RESUMO
INTRODUCTION: Unlike Western countries, many low- and middle-income countries (LMIC), like India, have a de-centralized emergency medical services (EMS) involving both semi-government and non-government organizations. It is alarming that due to the absence of a common ecosystem, the utilization of resources is inefficient, which leads to shortage of available vehicles and larger response time. Fragmentation of emergency supply chain resources motivates us to propose a new vehicle routing and scheduling model equipped with novel features to ensure minimal response time using existing resources. MATERIALS AND METHODS: The data set of medical and fire-related emergencies from January 2018 to May 2018 of Uttarakhand State in India was provided by GVK Emergency Management and Research Institute (GVK EMRI) also known as 108 EMSs was used in the study. The proposed model integrates all the available EMS vehicles including partial outsourcing to non-ambulatory vehicles like police vans, taxis, etc., using a novel two-echelon heuristic approach. In the first stage, an offline learning model is developed to yield the deployment strategy for EMS vehicles. Seven well researched machine learning (ML) algorithms were analyzed for parameter prediction namely random forest (RF), convolutional neural network (CNN), k-nearest neighbor (KNN), classification and regression tree (CART), support vector machine (SVM), logistic regression (LR), and linear discriminant analysis (LDA). In the second stage, a real-time routing model is proposed for EMS vehicle routing at the time of emergency, considering partial outsourcing. RESULTS AND DISCUSSION: The results indicate that the RF classifier outperforms the LR, LDA, SVM, CNN, CART and NB classifier in terms of both accuracy as well as F-1 score. The proposed vehicle routing and scheduling model for automated decision-making shows an improvement of 42.1%, 54%, 27.9% and 62% in vehicle assignment time, vehicle travel time from base to scene, travel time from scene to hospital, and total response time, respectively, in urban areas.
RESUMO
Concrete, a cement-based product is the highest manufactured and second highest consumed product after water on earth. Across the world, production of cement is the most energy and emission intensive industry hence, the cement industry is currently under pressure to reduce greenhouse gases emissions (GHGEs). However, reducing the GHGEs of the cement industry especially for developing country like India is not an easy task. Cement manufacturing industry needs to focus on significant climate change mitigation strategies to reduce the GHGEs to sustain its production. This study aims at identifying significant climate change mitigation strategies of the cement manufacturing industry in the context of India. Extant literature review and expert opinion are used to identify climate change mitigation strategies of the cement manufacturing industry. In the present study, a model projects by applying both AHP and DEMATEL techniques to assess the climate change mitigation strategies of the cement industry. The AHP technique help in establishing the priorities of climate change mitigation strategies, while the DEMATEL technique forms the causal relationships among them. Through AHP, the results of this research demonstrate that Fuel emission reduction is on top most priority while the relative importance priority of the main remaining factors is Process emission reduction - Electric energy-related emission - Emission avoidance and reduction - Management mitigation measures. The findings also indicate that the main factors, Process emission reduction, and Fuel emission reduction are categorized in cause group factors, while the remaining factors, Electric energy-related emission, Emission avoidance and reduction and Management mitigation measures are in effect group factors. Present model will help supply chain analysts to develop both short-term and long-term decisive measures for effectively managing and reducing GHGEs.
