In the new “gig” economy, a user plays the role of a consumer as well as a service provider. As a service provider, drivers travelling from a source to a destination may opportunistically pickup and drop-off packages along the way if that does not add significantly to their trip distance or time. This gives rise to a new business offering called Package Delivery as a Service (PDaaS) that brokers package pickups and deliveries at one end and connects them to drivers on the other end, thus creating an ecosystem of supply and demand. The dramatic cost savings of such a service model come from the fact that the driver is already en-route to their destination and the package delivery adds a small overhead to an already pre-planned trip. From a technical perspective, this problem introduces new technical challenges that are uncommon in the literature. The driver may want to optimise for distance or time. Furthermore, new packages arrive for delivery all the time and are assigned to various drivers continuously. This means that the algorithm has to work in an environment that is dynamic, thereby precluding most standard road network precomputation efforts. Furthermore, the number of packages that are available for delivery could be in the hundreds of thousands, which has to be quickly pruned down for the algorithm to scale. The paper proposes a variation called dual Dijkstra’s that combines a forward and a backward scan in order to find delivery options that satisfy the constraints specified by the driver. The new dual heuristic improves the standard single Dijkstra’s approach by narrowing down the search space, thus resulting in significant speed-ups over the standard algorithms. Furthermore, a combination of dual Dijkstra’s with a heuristic landmark approach results in a dramatic speed-up compared to the baseline algorithms. Experimental results show that the proposed approach can offer drivers a choice of packages to deliver under specified constraints of time or distance, and providing sub-second response time despite the complexity of the problem involved. As the number of packages in the system increases, the matchmaking process becomes easier resulting in faster response times. The scalability of the PDaaS infrastructure is demonstrated using extensive experimental results.

在新的“零工”经济中，用户扮演着消费者和服务提供者的角色。作为服务提供商，从源头到目的地的司机可能会在沿途投递包裹，如果这不会显着增加他们的行程距离或时间的话。这产生了一种新的业务产品，称为“包裹递送即服务”(PDaaS)，在一端代理打包取件和送货，并将它们连接到另一端的司机，从而创建一个供需生态系统。这种服务模式可显着节省成本，因为司机已经在前往目的地的途中，而包裹递送会为已经预先计划好的行程增加少量开销。从技术角度来看，这个问题引入了文献中不常见的新的技术挑战。驾驶员可能想要优化距离或时间。此外，新包裹一直到达交付，并不断分配给不同的司机。这意味着该算法必须在动态环境中工作，从而排除了大多数标准道路网络预计算工作。此外，可交付的包裹数量可能有数十万个，必须迅速减少，以便算法进行扩展。该论文提出了一种变体，称为双重的Dijkstra 结合了前向和后向扫描，以找到满足驱动程序指定约束的交付选项。新的双重启发式算法通过缩小搜索空间改进了标准的单一 Dijkstra 方法，从而显着提高了标准算法的速度。此外，与基线算法相比，双重 Dijkstra 与启发式地标方法的结合导致了显着的加速。实验结果表明，所提出的方法可以为驾驶员提供在指定的时间或距离限制下交付的包裹选择，并提供亚秒级响应时间，尽管所涉及的问题很复杂。随着系统中包裹数量的增加，配对过程变得更加容易，从而加快了响应时间。