Yazar "Javadi, Sonya" seçeneğine göre listele

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    The impact of the COVID-19 pandemic on online grocery supply chain management: a case study in Istanbul
    (Gazi Üniversitesi, 2024-03) Javadi, Sonya; Keten, Olcay; Özer, A. İhsan; Alkan, R. Zeynep
    The COVID-19 pandemic has already crippled normal life all over the world. Its negative impact not only changed the human health system tragically but also disrupted the global economic system. One negative result was ended up in the global food supply chain. As the lockdown times have suspended the manufacturing and logistic activities, therefore, the customers have experienced unimaginable chaos in the shopping markets. Moreover, the purchasing habit of the consumers has remarkably changed compared to pre-pandemic. To meet this new demand pattern, many grocery retailers have tried to adapt to the new normal. While before COVID-19 offline grocery purchasing was popular, after the pandemic, online service got tremendous attention in market. In this study, online grocery supply chain management during the COVID-19 in Istanbul is considered. The aim is to find out how online grocery companies will serve more efficiently during the pandemics and which factors have more effect on the customer’s satisfaction. To do so, first, three popular grocery retailers in Istanbul were selected. Then, a related survey was designed to understand the consumer experience as doing online grocery shopping in COVID-19. Unsurprisingly, a result shows that 60% of the respondents did online shopping every 3-4 days in one week, and the delivery time is the most important factor for the customers. Then, the SWOT analyses were performed accordingly, and the related strategies were summarized. Finally, several managerial implications were given to may improve the company’s online services in COVID-19 and post COVID-19 in Turkey.
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    On computing the multivariate poisson probability distribution
    (Springer, 2023-06-20) Çekyay, Bora; Frenk, Johannes Bartholomeus Gerardus; Javadi, Sonya
    Within the theory of non-negative integer valued multivariate infinitely divisible distributions, the multivariate Poisson distribution plays a key role. As in the univariate case, any non-negative integer valued infinitely divisible multivariate distribution can be approximated by a multivariate distribution belonging to the compound Poisson family. The multivariate Poisson distribution is an important member of this family. In recent years, the multivariate Poisson distributions also has gained practical importance, since they serve as models to describe counting data having a positive covariance structure. However, due to the computational complexity of computing the multivariate Poisson probability mass function (pmf) and its corresponding cumulative distribution function (cdf), their use within these counting models is limited. Since most of the theoretical properties of the multivariate Poisson probability distribution seem already to be known, the main focus of this paper is on proposing more efficient algorithms to compute this pmf. Using a well known property of a Poisson multivariate distributed random vector, we propose in this paper a direct approach to calculate this pmf based on finding all solutions of a system of linear Diophantine equations. This new approach complements an already existing procedure depending on the use of recurrence relations existing for the pmf. We compare our new approach with this already existing approach applied to a slightly different set of recurrence relations which are easier to evaluate. A proof of this new set of recurrence relations is also given. As a result, several algorithms are proposed where some of them are based on the new approach and some use the recurrence relations. To test these algorithms, we provide an extensive analysis in the computational section. Based on the experiments in this section, we conclude that the approach finding all solutions of a set of linear Diophantine equations is computationally more efficient than the approach using the recurrence relations to evaluate the pmf of a multivariate Poisson distributed random vector.