2006 IEEE Conference on Cybernetics and Intelligent Systems最新文献_第6页

A Hybrid Discrete Particle Swarm Optimization Algorithm to Solve Flow Shop Scheduling Problems 求解流水车间调度问题的混合离散粒子群算法

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252316

S. Chandrasekaran, S. Ponnambalam, R. Suresh, N. Vijayakumar

引用次数: 38

On State-Space Neural Networks for Systems Identification: Stability and Complexity 用于系统辨识的状态空间神经网络:稳定性和复杂性

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252333

P. Gil, J. Henriques, A. Dourado, H. Duarte-Ramos

引用次数: 5

A Comment on the Formulation of an Aggregate Production Planning Problem 关于一个总体生产计划问题的提法的评论

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252270

B. Phruksaphanrat, A. Ohsato, P. Yenradee

{"title":"A Comment on the Formulation of an Aggregate Production Planning Problem","authors":"B. Phruksaphanrat, A. Ohsato, P. Yenradee","doi":"10.1109/ICCIS.2006.252270","DOIUrl":"https://doi.org/10.1109/ICCIS.2006.252270","url":null,"abstract":"Conventionally, a revenue function, a cost function and a profit function are selected to be the objective function for aggregate production planning (APP) problems. The theory of constraints (TOC) alternative consideration argues that instead of measuring by cost, factory should evaluate their performance by throughput. Even though, there are a lot of research works on formulations of APP problems, there has been no investigation, which formulation is the most appropriate for APP problems. In this research, the investigation of the formulation of existing APP problems is done. In order to clarify the difference of each objective function, a simple case study has been used to compare the performances of the APP problem with revenue, cost, and profit objective functions when resource constraints (limited processing time) are not included and included in the model. For the profit objective function, two formulations are also compared: profit objective function by TOC and profit objective function by linear programming. From the results, it can be shown that setting the objective function of an APP problem is very important because it may lead to a wrong decision in production planning","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132662914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Study of Distributed Scheduling Problem with Machine Maintenance 考虑机器维修的分布式调度问题研究

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252261

F. Chan, S. Chung, L. Chan

{"title":"A Study of Distributed Scheduling Problem with Machine Maintenance","authors":"F. Chan, S. Chung, L. Chan","doi":"10.1109/ICCIS.2006.252261","DOIUrl":"https://doi.org/10.1109/ICCIS.2006.252261","url":null,"abstract":"In this paper, we study the influence of machine maintenance to distributed scheduling problems. Distributed scheduling is aiming to maximize the system efficiency by simultaneously solving two problems: (i) allocation of jobs to suitable factories, and (ii) determination of the corresponding production scheduling in each factory. Scheduling of machine maintenance problems aim to reduce the effect of breakdown and maximize the facility availability at minimum cost. However, in many distributed scheduling problems, machine scheduling assumes that machines are available all the time. In fact, every machine requires maintenance, and the maintenance policy directly affects the machine's availability. Consequently, it interrupts the production scheduling determined. This paper designed a hypothetical distributed scheduling model with three different problem sizes to demonstrate the significance of simultaneously solving machine maintenance problem with distributed scheduling problem. We applied genetic algorithm with dominant genes methodology to solve the model. Several optimization approaches, including separating and integrating the two problems, are tested and compared. The results show the merit of integration","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114069423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

The Properties of the Zadeh Extension of Real Valued Exponential Function 实值指数函数的Zadeh扩展的性质

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252241

Wang Guixiang, Li Youming

引用次数: 0

Determining Optimal Polling Frequency Using a Learning Automata-based Solution to the Fractional Knapsack Problem 基于学习自动机的分式背包问题的最优轮询频率确定

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252228

O-C. Granmo, B. Oommen, S. A. Myrer, M. G. Olsen

{"title":"Determining Optimal Polling Frequency Using a Learning Automata-based Solution to the Fractional Knapsack Problem","authors":"O-C. Granmo, B. Oommen, S. A. Myrer, M. G. Olsen","doi":"10.1109/ICCIS.2006.252228","DOIUrl":"https://doi.org/10.1109/ICCIS.2006.252228","url":null,"abstract":"Previous approaches to resource allocation in Web monitoring target optimal performance under restricted capacity constraints (Pandey et al., 2003; Wolf et al., 2002). The resource allocation problem is generally modelled as a knapsack problem with known deterministic properties. However, for practical purposes the Web must often be treated as stochastic and unknown. Unfortunately, estimating unknown knapsack properties (e.g., based on an estimation phase (Pandey et al., 2003; Wolf et al., 2002)) delays finding an optimal or near-optimal solution. Dynamic environments aggravate this problem further when the optimal solution changes with time. In this paper, we present a novel solution for the nonlinear fractional knapsack problem with a separable and concave criterion function (Bretthauer and Shetty, 2002). To render the problem realistic, we consider the criterion function to be stochastic with an unknown distribution. At every time instant, our scheme utilizes a series of informed guesses to move, in an online manner, from a \"current\" solution, towards the optimal solution. At the heart of our scheme, a game of deterministic learning automata performs a controlled random walk on a discretized solution space. Comprehensive experimental results demonstrate that the discretization resolution determines the precision of our scheme. In order to yield a required precision, the current resource allocation solution is consistently improved, until a near-optimal solution is found. Furthermore, our proposed scheme quickly adapts to periodically switching environments. Thus, we believe that our scheme is qualitatively superior to the class of estimation-based schemes","PeriodicalId":296028,"journal":{"name":"2006 IEEE Conference on Cybernetics and Intelligent Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133038265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Scatter Bitmap: Space-Time Efficient Bitmap Indexing for Equality and Membership Queries 散点位图:时空有效位图索引等式和成员查询

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252354

S. Vanichayobon, J. Manfuekphan, L. Gruenwald

引用次数: 11

Determination of Erroneous Velocity Vectors by Co-operative Co-evolutionary Genetic Algorithms 基于协同进化遗传算法的错误速度矢量确定

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252288

K. Boonlong, K. Maneeratana, N. Chaiyaratana

引用次数: 1

Feasibility Study of a Force Feedback Controlled Robotic System for Bone Milling 力反馈控制骨铣机器人系统的可行性研究

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252240

Lee Beng Kwang, L. Phee Soo Jay

引用次数: 4

Usability Analysis of Mobile Phone Camera Software Systems 手机相机软件系统的可用性分析

2006 IEEE Conference on Cybernetics and Intelligent Systems Pub Date : 2006-06-07 DOI: 10.1109/ICCIS.2006.252258

Y. Qiu, Y. Chui, M. Helander

引用次数: 12