In recent times, industrial robots have gained immense significance and popularity in various industries. They not only enhance labor safety and reduce costs but also greatly improve productivity and efficiency in the production process. However, selecting the most suitable robot for a specific production process is a complex task. There are numerous criteria to consider, often conflicting with each other, making decision-making challenging. In order to tackle this problem, the multi-criteria decision-making (MCDM) method is employed, which aids in ranking decisions based on criteria weights. However, traditional MCDM methods are now considered outdated, and researchers are concentrating on hybrid models that include multiple MCDM techniques to tackle decision-making problems effectively. This study presents an effective MCDM model that integrates Fuzzy-AHP-TOPSIS to evaluate and choose the best robot. The Fuzzy-AHP is utilized to establish a set of weights for the evaluation criteria. Subsequently, the proposed technique analyzes, prioritizes, and chooses the best robot option from the ranking list for the factory. The experimental results demonstrate that by employing the integrated fuzzy analytical hierarchy process, taking into account parameter weights and expert judgment, the robots are identified in order of best to worst alternatives to factories. The outcomes of this research possess significant implications for robot selection and can be applied in various fields to cater to production requirements.

The known a posteriori error analysis of hybrid high-order methods treats the stabilization contribution as part of the error and as part of the error estimator for an efficient and reliable error control. This paper circumvents the stabilization contribution on simplicial meshes and arrives at a stabilization-free error analysis with an explicit residual-based a posteriori error estimator for adaptive mesh-refining as well as an equilibrium-based guaranteed upper error bound (GUB). Numerical evidence in a Poisson model problem supports that the GUB leads to realistic upper bounds for the displacement error in the piecewise energy norm. The adaptive mesh-refining algorithm associated to the explicit residual-based a posteriori error estimator recovers the optimal convergence rates in computational benchmarks.

The development of sustainable electricity technology is of utmost importance in addressing the increasing energy demand while mitigating greenhouse gas emissions. Fossil fuel-based electricity generation is the primary contributor to air pollution and climate change, necessitating a shift towards renewable energy sources. The efficient production, distribution, and utilization of energy resources, along with ensuring affordable energy access and environmental sustainability, are key policy objectives for any country's energy sector. However, assessing sustainable electricity technologies is a complex task due to the diverse range of evaluation criteria and impacts associated with the practical implementation of these solutions. To overcome this challenge, this study proposes a multi-criteria decision-making (MCDM) approach to select the optimal solution for the development of sustainable electricity technology. The study employs several reliable methods, including MAIRCA, SPOTIS, COMET, and the CRITIC weighting method, to perform ranking evaluations. Based on this, an evaluation Table of criteria using linguistic variables is constructed. Furthermore, a ranking of methods for developing sustainable electricity technology is established by combining MCDM optimization methods. The results indicate that future energy policies should prioritize sustainable energy technologies, particularly water and solar thermal solutions. These findings have significant implications for development policymakers as the transition towards a sustainable energy system becomes increasingly crucial. In the future, the findings of this research can be further developed on a regional level, enabling the identification of the most appropriate energy technologies for specific regions based on their unique characteristics and requirements

Enterprises must be able to make decisions for increasing process effectiveness and efficiency in view of shrinking natural resources. A globalised work force has to be made aware of the challenges in resource scarcity and material wealth. Special attention has also to be paid on health and safety at work due to recent demographic developments. We present two solutions based on human motion capture technology which can improve efficiency while enabling ergonomic working, thus addressing challenges in sustainable manufacturing. With a qualification module, intuitive work descriptions can be generated and distributed. An assistance module automatically adjusts the workplace to enable working in a comfortable pose. First tests regarding our qualification module show the ability to implicitly convey knowledge during simple working tasks. Studies on the assistance module show its capabilities to predict what test subjects working at the workplace would consider as configuration for “comfortable” and “ergonomic” working

This paper proposes a regularization of the Monge–Ampère equation in planar convex domains through uniformly elliptic Hamilton–Jacobi–Bellman equations. The regularized problem possesses a unique strong solution $u_\varepsilon$ and is accessible to the discretization with finite elements. This work establishes uniform convergence of $u_\varepsilon$ to the convex Alexandrov solution $u$ to the Monge–Ampère equation as the regularization parameter $\varepsilon$ approaches $0$ . A mixed finite element method for the approximation of $u_\varepsilon$ is proposed, and the regularized finite element scheme is shown to be uniformly convergent. The class of admissible right-hand sides are the functions that can be approximated from below by positive continuous functions in the $L^1$ norm. Numerical experiments provide empirical evidence for the efficient approximation of singular solutions $u$ .

In Social Life Cycle Assessment (SLCA), the health and safety aspect of workers is usually evaluated by considering the numbers of injuries and accidents; however, the work related musculoskeletal disorders (MSDs), which dominate occupational diseases, are often neglected in SLCA since the effects do not occur immediately. Thus, the MSDs lead to increased working absences and compensation costs, and also reduced productivity of workers. To address the gap, applying ergonomic assessment is proposed since it identifies and quantifies the health risks at work based on a set of pre-defined criteria e.g. force, posture, repetition and duration, and provides the numeric results analyzing the physical load and their sources. In the study, the application of ergonomic assessment and its indicators in SLCA is displayed to screen risks and to further improve working place design