Decision-making techniques are used to help evaluate the current suppliers' aim at classifying performance of individual suppliers against desired levels of performance, so as to design suitable ...plans to increase the performance and capabilities of suppliers. In this study, an integrated model is introduced and proposed for increasing the supplier selection and evaluation quality. The methodology is composed of two steps. The first stage is fuzzy decision-making trial and evaluation laboratory method in which the interactions between the evaluation criteria and the criteria weight have been computed. At the second stage, performances of suppliers are assessed using both the criteria weights obtained at the first stage and fuzzy c-means clustering algorithm by classifying the vendors according to their performances. Obtained results show that the proposed model is very well suited as a decision-making tool for supplier selection decisions.
This study uses artificial neural networks (ANNs) to examine the intricate relationship between air pollutants, meteorological factors, and respiratory disorders. The study investigates the ...correlation between hospital admissions for respiratory diseases and the levels of PM
10
and SO
2
pollutants, as well as local meteorological conditions, using data from 2017 to 2019. The objective of this study is to clarify the impact of air pollution on the well-being of the general population, specifically focusing on respiratory ailments. An ANN called a multilayer perceptron (MLP) was used. The network was trained using the Levenberg–Marquardt (LM) backpropagation algorithm. The data revealed a substantial increase in hospital admissions for upper respiratory tract diseases, amounting to a total of 11,746 cases. There were clear seasonal fluctuations, with fall having the highest number of cases of bronchitis (
N
= 181), sinusitis (
N
= 83), and upper respiratory infections (
N
= 194). The study also found demographic differences, with females and people aged 18 to 65 years having greater admission rates. The performance of the ANN model, measured using
R
2
values, demonstrated a high level of predictive accuracy. Specifically, the
R
2
value was 0.91675 during training, 0.99182 during testing, and 0.95287 for validating the prediction of asthma. The comparative analysis revealed that the ANN-MLP model provided the most optimal result. The results emphasize the effectiveness of ANNs in representing the complex relationships between air quality, climatic conditions, and respiratory health. The results offer crucial insights for formulating focused healthcare policies and treatments to alleviate the detrimental impact of air pollution and meteorological factors.
The dilemma between health concerns and the economy is apparent in the context of strategic decision making during the pandemic. In particular, estimating the patient numbers and achieving an ...informed management of the dilemma are crucial in terms of the strategic decisions to be taken. The Covid-19 pandemic presents an important case in this context. Sustaining the efforts to cope with and to put an end to this pandemic requires investigation of the spread and infection mechanisms of the disease, and the factors which facilitate its spread. Covid-19 symptoms culminating in respiratory failure are known to cause death. Since air quality is one of the most significant factors in the progression of lung and respiratory diseases, it is aimed to estimate the number of Covid-19 patients corresponding to the pollutant parameters (PM10, PM2.5, SO
2
, NO
X
, NO
2
, CO, O
3
) after determining the relationship between air pollutant parameters and Covid-19 patient numbers in Turkey. For this purpose, artificial neural network was used to estimate the number of Covid-19 patients corresponding to air pollutant parameters in Turkey. To obtain highest accuracy levels in terms of network architecture structure, various network structures were tested. The optimal performance level was developed with 15 neurons combined with one hidden layer, which achieved a network performance level as high as 0.97342. It was concluded that Covid-19 disease is affected from air pollutant parameters and the number of patients can be estimated depending on these parameters by this study. Since it is known that the struggle against the pandemic should be handled in all aspects, the result of the study will contribute to the establishment of environmental decisions and precautions.
Scheduling is an important decision-making problem in production planning and the resulting decisions have a direct impact on reducing waste, including energy and idle capacity. Batch scheduling ...problems occur in various industries from automotive to food and energy. This paper introduces the parallel p-batch scheduling problem with batch delivery, content-dependent loading/unloading times and energy-aware objective function. The problem has been motivated by a real system used for freezing products in a food processing company. A mixed-integer linear programming model (MILP) has been developed and explained through a numerical example. As it is not practical to solve large-size instances via a mathematical model, the discrete differential evolution algorithm has been improved (iDDE) and hybridised with the genetic algorithm (GA). A release-oriented vector generation procedure and a heuristic batch formation mechanism have been developed to efficiently solve the problem. The performance of the proposed approach (iDDEGA) has been compared with CPLEX, iDDE and GA through a comprehensive computational study. A case study was conducted based on real data collected from the freezing process of the company, which also verified the practical use and advantages of the proposed methodology.
For most of managers purchasing is a strategic issue. Thus, to select the suitable suppliers has strategic importance for every company. The objective of supplier selection is to reduce purchasing ...risk, maximize overall value to the purchaser and build a long term, reliable relationship between buyers and suppliers. Many methods have been proposed and used for supplier evaluation and selection; most of them try to rank the suppliers from the best to the worst and to choose the appropriate supplier(s). Supplier evaluation and selection is a complex and typical multi criteria decision-making problem. Because of human judgment needs in many area of supplier selection such as preferences on alternatives or on the attributes of suppliers or the class number and borders supplier selection becomes more difficult and risky.
In this study, a new tool for supplier selection is proposed. In this paper, we applied Fuzzy Adaptive Resonance Theory (ART)’s classification ability to the supplier evaluation and selection area. The proposed selection method, using Fuzzy ART not only selects the most appropriate supplier(s) and also clusters all of the vendors according to chosen criteria. To explain the Fuzzy ART method a real-life supplier selection problem is solved and suppliers are categorized according to their similarities. The obtained results show that the proposed method is well suited as a decision-making tool for supplier evaluation and selection problem.
This study presents a comparative study to determine ideal stock levels of a multi-national tire manufacturing company. The conventional inventory models can not be sufficient to optimize the ...production, the inventory quantity and the backorder simultaneously. Therefore, it is not possible to obtain a production policy by considering these objectives for all produced parts concurrently. In this paper, a production problem with three objectives is solved with mathematical modeling, greedy algorithm and genetic algorithm considering production constraints of a company. While existing inventory models based on conventional methods were applied for safety stock level determination, our proposed model uses the mathematical programming based optimization methods based on mathematical programming. Furthermore, the production planning policy is obtained with the optimum production amount and the stock is determined by considering the constraints defined by the firm. Finally, in our numerical results, we compare each solution methodology with respect to each objective criteria.
The aim of this study is to apply multivariate statistical methods in predicting ozone (O
3) concentrations at the ground level of the troposphere as the function of pollution and meteorological ...parameters. PM10, SO
2, NO, NO
2, CO, O
3, CH
4, NMHC, temperature, rainfall, humidity, pressure, wind direction, wind speed and solar radiation were measured hourly for one year period in order to predict O
3 concentrations of 1
h later. In the study, relationships between O
3 data and other variables were investigated by bivariate correlation analysis. CH
4, NMHC, NO
2 exhibited considerable negative correlations with O
3 described with the Pearson correlation coefficients of −
0.67, −
0.55, −
0.51, respectively whereas highest positive correlation was noted for temperature with correlation coefficient of 0.60. Multiple regression analysis (MLR) was used for modeling annual and seasonal O
3 concentrations. Adjusted R
2 values were determined as 0.90, 0.85 and 0.92 respectively for annual period, cooling and warming seasons. In order to decrease the number of input variables principle component analysis (PCA) was applied by using annual data. MLR analysis was repeated using four principle components and new adjusted R
2 was calculated as 0.63.
► We have used multivariate statistical methods for modeling tropospheric O
3. ► Relations between O
3 and other variables were examined by bivariate correlation analysis. ► Multiple regression analysis (MLR) was used for modeling O
3 concentrations. ► Principle component analysis was applied in order to reduce the number of input variables. ► Using principle components in MLR analysis provided lower standard error value.
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