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Estimating fatigue life is challenging due to the input parameters' statistical natures, such as the manufacturing process, scatter of service loads, microstructure, etc. Regarding fatigue life calculation in the aerospace industry, the importance of an accurate estimation becomes more critical due to strict safety, certification, service costs, and competitiveness regulations. The ability of soft computing methods to reveal complex relationships between multiple parameters and their computational speed could help predict fatigue life, especially in the service. This study compares random forest regression and artificial neural network methods to estimate the crack growth life of a fighter jet aircraft wing joint in terms of their computational time and accuracy. In addition, permutation feature importance and hyper parameter optimisation studies are conducted to extract essential features, investigate their effects on estimation performance, and fine-tune model parameters. The analysed joint is made of 7050-T7451 aluminium, widely used as a structural element in the aerospace industry. Since a hole is one of the major sources of stress concentration, and there may be many holes involved in any engineering structure, it is reasonable to assume that fatigue cracks may initiate at some of these holes during the service life of engineering structures. The crack type considered is a thru-crack around a hole, which is more severe than a corner crack. Load spectra are derived using the Fighter Aircraft Loading Standard for Fatigue (FALSTAFF) to calculate crack growth life. Considering particular service load conditions, ninety different spectra are developed, and the crack growth life of the joint is calculated based on linear elastic fracture mechanics correspondingly. Also, to simulate the worst-case scenario, friction between members and the retardation effect of load spectra are not considered when calculating crack growth life. Python's Tensor Flow and Scikit-learn libraries are utilised to build machine learning models. Then, ninety different load spectra are input for the thru-crack configuration to predict the crack propagation life. Eventually, the crack propagation life predictions of random forest regression and artificial neural network models are compared. The findings indicate that permutation feature importance and hyperparameter-optimisation significantly affect the model's accuracy and processing time performance. [ABSTRACT FROM AUTHOR]

One of the main causes of the significant commercial vehicle traffic in the city region is last-mile deliveries. Parcel lockers, which are one of the easiest and most environmentally friendly solutions for last-mile delivery, are one of the most studied subjects recently. The parcel locker ensures consumer privacy while being quick and efficient. Its full-time service can effectively address the issue of student and office worker pickup. In this paper, the location of a parcel locker intended to be established in the most convenient location in Beşiktaş district of İstanbul, Turkey has been determined. This problem can be solved using a multi-criteria decision-making (MCDM) structure due to the availability of numerous aspects that must be considered while choosing the optimum location. Additionally, the benefit of fuzzy logic is employed to translate expert opinions into mathematical expressions and incorporate them into decision-making processes. To choose the ideal location for the parcel locker, a novel model integrating the Bayesian Best Worst Method (B-BWM) and Pythagorean fuzzy Weighted Aggregated Sum Product Assessment (PF-WASPAS) approaches is proposed for the first time in the literature. Additionally, a sensitivity analysis is conducted to evaluate the model's robustness. As a consequence, the suggested model effectively identifies the best location for a parcel locker in Istanbul. [ABSTRACT FROM AUTHOR]

Limited resources must be used effectively, precisely, and damage-free considering the increase in the consumption of petroleum and petroleum-derived products. Therefore, the accurate and effective distribution of petroleum and related problems with petroleum distribution have attracted much attention among practitioners and optimization working researchers over the years. The petroleum distribution problem, as a version of the vehicle routing problem (VRP), deals with the planning of petroleum distribution from the depot(s) to the petrol stations safely and quickly. In this study, the petrol station replenishment problem (PSRP) is handled and a case study is presented for a public company located in Istanbul. The problem is considered as a time-dependent VRP with time windows. To handle the proposed time-dependent problem in a more realistic way, variable tanker speeds are considered based on traffic density. In this study, a novel mixed integer mathematical model to solve time dependent PSRP with time windows is proposed. The optimum route is determined in which risks such as environment and marine pollution may occur in case of possible accidents, and these risks are minimized by the proposed mathematical model considering factors such as traffic, vehicle speed, road structure, the road's proximity to the sea, and living areas. [ABSTRACT FROM AUTHOR]