Unified interactive multiple objective programming

Gardiner, Lorraine R.; Steuer, Ralph E.

doi:10.1016/0377-2217(94)90219-4

Cited by 91 publications

(37 citation statements)

References 33 publications

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“…This issue will be discussed in detail in Chapter 15. In order to accommodate the variety of methods in a single decision system, some authors have already proposed the creation of open architectures or combined systems (Gardiner and Steuer, 1994;Kaliszewski, 2004;Luque et al, 2007b). Some of such integrated systems have already been implemented.…”

Section: Discussionmentioning

confidence: 99%

Introduction to Multiobjective Optimization: Interactive Approaches

Miettinen

Ruiz

Wierzbicki

2008

Lecture Notes in Computer Science

210

197

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Abstract. We give an overview of interactive methods developed for solving nonlinear multiobjective optimization problems. In interactive methods, a decision maker plays an important part and the idea is to support her/him in the search for the most preferred solution. In interactive methods, steps of an iterative solution algorithm are repeated and the decision maker progressively provides preference information so that the most preferred solution can be found. We identify three types of specifying preference information in interactive methods and give some examples of methods representing each type. The types are methods based on trade-off information, reference points and classification of objective functions. IntroductionSolving multiobjective optimization problems typically means helping a human decision maker (DM) in finding the most preferred solution as the final one. By the most preferred solution we refer to a Pareto optimal solution which the DM is convinced to be her/his best option. Naturally, finding the most preferred solution necessitates the participation of the DM who is supposed to have insight into the problem and be able to specify preference information related to the objectives considered and different solution alternatives, as discussed in Chapter 1. There we presented four classes for multiobjective optimization methods according to the role of the DM in the solution process.

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Section: Discussionmentioning

confidence: 99%

Introduction to Multiobjective Optimization: Interactive Approaches

Miettinen

Ruiz

Wierzbicki

2008

Lecture Notes in Computer Science

210

197

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“…Descriptions of prominent interactive procedures are given in Gardiner and Steuer (1994) and Miettinen (1999). Although designed primarily for continuous case problems, many of them can be modified for navigating through large numbers of discrete solution points.…”

Section: Impact Of Increased Computational Powermentioning

confidence: 99%

Multiple Criteria Decision Making, Multiattribute Utility Theory: Recent Accomplishments and What Lies Ahead

Wallenius¹,

et al. 2008

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“…For that purpose there are a lot of interactive classical methods which are traditionally classified into two classes: information required from the user and the inner resolution strategy [19]. Some possible criteria to compare classical methods are e.g.…”

Section: Interactive Classical and Evolutionary Methodsmentioning

confidence: 99%