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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]

The rapid development of social network theory provides a new perspective for the research on counter-terrorism, however, current research mostly relates to terrorists and terrorist organizations. Firstly, the 'six-element' analysis method for terrorist activities based on social network is proposed in this paper, namely, a variety of sub-networks are constructed according to the correlation among the six elements-people, organization, time, location, manner and event. These sub-networks are assessed through using centrality analysis, cohesive subgroup analysis, spatial correlation analysis, invulnerability analysis and descriptive statistic analysis; the characteristics and laws of terrorist activities are revealed from several different perspectives. Then, the 'six-element' analysis method is applied to conduct empirical research on 'East Turkistan' terrorist activities since the founding of the People's Republic of China, so as to effectively identify core people and key organizations of the 'East Turkistan' terrorist activity network, to assess the invulnerability of the 'East Turkistan' terrorist network and to reveal the temporal and spatial distribution rules as well as the characteristics of the means and manners adopted in all previous terrorist activities. Lastly, the analysis results are interpreted qualitatively. This research can provide a basis for decision making and an analysis method for the identification of core terrorists and key terrorist organizations, determination of the key alert period, location of counter-terrorism and the early warning of the means of major terrorist activities. [ABSTRACT FROM AUTHOR]

This paper presents a novel application of operations research, data mining and geographic information-systems-based analytics to support decision making in blood supply chain management. This, blood reserve availability assessment, tracking, and management system (BRAMS), research project has been funded by the Office of the Secretary of Defense. (This DoD funded SBIR project is performed by the researchers at Knowledge Based Systems, Inc. (KBSI).) The rapidly increasing demand, criticality of the product, strict storage and handling requirements, and the vastness of the theater of operations, make blood supply-chain management a complex, yet vital problem for the department of defense. In order to address this problem a variety of contemporary analytic techniques are used to analyze inventory and consumption patterns, evaluate supply chain status, monitor performance metrics at different levels of granularity, and detect potential problems and opportunities for improvement. The current implementation of the system is being actively used by 130 mangers at different levels in the supply chain including facilities at Osan Air Force Base in South Korea and Incirlik Air Force Base in Turkey. [ABSTRACT FROM AUTHOR]

We study the delivery of mobile medical services and in particular, the optimization of the joint stop location selection and routing of the mobile vehicles over a repetitive schedule consisting of multiple days. Considering the problem from the perspective of a mobile service provider company, we aim to provide the most revenue to the company by bringing the services closer to potential customers. Each customer location is associated with a score, which can be fully or partially covered based on the proximity of the mobile facility during the planning horizon. The problem is a variant of the team orienteering problem with prizes coming from covered scores. In addition to maximizing total covered score, a secondary criterion involves minimizing total travel distance/cost. We propose a data-driven optimization approach for this problem in which data analyses feed a mathematical programming model. We utilize a year-long transaction data originating from the customer banking activities of a major bank in Turkey. We analyze this dataset to first determine the potential service and customer locations in Istanbul by an unsupervised learning approach. We assign a score to each representative potential customer location based on the distances that the residents have taken for their past medical expenses. We set the coverage parameters by a spatial analysis. We formulate a mixed integer linear programming model and solve it to near-optimality using Cplex. We quantify the trade-off between capacity and service level. We also compare the results of several models differing in their coverage parameters to demonstrate the flexibility of our model and show the impact of accounting for full and partial coverage. [ABSTRACT FROM AUTHOR]

We consider expected return, Conditional Value at Risk, and liquidity criteria in a multi-period portfolio optimization setting modeled by stochastic programming. We aim to identify a preferred solution of the decision maker (DM) by obtaining information on her/his preferences. We use a weighted Tchebycheff program to generate representative sets of solutions. Our approach models the stochasticity of market movements by stochastic programming. Working with multiple scenario trees, we construct confidence ellipsoids around representative solutions, and present them to the DM for her/him to make a choice. With each iteration of the approach, an increasingly concentrated set of ellipsoids around the DM's choices are generated. The procedure is demonstrated with tests performed using stocks traded on Borsa Istanbul. [ABSTRACT FROM AUTHOR]

We analyze the operational performance of 202 Turkish rural general hospitals. To help improve performance on both input and output space, we adopt a directional distance approach. We treat a mortality based measure as a 'needs indicator'. We derive pure technical, scale and output congestion inefficiency measures and show how they vary across size classes. We show that 'reducing mortality' involves sacrificing some good outputs. This is a trade off that holds at the potential output level. Second stage regressions of the inefficiency scores against hospital and rural district level variables, pinpoint critical areas for performance improvement. In particular we show the relative scarcity of nurses is linked to output congestion. [ABSTRACT FROM AUTHOR]

critical process in brass casting is blending of the raw materials in a furnace so that the specified metal ratios are satisfied. The uncertainties in raw material compositions may cause violations of the specification limits and extra cost. In this study, we proposed a chance-constrained stochastic programming approach for blending problem in brass casting industry to handle the statistical variations in raw material compositions. The proposed approach is a non-linear mathematical model that is solved global optimally by using GAMS/BARON solver. An application has been performed in MKEK brass factory in Kırıkkale, Turkey and the solution of the application has been compared with alternative solution approaches based on cost and specification violation risk conditions. This comparison demonstrates that the proposed model is the most effective solution approach for managing stochastic uncertainties in blending problems and successfully can be used other industries such as alloy steel or secondary aluminum production. [ABSTRACT FROM AUTHOR]

We consider a single-server queue subject to multiple types of operation-independent interruptions motivated by operations and vessel queueing at entrances of waterways. A case in point is the Strait of Istanbul. We are using waiting-time arguments and service completion time analysis to obtain the expected waiting time of a customer (vessel) in the aforementioned queue with single-class of customers and k non-simultaneous and possibly simultaneous service interruptions. In the analysis, we have used arguments and assumptions from the Strait of Istanbul that are also valid for narrow waterways at large. [ABSTRACT FROM AUTHOR]