Multidisciplinary topology optimization solved as a Nash game

In the present work, multidisciplinary optimization is formulated in the game theory framework. We choose a coupled heat transfer—thermoelastic system as the case study for which a topology design approach is developed. The multidisciplinary optimization problem is solved as a non‐cooperative game and we determine a Nash equilibrium. The game has two players and the parameterization of the design domain is such that the design variables describe the material density and a parameter which influences the heat flow by convection to the surrounding fluid. The first player controls the structure and the second player controls the temperature distribution in the structure. For the second player, we present mathematical proof of existence of a discrete valued optimal solution and it is concluded that no regularization of the suboptimization problem is needed. We present two numerical examples which illustrate the proposed methodology. One of the examples is also solved by weighting the objectives to a scalar valued objective function and the result is compared with the Nash game solution. Copyright © 2004 John Wiley & Sons, Ltd.     

Genetic algorithm quality of service design in resilient dense wavelength division multiplexing optical networks

The important role of quality of service (QoS) in deployment of a resilient dense wavelength division multiplexing (DWDM) backbone for global networks requires critical design-phase planning optimisation. The design issues of resilient DWDM networks for bandwidth and delay sensitive applications of dedicated path protection are addressed. A genetic algorithm (GA) model has been developed to solve the routing and wavelength assignment problem using binary variable-length chromosome encoding under two different schemes of bandwidth optimisation (BOS) and delay optimisation (DOS).The performance of the new GA-based resiliency model has been evaluated for four benchmark networks: PAN EUROPEAN, COST239, NSFNETand ARPA2. Simulation results show a superior capability and efficiency for the model to solve this complex, multi-constraint and nondeterministic polynomial-hard problem for BOS and DOS. The nonlinear nature of this process reveals a significant sensitivity for optical layer network topology on the optimum-design QoS. The results also demonstrate that the PAN EUROPEAN network shows the highest flexibility for primary path design, NSFNET for the secondary path and ARPA2 comes with the lowest design flexibility for both primary and secondary paths.     

Evolutionary algorithms applied to reliable communication network design

Several evolutionary algorithms (EAs) applied to a wide class of communication network design problems modelled under the generalized Steiner problem (GSP) are evaluated. In order to provide a fault-tolerant design, a solution to this problem consists of a preset number of independent paths linking each pair of potentially communicating terminal nodes. This usually requires considering intermediate non-terminal nodes (Steiner nodes), which are used to ensure path redundancy, while trying to minimize the overall cost. The GSP is an NP-hard problem for which few algorithms have been proposed. This article presents a comparative study of pure and hybrid EAs applied to the GSP, codified over MALLBA, a general purpose library for combinatorial optimization. The algorithms were tested on several GSPs, and asset efficient numerical results are reported for both serial and distributed models of the evaluated algorithms.     

Manufacturers’ coalition under a price elastic market – a quadratic production game approach

The paper addresses a supply network design problem in which a set of enterprises decides to organise itself as a multi-stage supply network by resource sharing and production coordination. A biform game theory formulation of the problem is obtained by combining the cooperative sub-game in the manufacturing network with the strategic sub-game of the Stackelberg type between the retailer and the manufacturers’ network. As a result of the interaction between these two sub-games, a new type of cooperative game, the quadratic production game, is formulated to describe the supply network design problem under anticipated price elastic demands from the market. The key problem of coalitional stability is addressed through the properties of rationality and fairness of the profit sharing agreement. We prove that the game is non-convex in general and thus, the fair solution given by the Shapley value allocation is not always rational. It is proposed to reinforce the coalition stability by selecting a minimal set of partner enterprises achieving the maximal expected profit and applying a profit sharing policy with guaranteed fairness restricted to the member enterprises.     

An optimal procedure for solving the hierarchical network design problem

The hierarchical network design problem consists of finding a minimum cost bilevel network that connects all the nodes in a set, created by a loopless main path and a forest. The main path is formed by primary (higher cost) arcs, providing a path between an origin node and a destination node. The forest, built using secondary (lower cost) arcs, connects all the nodes not on the main path, to the path itself. We state and prove some properties of the problem, which allow finding good upper bounds to the solution in polynomial time when the primary costs are proportional to secondary costs. We also propose an O(n⁴) procedure to improve on these bounds. In turn, these bounds are used to significantly reduce the number of nodes and arcs of the problem. Once the problem is reduced, large instances can be solved to optimality. At this stage, we use one of two linear integer optimization formulations. The first and preferred one is based on multicommodity flows, which avoids the formation of subtours. The second formulation avoids subtours by iteratively adding ad hoc constraints. We show some examples and provide computational experiments performed on networks with sizes up to 600 nodes and 14 000 arcs.     

Stochastic user equilibrium model with a tradable credit scheme and application in maximizing network reserve capacity

The tradable credit scheme (TCS) outperforms congestion pricing in terms of social equity and revenue neutrality, apart from the same perfect performance on congestion mitigation. This article investigates the effectiveness and efficiency of TCS on enhancing transportation network capacity in a stochastic user equilibrium (SUE) modelling framework. First, the SUE and credit market equilibrium conditions are presented; then an equivalent general SUE model with TCS is established by virtue of two constructed functions, which can be further simplified under a specific probability distribution. To enhance the network capacity by utilizing TCS, a bi-level mathematical programming model is established for the optimal TCS design problem, with the upper level optimization objective maximizing network reserve capacity and lower level being the proposed SUE model. The heuristic sensitivity analysis-based algorithm is developed to solve the bi-level model. Three numerical examples are provided to illustrate the improvement effect of TCS on the network in different scenarios.     

JOINT CONFIGURATION OF BACKBONE AND LOGICAL NETWORKS ON A RECONFIGURABLE PACKET-SWITCHED NETWORK WITH UNRELIABLE LINKS

Abstract

This paper considers a problem of configuring both backbone and logical networks in a reconfigurable packet-switched network where links are subject to failures. The objective is to design feasible backbone and logical networks at least cost where the network cost includes backbone link capacity expansion cost and average packet delay penalty cost due to link failures. The problem is formulated as a zero-one non-linear mixed integer programming problem, for which an effective solution procedure is developed by using a Lagrangean relaxation technique for finding a lower bound and a heuristic method is exploited for improving the upper bound of an intermediate solution. The solution procedure is tested for its effectiveness with various numerical examples.     

A network-wide exact optimization approach for multiobjective routing with path protection in multiservice multiprotocol label switching networks

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.     

基于Kano-QFD的健身游戏系统优化设计

目的 有效获取健身用户对健身游戏系统的功能需求及其权重,并确定健身游戏系统的关键性设计要求,从而提升健身游戏系统的用户满意度.方法 通过绘制用户旅程图对健身用户的行为路径进行分析,探求健身用户的需求,根据Kano模型对功能需求进行属性归类,并量化计算出用户需求重要度;然后基于QFD模型,获取健身游戏系统的设计要求,确定其重要度权重,通过对比分析得出关键性设计要求.结果 健身功能设计要求和教育指导设计要求的重要度权重最大,是健身游戏系统设计优化过程中的关键性设计要求.结论 通过用户旅程图、Kano模型和QFD模型的结合,以动感单车健身游戏为例进行研究,验证了该方法的可行性,从用户需求的角度为健身游戏系统的功能提供设计和优化的依据.     

Network Security Situation Awareness Framework based on Threat Intelligence

Network security situation awareness is an important foundation for network security management, which presents the target system security status by analyzing existing or potential cyber threats in the target system. In network offense and defense, the network security state of the target system will be affected by both offensive and defensive strategies. According to this feature, this paper proposes a network security situation awareness method using stochastic game in cloud computing environment, uses the utility of both sides of the game to quantify the network security situation value. This method analyzes the nodes based on the network security state of the target virtual machine and uses the virtual machine introspection mechanism to obtain the impact of network attacks on the target virtual machine, then dynamically evaluates the network security situation of the cloud environment based on the game process of both attack and defense. In attack prediction, cyber threat intelligence is used as an important basis for potential threat analysis. Cyber threat intelligence that is applicable to the current security state is screened through the system hierarchy fuzzy optimization method, and the potential threat of the target system is analyzed using the cyber threat intelligence obtained through screening. If there is no applicable cyber threat intelligence, using the Nash equilibrium to make predictions for the attack behavior. The experimental results show that the network security situation awareness method proposed in this paper can accurately reflect the changes in the network security situation and make predictions on the attack behavior.