Market conditions have become increasingly dynamic and demanding, resulting in a challenging competitive environment. To adapt to this dynamic environment, supply chains have become increasingly reliant on collaboration, integration, flexibility, and trust between stakeholders. Therefore, the importance of new technological applications in supply chains has grown even more. To improve supply chain flow control, modern specialized applications must be implemented. Blockchain technology has established itself as a necessary component of today’s competitive environment. Companies should invest in blockchain technology to respond quickly to changing market conditions and demands in today’s dynamic business environment. In this regard, this study investigates the effect of blockchain technology utilization on supply chain transparency, supply chain flexibility, and trust in suppliers. For this purpose, first, data were collected from the first 1000 exporting firms declared in 2019 by the Turkey Exporters Assembly using a survey method. Secondly, factor analyses and regression analyses were performed with the dataset obtained from 84 companies. As a result of the analysis, it was determined that blockchain technology utilization increases transparency, flexibility, and trust in supply chains. According to these results, it is suggested that companies should invest in blockchain technology to make their supply chain more transparent, flexible, and secure. Moreover, blockchain plays a crucial role in building mutual trust with supply chain stakeholders. Finally, the research includes some considerations on the positive implications and potential of blockchain in collaboration and integration.

This article focused on transportation sustainability during the COVID-19 pandemic situation under the Fermatean fuzzy environment. In both developed and developing nations, sustainability has risen to the top of the priority list for transportation policies and planning. We introduce a simplified presentation of the Fermatean fuzzy traveling seller problem solved by using a new computation approach. Several approaches for solving the traveling seller problem using fuzzy parameters have been described in the literature. Even so, all the current strategies use general fuzzy numbers as the parameters for the traveling salesman problems, but his study, focused on the new Fermatean fuzzy number, is more effective for representing real-life incidents. The Fermatean fuzzy scoring functions and numerical conditions in distinct models in the Fermatean fuzzy environment were described to construct the algorithm. New solution methodology developed through scoring functions to find the best solution to fulfill our goal of sustainable transportation for traveling sellers problem. Sustainable cost and the optimal path are obtained by this study.

Environmentally friendly goods are market-oriented goods that create less environmental damage. Their manufacture is related to a product development process designed to consider the environmental consequences that might develop throughout their life cycle. In reality, the global demand for herbal goods is expanding since herbal products are manufactured from plant extracts such as leaves, roots, flowers, and seeds, among others, and cause less environmental destruction. This study introduces a novel, eco-friendly demand determined by the usage of herbal and chemical substances in products. In this context, companies producing these products are encouraged. Firms are interested in producing eco-friendly products while keeping an eye on carbon emissions. This paper presents a sustainable inventory model of non-instantaneous decaying items that follow this eco-friendly demand under partially backlogged shortages. In this study, emission releases due to inventory setup, degradation, and holding were estimated, as were carbon emissions under cap and tax policies. This approach invests in green and preservation technologies to reduce carbon emissions and deterioration. To address the imprecision of the model’s cost parameters, we converted them to Pythagorean fuzzy numbers. The optimum profit of the inventory model with carbon emissions is estimated by considering the time that the inventory level takes to reach zero and the replenishment time as decision variables. Numerical examples and a sensitivity analysis of significant parameters have been conducted to examine the effect of variation in the optimal inventory policy.

Robots have started to be used in the service sector as well as in the manufacturing industry. The use of service robots in airport services, tourism, and hospital services also brings many advantages. Because of the COVID-19 pandemic, reducing physical contact and providing hygiene are essential benefits of service robots. For this purpose, the effect of COVID-19 fear on perceived trust in service robots and the intention to use them at airports was investigated in this study. For analysis, data were collected from 730 participants from 64 different countries, by questionnaire. The data were analyzed with the SPSS and AMOS package programs. As a result of the structural equation model analysis, it was determined that COVID-19 fear affects perceived trust in service robots and the intention to use them positively, and perceived trust also affects the intention to use service robots positively. In addition, as a result of the Process Macro analysis, it was found that perceived trust has a high mediating effect on the effect of COVID-19 fear on the intention to use. The research results show that people’s attitudes towards COVID-19 shape the perception of robot usage in air transport services.

Only recently, smart cities are taking shape, thanks to the rapid development of Internet of Things (IoT), cloud computing, and other similar technologies. Given the high demands placed on advanced technologies such as autonomous driving, cloud data services, and high-precision sensors, smart cities are creating an intelligent transportation environment conducive to the introduction of autonomous vehicles (AVs). In this context, the use of AVs in transportation is also considered a form of transportation innovation. As a result, AVs are considered more favorable to people interested in new technologies because they appear to be technologically superior. Their association with the most up-to-date technology can serve as a symbol for those who wish to demonstrate their interest in new technologies through their appearance. The positive image of technological innovation projected by AVs may influence their acceptance among technology enthusiasts to a significant degree. In this context, this study investigates the effects of perceived advantage, perceived risk, and perceived safety on the intention to use autonomous vehicles. For this purpose, data were collected from vehicle users living in Turkey by survey method. Secondly, factor analyses and regression analyses were performed with the data set obtained from 611 participants. As a result of the analyses, it has been determined that the perceived advantage and perceived security increase the intention to use autonomous vehicles. In contrast, the perceived risk reduces this intention to use. According to these results, recommendations were made to the companies about the level of acceptance of this technology by the users to assess their investments in autonomous vehicles better.

Cross-docking is an excellent way to reduce the space required to store goods, inventory management costs, and customer order delivery time. This paper focuses on cost optimization, scheduling incoming and outgoing trucks, and green supply chains with multiple cross-docking. The three objectives are minimizing total operating costs, truck transportation sequences, and carbon emissions within the supply chain. Since the linear programming model is an integer of zero and one and belongs to NP-hard problems, its solution time increases sharply with increasing dimensions. Therefore, the non-dominated sorting genetic algorithm-II (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) were used to find near-optimal solutions to the problem. Then, these algorithms were compared with criteria such as execution time and distance from the ideal point, and the superior algorithm in each criterion was identified.