An assessment approach for non-governmental organizations in humanitarian relief logistics and an application in Turkey

The ever-increasing natural disasters have been causing the loss of lives, properties and resources. By the preparedness and response ability of non-governmental organizations, it is aimed to minimize these losses. In this paper, first, the critical success factors of humanitarian relief logistics management operations are determined and categorized. Then, by considering these factors, a hybrid method that consists of trapezoidal interval type-2 fuzzy sets, AHP and TOPSIS, is proposed to evaluate emergency preparedness and response ability performance of non-governmental relief organizations. The proposed hybrid method is applied for non-governmental relief organizations in Turkey to evaluate their performance, and to the factors need to be improved for each determined organization. First published online 11 September 2015

The Usage of Artificial Neural Networks For Finite Capacity Planning

In this study finite scheduling and artificial neural networks are applied for finite capacity planning. Utilisation of artificial neural networks on solving finite scheduling problems is examined. Also a model is developed by using multi layer perceptron (MLP) networks and carried out to solve a real world problem in a job shop scheduling system, in an automotive firm

Artificial Neural Networks for Finite Capacity Scheduling: A Comparative Study

In this study artificial neural networks are applied for finite capacity scheduling. Utilisation of artificial neural networks on solving finite scheduling problems is examined. Also a comparative model is proposed by using multi layer perceptron (MLP) neural networks and branch-and-bound algorithm, and carried out to solve a real world problem in a job shop scheduling system

A Pythagorean fuzzy number-based integration of AHP and WASPAS methods for refugee camp location selection problem: a real case study for Istanbul, Turkey

There is an increase in the number of people who have changed their country for compulsory reasons due to the wars experienced worldwide. This raises the problem of identifying the regions where these people, called refugees, will live in the countries. In this regard, we determine in this paper where the camps established for refugees living in Istanbul should be located. The presence of many quantitative and qualitative factors which should be handled in determining the best location has enabled this problem to have multi-criteria decision-making structure. In addition, the advantage of fuzzy logic is used to convert the evaluations taken from experts into available numbers and to include them in decision-making process. For this purpose, a novel model with the integration of Pythagorean fuzzy AHP and Pythagorean fuzzy WASPAS methods is proposed for the first time in the literature, to select the best location for the refugee camp. In addition, a comparative analysis is applied to determine the validity of the results obtained and the sensitivity analysis is applied to check the robustness of the model. As a result, the most suitable location for a refugee camp in Istanbul is identified reasonably with the proposed model.WOS:0006632714000042-s2.0-8510823435

A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul

This paper provides a hierarchical customer satisfaction framework to measure rail transit lines’ performances in Istanbul. The problems related to rail transit line systems are addressed via customer satisfaction surveys. Then, a framework is proposed combining statistical analysis, fuzzy analytic hierarchy process, trapezoidal fuzzy sets and Choquet integral to evaluate customer satisfaction levels. Next, the criteria need to be improved are determined and specific recommendations to enhance the operation for specific lines are suggested. The proposed framework provides directions for the future investments and it also can be used at a more macroscopic level to determine the operational deficiencies. Furthermore, it can be generalized and applied to complex decision making problems that include uncertain and subjective data or vague information