Design of a case-based logistics strategy system – an integrated approach

Abstract: Logistics service providers are professionals who serve clients such as manufacturers, raw material suppliers, distributors, retailers and shippers within the supply chain. Traditionally, the formulation of logistics strategies to execute various logistics services has been based solely on experience. In this paper, an intelligent case-based logistics strategy system (CLSS) is designed to help logistics service providers to develop strategies under various customers' demands. The proposed system, which is suitable for use in warehouse operations in the Hong Kong/Pearl River Delta region, enhances the effectiveness of logistics strategies formulations by integrating case-based reasoning technology seamlessly with various data management software such as a data warehouse, on-line analytical processing and a multidimensional database management system. Through applying CLSS in Eastern Worldwide Company, the resource utilization is maximized while work efficiency is greatly enhanced. As a result, the overall logistics operations are smoothened significantly.     

Optimizing resource speed for two-stage real-time tasks

Multiple resource co-scheduling algorithms and pipelined execution models are becoming increasingly popular, as they better capture the heterogeneous nature of modern architectures. The problem of scheduling tasks composed of multiple stages tied to different resources goes under the name of “flow-shop scheduling”. This problem, studied since the ‘50s to optimize production plants, is known to be NP-hard in the general case. In this paper, we consider a specific instance of the flow-shop task model that captures the behavior of a two-resource (DMA-CPU) system. In this setting, we study the problem of selecting the optimal operating speed of the two resources with the goal of minimizing power usage while meeting real-time schedulability constraints. In particular, we derive an algorithm that finds the optimal speed of one resource while the speed of the other resource is kept constant. Then, we discuss how to extend the proposed approach to jointly optimize the speed of the two resources. In addition, applications to multiprocessor systems and energy minimization are considered. All the proposed algorithms run in polynomial time, hence they are suitable for online operation even in the presence of variable real-time workload.     

A parameterized liveness and ratio-enforcing supervisor for a class of generalized Petri nets

The work proposes a synthesis method of supervisors for flexible manufacturing systems modeled by a class of generalized Petri nets. A concept of resource usage ratios (RU-ratios) is first presented to describe the occupation degree of a resource by an operation. Next, an intrinsically live structure characterized by a special numerical relationship between arc-weights and initial markings is investigated from a perspective of RU-ratios. Then, a new kind of supervisors is synthesized on the ground of the generic nature of the intrinsically live structure. Such a supervisor can achieve the purposes of both liveness-enforcement and resource usage ratio-enforcement of the system under consideration. Given a plant, it is easy to determine the topological structure of such a supervisor and the number of monitors is bounded by that of resources used in the plant. In addition, when the configuration of the plant model changes, the supervisor can be reusable through adjusting control parameters only without rearrangement of connections. This makes it easy enough and intuitive to be used by industrial practitioners. Instead of maximal behavioral permissiveness, it pursues a precise usage of shared resources that are limited and valuable. Several examples are used to illustrate the proposed methods.     

Resource sharing among real-time components under multiprocessor clustered scheduling

In this paper we propose a general synchronization protocol for resource sharing among independently-developed real-time applications (components) on multi-core platforms. This protocol is a generalization of a previously proposed synchronization protocol (MSOS). In our proposed protocol, each component is statically allocated on a dedicated subset of processors (called cluster). A component has its own internal scheduler by which its tasks are scheduled. In this paper we focus on multiprocessor global fixed-priority preemptive scheduling algorithms to be used to schedule the tasks inside each component. Sharing the local resources is handled by the Priority Inheritance Protocol (PIP). For sharing the global resources (inter-component resource sharing) we have studied usage of FIFO and Round-Robin queues for access the resources across the components and usage of FIFO and prioritized queues inside the components. We have derived schedulability analysis for the different queue handling alternatives and compared their performance by using experimental evaluations. Finally, we have shown that the integration phase can be formulated in the form of a nonlinear integer programming problem where solution techniques in this domain can be used to minimize the total number of processors required to guarantee the schedulability of all components. As a proof of concept we have only provided the formulation for FIFO queues.     

Resource smoothening in repetitive projects

To manage construction projects properly, it is necessary to establish a project plan for resources utilization. This management function, usually, is carried out in two steps: resource allocation and resource usage smoothening. In this paper, a linear programming model for a single resource allocation and smoothening in repetitive construction project has been achieved. The linear programming model is based on one of the following objectives:

1. 1. The resource requirements nearly follow a desirable resource histogram, Model M1.

2. 2. Minimizing the number of changes in resource requirements, Model M2.

     

A social network approach to resolving group-level conflict in context-aware services

Group conflict that occurs when delivering context-aware services negatively affects service usage within a specific service space, and researchers have proposed few conflict resolution methods to address the issue. This paper presents a methodology to autonomously resolve the conflict arising between users in identical contexts, such as place and computing resources. To do so, a two-phase case-based reasoning method is proposed. To minimize privacy concerns, a social network model is applied to produce key social context data, secondary to personal data, and which is used in place of the users’ personal data for case-based reasoning. To show the feasibility of the idea proposed in this paper, a prototype system with simulation-based validation is tested to determine conflict resolution strategies and a simulation test has been performed.     

结构化查询语言动态功耗解析及建模

为构建节能的绿色数据库,提出一种基于结构化查询语言(SQL)资源(中央处理单元(CPU)、磁盘)消耗的最小单位的数据库动态能耗模型。该模型对系统动态能耗进行解析,将系统主要硬件(CPU、磁盘)的资源消耗映射成功率消耗,采用多元线性回归方法拟合模型关键参数,实时地估算系统动态功率,构建单位统一的动态功耗模型。实验结果表明,相比基于元组总数的模型,CPU指令总数能更好地反映CPU的功率消耗,所构模型在数据库管理系统(DBMS)独占系统资源的静态环境下,平均相对误差小于6%,绝对误差不超过9%。该动态功耗模型更适合于构建节能的绿色数据库。     

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.     

On the evaluation of AGVS-based warehouse operation performance

An automated guided vehicle system (AGVS) is an advanced material handling system widely used in various automated systems, particularly in e-commerce warehouses. The warehouse operation performance, mainly focusing on transportation efficiency, is affected by many factors, such as traffic-control policies and warehouse layouts. Based on the digraph theory and real-time control, two effective traffic-control policies with polynomial-time complexity are proposed to avoid the collision and to solve deadlocks. To accommodate more complicated situations, different types of warehouse layouts are tested. Extensive simulations are carried out to study the effects of policies, warehouse layouts, task densities and timing of an AGV to apply for a resource on the evaluation of the AGVS-based warehouse operation performance, which provide guidelines for warehouse designers.     

基于双层规划的高超声速飞行器预警资源分配方法

针对高超声速飞行器预警系统中资源难以合理利用的问题,提出一种基于双层规划的预警资源分配方法.首先,建立高超声速飞行器运动状态的马尔可夫模型,提出威胁评估的方法;其次,基于隐马尔可夫模型和卡尔曼滤波,提出双层规划的高超声速飞行器预警资源分配模型,下层规划以单位资源损耗下信息增益为目标函数,上层规划以风险的降低为目标函数;...     

面向Web服务工作流系统的访问控制模型

提出了一种面向Web服务工作流系统的访问控制模型（WSWF-RBAC）。在基于角色的访问控制模型基础上引入系统资源的概念,把Web服务对象、属性和其它功能模块当做资源统一管理。引XT资源访问模式的概念,在约束条件下通过与资源的运算生成系统权限,从而实现了对权限的精细管理和动态调整能力。通过扩展web服务访问的角色集和用户集,并引入角色扮演对象,实现了工作流系统中Web服务与其它模块访问控制的一致性。将该模型应用于库房供应链系统中,运行结果表明,该模型能够增强Web服务工作流系统授权的灵活性和安全性。     

A model for selecting an ERP system based on linguistic information processing

An enterprise resource planning (ERP) is an enterprise-wide application software package that integrates all necessary business functions into a single system with a common database. In order to implement an ERP project successfully in an organization, it is necessary to select a suitable ERP system. This paper presents a new model, which is based on linguistic information processing, for dealing with such a problem. In the study, a similarity degree based algorithm is proposed to aggregate the objective information about ERP systems from some external professional organizations, which may be expressed by different linguistic term sets. The consistency and inconsistency indices are defined by considering the subject information obtained from internal interviews with ERP vendors, and then a linear programming model is established for selecting the most suitable ERP system. Finally, a numerical example is given to demonstrate the application of the proposed method.     

Modeling a resource contention in the management of virtual organizations

A virtual organization provides a cost-efficient method allowing different autonomous entities, such as organizations, departments and individuals, to extend service offerings in a virtual marketplace. To support cost-efficient service provisioning, a suitable procedure must be applied to determine the amount of resources necessary for the operation of virtual organizations.We propose a new mathematical model for a quantitative performance evaluation of resource management in virtual organizations. We present an efficient algorithm to determine the steady state probabilities and the performance measures of the system. A comparison with a detailed simulation model and other numerical approaches shows that the proposed algorithm is fast and accurate. This algorithm can therefore be used for resource dimensioning to support the cost-efficient operation of virtual organizations.     

Catch-up TV forecasting: enabling next-generation over-the-top multimedia TV services

Due to recent developments in Over-The-Top (OTT) technologies, Pay-TV operators have begun a migration process of managed IP Television (IPTV) services to more appealing OTT approaches. In these scenarios, being able to predict when and what resources will be necessary at any given point is crucial to a high-quality, efficient, and cost-effective operation, especially when dealing with the dynamic and resource-intensive requirements of IPTV multimedia services. To evaluate the advantages of demand forecasting for efficient Catch-up TV delivery on OTT scenarios, this research work explores several classes of machine learning models regarding their accuracy, computational requirement trade-offs, and deployability. The training process relies on a dataset comprised of Catch-up TV usage logs acquired from an IPTV operator’s live production service containing over 1 million subscribers. A predictive and dynamic resource provisioning approach is proposed and evaluated in terms of bandwidth and storage savings. Results demonstrate that forecasting Catch-up TV demand is practical, suitable for integration in OTT solutions, and useful in improving efficiency, with benefits to operators and consumers. Significant savings in bandwidth and storage are shown to be achievable, enabling green and cost-effective resource usage.     

EasiSMP: A Resource-Oriented Programming Framework Supporting Runtime Propagation of RESTful Resources

In order to simplify programming for building sensor networks, macro-programming methods have been pro- posed in prior work. Most of them are designed for the dedicated networks and specific scenarios where devices are mostly homogeneous. Nevertheless the methods rarely consider those shared networks which are composed of heterogeneous de- vices, e.g., sensors, actuators, mobile devices, and share resources among themselves. In this paper, we present EasiSMP, a resource-oriented programming framework for these shared networks and generic application scenarios. In this framework, the devices and their functionalities are abstracted into RESTful virtual resources （VRs） each of which is labelled by a uni- form resource identifier （URI）. The post-deployment VR can be globally accessed and reused to propagate new resource（s） at runtime. To support the resource propagation, programming primitives are proposed and a virtual resource engine （VRE） is studied. To perform evaluation, EasiSMP is deployed into a relic monitoring network. Experimental results show that programming using Ea-siSMP is concise, and the average deployment overhead is decreased by up to 27% compared with the node-level programming.     

计算机硬件资源管理系统中数据库的设计

计算机硬件资源管理系统采用Windows操作系统,用SQLServer作为管理数据库,Delphi作为开发平台,根据硬件资源管理的具体需求并结合硬件资源管理的实际使用情况,提出了基于设备静态管理和运行状态动态检测管理相结合的方法。     

Deadlock resolution strategy for automated manufacturing systems including conjunctive resource service

Automated manufacturing systems (AMSs) can process different parts according to operation sequences sharing a finite number of resources. In these systems, deadlock situations can occur so that the flow of parts is permanently inhibited, and the processing of jobs is partially or completely blocked. Hence, one of the tasks of the control system is ruling resource allocation to prevent such situations from occurring. A large part of the existing literature focused on systems in which every operation is performed by only one resource. This paper proposes a deadlock strategy to avoid deadlock conditions in more complex systems where multiple resource acquisitions are allowed to complete a working operation conjunctive resource service system (CRSS). The AMS structure and dynamics is described by a colored timed Petri net model, suitable for following resource changes and working procedure updating. Moreover, digraphs characterize the complex interactions between resources and jobs so that the conditions for the deadlock occurrence are derived. Finally, an event-based controller is defined to avoid deadlock in CRSSs on the basis of the system state knowledge and of the given priority law ruling the concurrent job selection.     

面向可信嵌入式系统的随机实时任务能耗优化

针对可信嵌入式系统应用中将任务的最坏情况下的执行时间(WCET)作为任务的实际执行时间,导致系统资源的极大浪费的问题,提出了一种基于随机任务概率模型的方法。首先,考虑任务执行时间具有特定概率分布,并且任务具有不错过其死限的概率(NDVP)需求,同时考虑了动态电压和频率调整(DVFS)对系统可靠性的影响,利用该技术降低能耗。然后,基于动态规划算法,提出了一种具有多项式运行时间的优化算法,并进一步设计了状态剔除规则降低算法运行开销。仿真表明,所提算法与最坏执行时间模型下的最优算法相比,系统能耗降低了30%以上。实验结果表明,考虑任务的随机执行时间能在保证系统可靠性的同时大大节约系统资源。     

基于线性规划模型的高校排课系统

排课系统本质是一个多资源约束分配问题。本文对排课资源的约束条件和自身需求进行分析,建立了系统的线性规划模型,该模型具有直观性、易理解、可操作强等特点;在此基础上,采用蚁群算法进行仿真计算,实现资源分配的优化处理。系统用C#语言进行实现,效果良好。     

Dynamic QoS queuing control mechanism for multimedia differentiated services

An essential goal of communication networks is to provide multimedia services with QoS streaming. A properly designed multimedia QoS system must reserve requested resources according to user QoS requirements and the available network resources. However, the static resource allocation among priority queues in DiffServ networks leads to insufficient resource usage when a burst occurs in one priority queue while other queues starve. This study presents a User-Oriented QoS Streaming System to achieve perceptible satisfaction based on novel streaming and media differentiation policies in DiffServ networks. This study also proposes that the Dynamic QoS Queue Mapping (DQ²M) mechanism dynamically control queue scheduling by adaptively maximizing the utilization of queues and network resources according to the soft states of the DiffServ network. Evaluation results indicate that the proposed DQ²M algorithm can improve the fairness and efficiency of resource utilization for low-priority queues.