ABSTRACT
In today's competitive environment, having the best sequence of operations for production and distribution activities is a basic need for survival. As a result, one of the major challenges in fixed supply chain systems is unnecessary transportation costs and the inability to meet customer demand as quickly as possible. In order to meet these challenges, factories and mobile equipment have been considered in this study, and have recently been used in several industries, including pharmaceutical, chemical, and dairy. In the course of this study, a novel mathematical model was put forward for an integrated production and distribution scheduling problem taking into account some real-world features, focusing on reducing customer waiting time and also reducing production costs. A small-scale problem was resolved to check the model's accuracy. The accuracy of the model is affirmed given the example and its solution acquired from GAMS software. The results of the study prove the effectiveness of this model in reducing customer waiting time and production costs and also demonstrate that the model has the capacity to be utilized by all organizations that produce and distribute perishable products, including dairy and pharmaceutical products, chemical compounds and masks during the Coronavirus pandemic. © 2022,Mathematical Modelling of Engineering Problems.All Rights Reserved.
ABSTRACT
Nonparametric regression modelling for data applies the relationship between predictor and response variables without considering any particular trend. The main principle of nonparametric regression is estimating an unknown smooth function. Local polynomial regression is one of several methods for estimating smooth functions used in nonparametric regression models. There are two parameters in the local polynomial regression model the smoothing parameter and the polynomial degree parameter. Generalized Cross Validation is a classical method used to determine optimal smoothing parameter in nonparametric regression. The smoothing parameter that gives the minimum value of Generalized Cross-Validation is the optimal parameter. We apply the optimal smoothing parameter for local polynomial regression modelling using data on the regional domestic product growth rate of the business field in North Sumatra Province. The simulation results show a polynomial of degree two is better than degree one. Investigation of the effects of the Covid-19 pandemic shows that the predicted growth rate is far from the expected growth. © 2022 American Institute of Physics Inc.. All rights reserved.