The objective of this paper is to highlight the use of multiple criteria evaluation methods as a tool for the rating and selection of retail chains from the customers and suppliers perspective. We provide an assessment on the attractiveness of active retail chains on the Slovak market through multiple criteria methods used for the analysis of customer preferences. An analysis was conducted on a sample of consumers in Bratislava involving 11 389 respondents interviewed. The multi-attribute decision-making methods PROMETHEE II and V were used to assess the variants. In the first part of analysis the collected data uncover customers' preferences in the selection of retail chains. Findings suggest a ranking of evaluated retail chains and thus of customer preferences. Based on the obtained evaluation, in the second part of analysis, a set of retail chains was chosen under constraints concerning the effectiveness of advertising, market share of sales and the maximum number of chosen retail chains and a binary linear programming model was formulated as an outcome. Proposed procedure aims to assist the decision maker in selecting which retail chain to choose for distribution of supplier's products, and thus maximize benefits, which will result from consumer preferences and service satisfaction level in retail chain.
Background: The most significant changes caused by the COVID-19 crisis were the sharp increase in working from home and the growing importance of e-commerce, which affected the development of some industries. This change also affects the investors' investment operations, which are based on analysis to ensure an unquestionable certainty of the invested financial amount and a satisfactory return. It is, therefore, interesting to analyze the possible return of the chosen investment strategy based on the optimization model of portfolio selection based on the CVaR risk measure. Purpose: The paper aims to present the possible use of the analysis of returns of effective portfolios constructed based on the optimization model of portfolio selection based on the CVaR risk measure during the crisis (COVID-19) and the pre-crisis period. Study design/methodology/approach: Paper presents the impact of the COVID-19 crisis on investor decision-making through the CVaR risk measure, which was implemented on the historical data of the components of the Standard and Poor's 500 stock index (S&P 500) in the crisis period as well as in the pre-crisis period. Findings/conclusions: The presented approach based on the CVaR risk rate measure and the relevant portfolio selection model provides the investor with an effective tool for allocating funds to the financial market in particular segments in both monitored periods. Limitations/future research: Time series data are divided into two periods based on visible factors such as the number of COVID-19 cases. In future research, we aim to divide monitored periods based on unobservable factors influencing investors' decisions, such as bull or bear mood on the market.
Paper presents alternative solution seeking approach for portfolio selection problem with Omega function performance measure which allows determining capital allocation over the number of assets. Omega function computability is diffi-cult due to substandard structures and therefore the use of standard techniques seems to be relatively complicated. Dif-ferential evolution from the group of evolutionary algorithms was selected as an alternative computing procedure. Al-ternative approach is analyzed on the Down Jones Industrial Index data. Presented approach enables to determine good real-time solution and the quality of results is comparable with results obtained by professional software
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