Application of a biparametric perturbation method to large-deflection circular plate problems with a bimodular effect under combined loads

The large deflection condition of a bimodular plate may yield a dual nonlinear problem where the superposition theorem is inapplicable. In this study, the bimodular Föppl–von Kármán equations of a plate subjected to the combined action of a uniformly distributed load and a centrally concentrated force are solved using a biparametric perturbation method. First, the deflection and radial membrane stress were expanded in double power series with respect to the two types of loads. However, the biparametric perturbation solution obtained exhibited a relatively slow rate of convergence. Next, by introducing a generalized load and its corresponding generalized displacement, the solution is expanded in a single power series with respect to the generalized displacement parameter, thereby leading to the better convergence on the solution. A numerical simulation is also used to verify the correctness of the biparametric perturbation solution. The introduction of a bimodular effect will modify the stiffness of the plate to some extent. In particular, the bearing capacity of the plate will be strengthened further when the compressive modulus is greater than the tensile modulus.     

Asynchronous gradient algorithms for a class of convex separable network flow problems

We consider the single commodity strictly convex network flow problem. The dual of this problem is unconstrained, differentiable, and well suited for solution via distributed or parallel iterative methods. We present and prove convergence of gradient and asynchronous gradient algorithms for solving the dual problem. Computational results are given and analysed.     

A novel recurrent nonlinear neural network for solving quadratic programming problems

This paper presents a new neural network for solving quadratic programming problems. The new model has a simple form, furthermore it has a good convergence rate with a less number calculation operation than the old models. It converges very fast to exact solution of the dual problem and by substituting in a formulation, the optimal solution of the original problem is obtained. Neural network model with one of numerical method is solved. Finally, simple numerical examples are provided for more illustration.     

基于路网的电动汽车快速充电站布局决策研究

大力发展和推广纯电动汽车已成为全世界众多国家的共同选择。但电动汽车有限的续航能力及公共快速充电设施的缺乏,制约着人们的使用和长途出行。本研究针对我国公共快速充电网络建设亟待完善的问题,基于我国主干高速公路网络,对服务途中充电需求的快速充电站科学分布问题建立分布决策模型。研究表明,对续航能力低于200公里的电动汽车,应用最优策略分布的快速充电站数量从50座增加到250座时,可将途中充电需求覆盖率从50%左右提高到90%以上,而对续航能力超过250公里的电动汽车,150座按最优策略分布的快速充电站即能覆盖至少96.49%的途中充电需求。通过对不同续航能力和不同充电站数量约束下共30种情形的分析,本研究不仅能为多情形下充电站分布问题提供最优选址和数量组合决策方案,也为我国充电基础设施的完善及电动汽车产业可持续发展提供有力的理论支持和政策建议。     

A location–allocation problem for a web services provider in a competitive market

Web Services have become a viable component technology in distributed e-commerce platforms. Due to the move to high-speed Internet communication and tremendous increases in computing power, network latency has begun to play a more important role in determining service response time. Hence, the locations of a Web Services provider’s facilities, customer allocation, and the number of servers at each facility have a significant impact on its performance and customer satisfaction. In this paper we introduce a location–allocation model for a Web Services provider in a duopoly competitive market. Demands for services of these servers are available at each node of a network, and a subset of nodes is to be chosen to locate one or more servers in each. The objective is to maximize the provider’s profit. The problem is formulated and analyzed. An exact solution approach is developed and the results of its efficiency are reported.     

Design of optimal utility of wireless rechargeable sensor networks via joint spatiotemporal scheduling

In wireless rechargeable sensor networks, how to optimize energy resources for maximizing the sensor data is a challenging problem. In this paper, mobile charging vehicle scheduling, sensor charging time splitting and rate control with battery capacity constraints are considered together to maximize network utility. However, they are considered independently in exist works even though these problems are interdependent. In order to improve network performance through collaborative optimization of three problems, a joint optimization problem is formulated firstly. Then, a multistage approach is developed to jointly optimize the three subproblems iteratively. Furthermore, an accelerated distributed algorithm is integrated to improve the convergence speed of rate control. The results of extended experiments demonstrate that proposed approach can obtain higher network utility and charging efficiency compared to other charging scheduling methods.     

Existence and stability of almost periodic solutions in impulsive neural network models

The existence and global exponential stability of an almost periodic solution of an impulsive neural network model with distributed delays is considered in a matrix setting. The approach transforms the original network into a matrix analysis problem, where a set of sufficient conditions based on spectral radius is presented. A concrete Hopfield model shows the advantages in comparison with a classical norm approach.     

基于分布式序关系的双边匹配决策方法

针对分布式序关系的双边匹配问题,提出了一种基于证据推理的决策方法。首先,以双边匹配满意程度为目标,把双边的各个评价信息作为证据,并通过证据融合求出匹配的满意度;接着,构建基于满意度的决策模型来获得匹配方案。最后, 通过算例说明该方法的应用。     

分布式数据流处理系统管理中负载均衡问题建模与求解

对分布式数据流处理系统管理中,处理节点负载均衡问题进行了研究。阐述了分布式数据流处理系统的运行机理以及节点负载不均衡的成因,并提出了对系统负载均衡调整的优化方案;对提出的优化方案建立模型,并对模型的适用条件进行理论分析;然后采用蚁群算法对模型进行求解,并针对分布式数据流处理系统实时性的需求对算法进行改进;最后用实验证明本文所建立的模型及其求解方法对于解决分布式数据流处理系统管理中节点负载均衡问题的有效性。     

Success Guarantee of Dual Search in Integer Programming: p-th Power Lagrangian Method

Although the Lagrangian method is a powerful dual search approach in integer programming, it often fails to identify an optimal solution of the primal problem. The p-th power Lagrangian method developed in this paper offers a success guarantee for the dual search in generating an optimal solution of the primal integer programming problem in an equivalent setting via two key transformations. One other prominent feature of the p-th power Lagrangian method is that the dual search only involves a one-dimensional search within [0,1]. Some potential applications of the method as well as the issue of its implementation are discussed.     

Dynamic Network Flow with Uncertain Arc Capacities: Decomposition Algorithm and Computational Results

In a multiperiod dynamic network flow problem, we model uncertain arc capacities using scenario aggregation. This model is so large that it may be difficult to obtain optimal integer or even continuous solutions. We develop a Lagrangian decomposition method based on the structure recently introduced in G.D. Glockner and G.L. Nemhauser, Operations Research, vol. 48, pp. 233–242, 2000. Our algorithm produces a near-optimal primal integral solution and an optimum solution to the Lagrangian dual. The dual is initialized using marginal values from a primal heuristic. Then, primal and dual solutions are improved in alternation. The algorithm greatly reduces computation time and memory use for real-world instances derived from an air traffic control model.     

Optimal generation scheduling for constrained multi-area hydrothermal power systems with cascaded reservoirs

The optimization problem in this paper is targeted at large-scale hydrothermal power systems. The thermal part of the system is a multi-area power pool with tie-line constraints, and the hydro part is a set of cascaded hydrostations. The objective is to minimize the operation cost of the thermal subsystem. This is an integer nonlinear optimization process with a large number of variables and constraints. In order to obtain the optimal solution in a reasonable time, we decompose the problem into thermal and hydro subproblems. The coordinator between these subproblems is the system Lagrange multiplier. For the thermal subproblem, in a multi-area power pool, it is necessary to coordinate the area generations for reducing the operation cost without violating tie limits. For the hydro subsystem, network flow concepts are adopted to coordinate water usage over the entire study time span, and the reduced gradient method is used to overcome the linear characteristic of the network flow method in order to obtain the optimal solution. In this study, load forecasting errors and forced outages of generating units are incorporated in system reliability requirements. Three case studies for the proposed method are presented.     

Unit commitment in power generation – a basic model and some extensions

For the unit commitment problem in the hydro-thermal power system of VEAG Vereinigte Energiewerke AG Berlin we present a basic model and discuss possible extensions where both primal and dual solution approaches lead to flexible optimization tools. Extensions include staggered fuel prices, reserve policies involving hydro units, nonlinear start-up costs, and uncertain load profiles. This revised version was published online in June 2006 with corrections to the Cover Date.     

离散椭球分布下两阶段WCVaR风险利润优化模型及应用

本文研究随机变量非完全分布下的两阶段风险-利润优化问题。采用最坏情况下条件风险(Worst-case Conditional Value-at-Risk:WCVaR) 度量指标,在离散椭球分布下建立了两阶段WCVaR 约束下利润期望最大优化模型,运用优化对偶方法将复杂的Max-Min 结构化简,理论上证明了简化模型和原模型的同解性,以发电商电能分配组合优化为数值实例,验证了模型和计算方法的有效性。     

Determining common weights in data envelopment analysis based on the satisfaction degree

The traditional data envelopment analysis model allows the decision-making units (DMUs) to evaluate their maximum efficiency values using their most favourable weights. This kind of evaluation with total weight flexibility may prevent the DMUs from being fully ranked and make the evaluation results unacceptable to the DMUs. To solve these problems, first, we introduce the concept of satisfaction degree of a DMU in relation to a common set of weights. Then a common-weight evaluation approach, which contains a max–min model and two algorithms, is proposed based on the satisfaction degrees of the DMUs. The max–min model accompanied by our Algorithm 1 can generate for the DMUs a set of common weights that maximizes the least satisfaction degrees among the DMUs. Furthermore, our Algorithm 2 can ensure that the generated common set of weights is unique and that the final satisfaction degrees of the DMUs constitute a Pareto-optimal solution. All of these factors make the evaluation results more satisfied and acceptable by all the DMUs. Finally, results from the proposed approach are contrasted with those of some previous methods for two published examples: efficiency evaluation of 17 forest districts in Taiwan and R&D project selection.     

Web server load balancing: A queueing analysis

Over the last few years, the Web-based services, more specifically different types of E-Commerce applications, have become quite popular, resulting in exponential growth in the Web traffic. In many situations, this has led to unacceptable response times and unavailability of services, thereby driving away customers. Many companies are trying to address this problem using multiple Web servers with a front-end load balancer. Load balancing has been found to provide an effective and scalable way of managing the ever-increasing Web traffic. However, there has been little attempt to analyze the performance characteristics of a system that uses a load balancer. This paper presents a queuing model for analyzing load balancing with two Web servers. We first analyze the centralized load balancing model, derive the average response time and the rejection rate, and compare three different routing policies at the load balancer. We then extend our analysis to the distributed load balancing and find the optimal routing policy that minimizes the average response time.     

Robust placement and tuning of UPFC via a new multiobjective scheme‐based fuzzy theory

This article addresses a new modified honey bee mating optimization namely multiobjective honey bee mating optimization (MOIHBMO) based fuzzy multiobjective methodology for optimal locating and parameter setting of unified power flow controller (UPFC) in a power system for a long‐term period. One of the profits obtained by UPFC placement in a transmission network is the reduction in total generation cost due to its ability to change the power flow pattern in the network. Considering this potential, UPFC can be also used to remove or at least mitigate the congestion in transmission networks. The other issue in a power system is voltage violation which could even render the optimal power flow problem infeasible to be solved. Voltage violation could be also mitigated by proper application of UPFC in a transmission system. These objectives are considered simultaneously in a unified objective function for the proposed optimization algorithm. At first, these objectives are fuzzified and designed to be comparable against each other and then they are integrated and introduced to a MOIHBMO method to find the solution which maximizes the value of integrated objective function in a 3‐year planning horizon, considering the load growth. A power injection model is adopted for UPFC. Unlike, the most previous works in this field the parameters of UPFC are set for each load level to avoid inconvenient rejection of more optimal solutions. IEEE reliability test system is used as an illustrative example to show the effectiveness of the proposed method. © 2014 Wiley Periodicals, Inc. Complexity 21: 126–137, 2015     

基于两阶段求解策略的动态电动车辆路径优化研究

由于政府对新能源汽车的补贴政策和市区对燃油车限行政策的实时,越来越多的物流公司在城市配送中广泛采用电动汽车。然而,电动车续航里程受限,需要在途充电或者换电,同时客户需求的动态性以及充/换电设施的排队等现实因素也应该被考虑。为此,提出了分阶段策略求解动态电动车辆路径优化问题,并建立了两阶段的EVRP模型。其中第一阶段针对静态客户建立了静态EVRP模型,第二阶段在设计了换电站及动态客户插入策略的基础上,建立了动态EVRP模型以路径更新策略。最后,设计改进的CW-TS混合启发式算法来求解静态模型,设计贪婪算法求解动态模型。实验结果表明,模型与算法具有较好的适用性和有效性。     

Time-dependent discrete road network design with both tactical and strategic decisions

This paper aims to model and investigate the discrete urban road network design problem, using a multi-objective time-dependent decision-making approach. Given a base network made up with two-way links, candidate link expansion projects, and candidate link construction projects, the problem determines the optimal combination of one-way and two-way links, the optimal selection of capacity expansion projects, and the optimal lane allocations on two-way links over a dual time scale. The problem considers both the total travel time and the total CO emissions as the two objective function measures. The problem is modelled using a time-dependent approach that considers a planning horizon of multiple years and both morning and evening peaks. Under this approach, the model allows determining the sequence of link construction, the expansion projects over a predetermined planning horizon, the configuration of street orientations, and the lane allocations for morning and evening peaks in each year of the planning horizon. This model is formulated as a mixed-integer programming problem with mathematical equilibrium constraints. In this regard, two multi-objective metaheuristics, including a modified non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective B-cell algorithm, are proposed to solve the above-mentioned problem. Computational results for various test networks are also presented in this paper.     

An Iterative Exact Solution for the Dual Power Management Problem in Wireless Sensor Network

We study the dual power management problem in wireless sensor networks. Given a wireless sensor network with two possible power levels (heigh and low) for each sensor, the problem consists in minimizing the number of sensors assigned heigh power while ensuring the connectivity of the network. We formulate the problem by a binary integer programming model to minimize the total power consumption. Since the problem is NP-complete, we provide an iterative approximation based on iterative methods in combinatorial optimization. We solve the separation subproblem as a minimum spanning tree.