Minimizing the driving distance in ride sharing systems

Reducing the number of cars driving on roads is an important objective for smart sustainable cities, for reducing emissions and improving traffic flow. To assist with this aim, ride-sharing systems match intending drivers with prospective passengers. The matching problem becomes more complex when drivers can pick-up and drop-off several passengers, both drivers and passengers have to travel within a time-window and are willing to switch roles. We present a mixed integer programming model for this switching rider problem, with the objective of minimizing the total distance driven by the population. We exhibit how the potential saving in kilometres increases as the driver flexibility and the density of the distribution of participants increases. Further, we show how breaking symmetries among the switchers improves performance, gaining over an order of magnitude speed up in solving time, and allowing approximately 50% more participants to be handled in the same computation time

A distributed asynchronous solver for Nash Equilibria in hypergraphical games

Hypergraphical games provides a compact model of a network of self-interested agents, each involved in simultaneous subgames with its neighbors. The overall aim is for the agents in the network to reach a Nash Equilibrium, in which no agent has an incentive to change their response, but without revealing all their private information. Asymmetric Distributed constraint satisfaction (ADisCSP) has been proposed as a solution to this search problem. In this paper, we propose a new model of hypergraphical games as an ADisCSP based on a new global constraint, and a new asynchronous algorithm for solving ADisCSP that is able to find a Nash Equilibrium. We show empirically that we significantly reduce both message passing and computation time, achieving an order of magnitude improvement in messaging and in non-concurrent computation time on dense problems compared to state-of-the art algorithms

Inferring destination from mobility data

Destination prediction in a moving vehicle has several applications such as alternative route recommendations even in cases where the driver has not entered their destination into the system. In this paper a hierarchical approach to destination prediction is presented. A Discrete Time Markov Chain model is used to make an initial prediction of a general region the vehicle might be travelling to. Following that a more complex Bayesian Inference Model is used to make a fine grained prediction within that destination region. The model is tested on a dataset of 442 taxis operating in Porto, Portugal. Experiments are run on two maps. One is a smaller map concentrating specificially on trips within the Porto city centre and surrounding areas. The second map covers a much larger area going as far as Lisbon. We achieve predictions for Porto with average distance error of less than 0.6 km from early on in the trip and less than 1.6 km dropping to less than 1 km for the wider area

Inferring waypoints in the absence of knowledge of driving style

We present an algorithm for predicting intervals which contain waypoints from a GPS trace of a multi-part trip without having access to historical data about the driver or any other aggregated data sets. We assume the driver’s driving style is not known, but that it can be approximated by one of a set of cost preferences. The method uses a set of repeated forward and backward searches along the trace, where each of the searches represents one of the driving costs. We evaluate the algorithm empirically on multi-part trips on real route maps. The algorithm selects the results of the search with the fewest number of intervals and we achieve over 95% recall on estimating waypoints while the intervals cover less than 9% of the tra

Inferring waypoints using shortest paths

We present a method for reconstructing intermediate destinations from a GPS trace of a multi-part trip, without access to aggregated statistics or datasets of previous traces. The method uses repeated forwards and backwards shortest-path searches. We evaluate the algorithm empirically on multi-part trips on real route maps. We show that the algorithm can achieve up to 97% recall, and that the algorithm degrades gracefully as the GPS traces become sparse and irregular

Maximising the number of participants in a ride-sharing scheme: MIP versus CP formulations

Ride sharing schemes aim to reduce the number of cars in congested cities, while providing the participants with a cheaper alternative to solo driving. To ensure a ride-sharing scheme thrives, it is important to maintain a high participation rate. This requires an adequate balance between drivers and riders. And thus ride matches should be proposed which maximize the number of participants. Different variants of the ride sharing problem have been solved using mixed integer programming. In this paper, we introduce a constraint programming formulation for the problem that uses cumulative constraints with dependencies between trip times. In experiments based on collected trip schedules from four different regions, the constraint model outperforms the MIP model. However, when we change the problem by assuming all drivers have flexible roles, the MIP model allows faster solution times than the CP model

Preference Elicitation and Reasoning While Smart Shifting of Home Appliances

AbstractA crucial part of the total electricity demand is energy consumption in the residential sector. In parallel to optimizing energy consumption within houses, user comfort is still an essential success criterion for automated solutions used within the house. Choosing the most comfortable appliance schedule is often a challenging task for the members of the house. To bring focus on this challenge, residential customer involvement is enhanced by a trend towards automation of appliances. This trend is reflected by pilot projects such as Linear which uses automated smart appliances at the demand side to attain more flexibility in the electricity system. Moreover, industrial interest from the Telecom, energy and household appliance sector to promote smart schedules for appliances is growing. To meet this trend, this paper describes new ways to model and reason with the user preferences when scheduling appliances in a household under dynamic pricing schemes given different user preferences. These methods have been proven to be efficient in eliciting and computing the user preferences to increase the user comfort in the house

An Efficient MIP Model for the Capacitated Lot-sizing and Scheduling Problem with Sequence-dependent Setups

This paper presents a novel mathematical programming approach to the singlemachine capacitated lot-sizing and scheduling problem with sequence-dependent setup times and setup costs. The approach is partly based on the earlier work of Haase and Kimms (2000) which determines during pre-processing all item sequences that can appear in given time periods in optimal solutions. We introduce a new mixed-integer programming model in which binary variables indicate whether individual items are produced in a period, and parameters for this program are generated by a heuristic procedure in order to establish a tight formulation. Our model allows us to solve in reasonable time instances where the product of the number of items and number of time periods is at most 60–70. Compared to known optimal solution methods, it solves significantly larger problems, often with orders of magnitude speedup. Keywords: Lot-sizing, scheduling, sequence-dependent setups, mixed-integer programming.

Fault-tolerant relay deployment for k node-disjoint paths in wireless sensor networks

Ensuring that wireless sensor networks (WSNs) are robust to failures requires that the physical network topology will offer alternative routes to the sinks. This requires sensor network deployments to be planned with an objective of ensuring some measure of robustness in the topology, so that when failures occur that routing protocols can continue to offer reliable delivery. Our contribution is a solution that enables fault-tolerant WSN deployment planning by judicious use of a minimum number of additional relay nodes. A WSN is robust if at least one route to a sink is available for each remaining sensor node after the failure of up to k-1 nodes. In this paper, we define the problem for increasing WSN reliability by deploying a number of additional relay nodes to ensure that each sensor node in the initial design has k node-disjoint paths to the sinks. We present GRASP-ARP, a centralised offline algorithm to be run during the initial topology design to solve this problem. We have implemented this algorithm and demonstrated in simulation that it improves the efficiency of relay node placement for k node-disjoint paths compared to the most closely related published algorithms

Multiple sink and relay placement in wireless networks

Wireless sensor networks are subject to failures. Deployment planning should ensure that when a sink or sensor node fails, the remaining network can still be connected, and so may require placing multiple sinks and relay nodes in addition to sensors. For network performance requirements, there may also be path-length constraints for each sensor node. We propose two local search algorithms, GRASP-MSP and GRASP-MSRP, to solve the problem of multiple sink placement and the problem of multiple sink and relay placement, respectively. GRASP-MSP minimises the deployment cost, while ensuring that each sensor node in the network is double-covered, i.e. it has two length-constrained paths to two sinks. GRASP-MSRP deploys sinks and relays to minimise the deployment cost and to guarantee that all sensor nodes in the network are double-covered and noncritical. A sensor node is noncritical if upon its removal, all remaining sensor nodes still have length-constrained paths to sinks. We evaluate the algorithms empirically and show that both GRASP-MSP and GRASP-MSRP outperform the closely-related algorithms from the literature for the lowest total deployment cost