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2 Operations Research and Decisions

2 Sielska A.

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The impact of weights on the quality of agricultural producers' multicriteria decision models

Sielska A.

Operations Research and Decisions

2015

Vol. 25, No. 4

51--69

Decisions regarding agricultural production involve multiple goals. A multicriteria approach allows decision makers to consider more aspects of the decision scenario, although it also leads to other problems, such as difficulties with the selection of goals or criteria, as well as assigning them appropriate weights. It is argued that not only do goals vary depending on the decision-makers’ socioeconomic features, but their relative importance changes as well. A simulation study has been conducted based on the Farm Accountancy Data Network (FADN) database. We use the distance-to-the-negative-solution maximization model. Seven sets of criteria and different sets of weights are considered. The main purpose of the study is to determine the impact of weights on the quality of the model. Quality is assessed by comparing the optimal and observed values of the decision variables. The results lead to the conclusion that the differences between the quality of various models are small.

Multicriteria rankings of open-end investment funds and their stability

Sielska A.

Operations Research and Decisions

2010

Vol. 20, No. 1

111-129

For research purposes, three multicriteria outranking methods (PROMETHEE, WSA and TOPSIS) were used to construct rankings of investment funds to assess their performance in the time period from January to July 2008. Nine indicators related to the distributions of return rates, purchase and management costs and to customers’ convenience were included in the set of criteria. The weight of each criterion was calculated on the basis of the relative volatility rate of the given criterion. In order to assess the stability of the rankings, the weight of a single criterion was changed (using each criterion in turn) and new rankings were constructed using the modified weights. The similarity of rankings built before and after these changes was assessed on the basis of the maximum difference between ranks and the Spearman correlation coefficient. The results obtained enable assessment not only of the stability of each outranking method, but the similarity of results obtained by different methods as well. All calculations were done using the SANNA software.