共查询到10条相似文献,搜索用时 187 毫秒
1.
Rita Girão-Silva José Craveirinha Teresa Gomes Lúcia Martins João Clímaco João Campos 《工程优选》2017,49(7):1226-1246
A multiobjective routing model for multiprotocol label switching networks with multiple service types and path protection is presented in this article. The routing problem is formulated as a biobjective integer program, where the considered objectives are formulated according to a network-wide optimization approach, i.e. the objective functions of the route optimization problem depend explicitly on all traffic flows in the network. A disjoint path pair is considered for each traffic trunk, which guarantees protection to the associated connection. A link-path formulation is proposed for the problem, in which a set of possible pairs of paths is devised in advance for each traffic trunk. An exact method (based on the classical constraint method for solving multiobjective problems) is developed for solving the formulated problem. An extensive experimental study, with results on network performance measures in various randomly generated networks, is also presented and discussed. 相似文献
2.
This paper addresses a daily caregiver scheduling and routing problem arising in home health care or home care service providers. The problem is quite challenging due to its uncertainties in terms of travel and service times derived from changes in road traffic conditions and customer health status in practice. We first model the problem as a stochastic programme with recourse, where the recourse action is to skip customers without services if the caregiver arrives later than their latest starting service time (i.e. hard time window requirements). Then, we formulate the problem as a set partitioning model and solve it with a branch-and-price (B&P) algorithm. Specifically, we devise an effective discrete approximation method to calculate the arrival time distribution of caregivers, incorporate it into a problem-specific label algorithm, and use a removal-and-insertion-based heuristic and the decremental state-space relaxation technique to accelerate the pricing process. Finally, we conduct numerical experiments on randomly generated instances to validate the effectiveness of the discrete approximation method and the proposed B&P algorithm. 相似文献
3.
The design of dynamic Label-Switched Paths (LSP’s) in MultiProtocol Label Switched (MPLS) networks is an NP-hard optimization
problem. An LSP is a logical path between two nodes in the network. This path has a pre-reserved amount of bandwidth that
defines its size. The LSP design problem consists of determining the number of these logical links and configuring the physical
path and the size of each LSP. This paper presents an optimization model based on game theory. In this approach, connection
requests are modeled as competitive players in a noncooperative game context. The transport network bandwidth constitutes
the resource for which optimization is sought. The outcome of this optimization is a set of LSPs upon which the competing
connections are routed. 相似文献
4.
Internet is a worldwide network composed of interconnected but independent networks, called Autonomous Systems. Each network
owner has to decide which other networks to interconnect with and how to allocate its traffic among its providers. The financial
flows between Autonomous Systems depend on these decisions and raise the key issue of revenue management. In this paper, we
propose some models and exact methods for the joint optimization problem of interconnection policy and traffic allocation
for a customer AS. This problem is analyzed in the top-percentile pricing framework for the interconnection agreements, and
we assess the solution methods using real-life instances. 相似文献
5.
Solving optimization problems with multiple objectives under uncertainty is generally a very difficult task. Evolutionary
algorithms, particularly genetic algorithms, have shown to be effective in solving this type of complex problems. In this
paper, we develop a simulation-based multi-objective genetic algorithm (SMOGA) procedure to solve the build-operate-transfer
(BOT) network design problem with multiple objectives under demand uncertainty. The SMOGA procedure integrates stochastic
simulation, a traffic assignment algorithm, a distance-based method, and a genetic algorithm (GA) to solve a multi-objective
BOT network design problem formulated as a stochastic bi-level mathematical program. To demonstrate the feasibility of SMOGA
procedure, we solve two mean-variance models for determining the optimal toll and capacity in a BOT roadway project subject
to demand uncertainty. Using the inter-city expressway in the Pearl River Delta Region of South China as a case study, numerical
results show that the SMOGA procedure is robust in generating ‘good’ non-dominated solutions with respect to a number of parameters
used in the GA, and performs better than the weighted-sum method in terms of the quality of non-dominated solutions. 相似文献
6.
Abida Sharif Imran Sharif Muhammad Asim Saleem Muhammad Attique Khan Majed Alhaisoni Marriam Nawaz Abdullah Alqahtani Ye Jin Kim Byoungchol Chang 《计算机、材料和连续体(英文)》2023,75(3):5379-5393
The Internet of Vehicles (IoV) is a networking paradigm related to the intercommunication of vehicles using a network. In a dynamic network, one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion. Therefore, optimal path selection to route traffic between the origin and destination is vital. This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access. Firstly, this work proposed a novel use of the Ant Colony Optimization (ACO) algorithm and formulated the path planning optimization problem as an Integer Linear Program (ILP). This integrates the future estimation metric to predict the future arrivals of the vehicles, searching the optimal routes. Considering the mobile nature of IOV, fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path. The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path. Thus, this work strongly supports its use in applications having stringent Quality of Service (QoS) requirements for the vehicles. 相似文献
7.
8.
The nodes in the sensor network have a wide range of uses, particularly on under-sea links that are skilled for detecting, handling as well as management. The underwater wireless sensor networks support collecting pollution data, mine survey, oceanographic information collection, aided navigation, strategic surveillance, and collection of ocean samples using detectors that are submerged in water. Localization, congestion routing, and prioritizing the traffic is the major issue in an underwater sensor network. Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource. Minimization of localization error using the proposed angle-based forwarding scheme is explained in this paper. We choose the shortest path to the destination using the fitness function which is calculated based on fault ratio, dispatching of packets, power, and distance among the nodes. This work contemplates congestion conscious forwarding using hard stage and soft stage schemes which reduce the congestion by monitoring the status of the energy and buffer of the nodes and controlling the traffic. The study with the use of the ns3 simulator demonstrated that a given algorithm accomplishes superior performance for loss of packet, delay of latency, and power utilization than the existing algorithms. 相似文献
9.
Xiaoli He Yu Song Yu Xue Muhammad Owais Weijian Yang Xinwen Cheng 《计算机、材料和连续体(英文)》2022,70(1):195-212
Spectrum resources are the precious and limited natural resources. In order to improve the utilization of spectrum resources and maximize the network throughput, this paper studies the resource allocation of the downlink cognitive radio network with non-orthogonal multiple access (CRN-NOMA). NOMA, as the key technology of the fifth-generation communication (5G), can effectively increase the capacity of 5G networks. The optimization problem proposed in this paper aims to maximize the number of secondary users (SUs) accessing the system and the total throughput in the CRN-NOMA. Under the constraints of total power, minimum rate, interference and SINR, CRN-NOMA throughput is maximized by allocating optimal transmission power. First, for the situation of multiple sub-users, an adaptive optimization method is proposed to reduce the complexity of the optimization solution. Secondly, for the optimization problem of nonlinear programming, a maximization throughput optimization algorithm based on Chebyshev and convex (MTCC) for CRN-NOMA is proposed, which converts multi-objective optimization problem into single-objective optimization problem to solve. At the same time, the convergence and time complexity of the algorithm are verified. Theoretical analysis and simulation results show that the algorithm can effectively improve the system throughput. In terms of interference and throughput, the performance of the sub-optimal solution is better than that of orthogonal-frequency-division-multiple-access (OFDMA). This paper provides important insights for the research and application of NOMA in future communications. 相似文献
10.
Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve larger instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other non-standard features. Simulation optimisation is an alternative approach that has recently been applied to such problems, using policies that require only a few decision variables to be determined. However, to find optimal or near-optimal solutions we must consider exponentially large scenario trees with a corresponding number of decision variables. We propose instead a neuroevolutionary approach: using an artificial neural network to compactly represent the scenario tree, and training the network by a simulation-based evolutionary algorithm. We show experimentally that this method can quickly find high-quality plans using networks of a very simple form. 相似文献