We study a finite-source, finite-capacity queueing game with triadic control, working vacations, and impatient customers. The model is formulated as a finite continuous-time Markov chain with level-dependent feasible phases under single and multiple working-vacation policies. Unlike infinite-source models, the finite-source structure requires a careful distinction between aggregate request opportunities in the complete system and the decision environment faced by a tagged customer. The exogenous balking mechanism of the underlying finite-source service model is replaced by a symmetric strategic joining probability. This allows us to characterize the individual equilibrium, define the socially optimal admission probability, and compare decentralized and socially optimal behavior. Numerical experiments indicate that the working-vacation policy is the main determinant of equilibrium behavior and welfare in the retained parameter regime, while impatience can locally affect the socially desirable admission level.
The present work formalizes and establishes a framework for processing expert information where expert responses are not necessarily symmetric. A generalization of the Cloud model is presented and applied during the execution of Delphi rounds. In contrast to conventional approaches, this method captures the cognitive anisotropy intrinsic to expert estimation through the use of split-entropy. Furthermore, a hybrid evolutionary operator is proposed to control inter-round hyper-entropy, taking into account not only opinion variability but also the experts' self-assessment regarding their confidence level in the issued judgment. Boundary conditions and system stopping criteria are also formalized using infinitesimal entropic variation metrics, thereby guaranteeing the stability and robustness of the synthesized consensus. The results are implemented in a real-world scenario, and the data are processed and visualized using a script programmed in the R statistical language.
While developing economic order quantity model, there occurs a tradeoff between mathematical stand point and the practical scenario. The relevant parameters like deterioration rate, demand, various costs are normally taken as constant. But it is not realistic due to uncertainty. In the present study, an inventory model is designed by incorporating the manner of two warehouses (one is selling point and another is additional storage house). In this design, demand is considered as stock dependent and shortages are allowed. The parameters are executed by triangular neutrosophic number to relate the fashion of uncertainty in today’s scenario. The concept of graded mean integration is applied to manipulate the huge data of neutrosophic number. The model aims to minimize the entire inventory cost characterised by a non linear function of two time variables viz. time of stock out and the full time of an inventory cycle. The proposed model is numerically executed. A sensitivity analysis is performed to explore the changes of value of some system parameters.
We studied vehicle traffic at a signalized intersection using an Mt/Mt/1/N queueing model with two types of customers, who compete for service from a single server. Arrival of vehicles is according to a Poisson process with time-dependent parameter λ(t), and their service time follows an exponential distribution with a time-dependent parameter μ(t). If an approaching customer has to queue, they may join a queue of negative customers instead of the main queue of positive customers. Formation of negative customers occurs from the main Poisson stream according to the following rule: a customer, upon entering the system, decides to join the queue of negative customers with probability δ if the number of negative customers is less than or equal to that of positive customers. When the server is active, at each service completion epoch, a negative customer may forcefully get service with probability β1. Negative customers may thus extend a positive customer’s waiting time and further increase congestion. The bad effect caused by negative customers is studied on the basis of the delayexperienced by different types of customers. We conducted an extensive numerical study based on simulated and real-world data, which includes a comparison of the delays computed by the Mt/Mt/1/N queueing model and those by the incremental queue accumulation (IQA) method.
Macroeconomic forecasting in small open economies is challenging because samples are short, external shocks are important, and predictor sets are often high dimensional. This paper evaluates whether machine learning methods improve forecasts of Slovak industrial production relative to standard time series and factor based benchmarks. Using rolling pseudo real time forecasts at horizons of 1, 3, 6, 9, and 12 months, we compare regularized linear models, tree based methods, support vector machines, factor based approaches, and several forecast combination schemes. The analysis considers three settings: a pre-COVID hard data sample, a full sample hard data setting that includes the COVID shock and recovery period, and a sample based only on soft survey indicators. Across the hard data specifications, Lasso and Elastic Net consistently deliver the strongest out of sample performance in terms of both forecast accuracy and directional accuracy, while discounted mean squared forecast error weighting is the most competitive combination approach. These gains remain strong in the sample that includes the COVID period, indicating robustness under heightened volatility and structural disruption. By contrast, soft indicators used on their own provide only limited and unstable forecasting gains. The results show that, in a small open economy, sparse regularization offers a more effective forecasting strategy than broader shrinkage, factor compression, or standard univariate benchmarks.
