Clustering algorithms for the design of a cellular manufacturing system - an analysis for their performance

This paper presents results from an analytical study performed to determine the severity of chaining problem besides other performance characteristics associated with the clustering process of four selected algorithms. The four algorithms were Single linkage clustering (SLINK), Average linkage clustering (ALINK), Weighted average linkage clustering (WLINK), and Complete linkage clustering (CLINK). A sample of fifty problems with randomly generated data sets was used to determine feasible solutions consisting of machine cells and corresponding part families from each of the four algorithms. A quantitative measure is proposed for evaluating the performance of different algorithms. The study concludes that the chaining effect for CLINK, WLINK, ALINK and SLINK progressively worsens when the use of clustering algorithm is changed from CLINK to SLINK in the same order.     

Analyzing factors that influence end-to-end Web performance

Web performance impacts the popularity of a particular Web site or service as well as the load on the network, but there have been no publicly available end-to-end measurements that have focused on a large number of popular Web servers examining the components of delay or the effectiveness of the recent changes to the HTTP protocol. In this paper we report on an extensive study carried out from many client sites geographically distributed around the world to a collection of over 700 servers to which a majority of Web traffic is directed. Our results show that the HTTP/1.1 protocol, particularly with pipelining, is indeed an improvement over existing practice, but that servers serving a small number of objects or closing a persistent connection without explicit notification can reduce or eliminate any performance improvement. Similarly, use of caching and multi-server content distribution can also improve performance if done effectively.     

Genetic algorithms for optimal logical database design

The focus of this paper is database design using automated database design tools or more general CASE tools. We present a genetic algorithm for the optimization of (internal) database structures, using a multi-criterion objective function. This function expresses conflicting objectives, reflecting the well-known time/space trade-off. This paper shows how the solution space of the algorithm can be set up in the form of tree structures (forests), and how these are encoded by a simple integer assignation. Genetic operators (database transformations) defined in terms of this encoding behave as if they manipulate tree structures. Some basic experimental results produced by a research prototype are presented.     

Path based algorithms for metro network design

This paper proposes a practical methodology for the problem of designing a metro configuration under two criteria: population coverage and construction cost. It is assumed that a set of corridors defining a rough a priori geometric configuration is provided by the planners. The proposed algorithm consists of fine tuning the location of single alignments within each corridor. This is achieved by means of a bicriteria methodology that generates sets of non-dominated paths. These alignments are then combined to form a metro network by solving a bicriteria integer linear program. Extensive computational experiments confirm the efficiency of the proposed methodology.     

Multi-objective optimization design of a complex building based on an artificial neural network and performance evaluation of algorithms

While optimization studies focusing on real-world buildings are somewhat limited, many building optimization studies to date have used simple hypothetical buildings for the following three reasons: (1) the shape and form of real buildings are complex and difficult to mathematically describe; (2) computer models built based on real buildings are computationally expensive, which makes the optimization process time-consuming and impractical and (3) although algorithm performance is crucial for achieving effective building performance optimization (BPO), there is a lack of agreement regarding the proper selection of optimization algorithms and algorithm control parameters. This study applied BPO to the design of a newly built complex building. A number of design variables, including the shape of the building’s eaves, were optimized to improve building energy efficiency and indoor thermal comfort. Instead of using a detailed simulation model, a surrogate model developed by an artificial neural network (ANN) was used to reduce the computing time. In this study, the performance of four multi-objective algorithms was evaluated by using the proposed performance evaluation criteria to select the best algorithm and parameter values for population size and number of generations. The performance evaluation results of the algorithms implied that NSGA-II (with a population size and number of generations of 40 and 45, respectively) performed the best in the case study. The final optimal solution significantly improves building performance, demonstrating the success of the BPO technique in solving complex building design problems. In addition, the findings on the performance evaluation of the algorithms provide guidance for users regarding the selection of suitable algorithms and parameter settings based on the most important performance criteria.     

Genetic algorithms with local improvement for composite laminate design

This paper describes the application of a genetic algorithm to the stacking sequence optimization of a laminated composite plate for buckling load maximization. Two approaches for reducing the number of analyses required by the genetic algorithm are described. First, a binary tree is used to store designs, affording an efficient way to retrieve them and thereby avoid repeated analyses of designs that appeared in previous generations. Second, a local improvement scheme based on approximations in terms of lamination parameters is introduced. Two lamination parameters are sufficient to define the flexural stiffness and hence the buckling load of a balanced, symmetrically laminated plate. Results were obtained for rectangular graphite-epoxy plates under biaxial in-plane loading. The proposed improvements are shown to reduce significantly the number of analyses required for the genetic optimization.Presented at the ASME Winter Annual Meeting Structures and Controls Optimization, pp. 13–28. Printed with permission from ASME.     

DICOM中网络通信协议的研究与设计

DICOM(Digital Imaging and Communications in Medicine)涵盖了数字图像信息构成和网络通信两个领域,内容极其复杂庞大,目前没有任何软件系统可以支持所有的DICOM服务,针对DICOM中的网络通信协议进行了详细的理论分析。     

Clustering algorithms for the design of a cellular manufacturing system—an analysis of their performance

The heuristic clustering methods based on similarity coefficient are considered to be very efficient for providing modularity and flexibility in a cellular manufacturing systems (CMS's). Various algorithms have been implemented in these heuristic methods. However, these algorithms suffer from string effect which is also known as “chaining”. Sedveral studies have reported this problem, yet not much research has been conducted to investigate its impact on actual clustering process.

This paper presents results from an analytical study performed to determine the severity of chaining problem and other characteristics associated with the clustering process of four selected algorithms. The four algorithms are Single linkage clustering (SLINK), Average linkage clustering (ALINK), Weighted average linkage clustering (WLINK), and Complete linkage clustering (CLINK). A sample of fifty problems with randomly generated data sets was used to determine feasible solutions consisting of machine cells and corresponding part families from each of the four algorithms. A quantitative measure is proposed for evaluating the performance of different algorithms. The study concludes that the chaining effect for CLINK, WLINK, ALINK and SLINK progresively worsens from CLINK to SLINK in the same order. The study also provides important guidelines to designers of a CMS in selecting the most efficient algorithm for a given problem data. Several important statistical results are also presented.     

The design of civic technology: factors that influence public participation and impact

Civic technology needs to be better understood in terms of the factors that promote representative public participation and impact. This paper reports on a mixed-methods study of a civic tech platform that enabled the public to provide feedback on public transport to the service providers. The overall aim of this research was to investigate the public’s use of a leading civic tech platform, FixMyTransport. The key findings were that: an effective and easy-to-use civic technology platform enables broad participation; data and process complexity need to be removed; factual information can be captured in situ with impacts, consequences and opinions added later; emotions (if important) need to be explicitly elicited; feedback to, and a ‘conversation’ with, the users is important for engagement, as is a feeling of being part of a community. These findings can contribute to the future design of civic technology platforms.

Practitioner Summary: There is a lack of understanding of how ‘civic tech’ platforms are used and how they may be designed for maximum effectiveness. Multiple data collection methods were used to investigate a well-developed example of civic tech. Effective civic tech can enable broad democratic participation to improve public services.     

Evaluating factors that influence hand-arm stress while operating an electric screwdriver

The objective of this study is to evaluate the factors contributing toward hand-arm stress while operating an electric screwdriver. Hand-arm stress was investigated in terms of individual finger force exertion, flexor digitorum EMG, and hand-transmitted vibration. Two activation modes (push and push plus trigger (P + T)), two preset shut-off torque levels (low and high) and three horizontal operating distances (far, middle, and near) were evaluated. Thirteen healthy male subjects drove screws into a horizontally mounted iron plate with pre-tapped screw holes using an in-line electric screwdriver in randomly ordered experimental combinations. The results indicate that using push-to-start mode at low torque level was better than the other combinations of activation mode x torque because it resulted in less hand-arm stress. In addition, the far distance level (33-45 cm away from the work table edge) caused greater stress than the middle and near distances, and hence is best avoided. While operating an in-line electrical screwdriver, the force contribution of the small finger was greatest, followed by the ring finger. The average force contributions of the index, middle, ring, and small fingers were 19, 25, 27, and 30%, respectively, while operating with push-to-start mode.     

多阶段煤炭供应链网络设计及其遗传算法

煤炭供应链网络设计旨在为大型煤炭集团选择合理的设施网络布局与最佳运量,以便提高效率并降低成本。考虑配煤加工与流量平衡等特有约束,建立了煤炭供应链网络混合整数规划模型,其优化目标是最小化固定设施成本、运输总成本与采购成本。考虑到模型求解的复杂度,设计了一种遗传算法,结合优先权与整数规则对染色体进行了编码与解码。实验算例表明所建立的模型能够真实地模拟煤炭供应链网络中设施布局与最佳运量的决策环境,其算法能够在允许的运算时间内获得稳定的满意解,随着算例规模的增大,其计算时间与优化结果均优于LINGO软件。     

A hybrid AI/OR decision support tool for backbone communications network design

Much of existing DSS literature views the role of human expertise as primarily that of selecting appropriate formal models for solving a problem or synthesizing sequences thereof. Once a model (or model sequence) is determined, values of decision variables are determined by the model(s) alone. Hence, automated methods for facilitating model selection and synthesis have received considerable attention. However, a single model is often not an accurate abstraction of reality. Also, results from multiple formal models often have to be combined heuristically to obtain practical solutions. Thus, in this paper we explore the premise that human expertise needs to interact with formal models during the process of searching for solution values. Specifically, we describe a hybrid decision support tool for the design of backbone communication networks, a problem recognized as being of considerable complexity. An internal representation of the design process that employs a blackboard, a truth maintainence system and dependency directed backtracking, allows human expertise and formal models to jointly determine decision variable values in a uniform manner. The design tool has been implemented using a combination of Lisp and Fortran. Computational experiments indicate that incorporating human expertise during the search process results in superior complete solutions and added flexibility in satisfying ad hoc requirements. We conjecture that this hybrid search approach is not limited to the telecommunication network design problem and can be extended to other applications.     

Genetic design of real-time neural network controllers

The use of genetic algorithms to design neural networks for real-time control of flows in sewerage networks is discussed. In many control applications, standard supervised learning techniques (such as back-propagation) cannot be used through lack of training data. Reinforcement learning techniques, such as genetic algorithms, are a computationally-expensive but viable alternative if a simulator is available for the system in question. The paper briefly describes why genetic algorithms and neural networks were selected, then reports the results of a feasibility study. This demonstrates that the approach does indeed have merits. The implications of high computational cost are discussed, in terms of scaling up to significantly complex problems.     

The impact of network topology on the performance of MAP selection algorithms

The performance of the Hierarchical Mobile IPv6 (HMIPv6) protocol is affected by the Mobility Anchor Point (MAP) selection. Many MAP selection algorithms have been proposed. Researchers have based their algorithms on different operating principles, movement patterns and evaluation metrics, while the network topology model has remained essentially the same – a simple tree. Our study abolishes this restriction by expanding the research to different types of topologies. They are compared both analytically and by simulation. The results show that trees differ from other topologies in an important aspect. They do not allow the simultaneous reduction of average distance from Mobile Node (MN) to MAP and frequency of MAP changes. As a consequence, widely accepted cost functions can only be reduced by careful consideration of user-specific parameters such as speed and communication activity. We show that in other topologies, including internet models, there is no such limitation. The paper also analyses the topology characteristics that are beneficial to MAP selection, leading to simultaneous reduction of MAP distances and frequency of MAP changes. The demonstrated characteristics are verified by simulations of topology evolution.     

Analysing the factors that influence the Pareto frontier of a bi‐objective supply chain design problem

In this paper a bi‐objective multi‐product model for the design of a production/distribution supply chain logistic network with four echelons is considered. The proposed optimization model minimizes the total cost of the network (including the fixed cost to open facilities and the transportation costs between them) and the total CO₂ emissions. Five factors (network size, product complexity, cost variability, CO₂ emissions generation and over‐capacity) are considered for the experimental framework. The problem is solved using the ε‐constraint method and the resulting Pareto frontiers (PF) are characterized using five new metrics specifically developed for analysing how those factors affect the resulting optimal configurations. The results show that over‐capacity and product complexity are the two most influential factors regarding the characteristics of the PF, and that their effects are in the same direction: more complexity and capacity mean a wider set of optima alternatives, some close to the ideal point, and in general with a smaller number of links used.     

Genetic algorithms for door-assigning and sequencing of trucks at distribution centers for the improvement of operational performance

In a supply chain, cross docking is one of the most innovative systems for improving the operational performance at distribution centers. By utilizing this cross docking system, products are delivered to the distribution center via inbound trucks and immediately sorted out. Then, products are shipped to customers via outbound trucks and thus, no inventory remains at the distribution center. In this paper, we consider the scheduling problem of inbound and outbound trucks at distribution centers. The aim is to maximize the number of products that are able to ship within a given working horizon at these centers. In this paper, a mathematical model for an optimal solution is derived and intelligent genetic algorithms are proposed. The performances of the genetic algorithms are evaluated using several randomly generated examples.     

On design and performance evaluation of an integrated SIP and HCoP-B architecture for nested network mobility

This paper proposes a fully-integrated SIP + HCoP-B architecture to provide efficient mobility management of the nested mobile network. It achieves the following merits, which are rare in the literature. First, it reduces network deployment costs by only equipping an integrated SIP mobile server. Second, it supports both SIP-based and non-SIP-based applications. Third, by adopting the analytical model proposed in Mohanty and Akyildiz (2007) [19], mathematical analyses are provided to investigate six performance metrics of SIP + HCoP-B and the other four well-known SIP's over NEMO schemes over the error-prone wireless link. Finally, it is shown that SIP + HCoP-B outperforms these four traditional schemes through intensive simulations.