Forecasting, production planning, and control
Samrad Jafarian-Namin; Davood Shishebori; Alireza Goli
Abstract
The temperature has been a highly discussed issue in climate change. Predicting it plays an essential role in human affairs and lives. It is a challenging task to provide an accurate prediction of air temperature because of its complex and chaotic nature. This issue has drawn attention to utilizing the ...
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The temperature has been a highly discussed issue in climate change. Predicting it plays an essential role in human affairs and lives. It is a challenging task to provide an accurate prediction of air temperature because of its complex and chaotic nature. This issue has drawn attention to utilizing the advances in modelling capabilities. ARIMA is a popular model for describing the underlying stochastic structure of available data. Artificial Neural Networks (ANNs) can also be appropriate alternatives. In the literature, forecasting the temperature of Tehran using both techniques has not been presented so far. Therefore, this article focuses on modelling air temperatures in the Tehran metropolis and then forecasting for twelve months by comparing ANN with ARIMA. Particle Swarm Optimization (PSO) can help deal with complex problems. However, its potential for improving the performance of forecasting methods has been neglected in the literature. Thus, improving the accuracy of ANN using PSO is investigated as well. After evaluations, applying the seasonal ARIMA model is recommended. Moreover, the improved ANN by PSO outperforms the pure ANN in predicting air temperature.
Supply chain management
Somayeh Sazegari; Sayyed Mohammad Reza Davoodi; Alireza Goli
Abstract
Today, most supply chains are moving towards green business with a greater focus on environmental protection as a competitive advantage. Among them, the design of a three-stage green supply chain with optimal allocation, a multiple supply chain that includes supplier (first stage), manufacturer (second ...
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Today, most supply chains are moving towards green business with a greater focus on environmental protection as a competitive advantage. Among them, the design of a three-stage green supply chain with optimal allocation, a multiple supply chain that includes supplier (first stage), manufacturer (second stage) and distributor (third stage), based on maximum efficiency and considering the internal processes and products between these three levels, can be of special importance; because, it will increase the economic and environmental performance of the supply chain. One of the methods used to evaluate efficiency in Green Supply Chain Management (GSCM) is Data Envelopment Analysis (DEA). Therefore, performance evaluation is vital for companies to improve the effectiveness and efficiency of the supply chain. In this study, using the three-stage approach of DEA, the data collected in 2020 from 9 Selected home appliance companies have been analyzed. The results show that company 1 has the best efficiency and the greenest supply chain and company 7 has the worst value of efficiency, which makes it necessary to pay more attention to low performance companies. In order to show the capability of the proposed model, the developed model was compared with its equivalent base model, and companies 1 and 2 were identified as inefficient in the proposed model, but identified as efficient in the base model. Given that the efficiency score in the proposed model is always lower than the base model, so the accuracy of the developed model can be concluded.
