A Theoretical Framework for Analysing the Notion of Relative Importance of Criteria

Roy, Bernard; Mousseau, Vincent

doi:10.1002/(sici)1099-1360(199606)5:2<145::aid-mcda99>3.3.co;2-x

Cited by 14 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in general, the role of the weights may be greatly misleading and it is not at all clear what the concept 'relative importance of an objective' means (Podinovski, 1994;Roy and Mousseau, 1996). Besides, giving weights implies eliciting global preferences which may be hard if not impossible.…”

Section: Weighting Methodsmentioning

confidence: 99%

Why Use Interactive Multi-Objective Optimization in Chemical Process Design?

Miettinen

Hakanen

2017

Multi-Objective Optimization

View full text Add to dashboard Cite

Section: Weighting Methodsmentioning

confidence: 99%

Why Use Interactive Multi-Objective Optimization in Chemical Process Design?

Miettinen

Hakanen

2017

Multi-Objective Optimization

View full text Add to dashboard Cite

“…The weights are often viewed as the relative priority of different users, but the coupling is complicated and can in general be misleading. First, the notion of priority makes sense in a local area of the performance region, but the global interpretation of the weighting is not easily characterized [216]. This is particularly evident for nonconvex performance regions, because a small perturbation in the weights can greatly affect the optimal operating point; see Figure 1.16(b).…”

Section: Remark 13 (Shortcomings Of Weighted Arithmetic Mean)mentioning

confidence: 99%

Optimal Resource Allocation in Coordinated Multi-Cell Systems

Björnson

2013

FNT in Communications and Information Theory

283

299

View full text Add to dashboard Cite

The use of multiple antennas at base stations is a key component in the design of cellular communication systems that can meet high-capacity demands in the downlink. Under ideal conditions, the gain of employing multiple antennas is well-recognized: the data throughput increases linearly with the number of transmit antennas if the spatial dimension is utilized to serve many users in parallel. The practical performance of multi-cell systems is, however, limited by a variety of nonidealities, such as insufficient channel knowledge, high computational complexity, heterogeneous user conditions, limited backhaul capacity, transceiver impairments, and the constrained level of coordination between base stations. This tutorial presents a general framework for modeling different multi-cell scenarios, including clustered joint transmission, coordinated beamforming, interference channels, cognitive radio, and spectrum sharing between operators. The framework enables joint analysis and insights that are both scenario independent and dependent.The performance of multi-cell systems depends on the resource allocation; that is, how the time, power, frequency, and spatial resources are divided among users. A comprehensive characterization of resource allocation problem categories is provided, along with the signal processing algorithms that solve them. The inherent difficulties are revealed: (a) the overwhelming spatial degrees-of-freedom created by the multitude of transmit antennas; and (b) the fundamental tradeoff between maximizing aggregate system throughput and maintaining user fairness. The tutorial provides a pragmatic foundation for resource allocation where the system utility metric can be selected to achieve practical feasibility. The structure of optimal resource allocation is also derived, in terms of beamforming parameterizations and optimal operating points.This tutorial provides a solid ground and understanding for optimization of practical multi-cell systems, including the impact of the nonidealities mentioned above. The Matlab code is available online for some of the examples and algorithms in this tutorial. IntroductionThis section describes a general framework for modeling different types of multi-cell systems and measuring their performance -both in terms of system utility and individual user performance. The framework is based on the concept of dynamic cooperation clusters, which enables unified analysis of everything from interference channels and cognitive radio to cellular networks with global joint transmission. The concept of resource allocation is defined as allocating transmit power among users and spatial directions, while satisfying a set of power constraints that have physical, regulatory, and economic implications. A major complication in resource allocation is the inter-user interference that arises and limits the performance when multiple users are served in parallel. Resource allocation is particularly complex when multiple antennas are employed at each base station. However, the throu...

show abstract

“…In the literature some applications of Multi-attribute Utility Theory (MAUT) and Multi-criteria decision making (MCDM) in Project Management appeared. Mota and Almeida [30] propose the iterative outranking MCDM method for classifying of the project activities according to the tasks characteristic using ELECTRE TRI-C method [31,32]. Application of the PROMETHEE method for critical path analysis is shown in [29].…”

Section: Quantitative Approach To Task Criticalnessmentioning

confidence: 99%