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Nowadays, logistics is one of the most important tools in disaster relief operations. The logistic planning is essential and a key component in covering the initial needs in the immediate aftermath of any disaster. Planning is both necessary and practical, as it is generally possible to predict the types of disasters that should affect a given location and the needs that such disasters will be likely to cause. Transport planning, reception and distribution of emergency supplies, type and quantity of the resources, the way of procurement and storage of the supplies, the tools of the tracking and means transportation to the stricken area, the specialization of teams participating in the operation and plan of cooperation between these teams, are some vital life-saving coordination roles after natural disasters are connected directly to logistic planning. Turkey is located in one of the most active earthquake and volcanic regions which causes to many major earthquake-prone, in the world with a majority of the population living in these earthquake-prone areas. Earthquakes are one of the major disasters that require emergency logistic planning strategies due to their devastating effects, the large-scale natural disasters could cause major problem on commodities such as food, medicine etc. In this paper, a game theoretical model for emergency logistic planning is developed. To do this a cooperative game model is constructed from a flow problem which occurred after an earthquake in Istanbul. Several solution concepts for maximizing the transferred commodity are given. The paper ends with a conclusion and outlook to future studies. [ABSTRACT FROM AUTHOR]

A Rapid Needs Assessment process is carried out immediately after the onset of a disaster to investigate the disaster's impact on affected communities, usually through field visits. Reviewing practical humanitarian guidelines reveals that there is a great need for decision support for field visit planning in order to utilize resources more efficiently at the time of great need. Furthermore, in practice, there is a tendency to use simple methods, rather than advanced solution methodologies and software; this is due to the lack of available computational tools and resources on the ground, lack of experienced technical staff, and also the chaotic nature of the post-disaster environment. We present simple heuristic algorithms inspired by the general procedure explained in practical humanitarian guidelines for site selection and routing decisions of the assessment teams while planning and executing the field visits. By simple, we mean methods that can be implemented by practitioners in the field using primary resources such as a paper map of the area and accessible software (e.g., Microsoft Excel). We test the performance of proposed heuristic algorithms, within a simulation environment , which enables us to incorporate various uncertain aspects of the post-disaster environment in the field, ranging from travel time and community assessment time to accessibility of sites and availability of community groups. We assess the performance of proposed heuristics based on real-world data from the 2011 Van earthquake in Turkey. Our results show that selecting sites based on an approximate knowledge of community groups' existence leads to significantly better results than selecting sites randomly. In addition, updating initial routes while receiving more information also positively affects the performance of the field visit plan and leads to higher coverage of community groups than an alternative strategy where inaccessible sites and unavailable community groups are simply skipped and the initial plan is followed. Uncertainties in travel time and community assessment time adversely affect the community group coverage. In general, the performance of more sophisticated methods requiring more information deteriorates more than the performance of simple methods when the level of uncertainty increases. [ABSTRACT FROM AUTHOR]

This paper investigates the variation in nonperforming loans over the economic cycle and the effect of past returns based on a nonparametric quantile analysis of the largest Islamic banks in the United Kingdom and Turkey from 2010 to 2019. The findings show a weak variation in nonperforming loans that increases with an increasing return on assets and a decreasing return on equity and decreases in an inverse scenario. As a result, the credit risk of Islamic banks is countercyclical. We suggest that the inverse relationships evidence the existence of trade-offs within bank returns and credit risk. Thus, banks' past profitability and risk mitigation are determinants of asset quality. These findings provide support for risk-taking and risk-sharing principles in which flight-to-safety mirrors the calibration of risk factors in a disruptive economy. Our estimates indicate that nonparametric quantile regression captures considerably more variation in a risk-return analysis. [ABSTRACT FROM AUTHOR]

In a natural disaster such as an earthquake, due to the extensive number of injured, demand for blood units sharply increases in health care centers. Thus, designing an efficient supply chain network for managing blood distribution is one of the major concerns for the healthcare systems under uncertain conditions. This study aims to propose a methodology to minimize the total cost of the blood supply network and total blood shortage. We proposed a new fuzzy multi-period mathematical model using trapezoidal fuzzy numbers by Spherical Fuzzy membership degrees for the blood supply network design. The proposed methodology has considered the possibility of natural disasters in Istanbul (Asian side), the economic and cultural center of Turkey. The proposed model is formulated by fuzzy objective function and fuzzy variables; thus, the final results remained in the fuzzy form. The uncertain parameters are the demand for blood from different blood types in each hospital, the amount of the blood supply in each blood donation facility, the percentage of usable blood, failure rates of the network's components, and network costs. These parameters are represented by trapezoidal fuzzy numbers so that they have membership degrees stated by spherical fuzzy sets. To aggregate decision-makers viewpoints about parameters, spherical fuzzy number algebraic weighted harmonic mean (SFNAWHM) aggregation operator is employed. The relevant network consists of four layers: blood donation areas, blood collection centers, main blood centers, and hospitals. We considered the concept of disruption throughout the supply chain by considering failure rates for all network components. Finally, a sensitivity analysis is performed to validate the proposed fuzzy model and solution methodology. [ABSTRACT FROM AUTHOR]