Minimizing the number of late jobs for the permutation flowshop problem with secondary resources

This paper considers a permutation flowshop problem with secondary resources with the objective of minimizing the number of tardy jobs. The number of secondary resources assigned to the machines (workcenters), as well as the allocation of resources among the various machines, will play a significant role in the time required to process each job by its specified due date. This problem finds application in a large number of environments including manufacturing, maintenance, warehousing operations, as well as in healthcare. The research presents a lower bound for the permutation flowshop problem and evaluates its performance against the optimal solution for small, medium, and large instances. Several heuristics, including neighborhood search and simulated annealing, are presented to generate the secondary resource assignment and the allocation of jobs to the schedule. The computational complexity of the lower bound and computational examples for the heuristics are discussed.     

Cost minimization in utility computing systems

Utility computing is a form of computer service whereby the company providing the service charges the users for using the system resources. In this paper, we present system‐optimal and user‐optimal price‐based job allocation schemes for utility computing systems whose objective is to minimize the cost for the users. The system‐optimal scheme provides an allocation of jobs to the computing resources that minimizes the overall cost for executing all the jobs in the system. The user‐optimal scheme provides an allocation that minimizes the cost for individual users in the system for providing fairness. The system‐optimal scheme is formulated as a constraint minimization problem, and the user‐optimal scheme is formulated as a non‐cooperative game. The prices charged by the computing resource owners for executing the users jobs are obtained using a pricing model based on a non‐cooperative bargaining game theory framework. The performance of the studied job allocation schemes is evaluated using simulations with various system loads and configurations. Copyright © 2012 John Wiley & Sons, Ltd.     

A reinforcement learning approach to dynamic resource allocation

This paper presents a general framework for performing adaptive reconfiguration of a distributed system based on maximizing the long-term business value, defined as the discounted sum of all future rewards and penalties. The problem of dynamic resource allocation among multiple entities sharing a common set of resources is used as an example. A specific architecture (DRA-FRL) is presented, which uses the emerging methodology of reinforcement learning in conjunction with fuzzy rulebases to achieve the desired objective. This architecture can work in the context of existing resource allocation policies and learn the values of the states that the system encounters under these policies. Once the learning process begins to converge, the user can allow the DRA-FRL architecture to make some additional resource allocation decisions or override the ones suggested by the existing policies so as to improve the long-term business value of the system. The DRA-FRL architecture can also be deployed in an environment without any existing resource allocation policies. An implementation of the DRA-FRL architecture in Solaris 10 demonstrated a robust performance improvement in the problem of dynamically migrating CPUs and memory blocks between three resource partitions so as to match the stochastically changing workload in each partition, both in the presence and in the absence of resource migration costs.     

基于GPU的并行协同差分进化算法研究

针对大规模高维数复杂非线性函数优化的问题,提出一种新的基于GPU的协同差分进化算法。该方法将协同进化的思想引入启发式差分进化算法,随机分解大规模计算问题,利用GPU处理数据的并行性,同步计算分解后的子问题,加快算法的精度和收敛速度。实验对比结果表明,所提出的基于GPU的协同差分进化算法对大规模非线性函数优化具有更高的精度和效率。     

面向Flink迭代作业的动态资源分配策略

新兴分布式计算框架Apache Flink支持在集群上执行大规模的迭代程序,但其默认的静态资源分配机制导致无法进行合理的资源配置来使迭代作业按时完成.针对这一问题,应该依靠用户来主动表达性能约束而不是被动地进行资源保留,故提出了一种基于运行时间预测的动态资源分配策略RABORP (resource allocation...     

Large scale evolutionary optimization using cooperative coevolution

Evolutionary algorithms (EAs) have been applied with success to many numerical and combinatorial optimization problems in recent years. However, they often lose their effectiveness and advantages when applied to large and complex problems, e.g., those with high dimensions. Although cooperative coevolution has been proposed as a promising framework for tackling high-dimensional optimization problems, only limited studies were reported by decomposing a high-dimensional problem into single variables (dimensions). Such methods of decomposition often failed to solve nonseparable problems, for which tight interactions exist among different decision variables. In this paper, we propose a new cooperative coevolution framework that is capable of optimizing large scale nonseparable problems. A random grouping scheme and adaptive weighting are introduced in problem decomposition and coevolution. Instead of conventional evolutionary algorithms, a novel differential evolution algorithm is adopted. Theoretical analysis is presented in this paper to show why and how the new framework can be effective for optimizing large nonseparable problems. Extensive computational studies are also carried out to evaluate the performance of newly proposed algorithm on a large number of benchmark functions with up to 1000 dimensions. The results show clearly that our framework and algorithm are effective as well as efficient for large scale evolutionary optimisation problems. We are unaware of any other evolutionary algorithms that can optimize 1000-dimension nonseparable problems as effectively and efficiently as we have done.     

Note on a unified approach to the single-machine scheduling problem with a deterioration effect and convex resource allocation

In many resource allocation problems in physical or economic systems, a linear resource consumption function is commonly considered, and job processing times are assumed to be fixed parameters. However, the former assumption fails to reflect the law of diminishing returns, and the latter may be controlled by changing the allocation of resources to jobs. Motivated by these observations, we provide a unified model for solving single-machine scheduling problems in which each job's processing time is a function of its starting time and convex resource allocation. The objective is to find the optimal sequence of jobs subject to a limited resource consumption. We first show how this unified model can be useful in solving scheduling problems under due date assignment considerations. We analyze the problem with four different due date assignment methods, and our objective function includes costs for earliness, tardiness and due date assignments. We also consider scheduling problems without involving due date assignment decisions. The objective function is to minimize the makespan, total completion time, total absolute variation in completion times, and total absolute variation in waiting times. We show that several existing well-known problems can be reduced to a special case of our unified model and solved in O(nlogn) time.     

基于拍卖机制的网格资源分配方法

网格资源的动态性、异构性、自治性等特点,使得网格资源分配成为一个难点。目前存在的大多数分配方法仅关注分配效率,却对提高资源分配的公平性缺乏深入的研究。针对此问题,提出一种基于拍卖机制的网格资源分配方法,利用资源分配比例的算法分配资源。通过仿真实验表明该方法适合网格系统中的资源分配,能有效分配资源,提高了资源利用率,同时资源分配的公平性也得到显著的提高。     

PIVOT: An adaptive information discovery framework for computational grids

In a traditional computational grid environment, the owners of resources usually provide information about their resources extracted by pre-configured information services or web services. However, such information is not sufficient for the scheduler in the high-performance distributed computing. To solve this problem, we propose a scalable grid information service framework, named PIVOT (adaPtive Information discoVery framewOrk for compuTational grid). By using deadline-constrained flooding collector dissemination and P2P-like information collection schemes, PIVOT provides an active mechanism to collect application-specific resource information. In particular, PIVOT provides a resource information service for application-specific schedulers. The best-effort performance on overhead traffic and communication latency during information discovery is guaranteed by two new distributed cooperative algorithms. The experimental results in the simulations and real computational grid platform demonstrate that PIVOT has a high level of adaptability for application-specific resource information discovery, and also improves the accuracy of resource allocation and the efficiency of executing parallel tasks in traditional information services.     

求解大规模全局优化问题的新型三层递归差分分组方法

协同进化算法在求解大规模全局优化问题上具有较好的效果,其核心思想是利用分而治之的策略将一个高维问题分解成若干个子问题,然后分别优化每个子问题.然而,现有的分解方法通常需要花费大量的计算成本来获得精确的变量分组.通过采用递归交互检测中的历史信息简化分组过程,能够避免检测某些集合的相互关系,本文提出了一种新型三层递归差分分组策略(NTRDG).与其他4种现有的分组方法相比, NTRDG在不影响分组精度的情况下计算成本消耗较低.仿真结果表明, NTRDG在求解大规模全局优化问题时具有很强的竞争力.     

云环境下求解大规模优化问题的协同差分进化算法

差分进化是一种求解连续优化问题的高效算法。然而差分进化算法求解大规模优化问题时,随着问题维数的增加,算法的性能下降,且搜索时间呈指数上升。针对此问题,本文提出了一种新的基于Spark的合作协同差分进化算法（SparkDECC）。SparkDECC采用分治策略,首先通过随机分组方法将高维优化问题分解成多个低维子问题,然后利用Spark的弹性分布式数据模型,对每个子问题并行求解,最后利用协同机制得到高维问题的完整解。通过在13个高维测试函数上进行的对比实验和分析,实验结果表明算法加速明显且可扩展性好,验证了SparkDECC的有效性和适用性。     

Cooperative coevolution of artificial neural network ensembles for pattern classification

This paper presents a cooperative coevolutive approach for designing neural network ensembles. Cooperative coevolution is a recent paradigm in evolutionary computation that allows the effective modeling of cooperative environments. Although theoretically, a single neural network with a sufficient number of neurons in the hidden layer would suffice to solve any problem, in practice many real-world problems are too hard to construct the appropriate network that solve them. In such problems, neural network ensembles are a successful alternative. Nevertheless, the design of neural network ensembles is a complex task. In this paper, we propose a general framework for designing neural network ensembles by means of cooperative coevolution. The proposed model has two main objectives: first, the improvement of the combination of the trained individual networks; second, the cooperative evolution of such networks, encouraging collaboration among them, instead of a separate training of each network. In order to favor the cooperation of the networks, each network is evaluated throughout the evolutionary process using a multiobjective method. For each network, different objectives are defined, considering not only its performance in the given problem, but also its cooperation with the rest of the networks. In addition, a population of ensembles is evolved, improving the combination of networks and obtaining subsets of networks to form ensembles that perform better than the combination of all the evolved networks. The proposed model is applied to ten real-world classification problems of a very different nature from the UCI machine learning repository and proben1 benchmark set. In all of them the performance of the model is better than the performance of standard ensembles in terms of generalization error. Moreover, the size of the obtained ensembles is also smaller.     

基于非支配排序差异演化的应急资源多目标分配算法

应急资源分配（Emergency resource allocation,ERA）是灾害应急管理中的核心环节,主要研究如何高效合理地把各储备点的应急救援物资分配给各发放点.然而,在大规模突发灾害发生后,每个发放点极可能会同时向多个储备点请求多种救援物资,从而带来潜在的应急资源冲突.为此,本文首先构建了考虑应急资源冲突消解的多储备点、多发放点、多种救援物资的应急资源多目标优化模型,并提出了一种基于非支配排序差异演化和编码修正机制的应急资源多目标分配算法.对比实验结果表明,该算法在大规模样本下能够从全局角度同时给出多个发放点的应急资源分配方案,有效实现多个储备点同时为多个发放点协同配备应急资源,而且不会产生任何应急资源冲突,为解决应急资源受限情况下的大规模应急资源分配问题提供了一个有益的尝试.     

Resource allocation decisions under various demands and cost requirements in an unreliable flow network

This paper considers resource allocation decisions in an unreliable multi-source multi-sink flow network, which applies to many real-world systems such as electric and power systems, telecommunications, and transportation systems. Due to uncertainties of components in such an unreliable flow network, transmitting resources successfully and economically through the unreliable flow network is of concern to resource allocation decisions at resource-supplying (source) nodes. We study the resource allocation decisions in an unreliable flow network for a range of demand configurations constrained by demand-dependent and demand-independent cost considerations under the reliability optimization objective. Solutions to these problems can be obtained by computing the resource allocation for each demand configuration independently. In contrast, we pursue an updating scheme that eludes time-consuming enumeration of flow patterns, which is necessary in independent computation of resource allocations for different demand configurations. We show that updating is attainable under both demand-independent and demand-dependent cost constraints when demand incurs an incremental change, and demonstrate the proposed updating scheme with numerical examples.     

Algorithmic mechanism design for load balancing in distributed systems

Computational grids are promising next-generation computing platforms for large-scale problems in science and engineering. Grids are large-scale computing systems composed of geographically distributed resources (computers, storage etc.) owned by self interested agents or organizations. These agents may manipulate the resource allocation algorithm in their own benefit, and their selfish behavior may lead to severe performance degradation and poor efficiency. In this paper, we investigate the problem of designing protocols for resource allocation involving selfish agents. Solving this kind of problems is the object of mechanism design theory. Using this theory, we design a truthful mechanism for solving the static load balancing problem in heterogeneous distributed systems. We prove that using the optimal allocation algorithm the output function admits a truthful payment scheme satisfying voluntary participation. We derive a protocol that implements our mechanism and present experiments to show its effectiveness.     

Using Constraint Satisfaction Problem approach to solve human resource allocation problems in cooperative health services

In developing countries, the increasing utilization of health services, due to a great life expectancy, is followed by a reduction in incomes from the public health system and from private insurance companies, to the payment of medical procedures. Beyond this scenery, it is mandatory an effective hospital cost control though the utilization of planning tools.This work is intended to contribute to the reduction of hospital costs, proposing a new tool for planning human resources utilization in hospital plants. Specifically, it is proposed a new tool for human resources allocation in health units. The solution to the allocation problem uses the CSP technique (Constraint Satisfaction Problem) associated with the backtracking search algorithm. With the objective of enhancing the backtracking search algorithm performance a new heuristics is proposed. Through some simulations the performance of the proposed heuristics is compared to the other heuristics previously published in literature: remaining minimum values, forward checking and grade heuristics.Another important contribution of this work is the mathematical modeling of the constraints, that could be unary, multiple, numeric and implicit constraints. In the results it is presented a case study of a human resource allocation in a cooperative health service.Based on the results, it is proposed that for a real allocation problems solution, the best approach is to combine the remaining minimum values heuristics with the grade heuristics, to select the best unit allocation to be filled, and then use the proposed heuristic to select the best physician to the chosen unit allocation. This association shows a satisfactory result for the human resource allocation problem of the case study, with an algorithm convergence time of 46.7 min with no backtracks. The same problem when manually resolved took about more than 50 h.     

Game Theoretic subcarrier and power allocation for wireless OFDMA networks

This paper proposes a bargaining game theoretic resource(including the subcarrier and the power) allocation scheme for wireless orthogonal frequency division multiple access(OFDMA) networks.We define a wireless user s payoff as a function of the achieved data-rate.The fairness resource allocation problem can then be modeled as a cooperative bargaining game.The objective of the game is to maximize the aggregate payoffs for the users.To search for the Nash bargaining solution(NBS) of the game,a suboptimal subcarrier allocation is performed by assuming an equal power allocation.Thereafter,an optimal power allocation is performed to maximize the sum payoff for the users.By comparing with the max-rate and the max-min algorithms,simulation results show that the proposed game could achieve a good tradeoff between the user fairness and the overall system performance.     

任务分配与调度的共同进化方法

并行与分布式计算环境中随着独立任务的增多,传统进化类单种群的任务分配与调度算法的效率与效力随之大为降低,该文在分析传统解完整编码单种群进化类算法的基础上,基于生物界多物种间共同进化的机制提出了任务分配与调度的合作式共同进化计算模型,并探讨了任务分配与调度问题中的子种群合作方式与个体的适应值计算方法。此外,从数学上分析了基于合作式共同进化的任务分配与调度算法的性能,指出共同进化调度方法中好的调度方案能以高于传统单种群进化算法的递增指数递增。仿真分析证实了算法的理论分析结果,算法具有实际工程价值。     

Radio resource allocation problems for OFDMA cellular systems

Orthogonal frequency division multiple-access (OFDMA) manages to efficiently exploit the inherent multi-user diversity of a cellular system by performing dynamic resource allocation. Radio resource allocation is the technique that assigns to each user in the system a subset of the available radio resources (mainly power and bandwidth) according to a certain optimality criterion on the basis of the experienced link quality. In this paper we address the problem of resource allocation in the downlink of a multi-cellular OFDMA system. The allocation problem is formulated with the goal of minimizing the transmitted power subject to individual rate constraint for each user. Exact and heuristic algorithms are proposed for the both the single-cell and the multi-cell scenario. In particular, we show that in the single-cell scenario the allocation problem can be efficiently solved following a network flow approach. In the multi-cell scenario we assume that all cells use the same frequencies and therefore the allocation problem is complicated by the presence of strong multiple access interference. We prove that the problem is strongly NP-hard, and we present an exact approach based on an MILP formulation. We also propose two heuristic algorithms designed to be simple and fast. All algorithms are tested and evaluated through an experimental campaign on simulated instances. Experimental results show that, although suboptimal, a Lagrangian-based heuristic consisting in solving a series of minimum network cost flow problems is attractive for practical implementation, both for the quality of the solutions and for the small computational times.