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Spare parts inventory management is crucial in the success of a service providing company. In this study, the spare parts of a service provider in the automotive sector are classified according to their characteristics in groups and different inventory control policies are applied to the categorized groups. The Analytical Hierarchy Process (AHP), one of the Multi-Criteria Decision Making (MCDM) methods, is used to classify the spare parts into groups. As a result of the application of AHP, classes of spare parts are determined according to the VED analysis, classifying the spare parts according to their criticality. Furthermore, the ABC analysis performed by the company was improved by using cost and demand criteria. After performing both analysis, three new classes of spare parts are determined with the combination of ABC and VED classification techniques. For each class, an appropriate inventory control policy is decided according to the spare parts importance and criticality. Based on the literature review, the (R, S, s) inventory control policy is chosen to be applied in each class, taking into consideration the review period, order up-to-level and reorder point of items. In the inventory control model, the review period for the same class items is assumed to be constant based on the information provided by the company. For verification purposes, necessary cost calculations including total ordering and holding costs are performed by means of Microsoft Excel. In order to be able to vastly observe the system behavior, different cost scenarios are generated by increasing and decreasing the service level and review period of the system. Using, OptQuest, an optimization tool, embedded into ARENA simulation software, the different scenarios were analyzed and the total minimum cost is reached. For supporting the daily operations of the company, a user-friendly decision support system is built, where the end-user can easily add/remove spare parts to/from the system, classify them and compare the results of inventory control policies with the current system. The DSS will also assist the company to manage and control their real-time inventory and perform spare parts stock level tracking and decide when to place orders.

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Water is a product that cannot be substituted for living creatures to survive. The availability of water resources is crucial. The balance of water availability can change with environmental factors, and as a result, living creatures struggle with water scarcity. This study includes the policy of water pricing in the Izmir district. A multiperiod water pricing model based on transportation and inventory carrying problems is developed for encouraging water consumption reduction. Different pricing policy scenarios are proposed using the developed model to provide sustainable water management and penalize high water use. The aim is to maximize consumer welfare and ensure fair distribution among groups.

The impact of climate change has led to significant changes in hydroclimatic patterns and continuous stress on water resources through frequent wet and dry spells. Hence, understanding and effectively addressing the escalating impact of climate change on hydroclimatic patterns, especially in the context of meteorological drought, necessitates precise modeling of these phenomena. This study focuses on assessing the accuracy of drought modeling using the well-established Standard Precipitation Index (SPI) in the Aegean region of Türkiye. The study utilizes monthly precipitation data from six stations in Cesme, Kusadasi, Manisa, Seferihisar, Selcuk and Izmir at Kucuk Menderes Basin covering the period from 1973 to 2020. The dataset is divided into three sets, training (60%), validation (20%), and testing (20%) sets. The study aims to determine the SPI-3, SPI-6 and SPI-12 using a multi-station prediction technique. Three boosting regression models (BRMs), namely Extreme Gradient Boosting (XgBoost), Adaptive Boosting (AdaBoost), and Gradient Boosting (GradBoost), were employed and optimized with the help of the Weighted Mean of Vectors (INFO) technique. Model performances were then evaluated with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient of Determination (R2) and the Willmott Index (WI). Results demonstrated a distinct superiority of the XgBoost model over AdaBoost and GradBoost in terms of accuracy. During the test phase, the XgBoost model achieved RMSEs of 0.496, 0.429 and 0.389 for SPI-3, SPI-6 and SPI-12, respectively. The WIs were 0.899, 0.901 and 0.825 for SPI-3, SPI-6 and SPI-12, respectively. These are considerably lower than the corresponding values obtained by the other models. Yet, the comparative statistical analysis further underscores the effectiveness of XgBoost in modeling extended periods of drought in the Aegean region of Türkiye.