An integrated simulation-optimization framework for the operational planning of seaport container terminals

In this article the operational planning of seaport container terminals is considered by defining a suitable integrated framework in which simulation and optimization interact. The proposed tool is a simulation environment (implemented by using the Arena software) representing the dynamics of a container terminal. When the system faces some particular conditions (critical events), an optimization procedure integrated in the simulation tool is called. This means that the simulation is paused, an optimization problem is solved and the relative solution is an input for the simulation environment where some system parameters are modified (generally, the handling rates of some resources are changed). For this reason, in the present article we consider two modelling and planning levels about container terminals. The simulation framework, based on an appropriate discrete-event model, represents the dynamic behaviour of the terminal, thus it needs to be quite detailed and it is used as an operational planning tool. On the other hand, the optimization approach is devised in order to define some system parameters such as the resource handling rates; in this sense, it can be considered as a tool for tactical planning. The optimization procedure is based on an aggregate representation of the terminal where the dynamics is modelled by means of discrete-time equations.     

Monte Carlo Algorithms for Identifying Densely Connected Subgraphs

The problem of finding densely connected subgraphs in a network has attracted a lot of recent interest. Such subgraphs are sometimes referred to as communities in social networks or molecular modules in protein networks. In this article, we propose two Monte Carlo optimization algorithms for identifying the densest subgraphs with a fixed size or with size in a given range. The new algorithms combine the idea of simulated annealing and efficient moves for the Markov chain, and both algorithms are shown to converge to the set of optimal states (densest subgraphs) with probability 1. When applied to a yeast protein interaction network and a stock market graph, the algorithms identify interesting new densely connected subgraphs. Supplementary materials for the article are available online.     

供给能力一定条件下的物流网络优化研究

第三方物流为了给客户提供正确且高效的服务,对物流进行了领域整合,建立了连接供应商和顾客的多级物流网络,网络包括了运输、流通加工、储存及配送等物流子系统。为降低网络系统成本,提高服务效率,针对网络中节点和通道上固定的物流供给能力,设计了以运营成本为目标的优化模型,通过变分不等式求出了最佳的物流组织方案,同时根据运行方案对网络中基础设施进行了重新设计改造,避免了资源不必要的浪费。最后通过算例对模型和算法进行了验证。     

Fast, Efficient Equipment Placement Heuristics for Broadband Switched or Internet Router Networks

Planning and designing the next generation of IP router or switched broadband networks seems a daunting challenge considering the many complex, interacting factors affecting the performance and cost of such networks. Generally, this complexity implies that it may not even be clear what constitutes a “good” network design for a particular specification. Different network owners or operators may view the same solution differently, depending on their unique needs and perspectives. Nevertheless, we have observed a core common issue arising in the early stages of network design efforts involving leading-edge broadband switched technologies such as ATM, Frame Relay, and SMDS; or even Internet IP router networks. This core issue can be stated as follows: Given a set of service demands for the various network nodes, where should switching or routing equipment be placed to minimize the Installed First Cost of the network? Note that the specified service demands are usually projections for a future scenario and generally entail significant uncertainty. Despite this uncertainty, we have found that network owners and operators generally feel it is worthwhile to obtain high-level advice on equipment placement with a goal of minimizing Installed First Cost. This paper reports on a heuristic approach we have implemented for this problem that has evolved out of real network design projects. A tool with both a Solution Engine and an intuitive Graphical User Interface has been developed. The approach is highly efficient; for example, the tool can often handle LATA-sized networks in seconds or less on a workstation processor. By using only nodal demands rather than the more complex point-to-point demands usually required in tools of this sort, we have created an approach that is not only highly efficient, but is also a better match to real design projects in which demand data is generally scant and highly uncertain.     

优化和均衡的等价性

通过向量优化问题, 向量变分不等式问题以及向量变分原理来分析优化问题及均衡问题的一致性.从而显然, 可以用统一的观点来处理数值优化、向量优化以及博弈论等问题.进而为非线性分析提供了一个新的发展空间.     

Structural balance in fully signed networks

Node attributes play an important role in shaping network structures, but are generally ignored in transformations of structural balance. A fully signed network consisting of signs of edges and nodes expresses both properties of relationship and node attributes. In this article, we generalize the definition of structural balance in fully signed networks. We transform the unbalanced fully signed network by not only changing signs of edges but also changing the signs of nodes. We propose a memetic algorithm to transform unbalanced networks at the lowest cost. Experiments show that our algorithm can solve this problem efficiently, and different node attribute assignments may lead to different optimized structures. © 2016 Wiley Periodicals, Inc. Complexity 21: 497–511, 2016     

An elementary method of calculating an explicit form of Young measures in some special cases

We present an elementary method of explicit calculation of Young measures for a certain class of functions. This class contains, in particular, functions of a highly oscillatory nature which appear in optimization problems and homogenization theory. In engineering such situation occurs, for instance, in nonlinear elasticity (solid–solid phase transition in certain elastic crystals). Young measures associated with oscillating minimizing sequences gather information about their oscillatory nature and therefore about underlying microstructure. The method presented in this article makes no use of functional analytic tools. There is no need to use a generalized version of the Riemann–Lebesgue lemma and to calculate weak* limits of functions. The main tool is the change of variable theorem. The method applies both to sequences of periodic and nonperiodic functions.     

Quasi-relative interior-type constraint qualifications ensuring strong Lagrange duality for optimization problems with cone and affine constraints

In this article we provide weak sufficient strong duality conditions for a convex optimization problem with cone and affine constraints, stated in infinite dimensional spaces, and its Lagrange dual problem. Our results are given by using the notions of quasi-relative interior and quasi-interior for convex sets. The main strong duality theorem is accompanied by several stronger, yet easier to verify in practice, versions of it. As exemplification we treat a problem which is inspired from network equilibrium. Our results come as corrections and improvements to Daniele and Giuffré (2007) [9].     

What is a Graph?

In the past few years, the subject of graph theory (or network analysis) has come very much to the fore, not only as an important mathematical discipline in its own right, but also as a useful mathematical tool in a wide variety of subjects, ranging from organic chemistry and probability, through operational research and geography, to sociology and linguistics. In this article, we shall present some simple applications of graph theory, chosen in such a way as to be as elementary but as varied as possible. The reader who is interested in pursuing the subject further should consult Reference 1; for a lengthy account of some further applications of graph theory, see Chapter six of Reference 2.     

Fast and Flexible ADMM Algorithms for Trend Filtering

This article presents a fast and robust algorithm for trend filtering, a recently developed nonparametric regression tool. It has been shown that, for estimating functions whose derivatives are of bounded variation, trend filtering achieves the minimax optimal error rate, while other popular methods like smoothing splines and kernels do not. Standing in the way of a more widespread practical adoption, however, is a lack of scalable and numerically stable algorithms for fitting trend filtering estimates. This article presents a highly efficient, specialized alternating direction method of multipliers (ADMM) routine for trend filtering. Our algorithm is competitive with the specialized interior point methods that are currently in use, and yet is far more numerically robust. Furthermore, the proposed ADMM implementation is very simple, and, importantly, it is flexible enough to extend to many interesting related problems, such as sparse trend filtering and isotonic trend filtering. Software for our method is freely available, in both the C and R languages.     

A differential inclusion-based approach for solving nonsmooth convex optimization problems

This article presents a differential inclusion-based neural network for solving nonsmooth convex programming problems with inequality constraints. The proposed neural network, which is modelled with a differential inclusion, is a generalization of the steepest descent neural network. It is proved that the set of the equilibrium points of the proposed differential inclusion is equal to that of the optimal solutions of the considered optimization problem. Moreover, it is shown that the trajectory of the solution converges to an element of the optimal solution set and the convergence point is a globally asymptotically stable point of the proposed differential inclusion. After establishing the theoretical results, an algorithm is also designed for solving such problems. Typical examples are given which confirm the effectiveness of the theoretical results and the performance of the proposed neural network.     

混沌粒子群神经网络在上证指数预测中的应用

上证指数预测是一个非常复杂的非线性问题,为了提高对上证指数预测的准确性,本文采用基于混沌粒子群(CPSO)算法对BP神经网络算法改进的方法来进行预测.BP神经网络算法目前已经应用到预测、聚类、分类等许多领域,取得了不少的成果.但自身也有明显的缺点,比如易陷入局部极小值、收敛速度慢等.用混沌粒子群算法改进BP神经网络算法的基本思想是用混沌粒子群算法优化BP神经网络算法的权值和阈值,在粒子群算法中加入混沌元素,提高粒子群算法的全局搜索能力.对上证指数预测的结果表明改进后的预测方法,具有更好的准确性.     

The role of crossover operator in the genetic optimization of magnetic models

The Ising model, introduced almost 100 years ago by Wilhelm Lenz and Ernst Ising, is the formalism still popular as a tool to describe magnetic properties of a wide class of materials. Among many issues which arise when using this model there exist problems related to the process of finding minimum energy of the system. Since these problems are NP-hard, optimizations can either be performed for some approximated cases or be the subject of global optimization techniques. In this paper we present an analysis of the effect of different crossover operators on the efficiency of genetic algorithm used to minimize energy in the Ising model. Although it is not a benchmark tool, we hope it may be interesting as a testing tool.     

质量保证策略下服务商的质量缺陷补偿决策分析

随着竞争的日趋激烈,越来越多的服务商采用质量保证策略作为竞争手段以吸引顾客,然而现有研究缺乏对该策略下的质量缺陷补偿问题的深入分析。鉴于此,本文系统地分析了质量保证策略下服务商的质量缺陷补偿问题:首先分析了当服务价格外生时,服务商的最优质量缺陷补偿决策;然后分别探讨在服务价格内生变化和服务供给变动的影响下,服务商的最优质量缺陷补偿;最后,通过数值分析,探寻在服务价格和服务供给的双重变动影响下,服务商的最优质量缺陷补偿。本文拓展了服务质量的研究,对于指导服务型企业如何制定质量缺陷补偿具有较强的实践启示意义。     

A cellular automata model can quickly approximate UDP and TCP network traffic

Brooks and Orr [R.R. Brooks and N. Orr, A model for mobile code using interacting automata. IEEE Trans Mobile Computing 2002, 1(4)] present a model for analysis and simulation of mobile code systems based on cellular automata (CA) abstractions. One flaw with that article was a lack of experimental support showing that CA can model IP networks. This article presents CA models, consistent with those in the work of Brooks and Orr, that model the transport layer of IP networks. We show how these models may be generalized for more complicated network topologies. We provide quantitative results comparing the quality of our CA implementation versus the standard network modeling tool ns‐2. The results from the CA model are qualitatively similar to ns‐2, but the CA simulation runs significantly faster and scales better. © 2004 Wiley Periodicals, Inc. Complexity 9:32–40, 2004     

A simulation optimization-based decision support tool for mitigating traffic congestion

Traffic congestion has grown considerably in the United States over the past 20 years. In this paper, we develop a robust decision support tool based on simulation optimization to evaluate and recommend congestion mitigation strategies to transportation system decision-makers. A tabu search-based optimizer determines different network design strategies on the road network while a traffic simulator evaluates the goodness of fit. The tool is tested with real-world traffic data.     

Optimizing flow rates in a queueing network with side constraints

In this paper, modified versions of the classical deterministic maximum flow and minimum cost network flow problems are presented in a stochastic queueing environment. In the maximum flow network model, the throughput rate in the network is maximized such that for each arc of the network the resulting probability of finding congestion along that arc in excess of a desirable threshold does not exceed an acceptable value. In the minimum cost network flow model, the minimum cost routing of a flow of given magnitude is determined under the same type of constraints on the arcs. After proper transformations, these models are solved by Ford and Fulkerson's labeling algorithm and out-of-kilter algorithm, respectively.     

Survivable network design with demand uncertainty

The objective in designing a communications network is to find the most cost efficient network design that specifies hardware devices to be installed, the type of transmission links to be installed, and the routing strategy to be followed. In this paper algorithmic ideas are presented for improving tractability in solving the survivable network design problem by taking into account uncertainty in the traffic requirements. Strategies for improving separation of metric inequalities are presented and an iterative approach for obtaining solutions, that significantly reduces computing times, is introduced. Computational results are provided based on data collected from an operational network.     

Permutatorial optimization via the permutahedron

A water company decides to expand its network with a set of water lines, but it cannot build them all at once. However, it starts reaping benefits from a partial expansion. In what order should the company build the lines? We formalize a class of permutatorial problems with combinatorial/continuous subproblems capturing applications of incremental deployment. We show that, for additive/linear objective functions, efficient polyhedral methods for the subproblems extend to the permutatorial problem. Our main technical ingredient is the permutahedron.     

无中心优化的算子分裂方法

在某些多智能体系统中,由于受到通讯等因素的限制,单个智能体只能进行本地计算,再与相邻智能体交换数据.与传统的并行和分布式计算不同,这种数据交换方式不再使用中心节点或者共享内存,而仅限于相邻节点之间.这种通过局部数据交换而实现全网目标的方式叫做无中心计算.比如,从任意的多个数开始,所有智能体通过不断地计算其局部平均,就都能收敛到这些数的平均值.无中心计算有不易形成通讯和计算瓶颈的优点,更适合分布的节点,因此受到一些应用的欢迎. 本文介绍求解一致最优化问题的若干无中心算法.一致最优化问题的目标是全网所有节点的变量收敛到同一个、并使所有目标函数之和最小的值.我们可以通过推广求平均的无中心方法去实现这个目标,但是得到算法比普通（有中心的）优化算法收敛得更慢,有阶数差距.近年来,一些新的无中心算法弥补了这个阶数差距.本文采用算子分裂的统一框架,以比这些算法原文更为简单的形式介绍这些方法.