The Experts below are selected from a list of 126 Experts worldwide ranked by ideXlab platform
Yali Wang - One of the best experts on this subject based on the ideXlab platform.
-
Analysis of Equilibrium Strategies in Markovian Queues With Negative Customers and Working Breakdowns
IEEE Access, 2019Co-Authors: Ruiling Tian, Yali WangAbstract:We consider the Customers' strategic behavior in Markovian queues with negative Customers and working breakdowns. when a negative Customer Arrives, the Customer being served is forced to leave the system. At the same time, the server breaks down and the service rate decreases. Arriving Customers decide whether to enter the system or balk based on a natural reward-cost structure and the information of the system. By using the probability generating functions, we analyze the steady-state distribution and obtain the mean sojourn time of the arriving Customers. We investigate the Customers' behavior under different information levels of the system and derive equilibrium strategy for the Customers in the fully observable, almost observable, almost unobservable and fully unobservable cases. Finally, some numerical results are provided to illustrate the effect of the system parameters on equilibrium strategies of the observable case and Customers' sojourn time for unobservable cases.
Ruiling Tian - One of the best experts on this subject based on the ideXlab platform.
-
Analysis of Equilibrium Strategies in Markovian Queues With Negative Customers and Working Breakdowns
IEEE Access, 2019Co-Authors: Ruiling Tian, Yali WangAbstract:We consider the Customers' strategic behavior in Markovian queues with negative Customers and working breakdowns. when a negative Customer Arrives, the Customer being served is forced to leave the system. At the same time, the server breaks down and the service rate decreases. Arriving Customers decide whether to enter the system or balk based on a natural reward-cost structure and the information of the system. By using the probability generating functions, we analyze the steady-state distribution and obtain the mean sojourn time of the arriving Customers. We investigate the Customers' behavior under different information levels of the system and derive equilibrium strategy for the Customers in the fully observable, almost observable, almost unobservable and fully unobservable cases. Finally, some numerical results are provided to illustrate the effect of the system parameters on equilibrium strategies of the observable case and Customers' sojourn time for unobservable cases.
-
Equilibrium strategies in an observable queue with single exponential vacation
2011 Chinese Control and Decision Conference (CCDC), 2011Co-Authors: Ruiling TianAbstract:We consider an observable queue with single exponential vacation. Whenever the system becomes empty, the server begins a vacation at the instant. And when the vacation ends, if there are Customers in the queue, the server begins work. Otherwhise, the server stays idle until the next Customer Arrives. A Customer arriving at an idle server does not wait, while a Customer arriving during a server's vacation must wait until the end of the vacation. We assume both the fully and almost observable cases, then derive equilibrium threshold strategies for the Customers and analyze the stationary behavior of the systems under these strategies. We also illustrate the equilibrium thresholds via numerical experiments.
Barry L. Nelson - One of the best experts on this subject based on the ideXlab platform.
-
Winter Simulation Conference - Estimating and interpreting the waiting time for Customers arriving to a non-stationary queueing system
2015 Winter Simulation Conference (WSC), 2015Co-Authors: Jeffrey S. Smith, Barry L. NelsonAbstract:When a Customer Arrives to a service system, how long should they expect to wait, and how long might their wait actually be? Computer simulation is an ideal tool for answering such questions for very general and complex queueing systems, but they are not always answered by the automatic statistical summary generated by commercial simulation languages. Using an illustration based on passenger check-in at an airport, we demonstrate how standard summary measures go wrong and provide methods that correctly answer these questions.
-
Estimating and interpreting the waiting time for Customers arriving to a non-stationary queueing system
2015 Winter Simulation Conference (WSC), 2015Co-Authors: Jeffrey S. Smith, Barry L. NelsonAbstract:When a Customer Arrives to a service system, how long should they expect to wait, and how long might their wait actually be? Computer simulation is an ideal tool for answering such questions for very general and complex queueing systems, but they are not always answered by the automatic statistical summary generated by commercial simulation languages. Using an illustration based on passenger check-in at an airport, we demonstrate how standard summary measures go wrong and provide methods that correctly answer these questions.
Daniela Rus - One of the best experts on this subject based on the ideXlab platform.
-
Rebalancing the rebalancers: optimally routing vehicles and drivers in mobility-on-demand systems
2013 American Control Conference, 2013Co-Authors: Stephen L. Smith, Mac Schwager, Marco Pavone, Emilio Frazzoli, Daniela RusAbstract:In this paper we study rebalancing strategies for a mobility-on-demand urban transportation system blending Customer-driven vehicles with a taxi service. In our system, a Customer Arrives at one of many designated stations and is transported to any other designated station, either by driving themselves, or by being driven by an employed driver. When some origins and destinations are more popular than others, vehicles will become unbalanced, accumulating at some stations and becoming depleted at others. This problem is addressed by employing rebalancing drivers to drive vehicles from the popular destinations to the unpopular destinations. However, with this approach the rebalancing drivers themselves become unbalanced, and we need to “rebalance the rebalancers” by letting them travel back to the popular destinations with a Customer. In this paper we study how to optimally route the rebalancing vehicles and drivers so that the number of waiting Customers remains bounded while minimizing the number of rebalancing vehicles traveling in the network and the number of rebalancing drivers needed; surprisingly, these two objectives are aligned, and one can find the optimal rebalancing strategy by solving two decoupled linear programs. We determine the minimum number of drivers and minimum number of vehicles needed to ensure stability in the system. Our simulations suggest that, in Euclidean network topologies, one would need between 1/3 and 1/4 as many drivers as vehicles.
Stephen L. Smith - One of the best experts on this subject based on the ideXlab platform.
-
Rebalancing the rebalancers: optimally routing vehicles and drivers in mobility-on-demand systems
2013 American Control Conference, 2013Co-Authors: Stephen L. Smith, Mac Schwager, Marco Pavone, Emilio Frazzoli, Daniela RusAbstract:In this paper we study rebalancing strategies for a mobility-on-demand urban transportation system blending Customer-driven vehicles with a taxi service. In our system, a Customer Arrives at one of many designated stations and is transported to any other designated station, either by driving themselves, or by being driven by an employed driver. When some origins and destinations are more popular than others, vehicles will become unbalanced, accumulating at some stations and becoming depleted at others. This problem is addressed by employing rebalancing drivers to drive vehicles from the popular destinations to the unpopular destinations. However, with this approach the rebalancing drivers themselves become unbalanced, and we need to “rebalance the rebalancers” by letting them travel back to the popular destinations with a Customer. In this paper we study how to optimally route the rebalancing vehicles and drivers so that the number of waiting Customers remains bounded while minimizing the number of rebalancing vehicles traveling in the network and the number of rebalancing drivers needed; surprisingly, these two objectives are aligned, and one can find the optimal rebalancing strategy by solving two decoupled linear programs. We determine the minimum number of drivers and minimum number of vehicles needed to ensure stability in the system. Our simulations suggest that, in Euclidean network topologies, one would need between 1/3 and 1/4 as many drivers as vehicles.
-
rebalancing the rebalancers optimally routing vehicles and drivers in mobility on demand systems
arXiv: Optimization and Control, 2013Co-Authors: Stephen L. Smith, Mac Schwager, Marco Pavone, Emilio FrazzoliAbstract:In this paper we study rebalancing strategies for a mobility-on-demand urban transportation system blending Customer-driven vehicles with a taxi service. In our system, a Customer Arrives at one of many designated stations and is transported to any other designated station, either by driving themselves, or by being driven by an employed driver. The system allows for one-way trips, so that Customers do not have to return to their origin. When some origins and destinations are more popular than others, vehicles will become unbalanced, accumulating at some stations and becoming depleted at others. This problem is addressed by employing rebalancing drivers to drive vehicles from the popular destinations to the unpopular destinations. However, with this approach the rebalancing drivers themselves become unbalanced, and we need to "rebalance the rebalancers" by letting them travel back to the popular destinations with a Customer. Accordingly, in this paper we study how to optimally route the rebalancing vehicles and drivers so that stability (in terms of boundedness of the number of waiting Customers) is ensured while minimizing the number of rebalancing vehicles traveling in the network and the number of rebalancing drivers needed; surprisingly, these two objectives are aligned, and one can find the optimal rebalancing strategy by solving two decoupled linear programs. Leveraging our analysis, we determine the minimum number of drivers and minimum number of vehicles needed to ensure stability in the system. Interestingly, our simulations suggest that, in Euclidean network topologies, one would need between 1/3 and 1/4 as many drivers as vehicles.