一种两级分布调度算法

针对传统分布式调度算法研究中将调度策略与机制相分离的不足，本文提出了两级分布调度算法，它能较好地结合两种调度机制（远程执行与进程迁移），提高了系统的性能。     

一个实时CORBA的动态调度服务模型

在实时CORBA中引入调度服务简化了实时应用的开发,但目前实时CORBA1．0所定义的调度服务使用固定优先级调度策略只能适合于一个封闭的CORBA应用系统．本文针对这一局限性,提出了一个基于客户调度器和服务调度器相协作的动态调度服务模型对其进行扩展,使得调度服务可以适用于一个更为开放的实时CORBA系统．     

网格调度综述

简要地分析了网格资源的特点,给出了网格作业调度过程中所面临的主要挑战.为了便于对网格调度有更清晰的认识,介绍了包含网格系统主要功能模块的网格调度体系结构,并结合特定作业具体介绍了网格作业调度流程.从3个不同的角度重点对网格环境中的作业调度算法进行了详尽介绍,并就各种调度算法的优缺点和适用情形进行了比较和分析.对网格调度进行了总结,并从几个不同的方面对网格调度算法的研究进行了展望.     

实时系统调度算法综述

在多道程序环境下,主存中有多个进程,其数目往往多于处理机数目。操作系统通过处理机调度程序,按照某种调度算法动态地把处理机分配给就绪队列中的一个进程,使之执行。处理机是重要的计算机资源,提高处理机的利用率及改善系统性能（吞吐量、响应时间）,很大程度上取决于处理机调度性能的好坏,因而操作系统的调度算法是非常重要的。通过研究基本的操作系统作业（进程）调度算法,详尽分析和对比这些调度算法的优势和劣势。最后对新兴的实时系统研究现状进行介绍和展望,为以后实时系统调度算法研究提供了有效的参考价值。     

非规则循环的OpenMP调度算法

针对非规则循环结构,评估各种不同OpenMP预定义的调度策略的调度效果及各自优缺点,在分析已有调度策略存在不足的基础上,采用分担任务的思想以及插桩技术设计一种适合非规则循环结构的均衡调度算法Balance,实现对非规则循环调度的负载平衡。测试结果表明该调度算法是有效的。     

EPA通信调度测试方法与实现技术

介绍了EPA通信协议模型和EPA通信调度规程，根据通信调度的原理和要求，研究了EPA协议确定性调度的测试原理，提出了测试EPA确定性调度的方法和系统结构，对调度偏差和调度顺序分别论述了其相应的测试流程。通过测试系统的实际应用证明这种测试方法能够准确地测试被测设备调度实体的实现与EPA标准的符合程度。     

液压支架单片机分布式控制系统调度监控软件的设计

在介绍了液压支架单片机分布控制系统原理和功能基础上,着重介绍了系统调度监控软件的设计,调度表编制,调度计划的签发,调度程序的运行.以及被控对象结构功能变化时,在不修改系统硬件和基本软件的条件下,通过修改调整调度表,使系统适应被控对象结构功能的变化.     

并行作业调度系统的设计与实现

在并行计算的作业调度过程中，涉及到调度系统两个方面的内容：调度策略和调度算法。文章讨论了调度策略的设计和调度算法的选择．并通过一个实际的并行处理系统加以说明。它使调度系统更好地满足了并行处理系统作业调度的需要，提高了系统资源的利用率。     

分布式系统下的DAG任务调度研究综述

近年来随着网格、云计算工作流等分布式计算技术的发展,关于DAG(有向无环图)模型任务在分布式系统环境下的调度问题逐渐成为备受关注的研究热点。根据最新研究进展,对分布式系统下的DAG任务调度问题和有关技术进行了研究与讨论,主要包括四个方面:系统地描述了分布式系统和异构分布式系统的有关概念,异构分布式系统下的DAG任务调度问题、调度模型及其典型应用;对现有分布式系统下DAG任务调度的研究按照不同的方式进行了分类;探讨了多DAG共享异构分布式资源调度的研究现状;讨论了目前多DAG共享异构分布式资源调度研究存在的问题和未来可能的研究方向。     

基于强化学习的智能车间调度策略研究综述

智能制造是我国制造业发展的必然趋势,而智能车间调度是制造业升级和深化“两化融合”的关键技术。主要研究强化学习算法在车间调度问题中的应用,为后续的研究奠定基础。其中车间调度主要包括静态调度和动态调度;强化学习算法主要包括基于值函数和AC（Actor-Critic）网络。首先,从总体上阐述了强化学习方法在作业车间调度和流水车间调度这两大问题上的研究现状;其次,对车间调度问题的数学模型以及强化学习算法中最关键的马尔可夫模型建立规则进行分类讨论;最后,根据研究现状和当前工业数字化转型需求,对智能车间调度技术的未来研究方向进行了展望。     

基于云计算神经网络物流车辆调度算法研究

研究了物流车辆调度优化问题。针对云计算下任务调度算法没有考虑调度的服务质量和用户满意度的问题,特别是在物流任务调度问题中存在复杂的计算网络,造成计算率降低,为了解决上述问题,提出了一种新的有关云计算和神经网络相结合的物流作业调度算法。算法充分考虑了调度的服务质量以及用户满意度,建立一个参数化的处理模型,计算用户在各个资源上的综合满意度,再将任务分配到满足用户需求和使系统资源达到均衡的资源上执行,最后采用改进的神经网络进行优化车辆调度。实验结果表明,改进算法不仅能满足用户的多种需求,提高了用户的满意度,同时也提高了资源调度率和系统资源的利用率。     

FCM-based customer expectation-driven service dispatch system

Maintaining long-term customer loyalty has been an important issue in the service industry. Although customer satisfaction can be enhanced with better service quality, delivering appropriate services to customers poses challenges to service providers, particularly in real-time and resource-limited dynamic service contexts. However, customer expectation management has been regarded as an effective way for helping service providers achieve high customer satisfaction in the real world that is nevertheless less real-time and dynamic. This study designs a FCM-based customer expectation-driven service dispatch system to empower providers with the capability to deal effectively with the problem of delivering right services to right customers in right contexts. Our evaluation results show that service providers can make appropriate decisions on service dispatch for customers by effectively managing customer expectations and arranging their contextual limited resources and time via the proposed service dispatch system. Meanwhile, customers can receive suitable service and obtain high satisfaction when appropriate services are provided. Accordingly, a high-performance ecosystem can be established by both service providers and customers who co-create value in the dynamic service contexts.     

The effect of online social value on satisfaction and continued use of social media

Social media offers customers a unique service value proposition. Recognizing service value as a pivotal concept, this study develops an aggregate construct of online social value, whereby customers evaluate utilitarian and hedonic benefits relative to what they must sacrifice in effort and risk in deriving a value calculation of online social networking services which predicts satisfaction and continued use of online social media, such as Facebook. By empirically testing a model that explains online social value, this research contributes to information systems (IS) theory by introducing a customer value perspective in the social media context and helps service providers by identifying factors predicting satisfaction and continued use that might be employed to improve offerings to keep customers coming back.     

Service interaction design: A Hawk-Dove game based approach to managing customer expectations for oligopoly service providers

In the “experience economy”, effectively delivering memorable and exciting customer experiences has become a key issue for service providers. Service experience delivery involves service encounters through which interactions between service providers and customers can be shaped into interactive artifacts managing customer expectations and dynamically delivering suitable services. Service interaction design aims to optimize customer interactions with services to match customer expectations and yield satisfactory service experiences. On the other hand, service providers typically make profits and cost the priority, despite knowing that high service quality can maximize satisfaction, particularly in markets served by an oligopoly, resulting in customers only accepting existing limited-value services. Hence, the oligopoly market can be regarded as a value-bounded context. Additionally, understanding customer expectations regarding a wide range of interactions is crucial to service providers selecting and designing services that match customer expectations. Therefore, this paper presents a service interaction design mechanism to help oligopoly service providers systematically and effectively manage customer expectations in dynamic interactions, even in value-bounded contexts. The proposed mechanism models this service interaction design problem as a series of Hawk-Dove games that approach an evolutionary stable state. The evaluation results suggest that oligopoly service providers should change their mindsets and design service interactions to manage customer expectations associated with service delivery, not only to ensure high satisfaction and profit but also to engage customers in co-creating value.     

A custom collaboration service system for idea management of mobile phone design

Customized collaborative service (CCS) systems are defined as the e‐services transforming a business process into a collaborative service model and aiming to facilitate interactions with customers and assist the providers in dealing with collaborative strategies and activities. The demand for such services has grown rapidly in a shift of people out of the manufacturing mindset into the service‐dominant mindset. For example, the mobile‐phone market now tends to customization rather than commoditization, and customer‐driven design strategies increasingly substitute for technology‐driven design strategies. This trend accordingly urges the mobile‐phone companies to center on a customer‐centric idea management process to assure customer idea originality but also sustain the process feasibility for realistic product design. However, a method to engineer such CCS systems has not been addressed. This article presents a prototype system named iMobileDesign to exemplify a CCS system. We present a new methodology to engineer this CCS system aiming to achieve semiautomated value coproduction with productivity and satisfaction. This method comprises two parts: simple service machine (SSM) and intelligent service machine (ISM). Usage of SSM and ISM would lead to the formation of analysis and design of the CCS system that joins the service provider efforts with their customers for ensuring a customer‐centric idea management process. © 2009 Wiley Periodicals, Inc.     

Willing to pay for quality personalization? Trade-off between quality and privacy

Online personalization presents recommendations of products and services based on customers’ past online purchases or browsing behavior. Personalization applications reduce information overload and provide value-added services. However, their adoption is hindered by customers’ concerns about information privacy. This paper reports on research undertaken to determine whether a high-quality recommendation service will encourage customers to use online personalization. We collected data through a series of online experiments to examine the impacts of privacy and quality on personalization usage and on users’ willingness to pay and to disclose information when using news and financial services. Our findings suggest that under certain circumstances, perceived personalization quality can outweigh the impact of privacy concerns. This implies that service providers can improve the perceived quality of personalization services being offered in order to offset customer privacy concerns. Nevertheless, the impact of perceived quality on personalization usage is weaker for customers who have experienced privacy invasion in the past. The results show that customers who are likely to use online personalization are also likely to pay for the service. This finding suggests that, despite privacy concerns, there is an opportunity for businesses to monetize high-quality personalization.     

多技能维修服务干扰管理研究

针对多技能维修工人调度系统的干扰管理问题，以最小化干扰对原调度计划的影响为目标建立了客户需求变化的干扰管理模型。利用前景理论度量多技能工维修调度系统中的三个利益主体客户、服务中心管理人员和多技能维修工人的干扰，同时考虑到多技能工掌握的技能与任务需要的技能的匹配的约束，以及服务时间的约束。利用遗传算法对模型进行求解，验证了干扰管理模型的有效性。     

A sharing-oriented service selection and scheduling approach for the optimization of resource utilization

We present a sharing-oriented service selection and scheduling approach capable of finding a trade-off between requirement satisfaction degree, service utilization rate and service sharing cost for limited quantities and capacities of available services. In traditional service selection approaches, each customer requirement is independently satisfied by optimally selecting a set of candidate service resources. However, in real-life service scenarios, it is usual for multiple customers to raise their requirements simultaneously, and available services need to be allocated between them. Especially, when available services are limited in both quantity and capacity, a traditional “first-come-first-serve” strategy would lead to a low service utilization rate, and some requirements cannot be satisfied at all (i.e., a low requirement satisfaction degree). Our approach makes use of the feature that some services can be shared by several customer requirements. Specifically, a virtualized service resource consisting of multiple candidate services is constructed and scheduled to satisfy multiple customer requirements simultaneously. Our approach searches for the global optimization on requirement satisfaction degree, service utilization rate, and service sharing cost. We build a mathematical model for this multi-objective optimization problem and propose a nested genetic algorithm mixed with a greedy strategy. Experiments in an ocean transportation service setting are conducted and our approach is compared with traditional approaches to validate its effectiveness.     

What to do after a data breach? Examining apology and compensation as response strategies for health service providers

Innovative IT-enabled health services promise tremendous benefits for customers and service providers alike. Simultaneously, health services by nature process sensitive customer information, and data breaches have become an everyday phenomenon. The challenge that health service providers face is to find effective recovery strategies after data breaches to retain customer trust and loyalty. We theorize and investigate how two widely applied recovery actions (namely apology and compensation) affect customer reactions after a data breach in the specific context of fitness trackers. Drawing on expectation confirmation theory, we argue that the recovery actions derived from practice, apology, and compensation address the assimilation-contrast model’s tolerance range and, thus, always lead to satisfaction with the recovery strategy, which positively influences customers’ behavior. We employ an experimental investigation and collect data from fitness tracker users during a running event. In the end, we found substantial support for our research model. Health service providers should determine specific customer expectations and align their data breach recovery strategies accordingly.

     

Product recommendation with latent review topics

Online customer reviews complement information from product and service providers. While the latter is directly from the source of the product and/or service, the former is generally from users of these products and/or services. Clearly, these two information sets are generated from different perspectives with possibly different sets of intentions. For a prospective customer, both these perspectives together provide a complementary set of information and support their purchase decisions. Given the different perspective and incentive structure, the information from these two source sets tends to be necessarily biased, clearly with the high probability of negative information omission from that provided by the product/service providers. Moreover, customers oftentimes face information overload during their attempts at deciphering existing online customer reviews. We attempt to alleviate this through mining hidden information in online customer reviews. We use a variant of the Latent Dirichlet Allocation (LDA) model and clustering to generate equivalent options that the customer could then use in their purchase decisions. We illustrate this using online hotel review data.