1,720,983 research outputs found
A Combined Fuzzy-AHP and Fuzzy-GRA Methodology for Hydrogen Energy Storage Method Selection in Turkey
No full textIn this paper, we aim to select the most appropriate Hydrogen Energy Storage (HES) method for Turkey from among the alternatives of tank, metal hydride and chemical storage, which are determined based on expert opinions and literature review. Thus, we propose a Buckley extension based fuzzy Analytical Hierarchical Process (Fuzzy-AHP) and linear normalization based fuzzy Grey Relational Analysis (Fuzzy-GRA) combined Multi Criteria Decision Making (MCDM) methodology. This combined approach can be applied to a complex decision process, which often makes sense with subjective data or vague information; and used to solve to solve HES selection problem with different defuzzification methods. The proposed approach is unique both in the HES literature and the MCDM literature
A hybrid data analytic methodology for 3PL transportation provider evaluation using fuzzy multi-criteria decision making
No full textThird-party logistics (3PL) service provider selection for a strategic alliance is not an easy decision, and is constantly associated with uncertainty and complexity. For this reason, in this study, a hybrid fuzzy multi-criteria decision-making methodology is proposed to provide a systematic decision support tool for 3PL provider evaluation, especially for 3PL transportation provider. The proposed evaluation methodology consists of several steps. First, the strategic goal and sub-attributes are identified for 3PL service provider evaluation. After constructing the hierarchy, Buckley's fuzzy-analytical hierarchy process (AHP) extension algorithm is used to determine the evaluation criteria weights. Then, by using fuzzy-AHP results as input weights, the fuzzy-Technique for Order Preference by Similarity to Ideal Solution technique is conducted in order to identify the most suitable third-party providers. Finally, a real-life case study in a confectionary company is presented to demonstrate the potential use of the methodology and a sensitivity analysis is performed to analyse the hybrid methodology proposed here. In the conclusion of the study, future recommendations are presented
A hierarchical customer satisfaction framework for evaluating rail transit systems of Istanbul
No full textArtificial Neural Networks for Finite Capacity Scheduling: A Comparative Study
No full textIn 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 passenger satisfaction approach based on interval type-2 fuzzysets for rail transit system
No full textA multiattribute customer satisfaction evaluation approach for rail transit network: A real case study for Istanbul, Turkey
No full textA multi-echelon inventory management framework for stochastic and fuzzy supply chains
No full textIn this paper, for effective multi-echelon Supply chains under stochastic and fuzzy environments, an inventory management framework and deterministic/stochastic-neuro-fuzzy cost models within the context of this framework are structured. Then, a numerical application in a three-echelon tree-structure chain is presented to show the applicability and performance of proposed framework. It can be said that, by our framework, efficient forecast data is ensured, realistic cost titles are considered in proposed models, and also the minimum total supply chain cost values under demand, lead time and expediting cost pattern changes are presented and examined in detail. (C) 2008 Elsevier Ltd. All rights reserved
A multiattribute customer satisfaction evaluation approach for rail transit network: A real case study for Istanbul, Turkey
No full textRail transit is one of the most important public transportation types, especially in big and crowded cities. Therefore, getting a high customer satisfaction level is an essential task for municipalities and governments. For this purpose, a survey is conducted to question the attributes related to rail transit network (metros, trams, light rail and funicular) in Istanbul. In this study, we present a novel framework which integrates statistical analysis, SERVQUAL, interval type-2 fuzzy sets and VIKOR to evaluate customer satisfaction level for the rail transit network of Istanbul. Level of crowdedness and density in the train, air-conditioning system of trains\u27 interior, noise level and vibration during the journey, and phone services are determined as the attributes need improvements. On the other hand, different improvement strategies are suggested for the rail transit network. The proposed approach provides directions for the future investments and can be generalized and applied to complex decision making problems encounter inexact, indefinite and subjective data or uncertain information
Artificial Neural Networks for Finite Capacity Scheduling: A Comparative Study
No full textIn 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
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