Publication detail

LANDFILLS MULTIPLE GOAL OPTIMIZATION USING EQUATIONLESS QUALITATIVE RELATIONS

Original Title

LANDFILLS MULTIPLE GOAL OPTIMIZATION USING EQUATIONLESS QUALITATIVE RELATIONS

Czech Title

LANDFILLS MULTIPLE GOAL OPTIMIZATION USING EQUATIONLESS QUALITATIVE RELATIONS

Language

cs

Original Abstract

Landfills are unique and difficult to measure. Their optimization must be solved with a severe lack of information. The privilege of not utilizing information items based on common sense cannot be afforded, as this represents an important part of the available ad hoc landfill knowledge related to e.g., economics, sociology. Therefore, a flexible, formal tool for dealing with the common sense knowledge and data of a non-numerical nature is required. The classical quantitative tools, e.g., statistics, are inefficient for dealing with such sets of non-quantitative information items as interviews. Qualitative quantification is information non-intensive. It is based on just three values-positive, zero and negative; increasing, constant and decreasing. A qualitative model can be used to generate all possible qualitative activities/scenarios. It means that the past history and future scenarios of the landfill under study are known, given the model is correct. The scenarios can be screened against the prescribed trends (maximization or minimization) of objective functions, to identify all possible ways of achieving optimal results. A case study with four mutually competing objective functions is presented in details. No prior knowledge of qualitative reasoning is required.

Czech abstract

Landfills are unique and difficult to measure. Their optimization must be solved with a severe lack of information. The privilege of not utilizing information items based on common sense cannot be afforded, as this represents an important part of the available ad hoc landfill knowledge related to e.g., economics, sociology. Therefore, a flexible, formal tool for dealing with the common sense knowledge and data of a non-numerical nature is required. The classical quantitative tools, e.g., statistics, are inefficient for dealing with such sets of non-quantitative information items as interviews. Qualitative quantification is information non-intensive. It is based on just three values-positive, zero and negative; increasing, constant and decreasing. A qualitative model can be used to generate all possible qualitative activities/scenarios. It means that the past history and future scenarios of the landfill under study are known, given the model is correct. The scenarios can be screened against the prescribed trends (maximization or minimization) of objective functions, to identify all possible ways of achieving optimal results. A case study with four mutually competing objective functions is presented in details. No prior knowledge of qualitative reasoning is required.

BibTex


@article{BUT106155,
  author="Mirko {Dohnal}",
  title="LANDFILLS MULTIPLE GOAL OPTIMIZATION USING EQUATIONLESS QUALITATIVE RELATIONS",
  annote="Landfills are unique and difficult to measure. Their optimization must be solved with a severe lack of information. The privilege of not utilizing information items based on common sense cannot be afforded, as this represents an important part of the available ad hoc landfill knowledge related to e.g., economics, sociology. Therefore, a flexible, formal tool for dealing with the common sense knowledge and data of a non-numerical nature is required. The classical quantitative tools, e.g., statistics, are inefficient for dealing with such sets of non-quantitative information items as  interviews. Qualitative quantification is information non-intensive. It is based on just three values-positive, zero and negative; increasing, constant and decreasing. A qualitative model can be used to generate all possible qualitative activities/scenarios. It means that the past history and future scenarios of the landfill under study are known, given the model is correct. The scenarios can be screened against the prescribed trends (maximization or minimization) of objective functions, to identify all possible ways of achieving optimal results. A case study with four mutually competing objective functions is presented in details. No prior knowledge of qualitative reasoning is required.",
  chapter="106155",
  doi="10.3844/ajessp.2014.26.34",
  number="1",
  volume="10",
  year="2014",
  month="february",
  pages="26--34",
  type="journal article in Scopus"
}