Performance evaluation of massively parallel processing architectures with three-dimensional optical interconnections

We evaluate the performance of three-dimensional optoelectronic computer architectures on the basis of basic database operations and parallel benchmark algorithms for numerical computations. We show that the select and the join database operations can be performed much faster with an optical interconnection network. Also, optoelectronic architectures can perform the fast Fourier transform and sorting benchmarks orders of magnitude faster than electronic supercomputers. An architecture with an adequately fast reconfigurable interconnection network can perform the conjugate-gradient benchmark faster than all parallel supercomputers, but its performance is not as impressive when a fixed network is used. In the case of the multigrid benchmark the three-dimensional optoelectronic architecture also can outperform the best parallel supercomputers.     

同一设备维护业务多次外包的相对有效性评价

选择合同价格或实际支付价格和支持成本作为输入指标,维护质量和维护工期作为输出指标,分别以输入倾向和输出倾向建立了BCC模型;比较了两种模型的评价结果,说明这对找出各决策单元中的绩效改进标杆和绩效改进的重点有利;最后给出了一个算例.     

Sample average approximation for multi-vehicle collection–disassembly problem under uncertainty

The implementation of the circular economy is increasingly supported by many governments. It is performed by integrating the activities of reverse supply chain (RSC) into those of forward supply chain. However, many companies that traditionally focus on the activities of forward supply chain have decided to collaborate with third-party reverse logistics providers to manage the RSC. This collaboration motivates the work presented in this paper to propose better decisions for decision makers in the providers under the fact that integrating decisions of the collection of End-of-Life products and their disassembly process proposes a RSC with better performance. In this paper, an integrated problem concerning those decisions is presented and formalised. It also deals with the uncertainty of the quality and the quantity of products as well as the demands of the associated components. Two approximate methods are developed to provide the solutions.     

基于服务价格和质量承诺的服务商竞争策略研究

现有研究仅关注单一服务商的质量承诺决策,而忽视多个服务商关于服务价格和质量承诺的竞争策略。为弥补这种研究不足,本文的研究并不仅停留在单一的服务承诺研究,而是扩展到服务价格层面进行综合研究,以此探讨服务商竞争策略。本文构建3种竞争模型,并运用最优化方法研究,得到结论如下。1) 在基于服务价格的竞争模型中,当质量承诺水平一定时,一旦有服务商涨价,其他服务商会制定相同的决策。然而,一旦有服务商提高质量承诺水平,其他服务商会制定降价决策予以应对。2) 在基于质量承诺水平的竞争模型中,当服务价格一定时,一旦有服务商提高质量承诺水平,其他服务商会适当地降低质量承诺水平来减少服务失败时的补偿。一旦有服务商从服务价格层面提升竞争力,那么,为应对这种竞争策略,其他服务商就会从质量承诺方面进行补偿。3) 本文基于服务竞争策略研究而建立了竞争模型,从模型研究中发现,各个服务商之间必然存在纳什均衡。本文对服务市场竞争环境下,关于服务价格和质量承诺的竞争策略的制定有一定的实践指导意义。     

Efficient Ordering Heuristics in Binary Decision Diagram–based Fault Tree Analysis

In binary decision diagram–based fault tree analysis, the size of binary decision diagram encoding fault trees heavily depends on the chosen ordering. Heuristics are often used to obtain good orderings. The most important heuristics are depth‐first leftmost (DFLM) and its variants weighting DFLM (WDFLM) and repeated‐event‐priority DFLM (RDFLM). Although having been used widely, their performance is still only vaguely understood, and not much formal work has been done. This article firstly identifies some basic requirements for a reliable benchmark and gives a benchmark generation method. Then, using the generated benchmark, the performance of DFLM and its variants is studied. Both the experimental results and some interesting findings for our research questions are proposed. This article also presents a new weighting DFLM (NWDFLM) heuristic and the underlying basic ideas and gives both the experimental results and conclusions on the performance comparison. As a final synthesis of all previous results, a practical suggestion of the order of heuristic selection to process a large fault tree is NWDFLM < WDFLM < RDFLM. Copyright © 2012 John Wiley & Sons, Ltd.     

Determining the optimal collaborative benchmarks in a supply chain

Supply chains are currently considered dynamic systems and will change with time and with the environment. Thus, the performance of a supply chain system will not only be influenced by the determined measuring factors but also by the adding or withdrawal of enterprises. Faced with these unstable systems, this study employs the Data Envelopment Analysis and sensitivity analysis methods in order to measure supply chain collaborative performance as well as relative individual company performance. This approach principally aims at finding: (1) an effective collaborative performance evaluation method that can cover the measuring factors admitted by all chain members; (2) the robust benchmark companies for closely related chain members. The results of this research clearly show the expected study target that will benefit chain members in performance improvement.     

Improved genetic algorithm based on particle swarm optimization-inspired reference point placement

This article investigates the use of optimal reference point placement to improve performance of non-dominated sorting genetic algorithm (NSGA). Placement of reference points for many-objective optimization is inspired by wheel and Von Neumann topologies of Particle Swarm Optimization (PSO). Results obtained show that the pattern of reference point placement determines performance efficiency of NSGA. The better-performing wheel topology (called wheel reference point genetic algorithm (wRPGA), is compared to three other many-objective evolutionary algorithms: knee-driven evolutionary algorithm (KnEA), non-dominated sorting genetic algorithm III (NSGAIII) and multi-objective evolutionary algorithm based on dominance and decomposition (MOEAD/D). The selected many-objective benchmark problems are Walking Fish Group 2 (WFG2) and Deb-Thiele-Laumanns-Zitzler 2 (DTLZ2). It is also tested on a 3-objective cost function for a hypothetical model of a stand-alone microgrid. Through the simulations, the wheel configuration performed 88.9% better than the Von Neumann configuration. The wheel topology also achieved better performance with respect to inverted generational distance (IGD) compared to KnEA, NSGAIII and MOEAD/D for 7 out of 15 IEEE Congress on Evolutionary Computation (CEC) 2017 benchmark problems. wRPGA gave a good approximation of the Pareto front for the 3-objective model representing the hypothetical microgrid.     

The once and future application of cost-effectiveness analysis.

BACKGROUND: Cost-effectiveness analysis (CEA) is used by payers to make coverage decisions, by providers to make formulary decisions, and by large purchasers/employers and policymakers to choose health care performance measures. However, it continues to be poorly utilized in the marketplace because of overriding financial imperatives to control costs and a low apparent willingness to pay for quality. There is no obvious relationship between the cost-effectiveness of life-saving interventions and their application. Health care decision makers consider financial impact, safety, and effectiveness before cost-effectiveness. WHY IS CEA NOT MORE WIDELY APPLIED? Most health care providers have a short-term parochial financial perspective, whereas CEA takes a long-term view that captures all costs, benefits, and hazards, regardless of to whom they accrue. In addition, a history of poor standardization of methods, unrealistic expectations that CEA could answer fundamental ethical and political issues, and society's failure to accept the need for allocating scarce resources more judiciously, have contributed to relatively little use of the method by decision makers. HOW WILL CEA FIND GREATER UTILITY IN THE FUTURE? As decision makers take a longer-term view and understand that CEA can provide a quantitative perspective on important resource allocation decisions, including the distributional consequences of alternative choices, CEA is likely to find greater use. However, it must be embedded within a framework that promotes confidence in the social justice of health care decision making through ongoing dialogue about how the value of health and health care are defined.     

Understanding ‘Misfits’: Aspirations and Systematic Deviations from Firm-Specific Optimal Multinationality

Transaction cost and internalization theory predicts that there are firm-specific optimal levels of multinationality, and deviations from firm-specific optimal multinationality should decrease performance. However, one important unanswered question is why some firms continuously deviate from their firm-specific optimal levels of multinationality. In this conceptual analysis, I argue that continuous deviations from firm-specific optimal levels of multinationality can be explained by decision-makers’ use of aspirations. Specifically, if a firm deviates from its optimal level of multinationality and performance decreases, but aspirations are still being met, decision makers will not perceive a problem and will not engage in search processes to identify and implement changes that align the firm with its optimal level of multinationality. In addition, decision makers in smaller firms may respond to shortfalls between aspirations and actual performance with greater rigidity. Additionally, narcissistic decision makers, and decision makers who are highly accountable to external audiences, may retrospectively revise aspirations in the face of shortfalls in actual performance.     

考虑公有与私有资源约束的多项目调度规则对比研究

为了识别出适用于具有公有资源与私有资源约束的多项目调度问题的优先规则,基于标准测试集MPSPLIB进行计算实验,并基于相对偏差指标对比分析25种经典优先规则在最小化最大完工时间、最小化总拖期、最小化加权总拖期3种决策目标下的表现。实验结果表明,优先规则的表现与决策目标、单项目任务数、并行项目数、资源利用系数等因素之间具有显著相关性。在最小化总拖期目标下,尽管大多数规则的表现与现有文献总体一致,但是MAXTWK和MINSLK规则的表现有着显著差异。所得实验结论对于工程实际多项目调度决策具有指导意义。     

A control engineering approach to the assessment of supply chain resilience

There is no consensus on the supply chain management definition of resilience. To aid in evaluating the dynamic behaviour of such systems we need to establish clearly elucidated performance criteria that encapsulate the attributes of resilience. A literature review establishes the latter as readiness, responsiveness and recovery. We also identify robustness as a necessary condition that would complement resilience. We find that the Integral of the Time Absolute Error (ITAE) is an appropriate control engineering measure of resilience when it is applied to inventory levels and shipment rates. We use the ITAE to evaluate an often used benchmark model of make-to-stock supply chains consisting of three decision parameters. We use both linear and nonlinear forms of the model in our evaluation. Our findings suggest that optimum solutions for resilience do not yield a system that is robust to uncertainties in lead-time. Hence supply chains will experience drastic changes in their resilience performance when lead-time changes.     

A review of: “Engineering Reliability: Fundamentals and Applications”. By R. RAMAKUMAR (Prentice-Hall, 1993) [Pp. xiii + 482].

In this work, an algorithm for rescheduling the affected operations in a job shop is presented and its performance, with respect to measures of efficiency and stability, is compared with that of Total Rescheduling and Right-Shift Rescheduling. Algorithms for all three methods are implemented in a rescheduling program which, based on keyed-in disruption information, produces three new schedules and outputs their related performance measures. A designed factorial experiment is performed on benchmark scheduling problems to study the effect of different rescheduling methods as well as various problem characteristics and disruption scenarios on the performance of the new schedules. The results demonstrate that the Affected Operations Algorithm overcomes the disadvantages associated with other rescheduling methods.     

企业研发项目中止决策的灰靶分析模型

中止决策是研发项目过程管理中不可或缺的环节,是企业提高研发效率、改善资源配置的有效手段。根据该问题模糊多属性分类决策的本质,以案例分析为起点,结合模糊集与灰靶决策理论构建求解属性权重、临界点等分类参数的优化模型,利用所得参数计算待判项目的综合靶心矩,最后按照综合靶心矩所属区间对待判项目进行分类决策。将所提方法应用于一个具体的决策分析过程,其可行性和合理性得到验证。     

需求依赖于价格环境下的批发价格契约

研究需求依赖于价格环境下的批发价格契约,建立了由单个零售商和单个制造商组成的供应链模型。该模型中零售商决定商品的订货量和销售价格,制造商决定商品的批发价格。以集中决策系统下供应链的利润、销售价格和销售量作为基准,研究分散情况下供应链的效率、销售价格以及销售量的变化,同时考虑在此情况下制造商提供的批发价格构成,在此基础上进一步探讨价格弹性对供应链效率和供应链利润分配比例的影响。     

A Pareto block-based estimation and distribution algorithm for multi-objective permutation flow shop scheduling problem

Multi-objective flow shop scheduling plays a key role in real-life scheduling problem which attract the researcher attention. The primary concern is to find the best sequence for flow shop scheduling problem. Estimation of Distribution Algorithms (EDAs) has gained sufficient attention from the researchers and it provides prominent results as an alternate of traditional evolutionary algorithms. In this paper, we propose the pareto optimal block-based EDA using bivariate model for multi-objective flow shop scheduling problem. We apply a bivariate probabilistic model to generate block which have the better diversity. We employ the non-dominated sorting technique to filter the solutions. To check the performance of proposed approach, we test it on the benchmark problems available in OR-library and then we compare it with non-dominated sorting genetic algorithm-II (NSGA-II). Computational results show that pareto optimal BBEDA provides better result and better convergence than NSGA-II.     

Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder (DAE) for learning the robust feature representation and one-class support vector machine (OCSVM) for finding the more compact decision hyperplane for intrusion detection. Specially, the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously. This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection. Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model. First, the ablation evaluation on benchmark dataset, NSL-KDD validates the design decision of the proposed model. Next, the performance evaluation on recent intrusion dataset, UNSW-NB15 signifies the stable performance of the proposed model. Finally, the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.     

过度自信对投资项目后续增资决策的影响

目的采用情境模拟实验研究过度自信对不同投资绩效下投资项目增资决策的影响。方法采用2×3被试间和被试内混合实验设计,其中被试自信程度为被试间变量,项目运行一阶段后的绩效状态为被试内变量。结果过度自信的被试对投资项目运行后的评价要显著高于相对理性的被试,但两类被试在项目后续决策上基本上不存在显著差异,仅仅是当投资绩效为模糊状态时,高估投资收益型过度自信的被试更少放弃投资。高估投资收益和对投资收益的定位过高型过度自信的被试比理性被试增资的持续阶段更长,但仅在投资绩效为积极状态时,在持续增资的第二阶段,对投资收益的定位过高型过度自信的被试增资程度更高。结论过度自信具有情境性,采用与投资决策任务有关的方式测量被试信心时,过度自信对投资项目后续增资决策存在显著影响。     

New formulations for the setup assembly line balancing and scheduling problem

We present three new formulations for the setup assembly line balancing and scheduling problem (SUALBSP). Unlike the simple assembly line balancing problem, sequence-dependent setup times are considered between the tasks in the SUALBSP. These setup times may significantly influence the station times. Thus, there is a need for scheduling the list of tasks within each station so as to optimize the overall performance of the assembly line. In this study, we first scrutinize the previous formulation of the problem, which is a station-based model. Then, three new formulations are developed by the use of new sets of decision variables. In one of these formulations, the schedule-based formulation, SUALBSP is completely formulated as a scheduling problem. That is, no decision variable in the model directly denotes a station. All the proposed formulations will be improved by the use of several enhancement techniques such as preprocessing and valid inequalities. These improved formulations can be applied to establishing lower bounds on the problem. To assess the performance of new formulations, results of an extensive computational study on the benchmark data sets are also reported.     

Set-partitioning-based heuristic for balancing and configuration of automated flexible machining line

Flexible machining lines are used in a wide range of industries due to their ability of reconfiguration to meet high variety of customer demands. A novel problem is proposed in the current research to consider automated flexible machining line (AFML) with automated machining using computer numerical control machines and automated auxiliary operations using robots. A mixed-integer programming model for the current novel problem is developed. Moreover, a novel method named set-partitioning-based heuristic (SPH) is proposed to solve this new flexible machining line balancing problem to minimise the cycle time of the line and the performance is compared with both exact algorithm and random search algorithm. A set of benchmark instances based on different size of problems against different system parameters is made. Furthermore, sensitivity analysis of the system parameter in AFML is performed to know, how the number of machines and processing time can influence the cycle time and the utilisation of AFML. Computational experiments are performed to show the performance of the proposed method SPH against other methods and the results indicate that SPH performs best among all test methods in terms of solution quality and computation on both the proposed benchmark instances.