Automation and digitisation are the driving force of the Industrial Revolution 4.0. Industrial revolutions led to the mass production of goods, which increased the need for modern warehouses. Every year, the operation of warehouses becomes increasingly more complicated due to the increasing abundance of goods, thus the usual warehouse management strategies are no longer suitable. In order to cope with huge product flows, modern innovations should be used more extensively to manage these processes. Successful management will help provide quality service to rapidly changing business sectors. The Internet of Things (IoT) is a technology designed to process large amounts of data with maximum efficiency in real time. This technology can facilitate the implementation of smart identification, tracking, tracing, and management using radio frequency identification (RFID), infrared sensors, global positioning systems (GPS), laser scanners, and other detection tools. Such innovations as IoT have made a significant impact on warehousing operations. The aim of IoT is to perform administrative work, i.e., to efficiently manage warehouse data. IoT can be used to monitor and track goods, forecast demand trends, manage inventory, and perform other warehouse operations in real time. The key elements of a warehouse are sales and customer satisfaction. Implementing IoT improves financial performance, work productivity, and customer satisfaction. However, innovation requires additional investment in, for instance, implementation and maintenance. It is necessary to investigate how warehouse elements such as inventory accuracy or order processing time are affected by the internet of things in companies of different sizes. Research on the impact of IoT on warehouse management focuses on IoT advantages, disadvantages, mitigation risks, and the use of IoT in warehouses. The aim of this work is to research the impact of IoT on warehouse management in companies of different sizes and to determine whether the costs and benefits of IoT differ in the same scenario. As a result, the conceptual model for the adoption of IoT measures in warehouse companies was created, and its suitability was assessed by experts.

The investigation for new innovative solutions to reduce transport pollution is a priority for the European Union (EU). This study includes energy and a sustainable environment, as well as transport, logistics, and information and communication technologies. Energy ecological parameters of internal combustion depend on many factors: fuel, the fuel injection time, engine torque, etc. The engine’s energy ecological parameters were studied by changing engine torques, using different fuels, and changing the start of the fuel injection time. The selection of the optimum parameters is a complex problem. Multicriteria decision-making methods (MCDM) present powerful and flexible techniques for the solution of many sustainability problems. The article presents a new way of tackling transport pollution. The analysis of the energy ecological parameters of the experimental internal combustion engine is performed using the neutrosophic multi-objective optimization by a ratio analysis plus the full multiplicative form (MULTIMOORA) and step-wise weight assessment ratio analysis (SWARA) methods. The application of MCDM methods provides us with the opportunity to establish the best alternatives which reflect the best energy ecological parameters of the internal combustion engine.

The construction industry is considered as one of the most dangerous industries in terms of occupational safety and has a high rate of occupational incidents and risks compared to other industries. Given the importance of identifying and assessing the occupational hazards in this industry, researchers have conducted numerous studies using statistical methods, multi-criteria decision-making methods, expert-based judgments, and so on. Although, these researchers have used linguistic variables, fuzzy sets and interval-valued intuitionistic fuzzy sets to overcome challenges such as uncertainty and ambiguity in the risk assessment conducted by experts; the previous models lack in efficiency if the experts are hesitant in their assessment. This leads to the inability to assign a specific membership degree to any risk. Therefore, in this research, it is tried to provide an improved approach to the Failure Mode and Effects Analysis (FMEA) method using an Multi-Criteria Decision-Making (MCDM) method based on the hesitant fuzzy set, which can effectively cope with the hesitance of the experts in the evaluation. Also, Stepwise Weight Assessment Ratio Analysis (SWARA) method is applied for risk factor weighing in the proposed approach. This model is applied to a construction industry case study to solve a realistic occupational risk assessment. Moreover, a comparison is made between the results of this model and those obtained by the conventional FMEA and some other aggregation operators. The results indicate that the newly developed approach is useful and flexible to address complex FMEA problems and can generate logical and reliable priority rankings for failure modes.

Steel ropes are complex flexible structures used in many technical applications, such as elevators, cable cars, and funicular cabs. Due to the specific design and critical safety requirements, diagnostics of ropes remains an important issue. Broken wire number in the steel ropes is limited by safety standards when they are used in the human lifting and carrying installations. There are some practical issues on loose wires—firstly, it shows end of lifetime of the entire rope, independently of wear, lubrication or wrong winding on the drums or through pulleys; and, secondly, it can stick in the tight pulley—support gaps and cause deterioration of rope structure up to birdcage formations. Normal rope operation should not generate broken wires, so increasing of their number shows a need for rope installation maintenance. This paper presents a methodology of steel rope diagnostics and the results of analysis using multi-criteria analysis methods. The experimental part of the research was performed using an original test bench to detect broken wires on the rope surface by its vibrations. Diagnostics was performed in the range of frequencies from 60 to 560 Hz with a pitch of 50 Hz. The obtained amplitudes of the broken rope wire vibrations, different from the entire rope surface vibration parameters, was the significant outcome. Later analysis of the obtained experimental results revealed the most significant values of the diagnostic parameters. The evaluation of the power of the diagnostics was implemented by using multi-criteria decision-making (MCDM) methods. Various decision-making methods are necessary due to unknown efficiencies with respect to the physical phenomena of the evaluated processes. The significance of the methods was evaluated using objective methods from the structure of the presented data. Some of these methods were proposed by authors of this paper. Implementation of MCDM in diagnostic data analysis and definition of the diagnostic parameters significance offers meaningful results.

Air pollution with various materials, particularly with aerosols, increases with the advances in technological development. This is a complicated global problem. One of the priorities in achieving sustainable development is the reduction of harmful technological effects on the environment and human health. It is a responsibility of researchers to search for effective methods of reducing pollution. The reliable results can be obtained by combining the approaches used in various fields of science and technology. This paper aims to demonstrate the effectiveness of the multiple attribute decision-making (MADM) methods in investigating and solving the environmental pollution problems. The paper presents the study of the process of the evaporation of a toxic liquid based on using the MADM methods. A schematic view of the test setup is presented. The density, viscosity, and rate of the released vapor flow are measured and the dependence of the variation of the solution concentration on its temperature is determined in the experimental study. The concentration of hydrochloric acid solution (HAS) varies in the range from 28% to 34%, while the liquid is heated from 50 to 80 °C. The variations in the parameters are analyzed using the well-known VIKOR and COPRAS MADM methods. For determining the criteria weights, a new CILOS (Criterion Impact LOSs) method is used. The experimental results are arranged in the priority order, using the MADM methods. Based on the obtained data, the technological parameters of production, ensuring minimum environmental pollution, can be chosen.