As an alternative to traditional taxi services, Transportation Network Companies (TNCs) such as Uber and Lyft are playing an increasingly important role in the paradigm shifting from car ownership to mobility as a service. We consider an electric vehicle fleet charging scheduling problem in the TNC setting where taxi drivers, as freelancers, have their individual preferences regarding when and where to charge their vehicles. In this setting, obtaining social welfare maximizing schedules is particularly difficult as drivers may behave strategically in competing over shared charging resources to advance their own benefits rather than the system wide social welfare. We propose a negotiation mechanism which allows drivers to collectively evolve an incumbent schedule into a socially beneficial one through an iterative voting process. The proposed mechanism provides a platform which enables multilateral negotiation among a large number of drivers. We prove that, given the design of the proposed mechanism, drivers’ best response strategy is to truthfully vote their best valued candidate schedules according to the acceptance quota prescribed by the scheduler at each voting round. In addition, experiment results show that the mechanism achieves on average 93% efficiency compared with optimal solutions and scales well to larger problem instances.

This paper presents a multiagent systems model for patient diagnostic services scheduling. We assume a decentralized environment in which patients are modeled as self-interested agents who behave strategically to advance their own benefits rather than the system wide performance. The objective is to improve the utilization of diagnostic imaging resources by coordinating patient individual preferences through automated negotiation. The negotiation process consists of two stages, namely patient selection and preference scheduling. The contract-net protocol and simulated annealing based meta-heuristics are used to design negotiation protocols at the two stages respectively. In terms of game theoretic properties, we show that the proposed protocols are individually rational and incentive compatible. The performance of the preference scheduling protocol is evaluated by a computational study. The average percentage gap analysis of various configurations of the protocol shows that the results obtained from the protocol are close to the optimal ones. In addition, we present the algorithmic properties of the preference scheduling protocol through the validation of a set of eight hypotheses.

Consider a decentralized electric vehicle (EV) charging scheduling problem where the chargers and vehicles are modeled as utility maximizing agents. To schedule chargers to vehicles in a day-ahead market, we propose a double auction mechanism, where chargers report their time availabilities and charging costs and vehicles report their preferences on different chargers. This auction is conducted in an iterative manner which enables the computation of effective schedules through multilateral negotiation between agents. We evaluate the efficiency performance of the mechanism through a computational study. The result generated from the iterative double auction mechanism is on average 35% better than the first come first serve (FCFS) allocation policy.