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
The economic contribution of insect pollinators is evident as they contribute to higher crop yield quantity and quality. The management of bee species is key to crop production, especially where wild and domesticated bees are in low abundance. Several bee species have been identified as possible candidates for replacing, or at least supplementing, the decreasing number of honey bees. Our research seeks to address the location problem as regards nesting aids for Osmia cornuta bees in orchards using mathematical programming models for determining the optimal location of nesting aids and optimizing the management of solitary bees. A differential evolution algorithm is used to solve a location model of Osmia cornuta nesting aids for optimum pollination. Instead of a random ad hoc location of nesting aids in an orchard, or at the edge of an orchard, we utilize an effective optimization tool to determine locations which will optimize pollination by alternative pollinators, and increase the economic output of an agricultural business. The importance of this proposed model is likely to increase with agricultural intensification, and the decrease of the numbers of wild pollinators.
The paper presents heterogeneous fleet vehicle routing problem with selection of inter-depots. The goal of the proposed model is to schedule shipments of goods from the central depot to the inter-depots and finally to the endpoint customers, so that as inter-depots are considered the existing capacities of endpoints customers. In given type of transportation it can be expected to use two types of vehicles: large capacity vehicles ensuring the distribution of goods from the central warehouse and small capacity vehicles ensuring the delivery of goods from inter-depots for late shipment to other cus-tomers. The principle of the model is illustrated on three demonstrative instances
Research background: Using the marginal means and contrast analysis of the target variable, e.g., claim severity (CS), the actuary can perform an in-depth analysis of the portfolio and fully use the general linear models potential. These analyses are mainly used in natural sciences, medicine, and psychology, but so far, it has not been given adequate attention in the actuarial field. Purpose of the article: The article's primary purpose is to point out the possibilities of contrast analysis for the segmentation of policyholders and estimation of CS in motor third-party liability insurance. The article focuses on using contrast analysis to redefine individual relevant factors to ensure the segmentation of policyholders in terms of actuarial fairness and statistical correctness. The aim of the article is also to reveal the possibilities of using contrast analysis for adequate segmentation in case of interaction of factors and the subsequent estimation of CS. Methods: The article uses the general linear model and associated least squares means. Contrast analysis is being implemented through testing and estimating linear combinations of model parameters. Equations of estimable functions reveal how to interpret the results correctly. Findings & value added: The article shows that contrast analysis is a valuable tool for segmenting policyholders in motor insurance. The segmentation's validity is statistically verifiable and is well applicable to the main effects. Suppose the significance of cross effects is proved during segmentation. In that case, the actuary must take into account the risk that even if the partial segmentation factors are set adequately, statistically proven, this may not apply to the interaction of these factors. The article also provides a procedure for segmentation in case of interaction of factors and the procedure for estimation of the segment's CS. Empirical research has shown that CS is significantly influenced by weight, engine power, age and brand of the car, policyholder's age, and district. The pattern of age's influence on CS differs in different categories of car brands. The significantly highest CS was revealed in the youngest age category and the category of luxury car brands.
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