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On Multi-Criteria Approaches to Bandwidth Allocation

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Resource allocation decisions are concerned with the allocation of limited resources so as to achieve the best system performances. This paper deals with problems of bandwidth allocation within telecommunication networks. The development of the Internet has led to an increased role of the traffic carried by the IP protocol in telecommunication networks. Due to the use of packet switching, the IP protocol can provide greater network utilization (the so-called multiplexing gain). For these reasons, network management can be interested in designing networks which have a high throughput for the IP protocol. Moreover, a fair way of distribution of the bandwidth (or other network resources) among competing demands becomes a key issue in computer networks (Denda et al., 2000) and telecommunications network design, in general (Pi´ oro and Medhi, 2004). Therefore, we focus on the approaches that, while allocating resources, attempt to provide a fair (equal) treatment of all the activities (Luss, 1999; Ogryczak ´ and Sliwi´ nski, 2002). Note that data traffic carried by the TCP protocol (which is the most frequently used transport protocol in IP networks) has a unique characteristic. The TCP protocol will adapt its throughput to the amount of available bandwidth. It is therefore capable to use the entire available bandwidth, but it will also be able to reduce its throughput in the presence of contending traffic. This type of network traffic has been called elastic traffic. Network design today often considers the problem of designing networks that carry elastic traffic. The network design problem reduces to a decision about link capacities and possibly flow routing. Flow sizes are an outcome of the design problem, since it can be assumed that flows adapt to given network resources on a chosen path. If the network is also used for other types of communication that require guaranteed quality of service, the network design problem can be decomposed into two parts: first, design the network to carry non-elastic traffic in such a way that all demands for that communication are satisfied. Next, use the spare capacity to carry elastic traffic of the IP protocol. Resource allocation models may be used to help to solve such network design problems. Within a telecommunication network the data traffic is generated by a huge number of nodes exchanging data. In such a network, a relatively small subset of nodes are chosen to serve as hubs which can be used as intermediate switching points or to define the so-called backbone network (Pi´ oro and Medhi, 2004). The hub-based network organization allows the data traffic to be consolidated on the inter-hub links. The problem of network dimensioning with elastic traffic arises when there is a need to design the (inter-hub) link capacities to carry as much traffic as possible between a set of network nodes. This can occur in the case described above, when the network capacity available after considering all non-elastic demands has to be used for elastic traffic, or in another case: when the network capacity is insufficient to carry all non-elastic demands. In such a case, the problem is to determine how much traffic of the non-elastic
Abstract: Modern telecommunication networks face an increasing demand for services. Among these, an increasing number are services that can adapt to available bandwidth, and are therefore referred to as elastic traffic. Nominal network design for elastic traffic becomes increasingly significant. Typical resource allocation methods are concerned with the allocation of limited resources among competing activities so as to achieve the best overall performances of the system. In the network dimensioning problem for elastic traffic, one needs to allocate bandwidth to maximize service flows and simultaneously to reach a fair treatment of all the elastic services. Thus, both the overall efficiency (throughput) and the fairness (equity) among various services are important. In such applications, the so-called Max-Min Fairness (MMF) solution concept is widely used to formulate the resource allocation scheme. This approach guarantees fairness but may lead to significant losses in the overall throughput of the network. In this paper we show how the concepts of multiple criteria equitable optimization can be effectively used to generate various fair resource allocation schemes. We introduce a multiple criteria model equivalent to equitable optimization and we develop a corresponding reference point procedure to generate fair efficient bandwidth allocations. The procedure is tested on a sample network dimensioning problem and its abilities to model various preferences are demonstrated. Keywords: Multiple Criteria Optimization, Efficiency, Fairness, Equity, Reference Point Method, Resource Allocation, Network Design, Elastic Traffic

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ahp-topsisi英文参考文献

ahp-topsisi英文参考文献

ahp-topsisi英文参考文献The field of decision-making has long been a subject of interest and study, with various methodologies and techniques developed to aid in the process. Two prominent approaches in this domain are the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). These two methods have been widely applied in diverse contexts, from business and management to engineering and environmental studies. In this essay, we will explore the theoretical foundations and practical applications of AHP and TOPSIS, with a focus on the existing English-language references that have contributed to the development and understanding of these techniques.The Analytic Hierarchy Process (AHP), developed by Thomas L. Saaty in the 1970s, is a multi-criteria decision-making (MCDM) method that allows decision-makers to structure complex problems into a hierarchical framework. This approach involves the decomposition of a decision problem into a hierarchy of criteria, sub-criteria, and alternatives, enabling the systematic evaluation and prioritization of various options. The key steps in the AHP process include thepairwise comparison of elements within each level of the hierarchy, the calculation of local and global priorities, and the synthesis of these priorities to determine the overall ranking of alternatives.One of the seminal works on AHP is Saaty's book "The Analytic Hierarchy Process" (1980), which provides a comprehensive introduction to the method and its underlying principles. This publication has been widely cited and has served as a foundation for numerous subsequent studies and applications of AHP. Another influential reference is Saaty's article "Decision making with the analytic hierarchy process" (2008), which delves into the theoretical and practical aspects of the AHP methodology, including the use of eigenvalues and eigenvectors in the pairwise comparison process.In addition to Saaty's contributions, other researchers have expanded upon and refined the AHP approach. For instance, Dyer and Forman (1992) explored the use of AHP in group decision-making, highlighting the importance of consensus-building and the integration of individual judgments. Bhushan and Rai (2004) authored a book titled "Strategic Decision Making: Applying the Analytic Hierarchy Process," which provides a comprehensive guide to the application of AHP in various strategic contexts.The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), on the other hand, is another MCDM method that wasintroduced by Hwang and Yoon in 1981. TOPSIS is based on the concept of identifying the ideal and anti-ideal solutions and then ranking alternatives based on their relative proximity to these ideal points. This approach involves the normalization of decision criteria, the calculation of weighted normalized decision matrices, and the determination of the ideal and anti-ideal solutions.One of the seminal works on TOPSIS is the book "Multiple Attribute Decision Making: Methods and Applications" by Hwang and Yoon (1981), which presents the theoretical foundation and practical applications of the TOPSIS method. Another influential reference is the article "TOPSIS: A comprehensive review of the method and its applications" by Behzadian et al. (2012), which provides a thorough review of the TOPSIS methodology, its variants, and its applications in various fields.The integration of AHP and TOPSIS has also been a subject of interest among researchers. Combining the strengths of both methods, the AHP-TOPSIS approach has been utilized in a wide range of decision-making scenarios. Afshari et al. (2010) explored the integration of AHP and TOPSIS for the selection of the best alternative in a group decision-making context. Dağdeviren et al. (2009) applied the AHP-TOPSIS approach for the selection of the best maintenance strategy in a manufacturing system.The existing English-language references on AHP and TOPSIS have significantly contributed to the understanding, development, and application of these decision-making techniques. These publications have provided a solid theoretical foundation, practical guidance, and case studies that have aided researchers, practitioners, and decision-makers in various fields. The continued exploration and refinement of these methods, as well as their integration with other decision-making approaches, will undoubtedly lead to further advancements in the field of multi-criteria decision analysis.In conclusion, the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are two prominent MCDM methods that have gained widespread recognition and application. The existing English-language references on these techniques have played a crucial role in advancing the understanding and practical implementation of these decision-making tools. As the field of decision-making continues to evolve, the insights and contributions from these seminal works will continue to guide and inspire future research and applications in this domain.。

管理信息系统(英文文献)

管理信息系统(英文文献)

Decision models for information systems management Kweku-Muata Osei-Bryson&Ojelanki NgwenyamaPublished online:1May2008#Springer Science+Business Media,LLC20081IntroductionIn the last two decades,information systems(IS)manage-ment decision making has become more complex.Every-day,IS managers in organizations of all over the world manage billions dollars of assets and must make complex decisions that involve information security,outsourcing, disaster recovery,software,hardware and vendor selection, investment strategies,timing the implementation of com-plex software systems(such as enterprise resource planning (ERP)).Inadequate understanding of these decision prob-lems,poor decision analysis,and bad judgment can result in catastrophic outcomes for companies.However,there has been limited research on decision models and frame-works that could assist information technology(IT) managers with these complex decision problems.While there are researchers working in these areas,much of their work appears in journals outside the field of IS.The primary objective of this special issue of Information Systems Frontiers is to focus on providing solutions to significant IS management problems and to provide strategic knowledge for practicing managers.A second objective of this special issue to give impetus to the development of a line of research that would continue to focus attention on providing solutions for the many emerging complex problems that IS managers may face. 2Information systems decision contextInformation systems management decision problems such as information security,outsourcing,disaster recovery, software,hardware and vendor selection,investment strat-egies,timing the implementation of complex software systems(such as ERP)are extremely complex and can adversely impact the performance of the firm.The complexity of these decision problems derive from organi-zation and environmental conditions that include(a) multiple stakeholders with different goals and risk prefer-ences(b)multiple objectives and value trade-offs;(c) competitive environments with risk and uncertainty;and (d)inter dependencies,indefinite time horizons and strategic impacts of decision.Under these conditions decision makers often have difficulties identifying decision alternatives and must be creative and careful in their analysis,as the consequences of poor IS decision making found in the literature show(Lyttinen and Robey1999;Yeo 2002;Ngwenyama et al.2007).When managers face complex IS problems,decision analysis frameworks and methods can be extremely useful when they assist:(1) interrogating the problem domain;(2)structuring decision problems and generating alternatives;(3)analyzing alter-natives and assessing their impacts;(4)determining the preferences of decision makers;(5)evaluating and com-paring decision alternatives,and,analysis of implications. However,while general theories and frameworks exists for decision analysis,the complexity and diversity of IS problems necessitate research and development of frame-Inf Syst Front(2008)10:277–279DOI10.1007/s10796-008-9082-7K.-M.Osei-Bryson(*)The Information Systems Research Institute, Virginia Commonwealth University, Richmond,V A23284,USAe-mail:KMOsei@O.NgwenyamaInstitute for Research on Technology Management, Ryerson University,Toronto,ON,Canadae-mail:Ojelanki@Ryerson.caworks and models specific to the class of decision problems that IS managers face.3The papersIn this special issue we present eight papers that present a range of approaches and address a wide range of IS decision problems.In general the papers can be classified into two categories,frameworks for decision analysis and decision models.One of the papers presents a conceptual framework;five of the papers present structured decision analysis frameworks,and three present decision-theoretic based models.While all the papers follow the design science paradigm of research,the decision framework papers present empirical cases,and the decision-theoretic papers present demonstrations of analysis using the models.The Samoilenko paper(Information Systems Fitness and Risk in IS Development:Insights and Implications from Chaos and Complex Systems Theories)outlines a framework and principles for analyzing IS fitness and risk based on chaos and complexity theory.Samoilenko approach is more in the tradition of appreciative decision making,where the focus is on ways of thinking to help the decision maker appreciate the problem situation.Aydin and Bakker(Analyzing IT Maintenance Outsourcing Decision from a Knowledge Management Perspective)also follow this tradition but from an empirically grounded perspective outlining a framework for decision analysis of knowledge management issues in IS outsourcing.They focus mainly on strategies for interrogating the problem domain and structur-ing the decision problem.Alaranta and Henningsson(An Approach to Analyzing and Planning Post-Merger IS Integration:Insights from Two Field Studies)outline a framework and approach for post-merger information sys-tems integration planning.Post-merger IS integration is a complex multidimensional problem that is challenging many IS managers.Alaranta and Henningsson discuss the key decisions of the planning process and suggest strategies for approaching them.An important feature of their approach is its grounding in empirical case studies of favorable and unfavorable strategies.The Scheepers and Scheepers(A Process-focused Decision Framework for Analyzing the Business Value Potential of IT Investments)framework focuses on analyzing IT investments from the perspective of business value creation.These authors develop a model for exploring the business value potential of IT at the business process level;a challenging undertaking necessary for cost-benefit analysis.The last of this category is that of Barclay(Towards An Integrated Measurement of IS Project Performance:The Project Performance Scorecard),who presents a framework and approach for evaluating project performance based on existing theoretical models of IS success and the Balanced Scorecard.She focuses on defining dimensions of project measurement that are of interest to different stakeholders and a process for eliciting preferences.The three decision-theoretic papers also vary in topics that they cover.Like Barclay,Plaza(Team Performance and Information System Implementation Application of the Progress Curve to the Earned Value Method in an Information System Project)is concerned with IS project performance but comes to the issue from a different perspective.Plaza is interested in predicting implementa-tion times(and cost)for large scale IS projects and proposes a decision model based on the earned value method and the learning curve.Rao and Osei-Bryson(An Approach for Incorporating Quality-Based Cost–Benefit Analysis in Data Warehouse Design)focus on an aspect of data warehouse design where the major concern is the determination of the data that are to be produced and stored in the data warehouse.They present a cost-benefit model that incorporates quality issues in data warehouse design. Finally Turetken(Is Your Back-Up IT Infrastructure in a Safe Location?)presents a multi-criteria decision model for location of the IT infrastructure for business continuity planning.4Contribution of this issueThis issue of ISF makes a contribution to theory and practice of IS management from the following perspectives:(1)The papers present frameworks and models that have immediate application to various classes of IS decision problems.(2) The papers present possibilities for further research into decision models for the problems that the authors have addressed.However,this research can evolve along different lines:(a)the development of decision support systems for the addressed problems;(b)other approaches or techniques that might be applicable for extending the breadth and depth of the current approaches;(c)alternative approaches can be proposed for the set of problems and comparative analysis can be conducted.We wish to express our appreciation to all the authors who submitted papers.We also wish to express our gratitude to all the reviewers who diligently reviewed the papers in order to ensure that that there was appropriate quality and fit. ReferencesLyytinen,K.,&Robey,D.(1999).Learning failure in information systems rmation Systems Journal,9,85–101. Ngwenyama,O.,Guergachi,A.,&McLaren,T.(2007).Using the learning curve to maximize IT productivity:A decision analysismodel for timing software upgrades.International Journal of Production Economics,1005,524–535.Yeo,K.(2002).Critical failure factors in information systems projects.International Journal of Project Management,20,241–246. Kweku-Muata Osei-Bryson is Professor of Information Systems at Virginia Commonwealth University.He has also worked as an Information Systems practitioner in industry and government.He holds a Ph.D.in Applied Mathematics(Management Science& Information Systems)from the University of Maryland at College Park.His research areas include:Data Mining,Database Systems, Knowledge Management,IS Security,e-Commerce,Decision Support Systems,IT&Productivity,IS Outsourcing.He has published papers in various journals including:Journal of Database Management, Information Systems Frontiers,European Journal of Information Systems,Information&Management,Information Systems Journal, Journal of the Association for Information Systems,Expert Systems with Applications.He serves as an Associate Editor of the INFORMS Journal on Computing,on the Editorial Board of the Computers&Operations Research journal and the International Advisory Board of the Journal of the Operational Research Society.Ojelanki Ngwenyama,MS,(Roosevelt),MBA(Syracuse),Ph.D. (Computer Science,SUNYBinghamton),is Professor of Information Technology Management and Director of the Institute for Research on Technology Management and Organizational Learning at Ryerson University,Canada.Prior to joining Ryerson in2004,Ojelanki was Professor of Information Systems at Virginia Commonwealth Univer-sity.He is currently Visiting Research Professor at Aalborg University in Denmark,and University of Jyväskyläin Finland.Ojelanki’s current research focuses on ICT and productivity,IT management, software process improvement,non-traditional research methods in information systems and ICT and development.He was an Associate Editor for MISQ(2004-2001)and is a member of the Editorial Advisory Board of the Scandinavian Journal of Information System. He has also served on the editorial boards of the Journal of Information Technology and People and Journal of the Association of Information Systems and ICIS.。

Multi-criteria Reinforcement Learning

Multi-criteria Reinforcement Learning


Abstract1Fra bibliotekcision Problems, alternating games, ordered parallel subgoal decomposition
Keywords: reinforcement learning, multi-criteria problems, Markovian De-
We consider multi-criteria sequential decision making problems where the vector-valued evaluations are compared by a given, xed total ordering. Conditions for the optimality of stationary policies and the Bellman optimality equation are given. The analysis requires special care as the topology introduced by pointwise convergence and the order-topology introduced by the preference order are in general incompatible. Reinforcement learning algorithms are proposed and analyzed. Preliminary computer experiments con rm the validity of the derived algorithms. It is observed that in the medium-term multicriteria RL often converges to better solutions (measured by the rst criterion) than their single-criterion counterparts. These type of multicriteria problems are most useful when there are several optimal solutions to a problem and one wants to choose the one among these which is optimal according to another xed criterion. Example applications include alternating games, when in addition to maximizing the probability of win, the decision maker also minimizes the expected number of steps to win in states when it is possible to win, while in states when it is not possible to win it tries to play for time. Another application comes from specifying desired behaviors for robots in terms of a list of ordered, parallel sub-goals, e.g., a football playing robot's primary goal could be to shoot goals, while it's subordinate, parallel goal could be to keep clear of opponents as much as possible.
[自考英语二试题及答案]00015自考英语（二）教程课后试题答案

[自考英语二试题及答案]00015自考英语（二）教程课后试题答案

[自考英语二试题及答案]00015自考英语(二)教程课后试题答案篇一: 00015自考英语教程课后试题答案大学英语自学考试教程下册0015自考英语二课后习题答案unit1Unit 1Text AExercises for the TextI.1.d 2.c 3.c 4.a 5.dII.1.alternative 2.fundamental 3.accompany4.implement5.preccedent6.attain7.objectives 8.vary 9.multiple10.isolateIII.1.c 2.d 3.i 4.j 5.g 6.e 7.h 8.a 9.f 10.bIV.our ; helped ; form ; front; to; passed; it; same;V.1.Decision makers should be able to make the best guess at the future.2.Some people think that everything managers do involves decision making.3.If there are no correct alternatives ,there are no correct decisionsto be made.4.Since different people have different ideas about the same problem ,so the approaches to it vary from person to person.5.Decision makers usually hold the key to the business development of the company.V ocabulary ExercisesI.1.a.be organized anizational anization2.a.simple b.simplified c.simply d.simplification3.a.profit b.profitable c.profitability4.a.intention b.intended c.unintendedII.1.preccdent2.skilled3.achievement4.implement5.optimal6.goal7.accomplish 8.accompanies 9.tendencyIII.1.His friend accompanied him to a concert.2.He has argued her out of her decision.3.he owed his success in part to luck.4.According to his suggestion ,the formalities have been much simplified.5.The broadcasting station predicts that it will turn cold tomorrow.6.Motion is defined as a change in position or place.Text BExercises for the TextI.1.T 2.T 3.F 4.F 5.F 6.F 7.F 8.F 9.F 10.TII.1.preparation; confidence2.idea3.unattractive indifference4.hardworking; personality; interest5.speechless6.holidays; pay7.clean; neat; conservative8.the floor beside your chairPage 1 of 359.politely; naturally10.‖I beg your pardon?‖ or ―Could you please repeat it?‖ etc. V ocabulary ExercisesI.1.at a disadvantage2.conservative3.indifference4.make sure5.vague6.clutched7.turned down 8.to your advantage 9.neat10.prospects 11.take the trouble to 12.placeGrammar ExercisesI.1.连词；让步状语从句。