Radio Frequency Identification Device (RFID) has been widely recognized as a useful tool for effectively managing firms’ supply chain activities. Extant research on RFID in the supply chain domain has focused on implementing this technology in various supply chain activities, such as logistics and transportation, assembly and manufacturing. However, the application of RFID in warehousing management has received little attention. This study explores the implementation of RFID in warehousing in the construction sector. The study objectives are twofold. First, it explores the benefits of implementing RFID. Secondly, the study identifies major challenges associated with implementing RFID, which can prevent realizing the full potential of the technology in warehouse operations. The study adopts a qualitative research design by employing a case study approach to achieve these objectives. Specifically, the study undertakes three weeks of field observation and conducts interviews with 9 participants involved in warehouse operations in the construction sector. The findings show RFID implementations could bring significant values to warehouse management related to operational efficiency, service level and warehouse flexibility. However, the study also uncovered several challenges that can block the case firm from capturing the full potential of implementing RFID. The findings can contribute to a better understanding of the application of RFID technology in warehousing. Practitioners can understand the identified challenges to help them tackle them and inform their future decisions regarding further investment in this technology.

Silver is a metal that offers significant value to both investors and companies. The purpose of this study is to make an estimation of the price of silver. While making this estimation, it is planned to include the frequency of searches on Google Trends for the words that affect the silver price. Thus, it is aimed to obtain a more accurate estimate. First, using the Latent Dirichlet Allocation method, the keywords to be analyzed in Google Trends were collected from various articles on the Internet. Mining data from Google Trends combined with the information obtained by LDA is the new approach this study took, to predict the price of silver. No study has been found in the literature that has adopted this approach to estimate the price of silver. The estimation was carried out with Random Forest Regression, Gaussian Process Regression, Support Vector Machine, Regression Trees and Artificial Neural Networks methods. In addition, ARIMA, which is one of the traditional methods that is widely used in time series analysis, was also used to benchmark the accuracy of the methodology. The best MSE ratio was obtained as 0,000227131 ± 0.0000235205 by the Regression Trees method. This score indicates that it would be a valid technique to estimate the price of "Silver" by using Google Trends data using the LDA method.

COVID-19 has caused severe disruptions in global economic activities, and its impacts on stock markets cannot be overemphasized. The study employs market model and event study approach with four events (WHO announcement of COVID-19 as a global health emergency, confirmed infections, confirmed deaths, and vaccination) to examine the reactions of four African regional blocs’ markets to the pandemic from September 1, 2019, to August 31, 2021, to estimate the average abnormal returns of each regional bloc. On the day of the WHO announcement, we document insignificant negative average abnormal returns in the Northern bloc. We also document significant negative average abnormal returns for infections in all but the Northern bloc on the event day. The Western bloc generated the highest significant negative average abnormal return (-43 per cent) on the day COVID-19 death was confirmed on the continent. We finally document insignificant average abnormal returns from weeks 1 to 20 after the first vaccination in the Northern and Eastern blocs. The study recommends that investors, portfolio managers, and speculators not panic during similar pandemics since they can generate significant abnormal returns and diversify their investment holdings across the four regional blocs in Africa, as demonstrated by the COVID-19 pandemic.

Considering the acceleration in the development of alternative energy sources due to climate change and the net zero carbon commitments made in this direction, there are different assessments of how the capacity of the refining industry will change in the next two decades. Refinery companies are trying to adapt to altering conditions while also trying to determine their investment strategies. Project portfolio selection problem is one of the relevant issues to be considered in line with these changes. In this article, research has been undertaken to determine which criteria refinery companies take into consideration while selecting their project portfolios. Based on the identified criteria, it is also aimed to carry out a study that will guide sector practitioners in project selection. For this purpose, interviews were conducted with industry experts. The criteria were accredited by applying categorical content analysis to the data obtained and their importance weights were identified accordingly. The most deterministic criteria were abstracted from the findings and applied to a multi-criteria decision-making (MCDM) framework, namely fuzzy MULTIMOORA to suggest a decision support tool that ranks the projects against themselves. Some of the prominent outcomes of the study are also discussed, along with the previous studies and comparative results of the proposed decision support tool.

This study considers representing decision maker preferences by Choquet integral in existence of interactions among criteria. Parameters of the Choquet integral are capacities which assign weights not only to criteria but also to each subset of criteria. This property provides Choquet integral with the ability of modeling some types of interactions. Different capacity types with different degrees of complexity have been defined in the literature. After making a review on the dependence (interaction) and independence concepts used in the multiple criteria decision making literature, we study and represent structures of interactions that can be handled by different capacity types through intuitive graphical demonstrations. Afterwards, we provide guidelines for specifying the appropriate capacity type in practical applications. Such guidance has not been provided in the literature for the practitioners to the best of our knowledge.

This study proposes Q-learning-based iterated greedy (IGQ) algorithms to solve the blocking flowshop scheduling problem with the makespan criterion. Q learning is a model-free machine intelligence technique, which is adapted into the traditional iterated greedy (IG) algorithm to determine its parameters, mainly, the destruction size and temperature scale factor, adaptively during the search process. Besides IGQ algorithms, two different mathematical modeling techniques. One of these techniques is the constraint programming (CP) model, which is known to work well with scheduling problems. The other technique is the mixed integer linear programming (MILP) model, which provides the mathematical definition of the problem. The introduction of these mathematical models supports the validation of IGQ algorithms and provides a comparison between different exact solution methodologies. To measure and compare the performance of IGQ algorithms and mathematical models, extensive computational experiments have been performed on both small and large VRF benchmarks available in the literature. Computational results and statistical analyses indicate that IGQ algorithms generate substantially better results when compared to non-learning IG algorithms.

Due to complex structure of aortic valve (AV) leaflets and its strong interaction with the blood flow field, realistic and accurate modeling of the valve deformations comes with many challenges. In this study, we aimed to investigate the effect of AV material properties on the valve deformations, by implementing different non-linear properties of the AV leaflets in three different material models. In the computations, we captured the dynamics between the leaflet deformations and blood flow field variations by using an iterative implicit fluid-structure interaction (FSI) approach. By comparison of the FSI simulation results of these three models, the effects of hyperelasticity and anisotropy on the valve deformations have been studied in detail. The simulation results reveal the fact that the material characteristics strongly affect the deformation characteristics of the leaflets in the systolic phase. The material anisotropy stabilizes the leaflet movements during the systolic phase, which helps decreasing the flutters of the leaflets during the peak jet blood flow. Similarly, it has been observed that the hyperelastic behavior yields an increase in the valve opening area during systolic phase which prevents the risk of excessive work of the heart due to high pressure difference. Furthermore, simulation results indicate that the stress levels in hyperelastic model are much lower, compared to the stress levels in linear elastic one. This suggests that the non-linear material character of the leaflets decreases the risk of calcification.

The hybrid vehicle routing problem is an extension of the green vehicle routing problem where vehicles can use different fuels. In this research, a bi-objective hybrid vehicles routing problem is presented and solved bu considering alternative paths, various lengths and reliabilities. The first objective function minimizes overall system costs and fuel consumption and greenhouse gas emissions, whereas the second objective function maximizes the reliability of the entire system with alternative paths and several reliabilites. The proposed model is formulated as mixed-integer nonlinear programming and a bi-objective simulated annealing (MOSA) algorithm along with the e-constraint method is used as solution strategy. The implementation of the proposed method is presented using some numerical instances.

Operating rooms (OR) are one of the most expensive parts of a hospital with complex processes, and the efficient use of resources is of utmost importance. Therefore, proper management and operation of operating rooms are extremely crucial. OR scheduling ensures that the surgeries are performed at the proper time, patients are treated effectively and safely, resources are used effectively, and staff is increased in work efficiency. Furthermore, accurately scheduled surgeries are safer for patients' healing processes. This is dependent on factors such as the availability of qualified personnel at the appropriate time, the readiness of surgical equipment, and the provision of proper sterilization and hygienic conditions. Surgical team scheduling ensures that surgeries begin on time, are completed effectively, and patients are treated safely. It is also critical to reduce employee fatigue and balance the workload. As a result, integrating surgical teams into operating room scheduling problems provides significant benefits. Accordingly, 29 research articles focusing on the problem of OR scheduling, within the scope of constraints on surgical team members, scheduling strategies, uncertainties, and solution methods, are thoroughly reviewed in this study.

The Orienteering Problem (OP) is an optimization problem that finds the locations and routes that will return the highest profit/benefit, starting from the initial location of the traveler/vehicle, visiting these locations, and ending with the starting location of the tour within a given time or distance limit. There is no obligation to visit all locations in the problem structure. OP has many real-life applications, such as staff routing and disaster relief routing. In this study, OP with Time Windows (OPTW), an extension of OP, is discussed with hotel selection and mandatory visits. Although the main objective of OPTW is profit maximization, it is also essential to minimize the total travel time to complete the tour efficiently. For this reason, we consider the OPTW as a multi-objective problem. In the problem considered here, it is assumed that the profit/benefit, travel time between locations, service period, and time interval that each location can be visited are determined to be known. Within the scope of the study, first, a Mixed Integer Programming (MIP) model is prepared for the problem. Since the proposed mathematical model does not provide solutions in a reasonable time for large networks, the problem is solved by a Constraint Programming (CP) approach. Attractive tourist points of interest for Izmir, one of Turkey's major tourist cities, are determined, and the proposed method is applied to the real-life problem. The problem is modeled as Multi-Objective OPTW with MIP and CP and solved. Also, sensitivity analysis is performed by considering two different scenarios.