Volume 2 Issue 3
Authors: Chi-Chang Wang; Yiyo Kuo
Abstract: For any dynamic vehicle routing problem, if new routes are planned in response to a new dynamic request, and the new routes are very different from the original ones, the whole process becomes more complicated. In order to reduce the changes in the routes, this research proposes an insertion heuristic for solving a dynamic multi-depot vehicle routing problem where pick-up and delivery requests are both considered. The insertion heuristic can also take the current positions of vehicles into account. Five instances provided by a specialist webpage were tested to analyze the difference between solutions that do or do not consider the current positions of vehicles. The results show that considering the vehicles’ current positions reduces the completion time by 11.79%, but increases the number of rejected requests by 10.23% on average.
Keywords: Dynamic Vehicle Routing; Multi-depot; Pick-up and Delivery
Authors: Wu Ruizhen; Yang Yintang; Zhang Li
Abstract: In shared System-on-Chip (SoC) bus systems, masters may have requirements of priority and real-time guarantee when there exists bus contentions. Based on Fixed Priority Arbitration Algorithm, a real-time guaranteed and high-speed asynchronous fixed priority arbitration algorithm is proposed and implemented from an asynchronous circuit perspective. In NINP (NonIdling and NonPreemptive) model, the proposed algorithm has advantages in bandwidth utilization, power compared and can decrease the delay at least 8% with the commonly-used FP, RR and Lottery algorithms through verifications on Xilinx Virtex5 XC5VLX70T of 65nm CMOS technology and -2 speed grade. The proposed algorithm is suitable in various environments of SoC applications.
Keywords: Arbitration Algorithm; Asynchronous Circuits; Soc; Real-Time; Priority
Authors: G. Di Francesco; L. Mallozzi; P. De Paolis; C. de Nicola; d’ Argenio
Abstract: A computational methodology for designing an experimental test matrix is presented based on the concept of potential and repulsive fields. The problem consists in the optimal distribution of test points in a two-dimensional domain, pursuant to hard constraints (permitted boundaries of the domain) and soft constraints (minimization of potential). Each test point is assumed to be the source of different fields which expose all other points to repulsive forces, thus accelerations, acting in different directions. The result of the mutual repulsive forces is a dynamic evolution of the configuration of test points in the domain, which eventually converges to a condition of minimum potential. An iterative process is adopted to find a numerical solution where residual accelerations are below a desired threshold. The method has been extended to the additional task of dynamically relocating the remaining test points, after an initial subset has been performed and a need to change (either increase or reduce) the number of test points has arisen. The proposed technique allows for an easy accomplishment of the task with minor modifications to the algorithm. A large degree of flexibility in the algorithm is allowed to tune the relative weights to attribute to the different requirements. The method proved effective and computationally efficient, exhibiting satisfactory results in both the test matrix design task and the dynamic relocation task.
Keywords: Flight Test; Flutter; Envelope Expansion; Field Theory; Optimization; Spatial Location
Authors: Anshi Xie
Abstract: In this paper, the benchmarking learning algorithm (BLA), was proposed according to the benchmark learning theory in the business management. In BLA, a competitive learning mechanism based on dynamic niche was set up. First, by right of imitation and learning, all the individuals within population were able to approach to the high yielding regions in the solution space, and seek out the optimal solutions quickly. What is more, the premature convergence problem was solved through new optimal solution policy. Last but not least, BLA is able to accurately detect the slight changes of the environments and track the trajectory of the extreme points in the search space. And thus, it is naturally adaptable for the dynamic optimization problems. In this paper, the main differences between BLA and the existing intelligent optimization methods, such as genetic algorithm (GA), particle swarm optimization (PSO) et al were analyzed and revealed. The comparative experiments for both the static optimization problem and the dynamic optimization problem showed that BLA is robust and able to perform friendly interactive learning with the environments, whose search speed, optimization ability and dynamic tracking ability were far superior to other similar methods.
Keywords: Benchmark Learning; Search Pattern; Evolutionary Algorithm; Swarm Intelligence; Dynamic Environments
Authors: Jun Wang; Xinhui Zhang; Jing Sun; Li Ou
Abstract: Aviation fuel consumption is the major component of costs for air transport enterprises. The paper chooses the aviation fuel consumption volume as the research object and grey prediction, linear regression prediction and time series prediction as the individual prediction methods. Based on dominance matrix method, a combination prediction model is set up in the paper to forecast the aviation fuel consumption volume with better prediction results. It provides necessary ground for aviation fuel order and storage.
Keywords: Aviation Fuel Consumption Volume; Combination Prediction; Dominance Matrix Method