درخت حوزه‌های تخصصی

INTRODUCTION: The network of relief centers, which is a connected set of relief centers, rescue points, and rescue routes, is of utmost importance during an earthquake. This study aimed to model this network to identify the aid-receiving points and rescue routes, followed by the development of plans to improve the aid-giving process during an earthquake. METHODS: This descriptive-analytical study was conducted based on an applied research method and a quantitative approach. The required data were obtained through the library method (documents). The data used in this study included location information of relief centers (hospitals, crisis management centers, Red Crescent centers) and the transportation routes among them. These pieces of information were collected from related plans and documents. The obtained data related to the location of relief centers were analyzed using Spatial Analysis, and Network Analysis tools were utilized to analyze the transportation routes among the relief centers. FINDINGS: The findings in this study revealed the right places for receiving aid and the rescue routes that deliver aid from the aid-giving to the aid-receiving centers in the central zone of Tehran in the shortest time. Moreover, the relief centers were ranked based on their importance during a crisis. CONCLUSION: The results of this study show the number of required aid-receiving points in the central zone of Tehran through which the aid needed during a crisis can be delivered from rescue centers. The findings revealed which relief centers played a more significant role during an earthquake. The identification of these points and routes makes the provision of special planning possible during an earthquake.

Purpose – The purpose of this paper is to optimize Cost & time in the three echelon supply chain (SC) network. This paper developed a linear programing (LP) model to consider economic data. Design/methodology/approach – The overall objective of the present study is to use high-quality raw materials, at the same time in post COVID 19 pandemic and the highest profitability is achieved. The model in the problem is solved using two metaheuristic algorithms, namely, Cuckoo and Genetic. Optimization of supply chain performance indicators in minimization of cost and time and maximization of sustainability indexes of the system. Findings – The differences found between the genetic algorithms (GAs) and the LP approaches can be explained by handling the constraints and their various logics. To deal with ambiguity in the reverse logistics network, a fuzzy approach has been applied. To solve the problem in large dimensions, meta-heuristic algorithms of Cuckoo and Genetic were employed by applying MATLAB software. In order to compare two optimization algorithms, a series of sample problems have been generated then the results of two algorithms were compared and superiority of each of them was discussed.