A survey of approximability and inapproximability results for social welfare optimization in multiagent resource allocation

Multiagent resource allocation provides mechanisms to allocate bundles of resources to agents, where resources are assumed to be indivisible and nonshareable. A central goal is to maximize social welfare of such allocations, which can be measured in terms of the sum of utilities realized by the agents (utilitarian social welfare), in terms of their minimum (egalitarian social welfare), and in terms of their product (Nash product social welfare). Unfortunately, social welfare optimization is a computationally intractable task in many settings. We survey recent approximability and inapproximability results on social welfare optimization in multiagent resource allocation, focusing on the two most central representation forms for utility functions of agents, the bundle form and the k-additive form. In addition, we provide some new (in)approximability results on maximizing egalitarian social welfare and social welfare with respect to the Nash product when restricted to certain special cases.     

Minimising inequality in multiagent resource allocation

Annals of Mathematics and Artificial Intelligence - We analyse the problem of finding an allocation of resources in a multiagent system that is as fair as possible in terms of minimising inequality...     

Distributed optimal resource allocation of second‐order multiagent systems

In this paper, the resource allocation problems of multiagent systems are investigated. Different from the well‐studied resource allocation problems, the dynamics of agents are taken into account in our problem, which results that the problem could not be solved by most of existing resource allocation algorithms. Here, the agents are in the form of second‐order dynamics, which causes the difficulties in designing and analyzing distributed resource allocation algorithms. Based on gradient descent and state feedback, two distributed resource allocation algorithms are proposed to achieve the optimal allocation, and their convergence are analyzed by constructing suitable Lyapunov functions. One of the two algorithms can ensure that the decisions of all agents asymptotically converge to the exact optimal solution, and the other algorithm achieves the exponential convergence. Finally, numerical examples about the economic dispatch problems of power grids are given to verify the effectiveness of the obtained results.     

网络功能虚拟化资源配置及优化研究综述

网络功能虚拟化(network functions virtualization,NFV)利用虚拟化技术将网络功能从专用硬件中分离形成虚拟网络功能,然后把虚拟网络功能映射到通用服务器、交换机或者存储器中,能有效地降低网络投资成本和运营成本,并提高网络服务部署的灵活性。网络功能虚拟化资源配置是实施NFV需要解决的一个关键问题。为深入剖析网络功能虚拟化资源配置,介绍了NFV的体系结构,重点阐述了资源配置的三个阶段及资源配置的优化,最后对高效资源配置面临的挑战和有价值的研究方向进行了思考,为NFV的研究提供参考。     

Computational complexity of determining resource loops inre-entrant flow lines

This paper presents a comparison study of the computational complexity of the general job shop protocol and the more structured flow line protocol in a flexible manufacturing system. It is shown that the representative problem of finding resource invariants is NP-complete in the case of the job shop, while in the flow line case it admits a closed form solution. The importance of correctly selecting part flow and job routing protocols in flexible manufacturing systems to reduce complexity is thereby conclusively demonstrated     

Distributed resource allocation of second‐order multiagent systems with exogenous disturbances

In this paper, we study the resource allocation problem of second‐order multiagent systems with exogenous disturbances, and the communication networks are weight‐balanced digraphs. Different from the well‐studied resource allocation problems, our problem involves the disturbed second‐order dynamics of agents. In order to achieve the optimal allocation, we propose a distributed algorithm based on gradient descent and internal model approach. Furthermore, we analyze the convergence of the algorithm by constructing a suitable Lyapunov function. Moreover, we prove that the agents in the network can achieve the exact optimal allocation even in the presence of external disturbances. Finally, we provide two examples to illustrate our result.     

Weighted Coloring: further complexity and approximability results

Given a vertex-weighted graph G=(V,E;w), w(v)?0 for any v∈V, we consider a weighted version of the coloring problem which consists in finding a partition S=(S₁,…,Sk) of the vertex set of G into stable sets and minimizing where the weight of S is defined as . In this paper, we continue the investigation of the complexity and the approximability of this problem by answering some of the questions raised by Guan and Zhu [D.J. Guan, X. Zhu, A coloring problem for weighted graphs, Inform. Process. Lett. 61 (2) (1997) 77-81].     

移动音频带宽扩展算法计算复杂度优化

移动通信设备由于计算资源有限,对计算复杂度较为敏感。我国自主研发提出的移动音频编解码算法标准AVS P10中的带宽扩展算法有利于提高移动音频编解码质量,但其计算复杂度较高,不利于该编解码算法在移动环境中应用。通过分析该带宽扩展算法的流程,发现其计算复杂度较高的主要原因是时频变换次数过多,为此从算法和代码两个方面对该算法进行优化:算法方面通过减少快速傅里叶变换(FFT)次数来降低算法计算复杂度;而代码方面则通过空间换时间等方法来减少算法消耗时间。测试结果表明,在不降低整体音频编解码主观质量的情况下,带宽扩展模块在编码端和解码端运行时间比例分别降低了4.5和14.3个百分点,算法计算复杂度显著降低,这有利于进一步在移动音频领域推广应用该编解码算法。     

On the complexity of market equilibria with maximum social welfare

We consider the computational complexity of the market equilibrium problem by exploring the structural properties of the Leontief exchange economy. We prove that, for economies guaranteed to have a market equilibrium, finding one with maximum social welfare or maximum individual welfare is NP-hard. In addition, we prove that counting the number of equilibrium prices is #P-hard.     

基于广义资源日历约束的项目资源调配优化问题

资源受限项目调度问题(RCPSP)是最具代表性的项目调度问题之一,针对实际情况中考虑资源投入的必要性,建立一种以资源投入为变量的基于广义资源日历约束的项目调度优化模型.首先,引入组合优先关系的概念对广义资源日历的概念和具体内容进行整合和完善,为了避免传统网络图在表示组合优先关系时出现的网络循环等弊端,使用节点表示活动开始和结束的瞬时状态改进节点网络图;其次,考虑活动优先关系、活动持续时间、不可更新资源总量和资源日历约束,以项目工期最短和项目成本最小为优化目标,运用CP优化器求解所建立的多目标优化模型;最后,通过设计仿真算例并进行数值实验验证模型的准确性和高效性.     

Predefined-time distributed multiobjective optimization for network resource allocation

We consider the multiobjective optimization problem for the resource allocation of the multiagent network, where each agent contains multiple conflicting local objective functions. The goal is to find compromise solutions minimizing all local objective functions subject to resource constraints as much as possible, i.e., the Pareto optimums. To this end, we first reformulate the multiobjective optimization problem into one single-objective distributed optimization problem by using the weighted L<...     

Energy constrained resource allocation optimization for mobile grids

A mobile grid incorporates mobile devices into Grid systems. But mobile devices at present have severe limitations in terms of processing, memory capabilities and energy. Minimizing the energy usage in mobile devices poses significant challenges in mobile grids. This paper presents energy constrained resource allocation optimization for mobile grids. The goal of the paper is not only to reduce energy consumption, but also to improve the application utility in a mobile grid environment with a limited energy charge, ensuring battery lifetime and the deadlines of the grid applications. The application utility not only depends on its allocated resources including computation and communication resources, but also on the consumed energy, this leads to a coupled utility model, where the utilities are functions of allocated resources and consumed energy. Energy constrained resources allocation optimization is formulated as a utility optimization problem, which can be decomposed into two subproblems, the interaction between the two sub-problems is controlled through the use of a pricing variable. The paper proposes a price-based distributed energy constrained resources allocation optimization algorithm. In the simulation, the performance evaluation of our energy constrained resources allocation optimization algorithm is conducted.     

HAPS通信中的低复杂度OFDMA资源分配策略

针对配置于高空平台(HAPS)的OFDMA系统,研究了在平台功率受限情况下的资源分配问题。采用对偶理论,推导了在连续情况下,最小化发送功率时,OFDMA载波、功率、速率分配的最优解,同时利用统计近似法实现算法的实时配置。仿真结果表明,该算法性能与经典C.Y.Wong算法性能大致相同,而算法复杂度仅为线性复杂度,具有较强的实际应用价值。     

铁路集装箱中心站资源分配与作业调度联合优化

铁路集装箱中心站主作业区资源分配与作业调度联合优化对其经营效益和运作效率有重要影响.基于“轨道吊-集卡”协同装卸方案,引入轨道吊动态配置原则,以最大化作业均衡率和最小化作业成本为目标,构建多目标非线性混合整数规划模型,综合研究作业区域动态划分、贝位分配以及多轨道吊调度多层次联合优化问题. 根据问题特点,融合启发式规则、遗传算法和模拟退火算法,设计3层混合启发式算法求解模型.通过不同规模算例,对比Cplex与所设计算法的实验结果,验证模型的正确性和算法的有效性,并借助于不同划分原则、不同优化策略与不同间隔约束下的对比实验,验证优化模型与算法普适性.结果表明,新型装卸方案、动态作业区域以及联合优化策略可大幅度均衡轨道吊作业量,避免资源过度负载、降低中心站作业成本,为中心站的实际运营管理提供决策支持.     

Solving a dynamic resource allocation problem through continuous optimization

A class of dynamic resource allocation problems with infinite planning horizon are studied. We observe special structures in the dynamic programming formulation of the problem, which enable us to convert it to continuous optimization problems that can be more easily solved. Structural properties of the problems are discussed, and explicit solutions are given for some special cases.     

Modelling social norms in multiagent systems

Social norms are cultural phenomena that naturally emerge in human societies and help to prescribe and proscribe normative patterns of behaviour. In recent times, the discipline of multi-agent systems has been used to model social norms in an artificial society of agents. In this paper we review norms in multi-agent systems and then explore a series of norms in a simulated urban traffic setting. Using game-theoretic concepts we define and offer an account of norm stability. Particularly in small groups, a relatively small number of individuals with cooperative attitude are needed for the norm of cooperation to evolve and be stable. In contrast, in larger populations, a larger proportion of cooperating individuals are required to achieve stability.     

基于朗讯CDMA核心网资源配置优化的研究

针对朗讯CDMA核心网资源配置不平衡的问题,提出了一种基于基站区域重划分与5ESS硬件资源配置相结合的优化方案,利用系统统计数据对基站区域重新划分,从而减少基站的越区切换,并通过硬件槽位变动使得新区域获得必备的硬件资源。使用该方案对实际网络资源进行优化,结果表明该方案不仅解决了因为模块间通信瓶颈产生的通信质量问题,降低了切换掉话率,同时给网络未来的发展预留了足够的增长空间,提高了系统资源利用率。     

基于鲁棒优化的云医疗资源配置问题

医疗资源配置优化是云医疗系统高效运行的核心决策,然而,由于这种新型互联网医疗服务系统具有多组织协同、上下转诊以及诊疗时间不确定等特点,上述问题可以描述为需求不确定情形下核心医生服务时间分配优化问题.构建一个以最小化最大医疗服务成本为目标函数的云医疗资源鲁棒配置优化模型,通过引入决策者对患者诊疗时间和转诊概率两种不确定性...     

无线网状网中资源分配与选路联合优化研究*

针对无线网状网（WMN）的特点,对WMN中的资源管理问题和跨层设计方法进行了介绍,分析了跨层设计在WMN中的资源管理和选路设计中的重要性;对资源分配和选路联合优化问题以及现有的解决方法及研究进展进行了综述和分析,提出了待解决的问题和一些解决思想。     

PSO with path relinking for resource allocation using simulation optimization

This paper proposes a PSO-based optimization approach with a particular path relinking technique for moving particles. PSO is evaluated for two combinatorial problems. One under uncertainty, which represents a new application of PSO with path relinking in a stochastic scenario. PSO is considered first in a deterministic scenario for solving the Task Assignment Problem (TAP) and hereafter for a resource allocation problem in a petroleum terminal. This is considered for evaluating PSO in a problem subject to uncertainty whose performance can only be evaluated by simulation. In this case, a discrete event simulation is built for modeling a real-world facility whose typical operations of receiving and transferring oil from tankers to a refinery are made through intermediary storage tanks. The simulation incorporates uncertain data and operational details for optimization that are not considered in other mathematical optimization models. Experiments have been carried out considering issues that affect the choice of parameters for both optimization and simulation. The results show advantages of the proposed approach when compared with Genetic Algorithm and OptQuest (a commercial optimization package).