Purpose: This research aims to address the cost-oriented stochastic assembly line balancing problem (ALBP) and propose a chance-constrained programming model. Design/methodology/approach: The cost-oriented stochastic ALBP is solved for small- to medium-sized problems. Owing to the non-deterministic polynomial-time (NP)-hardness problem, a multiple rule-based genetic algorithm (GA) is proposed for large-scale problems. Findings: The experimental results show that the proposed GA has superior performance and efficiency compared to the global optimum solutions obtained by the IBM ILOG CPLEX optimization software. Originality/value: To the best of the authors’ knowledge, only one study has discussed the cost-oriented stochastic ALBP using the new concept of cost. Owing to the NP-hard nature of the problem, it was necessary to develop a heuristic or meta-heuristic algorithm for large data sets; this research paper contributes to filling this gap.

Owning a house is one of the most important decisions that low and middle income people make in their lives. The real estate market is a significant factor of the national economy as much as it is important for individuals. Therefore, predicting real estate values or real estate valuation is beneficial and necessary not only for buyers, but also for real estate agents, economists and policy makers. This issue represents an active area of research, as individuals, companies and governments hold considerable assets in real estate. In this context, the aim of the study is to predict real estate prices with Machine Learning methods using the real estate sales data set in June and July 2021 belonging to the province of Ankara. In particular, it is to perform a comprehensive comparison on Machine Learning regression types methods that give successful prediction results in various but similar tasks, which are not included in the real estate literature. Real estate data obtained over the Internet was first included in a detailed data preprocessing process, and then Linear, Lasso and Ridge Regression, XGBoost and Artificial Neural Networks (ANN) methods were used on this dataset. According to empirical findings, XGBoost and ANNs appear as very important alternatives in predicting real estate sales prices.

Dünyada milyarlarca kullanıcısı bulunan sosyal medya platformlarının yükselişiyle birlikte bilginin yayılması her zamankinden daha kolay hale gelmiştir. COVID-19 pandemisi aşılar da dâhil olmak üzere birçok konunun tartışılmasında sosyal medya kullanımını artırmıştır. Bu çalışmanın amacı, Türkiye’de, özellikle sosyal medya kullanıcılarının COVID-19 aşılarına ilişkin tutumunu ve endişelerini daha iyi anlamak adına Twitter üzerinde elde edilen aşıyla ilgili tweetlerin makine öğrenmesi ile kamu duyarlılığını analiz etmektir. Bu amaç doğrultusunda çalışma altı farklı sınıflandırma görevinde kullanılan makine öğrenmesi algoritması karşılaştırılarak en yüksek doğruluk oranına sahip Destek Vektör Makinesi, XGBoost ve Rastgele Orman ile bir kolektif öğrenme yöntemi olan çoğunluk oylama yöntemi geliştirilmiştir. Çoğunluk oylama yöntemlerinde birisi olan Yumuşak Oylama yöntemi hem Sert Oylama yaklaşımdan hem de bireysel diğer altı makine öğrenmesi yaklaşımlarından daha yüksek başarı oranı ile %90,5 başarı oranına ulaşmıştır. En yüksek doğruluk oranına sahip olan Yumuşak Oylama yöntemi ile Twitter’dan elde edilen 153 güne ait 412.588 adet günlük tweet analiz edilerek sonuçlar raporlanmıştır. Çalışmanın bulguları son derece çarpıcı olup, diğer ülkeler üzerine yapılan çalışmalardan da farklılık göstermektedir. Bu çalışma bildiğimiz kadarıyla Türkiye’de COVID-19 aşılarına yönelik duygu analizi gerçekleştiren ilk çalışma olmakla birlikte sosyal medya üzerinden duygu analizi yaklaşımıyla COVID-19 aşılarına ilişkin duyarlılığı izlemek için değerli ve kolayca uygulanan bir araç olduğunu göstermektedir.

As technology advances, the integration of robots in the assembly line has become widespread. While robots offer numerous benefits, such as increased productivity and improved product quality, they also result in higher energy usage. Finding the optimal line balance while considering energy consumption is a challenging task in a robotic assembly line that produces multiple product models in a mixed sequence. This paper addresses the mixed-model parallel robotic assembly line balancing and model sequencing (MPRALB/S) problem. The objectives of this problem are to minimize cycle time and energy consumption. The authors have not found any existing research on this topic in the literature. To solve the MPRALB/S problem, a modified non-dominated sorting genetic algorithm II (MNSGA-II) is developed. Since there is no existing benchmark data for the MPRALB/S problem, new datasets are generated for this study. The MPRALB/S problem is illustrated through a numerical example. The performance of MNSGA-II is evaluated with non-dominated sorting genetic algorithm II (NSGA-II) and restarted simulated annealing through commonly used performance metrics including hypervolume ratio (HVR), ratio of non-dominated solutions (RP) and generational distance (GD). According to the results of the computational study, MNSGA-II outperforms NSGA-II in approximately 81% of the problem instances for HVR, 71% for RP, and 76% for GD. The results show that MNSGA-II is an effective approach for solving the MPRALB/S problem and achieves competing performance compared to other algorithms.

For those who invest in real estate as an investment tool, as well as those who buy and sell real estate, the price of real estate should be predicted realistically and with the highest accuracy. It should be noted that the predict model should be the most appropriate representation of the underlying fundamentals of the market. Otherwise, the mistake to be made in the real estate valuation will cause some undesirable results such as inconsistent and unhealthy increase or decrease of the property tax, excessive gains or losses in favor of some groups, and adverse effects on investors and potential real estate owners. At this point, data-driven real estate valuation approaches are preferred more frequently to create highly accurate and unbiased estimates. However, the consistency, precision and accuracy of the models realized with machine learning approaches are directly related to the data quality. At this point, the effects of outlier detection on prediction performance in real estate valuation are investigated with a large data set obtained in this study. For this purpose, a heterogeneous data set with 70.771 real estate data and 283 variables, 4 different outlier detection methods were tested with 3 different machine learning approaches. The empirical findings reveal that the use of different outlier detection approaches increases the prediction performance in different ranges. With the best outlier detection approach, this performance increase was at a high 21,6% for Random Forest, with a 6,97% increase in average model performance.