We consider the problem of exploring travel demand statistics to optimize ride-sharing routing, which is to determine a route to transport multiple customers with similar itineraries and schedules in a cost-effective and timely manner. This problem is important for unleashing the economic and societal benefits of ride-sharing. Meanwhile, it is challenging due to the need to (i) meet the travel delay requirements of customers and (ii) make online decisions without knowing the exact travel demands beforehand. We present a general framework for exploring the new design space enabled by the demand-aware approach. We show that demand-aware ride-sharing routing is fundamentally a two-stage stochastic optimization problem. While the problem is combinatorial in nature and challenging, we exploit the two-stage structure to design an optimal solution that maximizes the expected reward of the route with polynomial time complexity, which makes it amenable to practical implementation. Our approach can be applied to optimize a variety of objectives in a ride-sharing trip, including revenue, driver’s profit, and total travel time reduction compared to serving customers individually. We carry out extensive simulations based on real-world travel demand traces in Manhattan. Simulation results show that applying our demand-aware solution increases the revenue by 26% against conceivable alternatives. As compared to the case without ride-sharing, our ride-sharing solution increases the driver’s profit per slot by 12% and decreases the total vehicle travel time (an indicator of greenhouse gas emission) by 16%.

We consider the scenario where an energy harvesting source sends its updates to a receiver. The source optimizes its energy allocation over a decision period to maximize a sum of time-varying functions of the age of information (AoI), representing the value of providing timely information. In a practical online setting, we need to make irrevocable energy allocation decisions at each time while the time-varying functions and the energy arrivals are only revealed sequentially. The problem is then challenging as 1) we are facing uncertain energy harvesting arrivals and time-varying functions, and 2) the energy allocation decisions and the energy harvesting process are coupled due to the capacity-limited battery. In this paper, we develop an optimal online algorithm$\textsf {CR-RePursuit}$and show it achieves$(\ln \theta +1)$-competitiveness, where$\theta $is a parameter representing the level of uncertainty of the time-varying functions. It is the optimal competitive ratio among all deterministic and randomized online algorithms. We also introduce an adaptive variant of the algorithm, +, that further exploits the revealed information to obtain significantly improved empirical performance. We conduct simulations based on real-world traces and compare our algorithms with conceivable alternatives. The results show that our algorithms achieve 15% performance improvement as compared to the state-of-the-art baseline.

We consider the scenario where an energy harvesting source sends its updates to a receiver. The source optimizes its energy allocation over a decision period to maximize a sum of time-varying functions of the age of information (AoI), representing the value of providing timely information. In a practical online setting, we need to make irrevocable energy allocation decisions at each time while the time-varying functions and the energy arrivals are only revealed sequentially. The problem is then challenging as 1) we are facing uncertain energy harvesting arrivals and time-varying functions, and 2) the energy allocation decisions and the energy harvesting process are coupled due to the capacity-limited battery. In this paper, we develop an optimal online algorithm CR-Reserve and show it achieves (lnθ + 1)-competitive, where θ is a parameter representing the level of uncertainty of the time-varying functions. It is the optimal competitive ratio among all deterministic and randomized online algorithms. We conduct simulations based on real-world traces and compare our algorithms with conceivable alternatives. The results show that our algorithms achieve 12% performance improvement as compared to the state-of-the-art baseline.

We consider an increasingly popular demand-response scenario where a home user schedules the flexible electric vehicle (EV) charging load in response to real-time electricity prices. The objective is to minimize the total charging cost with user dissatisfaction taken into account. We focus on the online setting where neither accurate prediction nor distribution of future real-time prices is available to the user when making irrevocable charging decisions in each time slot. The emphasis on considering user dissatisfaction and achieving optimal competitive ratio differentiates our work from existing ones and makes our study uniquely challenging. Our key contribution is two simple online algorithms with the optimal competitive ratio among all deterministic algorithms. The optimal competitive ratio is upper-bounded by$\min \left \{{ \sqrt {\alpha /p_{\min }},p_{\max }/p_{\min }}\right \} $and the bound is asymptotically tight with respect to$\alpha $, where$p_{\max }$and$p_{\min }$are the upper and lower bounds of real-time prices and$\alpha \geq p_{\min }$captures the consideration of user dissatisfaction. The bounds with respect to small and large values of$\alpha $suggest the fundamental difference of the problems with and without considering user dissatisfaction. We also extend the algorithms to take minimum charging requirement and short-term prediction into account. Simulation results based on real-world traces corroborate our theoretical findings and show that the empirical performance of our algorithms can be substantially better than the theoretical worst-case guarantees. Our algorithms also achieve notable performance gains under diverse settings as compared to conceivable alternatives.

We consider an increasingly popular demand-response scenario where a user schedules the flexible electric vehicle (EV) charging load in response to real-time electricity prices. The objective is to minimize the total charging cost with user dissatisfaction taken into account. We focus on the online setting where neither accurate prediction nor distribution of future real-time prices is available to the user when making irrevocable charging decision in each time slot. The emphasis on considering user dissatisfaction and achieving optimal competitive ratio differentiates our work from existing ones and makes our study uniquely challenging. Our key contribution is two simple online algorithms with the best possible competitive ratio among all deterministic algorithms. The optimal competitive ratio is upper-bounded by min {√α/pmin, pmax/pmin} and the bound is asymptotically tight with respect to α, where pmaxand pminare the upper and lower bounds of real-time prices and α ≥ pmincaptures the consideration of user dissatisfaction. The bounds under small and large values of α suggest the fundamental difference of the problems with and without considering user dissatisfaction. Simulation results based on real-world traces corroborate our theoretical findings and show that the empirical performance of our algorithms can be substantially better than its theoretical worst-case guarantee. Moreover, our algorithms achieve large performance gains as compared to conceivable alternatives. The results also suggest that increasing EV charging rate limit decreases overall cost almost linearly.

Ride-sharing is a modern urban-mobility paradigm with tremendous potential in reducing congestion and pollution. Demand-aware design is a promising avenue for addressing a critical challenge in ridesharing systems, namely joint optimization of request-vehicle assignment and routing for a fleet of vehicles. In this paper, we develop a probabilistic demand-aware framework to tackle the challenge. We focus on maximizing the expected number of passenger pickups, given the probability distributions of future demands. The key idea of our approach is to assign requests to vehicles in a probabilistic manner. It differentiates our work from existing ones and allows us to explore a richer design space to tackle the request-vehicle assignment puzzle with a performance guarantee but still keeping the final solution practically implementable. The optimization problem is non-convex, combinatorial, and NP-hard in nature. As a key contribution, we explore the problem structure and propose an elegant approximation of the objective function to develop a dual-subgradient heuristic. We characterize a condition under which the heuristic generates a (1 -- 1/e) approximation solution. Our solution is simple and scalable, amendable for practical implementation. Results of numerical experiments based on real-world traces in Manhattan show that, as compared to a conventional demand-oblivious scheme, our demand-aware solution improves the passenger pickups by up to 46%. The results also show that joint optimization at the fleet level leads to 19% more pickups than that by separate optimizations at individual vehicles.

We consider the problem of exploring travel demand statistics to optimize ride-sharing routing, in which the driver of a vehicle determines a route to transport multiple customers with similar itineraries and schedules in a cost-effective and timely manner. This problem is important for unleashing economical and societal benefits of ride-sharing. Meanwhile, it is challenging due to the need of (i) meeting travel delay requirements of customers, and (ii) making online decisions without knowing the exact travel demands beforehand. We present a general framework for exploring the new design space enabled by the demand-aware approach. We show that the demand-aware ride-sharing routing is fundamentally a two-stage stochastic optimization problem. We show that the problem is NP-Complete in the weak sense. We exploit the two-stage structure to design an optimal solution with pseudo-polynomial time complexity, which makes it amenable for practical implementation. We carry out extensive simulations based on real-world travel demand traces of Manhattan. The results show that using our demand-aware solution instead of the conventional greedy-routing scheme increases the driver's revenue by 10%. The results further show that as compared to the case without ride-sharing, our ride-sharing solution reduces the customers' payment by 9% and the total vehicle travel time (indicator of greenhouse gas emission) by 17%. The driver can also get 26% extra revenues per slot by participating in ride-sharing.