复杂网络的一种加权路由策略研究

复杂网络的传输能力是其功能正常运转的重要保障,提高网络的吞吐量有着重要意义.提出一种新的高效路由策略,以提高复杂网络的传输能力,称之为加权路由策略.即对网络的每一条边加权,权值与该边的两端节点的度相关,然后数据包按照这个加权网络的最短路径路由.这样的路径可以更均匀地经过各个节点,发挥它们的传输能力,极大地提高网络的吞吐量.可以避免数据包集中地通过个别度大的节点,在这些节点发生拥塞.仿真显示,该策略比传统的最短路径策略优越,对很多结构的网络,可以提高几十倍的吞吐量. 关键词： 复杂网络 路由策略 吞吐量 拥塞     

Blocking probability analysis of pruned optical banyan networks on vertical stacking scheme with extra banyan planes

Vertically stacked pruned optical banyan networks with extra planes (in short, EP-VSOB networks) have lower switch count and optimal time complexity (O(log₂ N)) for routing N input requests. However, blocking probability is relatively higher than that of a VSOB networks using regular banyan planes. In the EP-VSOB architecture, the number of pruned planes has always been considered as , and a few extra planes (regular banyan) have been added with these pruned planes. In this paper we present the results of blocking analysis of a more generalized architecture in which the number of pruned planes can be 2^x, where x ? 0 in addition to the variable extra planes. This generalization helps us make a compromise between different constraints and performance metrics. Our simulation results show that for some given performance requirements (e.g. cost, speed or blocking probability) we can choose a network that has lower switch count compared to -plane pruned crosstalk-free optical banyan networks. For example, to ensure blocking probability <0.02, previously we would chose a pruned network of 32 pruned planes and 1 extra planes (a regular banyan); however, our simulations results show that a network of 16 pruned planes and 2 extra planes is enough to ensure the same performance. It is notable that, the hardware cost decreases by 28.65% in this new combination of pruned and extra planes. We believe our results will provide more flexibility in choosing a particular EP-VSOB network satisfying given requirements.     

基于马赫-曾德尔干涉仪的单个光子操控

分别利用空间和光纤马赫—曾德尔干涉仪对空间和光纤传输中的单个光子的干涉现象进行了研究,干涉对比度可达到90％以上,实现了空间和光纤中的单个光子的路由操控。实验采用脉冲调制加衰减的方法产生单个光子,获得了每个脉冲中只包含0．1个光子的准单光子源。通过改变压电换能器的电压控制马赫—曾德尔干涉仪的单个光子在两个输出端的选择,实现了光子在节点上的路由。采用同步符合检测技术,利用重复频率为1kHz,脉冲宽度为100ns的同步信号对输出信号进行符合,实现了量子效率高于70％,暗计数小于0．2s^-1的单光子高灵敏度检测,观测到了单个光子在相位操控下出射到两个输出端的有序分配现象。验证了基于马赫—曾德尔干涉仪的单个光子路由操控实现的可能性。     

An optimization approach for communal home meal delivery service: A case study

This paper is the first to discuss the communal home meal delivery problem. The problem can be modelled as a multiple travelling salesman problem with time windows, that is closely related to the well-studied vehicle routing problem with time windows. Experimental results are reported for a real-life case study from Central Finland over several alternative scenarios using the SPIDER commercial solver. The comparison with current practice reveals that a significant savings potential can be obtained using off-the-shelf optimization tools. As such, the potential for supporting real-life communal routing problems can be considered to be important for VRP practitioners.     

Analysis,modeling and solution of the concrete delivery problem

This paper describes a specific local search approach to solve a problem arising in logistics which we prove to be NP-hard. The problem is a complex scheduling or vehicle routing problem where we have to schedule the tours of concrete mixer vehicles over a working day from concrete-producing depots to concrete-demanding customers and vice versa. We give a general mixed integer programming model which is too hard to solve for state of the art mixed integer programming optimizers in the case of the usually huge problem instances coming from practice. Therefore we present a certain local search approach to be able to handle huge practical problem instances.     

An approximate dynamic programming approach for the vehicle routing problem with stochastic demands

This paper examines approximate dynamic programming algorithms for the single-vehicle routing problem with stochastic demands from a dynamic or reoptimization perspective. The methods extend the rollout algorithm by implementing different base sequences (i.e. a priori solutions), look-ahead policies, and pruning schemes. The paper also considers computing the cost-to-go with Monte Carlo simulation in addition to direct approaches. The best new method found is a two-step lookahead rollout started with a stochastic base sequence. The routing cost is about 4.8% less than the one-step rollout algorithm started with a deterministic sequence. Results also show that Monte Carlo cost-to-go estimation reduces computation time 65% in large instances with little or no loss in solution quality. Moreover, the paper compares results to the perfect information case from solving exact a posteriori solutions for sampled vehicle routing problems. The confidence interval for the overall mean difference is (3.56%, 4.11%).     

An empirical study on the benefit of split loads with the pickup and delivery problem

Splitting loads such that the delivery of certain loads is completed in multiple trips rather than one trip has been shown to have benefit for both the classic Vehicle Routing Problem (VRP) and the Pickup and Delivery Problem (PDP). However, the magnitude of the benefit may be affected by various problem characteristics. In this paper, we characterize those real world environments in which split loads are most likely to be beneficial. Based on practitioner interest, we determine how the benefit is affected by the mean load size and variance, number of origins relative to the number of destinations, the percentage of origin–destination pairs with a load requiring service, and the clustering of origin and destination locations. We find that the magnitude of benefit is greatest for load sizes just over one half vehicle capacity as these loads can not be combined without splitting, while they are the easiest to combine on a vehicle with splitting; increases as the number of loads sharing an origin or destination increases because there are more potential load combinations to split at each stop; and increases as the average distance from an origin to a destination increases because splitting loads reduces the trips from origins to destinations.     

A deterministic tabu search algorithm for the fleet size and mix vehicle routing problem

The fleet size and mix vehicle routing problem consists of defining the type, the number of vehicles of each type, as well as the order in which to serve the customers with each vehicle when a company has to distribute goods to a set of customers geographically spread, with the objective of minimizing the total costs. In this paper, a heuristic algorithm based on tabu search is proposed and tested on several benchmark instances. The computational results show that the proposed algorithm produces high quality results within a reasonable computing time. Some new best solutions are reported for a set of test problems used in the literature.     

A Guided Tabu Search for the Vehicle Routing Problem with two-dimensional loading constraints

We present a metaheuristic methodology for the Capacitated Vehicle Routing Problem with two-dimensional loading constraints (2L-CVRP). 2L-CVRP is a generalisation of the Capacitated Vehicle Routing Problem, in which customer demand is formed by a set of two-dimensional, rectangular, weighted items. The purpose of this problem is to produce the minimum cost routes, starting and terminating at a central depot, to satisfy the customer demand. Furthermore, the transported items must be feasibly packed into the loading surfaces of the vehicles. We propose a metaheuristic algorithm which incorporates the rationale of Tabu Search and Guided Local Search. The loading aspects of the problem are tackled using a collection of packing heuristics. To accelerate the search process, we reduce the neighbourhoods explored, and employ a memory structure to record the loading feasibility information. Extensive experiments were conducted to calibrate the algorithmic parameters. The effectiveness of the proposed metaheuristic algorithm was tested on benchmark instances and led to several new best solutions.     

A variable neighbourhood search algorithm for the open vehicle routing problem

In the open vehicle routing problem (OVRP), the objective is to minimise the number of vehicles and then minimise the total distance (or time) travelled. Each route starts at the depot and ends at a customer, visiting a number of customers, each once, en route, without returning to the depot. The demand of each customer must be completely fulfilled by a single vehicle. The total demand serviced by each vehicle must not exceed vehicle capacity. Additionally, in one variant of the problem, the travel time of each vehicle should not exceed an upper limit.