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-Personal rapid transit (PRT) is getting attention as a new form of transportation. It is energy efficient and provides the high level of passenger service. In this study, the dynamic PRT dispatching and routing problem is dealt with. Passengers request transportation service on a complex network, and an operating system monitors passenger arrivals and coordinates vehicles in real time. A new online dispatching and routing algorithm is proposed, which minimizes the total travel distance of vehicles and the waiting time of passengers. The algorithm dispatches vehicles by considering multiple vehicles' state and multiple passengers at the same time. In particular, finding the shortest-time path is attempted by taking into account the future congestion on lanes. Discrete-event simulation is employed to validate the performance of the proposed algorithm. The results show the algorithm in this study outperforms others.

The trend of moving online in the retail industry has created great pressure for the logistics industry to catch up both in terms of volume and response time. On one hand, volume is fluctuating at greater magnitude, making peaks higher; on the other hand, customers are also expecting shorter response time. As a result, logistics service providers are pressured to expand and keep up with the demands. Expanding fleet capacity, however, is not sustainable as capacity built for the peak seasons would be mostly vacant during ordinary days. One promising solution is to engage crowdsourced workers, who are not employed full-time but would be willing to help with the deliveries if their schedules permit. The challenge, however, is to choose appropriate sets of tasks that would not cause too much disruption from their intended routes, while satisfying each delivery task's delivery time window requirement. In this paper, we propose a decision-support algorithm to select delivery tasks for a single crowdsourced worker that best fit his/her upcoming route both in terms of additional travel time and the time window requirements at all stops along his/her route, while at the same time satisfies tasks' delivery time windows. Our major contributions are in the formulation of the problem and the design of an efficient exact algorithm based on the branch-and-cut approach. The major innovation we introduce is the efficient generation of promising valid inequalities via our separation heuristics. In all numerical instances we study, our approach manages to reach optimality yet with much fewer computational resource requirement than the plain integer linear programming formulation. The greedy heuristic, while efficient in time, only achieves around 40-60% of the optimum in all cases. To illustrate how our solver could help in advancing the sustainability objective, we also quantify the reduction in the carbon footprint.

Personal rapid transit (PRT) is a new transportation system, which is energy efficient and brings high quality of customer service. Customers arrive dynamically at stations and request transportation service. In this paper, we propose a new online PRT dispatching algorithm for pickup and delivery of customers. We adopt the nearest neighbor dispatching rule, which is known as performing well in general. We extend the rule with bipartite matching in order to deal with multiple vehicles and customers at the same time. We suggest a systematic way for selecting vehicles that will be considered to be dispatched, since the scope with which vehicles are selected may affect the system performance. We regard the empty travel distance of vehicles and the customer waiting time as the performance measures. By using simulation experiments, it has been examined that the scope of dispatching affects the system performance. The proposed algorithm has been validated by comparing with other dispatching rules for transportation services. We have shown that our algorithm is more suitable for PRT operating environment than other dispatching rules.

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