Shelf Layout With Integrating Data Mining And Multi-Dimensional Scaling

Thanks to information, communication and technological improvements in these days, data mining method are used to obtain significant results from very large data sets. In terms of businesses, decisionmaking in product design, placement, layout and so on issues are of vital importance. Association rules taking part in data mining topic is used so much especially in marketing research in the market basket. The Multi- Dimensional scaling (MDS) method is also frequently used for the positioning of products in the marketing field. MDS is measured similarities between products, units and so on according to the method of Euclidean space. Relations between products or units are visualized in two or three dimensions using MDS method according to the purpose. The aim of this study is to determine the product shelf layout using association rules according to the relationship map of the products generated by MDS. Together with the association rules (conviction ratios) used in data mining field, proximity coefficients between products were calculated and used in MDS analyze. Product groups were created by using MDS and proximity coefficient combinations made up between products. Shelf layout ensuring similar products in line with side by side was determined with the help of association rules. The applicability of the proposed method for products and alternative shelf layout was presented visually. 750 shopping and customers who purchase products in the same shelf made up the data of this study. In this study, placement of the products designed to maximize the benefit level for customers in terms of time and convenience

Random search with adaptive boundaries algorithm for obtaining better initial solutions

Conventional random search techniques take a lot of time to reach optimum-like solutions. Thus, random search techniques with advanced competencies play an essential role in algorithms. In this study, we develop an algorithm that provides an adaptive initial solution, to some extent reducing the diversity of randomness in the initialization of the algorithms for continuous unconstrained/bounded nonlinear optimization problems. The algorithm meets this expectation by narrowing search space adaptively without trapping into local optimums. It also escapes from eliminating accidentally global optimum in multi-modal problems. For this reason, we configure the proposed algorithm on the principle of updating given upper-lower boundaries dynamically. It is worth mentioning that this procedure does not add an additional burden to existing solution methods; on the contrary, it contributes to problem-solving in terms of time and efficiency. To show its performance, we have incorporated with most frequently used unconstrained/bounded benchmarks and compared them with the solutions in the literature. In conclusion, the proposed algorithm converges solutions quickly and is applicable for later usage in further studies

Criteria Assessment for Covid-19 Vaccine Selection via BWM

The aim of this study is to discover the supreme and other most important criteria that count in decision making considering vital uncertainties associated with certain parameters, risks, and costs for individuals in order to select the right Covid-19 vaccine based on a set of remarkable criteria. A survey study for assessment according to the given most important criteria based on expert opinion is conducted through the Best-Worst Method (BWM). A form including pairwise comparison vectors was sent to the participants in order to reveal priorities against their subjective decision-making criteria for vaccine selection. The essence of the study addresses that the efficacy criterion has the highest score and it is followed by the other given criteria such as storage requirements, incorporated vaccine technology, and international acceptance criterion. Participants tend to prioritize the origin and price of the vaccine behind all other criteria. Long-sought Covid-19 vaccine and its alternatives with different disclosed criteria of them have led to increasing indecision of people who have an opportunity to choose individually and the government officials who are responsible for country-wide procurement and policymakers; as a result, criteria evaluation is a challenging task. To solve the mentioned multi-criteria decision-making (MCDM) problem, BWM is newly employed in vaccine selection problems and its robust approach reveals the subjective priority of the criteria