Carsharing services have successfully established their presence and are now growing steadily in many cities around the globe. Carsharing helps to ease traffic congestion and reduce city pollution. To be efficient, carsharing fleet vehicles need to be located on city streets in high population density areas and considering demographics, parking restrictions, traffic and other relevant information in the area to satisfy travel demand. This work proposes to formulate the initial placement of a fleet of cars for a round-trip carsharing service as a multi-objective optimization problem. The performance of state-of-the-art metaheuristic algorithms, namely, SPEA2, NSGA-II, and NSGA-III, on this problem is evaluated on a novel benchmark composed of synthetic and real-world instances built from real demographic data and street network. Inverted generational distance (IGD), spread and hypervolume metrics are used to compare the algorithms. Our findings demonstrate that NSGA-II yields significantly lower IGD and higher hypervolume than the rest and SPEA2 has a significantly better diversity if compared with NSGA-II and NSGA-III.

This paper presents a decentralized method for aligning three robots without communication and without explicit localization. Each robot is assumed to be equipped with an omnidirectional camera and is assumed to be able to detect the two other robots in the images produced by its own camera. Each robot decides of its own movements based on the continuous measures of angles between itself and the others. We present both the algorithm that performs such a task and its theoretical analysis. We prove the correctness of the algorithm when the robots are reduced to points, when collisions are not considered and when the initial formation is not an equilateral triangle. When collisions are considered, the probability for the algorithm to be successful is greater than $1 - \displaystyle \frac {1}{36}$. We then present some simulation for which a security distance $d_{\sec }$ has been introduced. During the execution of the algorithm by each robot, if the distance between any two robots is lower than this security distance we suppose that a collision is likely to occur and consider that the Algorithm has failed. Simulation results report that the larger $d_{\sec }$, the bigger the percentage of failures. However runs are always successful when $d_{\sec } = 0$ which suggests that the theoretical bound is not tight and could be improved. In addition, simulations reveal that the main source of failures was not the one expected by the theoretical analysis.

This paper investigates a self-organized critical approach for dynamically load-balancing computational workloads. The proposed model is based on the Bak-Tang-Wiesenfeld sandpile: a cellular automaton that works in a critical regime at the edge of chaos. In analogy to grains of sand, tasks arrive and pile up on the different processing elements or sites of the system. When a pile exceeds a certain threshold, it collapses and initiates an avalanche of migrating tasks, i.e., producing load-balancing. We show that the frequency of such avalanches is in power-law relation with their sizes, a scale-invariant fingerprint of self-organized criticality that emerges without any tuning of parameters. Such an emergent pattern has organic properties such as the self-organization of tasks into resources or the self-optimization of the computing performance. The conducted experimentation also reveals that the system has a critical attractor in the point in which the arrival rate of tasks equals the processing power of the system. Taking advantage of this fact, we hypothesize that the processing elements can be turned on and off depending on the state of the workload as to maximize the utilization of resources. An interesting side effect is that the overall energy consumption of the system is minimized without compromising the quality of service.

In ad hoc networks, the mean degree represents the average number of neighbors per station. The larger this value, the better the connectivity of the network. A direct consequence for the stations is that, when the mean degree increases, exchanging information becomes easier. In this work we study the sensitivity of the mean degree according to the variation of several characteristics related to the obstacles and to the environment in which the stations are positioned in. For that purpose, we provide a new method based on a careful geometric analysis and we show that, under a restricted set of assumptions, the value of the mean degree can be computed using a generic formula whose parameters depend on the size of the environment and on few geometric characteristics of the obstacles.

Abstract Motivation: A new heuristic algorithm for solving DNA sequencing by hybridization problem with positive and negative errors. Results: A heuristic algorithm providing better solutions than algorithms known from the literature based on tabu search method. Contact: [email protected] * To whom correspondence should be addressed.

This paper establishes the exact upper bound for Lawler's heuristic proving that its schedule of a UECT tree on m identical processors does not exceed an optimal solution by more than m/2 time units.