Among the supply chain risk types, disruptions that result from natural disasters, sanctions, transportation problems and equipment failure can seriously disrupt or delay the flow of material, information and cash. The aim of this research was to propose a hybrid model for disruption management, which is the process of achieving plans or strategies to reduce the expenses incurred by the disruption. For this purpose; first, we identified disruptions and mitigation strategies by using the nominal group technique. Then, the interaction between disruptions was formulated by the fuzzy DEMATEL technique. Consequently, with regard to the uncertainty of data, fuzzy logic was used for modeling the uncertainty of disruptions. Finally, mitigating strategies were selected and ranked with PROMETHEEΙΙ. Considering the existence of 4 types of responses of chain against risks, which include: 1- risk control and endurance 2-risk flexibility 3- risk avoidance 4- risk transfer and assignment; results show that according to the type of disorder, the risk management strategy changes and in general (taking into account the causal relationship between disorders), the risk transfer strategy it was more suitable پرونده مقاله

Abstract The rapid growth in demand for computing power has led to a shift towards a cloud-based model relying on virtual data centers. In order to meet the demand of cloud computing clients, cloud service providers need to maintain service quality parameters at optimum levels. This paper presents a hybrid algorithm dubbed PSOBAT-Greedy, which is expected to reduce cost and time while enhancing the efficiency of resources. The main idea behind the newly proposed algorithm is to find an optimal weight for local and global search using Range and Tuning functions as an important solution overcoming various problems in task scheduling and provide the right response within an acceptable time. The new hybrid algorithm is less time-consuming and costly than the other two algorithms. As compared to particle swarm optimization (PSO) algorithm and combined particle swarm optimization and bat algorithm (PSOBat), resource efficiency improves by 15% and 5%, respectively. Keywords: Quality of Service, Cloud Computing, Particle Swarm Optimization, Bat Algorithm پرونده مقاله