Dmitry Podkopaev

Towards Automatic Testing of Reference Point Based Interactive Methods

In order to understand strengths and weaknesses of optimization algorithms, it is important to have access to different types of test problems, well defined performance indicators and analysis tools. Such tools are widely available for testing evolutionary multiobjective optimization algorithms.

Multiple Criteria Decision Making and Multiobjective Optimization - A Toolbox

We present an integral approach to solving multiple criteria decision problems in sequences of intelligence, modeling, choice and review phases, often iterated, to identify the most preferred decision variant. The approach taken is human-centric, with the user taking the final decision being a sole and sovereign actor in the decision making process. To ensure generality, no assumption about the Decision Maker preferences or behavior is made. Likewise, no specific assumption about the underlying formal model is made.

Decision Problem: Selection of a Stock Portfolio

“Yes, that’s gold, but I’m too big to go running around like that after atoms.” “No problem, we’ll give you a suitable machine!” coaxed Trurl.

Decision Problems, Continuation

Without further ado I stocked my ship with necessary provisions, took off and, after numerous adventures we need not go into here, finally spotted in a great swarm of stars one that differed from all the rest, since it was a perfect cube.

Derivation of Efficient Portfolios

Towards the end of his second audience with the King, Klapaucius inquired if perhaps Trurl were on the planet and gave a detailed description of his comrade.

Solving Decision Problems

This chapter presents the decision process scheme, its principal phases and also introduces the generic idea of decision problem solving. The idea of the scheme is to repeat the principal phases of the process in cycles, till the DM concludes that among variants identified in the decision process, one variant can be regarded, in his/her opinion, as the most preferred variant.

Derivation of Efficient Variants

The subject of this chapter are methods for derivation of efficient variants in problems, in which variants are explicitly given as a list of variants.

Supporting the Process of the Most Preferred Variant Selection

Several police divisions rushed here and there, searched the grounds, every bush, every weed, and both x-rays and laboratory samples were diligently taken of everything imaginable.

Decision Problem: Selection of a Single Variant

In this chapter, we are concerned with decision problems in which a single variant has to be selected from a set of variants given explicitly (e.g., in the form of a list of variants). To illustrate our considerations, we use the problem which frequently appears in practice: selection of an investment variant. Data and the problem setting presented in this chapter are hypothetical. In subsequent sections, from Sects. 3.2 to 3.5, we follow the four-phased decision scheme presented in Chap. 2

Decision Problem: Selection of a Variant Portfolio—The Discrete Case

In Chap. 3, we have been concerned with the problem of selecting a single decision variant from a set of decision variants.