Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - Scopus: 52Solving an Ammunition Distribution Network Design Problem Using Multi-Objective Mathematical Modeling, Combined AHP-TOPSIS, and GIS(Elsevier Ltd, 2019-03) Akgün, Ibrahim; Erdal, HamitWe study a strategic-level ammunution distribution network design problem (ADNDP) where the purpose is to determine the locations and the service assignments of main, regional, and local depots in order to meet the ammunition needs of military units considering several factors, e.g., stock levels at the depots, costs, and risk levels of depot locations. ADNDP is a real-world and large-scale problem for which scientific decision making methods do not exist. We propose a methodology that uses multi-objective mathematical modeling, Analytic Hierarchy Process (AHP), The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Geographic Information System (GIS) to solve the problem. The multi-objective mathematical model determines the locations and the service assignments of depots considering two objectives, namely, to minimize transportation costs and to minimize risk scores of main depot locations. The risk score of a depot location indicates how vulnerable the location is to disruptions and is determined by a combined AHP-TOPSIS analysis where TOPSIS is used to compute the risk scores and AHP is used to compute the weights needed by TOPSIS for the identified risk attributes. The GIS analysis is conducted to determine the potential depot locations using map layers based on spatial criteria. We have applied the proposed methodology in designing and evaluating a real ammunition distribution network under different scenarios in collaboration and cooperation with the area experts. We have employed the weighted-sum method to find non-dominated solutions for each scenario and discussed their tradeoffs with the area experts. The purpose of this paper is to present the proposed methodology, findings, and insights. © 2019 Elsevier B.V., All rights reserved.Article Citation - WoS: 37Citation - Scopus: 44Selection of an Appropriate Acid Type for the Recovery of Zinc From a Flotation Tailing by the Analytic Hierarchy Process(Elsevier Sci Ltd, 2021-02) Kursunoglu, Sait; Kursunoglu, Nilufer; Hussaini, Shokrullah; Kaya, MuammerThe selection of acid type for metal dissolution from minerals is an important issue in leaching operations. Acids are used to recover valuable elements from the minerals by dissolving them in a solution. The acid must offer a high recovery at marginal cost and a low environmental effect. Many parameters can affect the acid type selection for high leaching recovery and low environmental effect and thus, the selection of an acid type is complex. In this study, based on the experimental results obtained from the bench-scale laboratory studies, the selection of acid type for the recovery of zinc from a flotation tailing was investigated using the analytic hierarchy process (AHP). The utilization of AHP was supported by the use of ExpertChoice (R) 2000 software. The outcomes demonstrated that sulfuric acid is the most desirable acid type with a ranking of 0.541, tracked by citric acid, and oxalic acid with scoring of 0.282 and 0.177, respectively. Furthermore, analyses of sensitivity were performed to examine the influence of the main criteria on the different acid type. It emerged that citric acid can be used when the environmental main criterion ascended from 7.8% to 75.3%. (C) 2020 Elsevier Ltd. All rights reserved.Article Citation - WoS: 15Citation - Scopus: 20Leaching Method Selection for Caldag Lateritic Nickel Ore by the Analytic Hierarchy Process (AHP)(Elsevier, 2017-08) Kursunoglu, Sait; Ichlas, Zela Tanlega; Kaya, MuammerLeaching is an important process in hydrometallurgical operations. This process is used to extract metals from the ores by dissolving them in a lixiviant. It is desired that the leaching method is able to provide high extraction rate at minimal capital and operational costs. There are many parameters that can affect the leaching efficiency and thus, the process of selecting a leaching method is complex. In this study, the use of Analytic Hierarchy Process (AHP) method to select an appropriate leaching method for Caldag lateritic nickel ore has been performed. The application of AHP is assisted with the use of ExperChoice 2000 (R) Software. The results shown that heap leaching (HL) is the most attractive leaching method with a rating of 0.592, followed by atmospheric leaching (AL), and high pressure acid leaching (HPAL) with ratings of 0.293 and 0.115, respectively. In addition, sensitivity analyses have been applied to investigate the impact of the main criteria on the alternative leaching methods. It was found that HPAL can be selected when economical main criteria decreased from 76.1% to 16.3%.Article Citation - WoS: 11Citation - Scopus: 12A Framework to Incorporate Decision-Maker Preferences into Simulation Optimization to Support Collaborative Design(IEEE-Inst Electrical Electronics Engineers Inc, 2016) Goren, Selcuk; Baccouche, Ahlem; Pierreval, HenriIn this paper, we are concerned with the use of simulation optimization to handle collaborative design problems where more than one decision-maker is involved. We assume that the designers cannot enumerate all their considerations in closed-form, precise mathematical expressions but they can examine the merits of solutions with respect to their preferences and can compare candidate solutions with one another. We propose a three-stage framework to take the decision-makers' such considerations into account. The first step is to obtain a diverse set of designs that can all be considered efficient in terms of a performance metric ( i.e.,the objective function values of the simulation optimization model). These solutions are then passed on to the decision-makers to be analyzed in terms of their preferences that could not have been previously considered. Finally, the most appropriate solution is chosen. We address the problem encountered in the first step as a multimodal optimization problem. We address the second and the third subproblems as a preference aggregation problem in the social choice theory. We also illustrate the effectiveness of the proposed approach through a supply chain design problem inspired from the literature. We use the crowding clustering genetic algorithm as an example to demonstrate the first step. We use a multiplicative variant of the popular analytic hierarchy process to illustrate how the second and the third steps can be handled.