This paper studies the multi-robot task assignment problem in which a fleet of dispersed robots needs to efficiently transport a set of dynamically appearing packages from their initial locations to corresponding destinations within prescribed time-windows. Each robot can carry multiple packages simultaneously within its capacity. Given a sufficiently large robot fleet, the objective is to minimize the robots' total travel time to transport the packages within their respective time-window constraints. The problem is shown to be NP-hard, and we design two group-based distributed auction algorithms to solve this task assignment problem. Guided by the auction algorithms, robots first distributively calculate feasible package groups that they can serve, and then communicate to find an assignment of package groups. We quantify the potential of the algorithms with respect to the number of employed robots and the capacity of the robots by considering the robots' total travel time to transport all packages. Simulation results show that the designed algorithms are competitive compared with an exact centralized Integer Linear Program representation solved with the commercial solver Gurobi, and superior to popular greedy algorithms and a heuristic distributed task allocation method.

Ride-hailing (ridesourcing) companies such as Uber, Lyft, and Didi Chuxing have been a disruptive force in the urban mobility landscape around the world during the past decade. In this paper, we analyse the working conditions, earnings, and job satisfaction of ride-hailing drivers. We begin by discussing the regulatory, labour, financial, and urban mobility effects of ride-hailing companies. Then, we present the results of a self-administered survey to ride-hailing drivers in Chile, which is complemented with the use of online tools for the estimation of driving earnings. Our findings show that the flexibility to choose work times is the most appreciated attribute of this job, even though most drivers follow a somewhat fixed routine each week. By contrast, the level of transparency with which ride-hailing apps determine driver pay is the attribute with the lowest satisfaction score. A large number of respondents drive for long daily and weekly periods, which is a health and safety hazard. Current drivers are not concerned about the future deployment of driverless vehicles for on-demand mobility services. Ordered probit models for job satisfaction show that ride-hailing was better evaluated by drivers who use it as a complement to another part-time job, by those who earn more money per week, and by those who have not experienced undesirable situations while working, such as harassment or traffic crashes.