripartite graph (i.e., a request can be part of at most one request group in the final assignment). While there have been myopic heuristic approaches employed for solving the online multi-capacity ridesharing problem, they do not provide any guarantees on the solution quality. To that end, this paper presents the first approach with bounds on the competitive ratio for online multi-capacity ridesharing (when resources rejoin the system at their initial location/depot after serving a group of requests).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
