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The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids. [ABSTRACT FROM AUTHOR]

Owing to the rise in population, lifestyle changes, high traffic rates in urban areas and environmental pollution, respiratory diseases have become much more prevalent on both regional and urban scales. Respiratory diseases affect over 300 million people worldwide and are thus among the major threats to humans' general well-being. The identification of underlying factors and the specification of accompanying risk areas for the temporal exacerbation of respiratory diseases are effective steps in managing the damage caused by such disorders. Here, we demonstrate a strategy for modelling the risk zone of respiratory diseases temporally, using a location-based social network (LBSN) and an artificial neural network (ANN). The main contribution of this paper is to consider the environmental and infrastructural factors and identify their relationships with the geographical locations of respiratory attacks. The study also utilizes Telegram, which is the most popular and conventional social media platform, in order to observe temporal changes in the location of respiratory attacks in Iran, in the form of a developed Telegram bot known as @respiratoryassociation. The relations between the factors behind and the location of respiratory attacks are determined using a multilayer perceptron (MLP) ANN. All the required data have been collected on a daily basis over a 5-year period from December 2013 to December 2018 in Tehran, Iran. The results indicated air pollution, especially pollution from carbon monoxide (CO) and suspended particulate matter (PM) as the most decisive factors. Following air pollution, the amount of exposure to the polluted area was determined as the second most decisive factor, which in turn increased as a result of escalations in traffic jams. Land use was determined as the third most decisive factor. Furthermore, the results revealed that the ANN performed satisfactorily, implying that the model can be used to examine the spatio-temporal behaviour of the time series of respiratory diseases with respect to environmental and infrastructural factors. [ABSTRACT FROM AUTHOR]

The basic purpose of this study is to scrutinize the asymmetric effect of oil price changes on environmental pollution in Canada, China, India, Iran, Germany, Japan, Russia, South Korea, Saudi Arabia, and the USA. The study uses time series annual data of selected courtiers from 1981 to 2018 and applies non-linear ARDL (NARDL) model to examine the long- and short-run asymmetries. The results show that positive shocks in diesel prices in the USA, India, Japan, Germany, South Korea, Iran, and Canada, while negative shocks in China and India reduce carbon emissions in the long run. However, an increase in gasoline prices in Russia and Iran while the decrease in gasoline prices in the USA, Russia, Japan, and Canada decreases in carbon emissions in the long run. Asymmetric findings also suggest that positive and negative changes in oil prices affect carbon emissions differently in China, the USA, India, Russia, Japan, Germany, South Korea, Iran, Saudi Arabia, and Canada in the short and long run. However, sign and magnitude of positive and negative shocks of oil prices are more important in environmental economics polices. Therefore, based on sign and magnitude, more taxation of fossil fuel and clean energy subsidies are recommended for the top carbon-emitting economies. [ABSTRACT FROM AUTHOR]