Central European Journal of Operations Research最新文献

One of the most important issues related to the management of an Automatic Teller Machine (ATM) network is the correct forecasting of the demand for cash. Typically, this demand, expressed as the value or number of ATM withdrawals, has some regularities that can be used to evaluate future values for these variables. However, forecasting becomes a challenge when a crisis occurs that could affect the behavior of ATM users. In this context, it is important to identify how the development of the crisis and the various information concerning it may affect people's attitudes to cash. This study aims to examine the impact of the COVID-19 pandemic on the behavior of ATM customers. On the basis of daily data from 81 ATMs, we analyze the changes in the value and number of withdrawals just before and during the COVID-19 pandemic in Poland. An event study analysis allows us to determine precisely the moments in which changes in user behavior took place. This means that it is also possible to examine the reaction of ATM users to the announcement and implementation of the pandemic restrictions, and to determine the factors that had an impact on the change in people's attitude to cash. Such a study is also important from a sociological point of view, as it enables one to understand people's reactions to the emerging crisis. Hence, its results may be useful not only for managers of ATM networks, but also for various authorities and policy makers.

This paper focuses on an innovative asset allocation strategy for risk averse investors who operate on very long-time horizons, such as endowments and the Italian foundations of banking origin (FBOs). FBOs play a pivotal role in supporting economic, financial and sustainable growth in the long term. In the search for a model which optimizes FBO portfolio choices in the light of regulatory constraints on their sizeable investable portfolio, we highlight the risk-adjusted performances obtained using a robust conditional VaR (R-CVaR) approach-assuming different risk profiles-which corrects some of the Markowitz approach pitfalls and accounts for tail risk. We compare the two models using a buy and hold strategy: the R-CVaR delivers better returns than a Markowitz portfolio, even when those performances are measured with a mean-variance metric.

The COVID19 virus, which first appeared in Wuhan, China, and has become a pandemic in a short time, has threatened the health system in many countries and put it into a bottleneck. Simultaneously, the second wave's expectation spread it necessary to plan the health services correctly. In this study, a location-allocation problem in the two-echelon system, which considers different test sampling alternatives, is examined to obtain test sampling centers' location-allocation. The problem is modeled as a goal programming model to create a network that tests samples at a minimum total distance, establishes a minimum number of test sampling centers, and reaches the distance of PCR test laboratories at minimum total distances. The proposed model is applied as a case study for the two cities located in Turkey, and the obtained locations and inventory levels of each location are presented. Besides, different scenarios are examined to understand the structure of the model. As a result, only testing in hospitals will increase the risk of contamination. Since testing at all points will not be possible administratively, it will be ensured that the most appropriate location-allocation decisions are taken by considering all the proposed model's objectives.

The countries are the units that procure the vaccines during the COVID-19 pandemic. The delivered quantities are huge. The countries must bear the inventory holding cost according to the variation of stock quantities. This cost depends on the speed of the vaccination in the country. This speed is time-dependent. The vaccinated portion of the population can be approximated by the cumulative distribution function of the Cauchy distribution. A model is provided for determining the minimal-cost inventory policy and its optimality conditions are provided. The model is solved for 20 countries for different numbers of procurements. The results reveal the individual behavior of each country.

A robust optimal control of discrete time Markov chains with finite terminal T and bounded costs or wealth using probability distortion is studied. The time inconsistency of these distortion operators and hence its lack of dynamic programming are discussed. Due to that, dynamic versions of these operators are introduced, and its availability for dynamic programming is demonstrated. Based on dynamic programming algorithm, existence of the optimal policy is justified and an application of the theory to portfolio optimization along with a numerical study is also presented.