The logistics sector should strive for sustainability alongside productivity by protecting its order pickers’ health and welfare. Existing storage assignment models are mainly based on the criterion of order picking time and, to a lesser extent, the human factor. In the paper, a solution to a storage assignment problem using a multi-objective model based on binary integer linear programing is presented by developing a solution that considers order picking time, energy expenditure and health risk. The Ovako Working Posture Assessment System (OWAS) method was used for health risk assessment. The downside of solely health risk-optimization is that the average order picking time increases by approximately 33% compared to solely time-optimization. Contrary to this, the developed multi-objective function emphasizing time has proven to be promising in finding a compromise between the optimal order picking time and eliminating work situations with a very-high risk for injuries. Its use increases the time by only 3.8% compared to solely time-optimization while significantly reducing health risk.

This paper studies a multiple-deep automated vehicles storage and retrieval system (AVS/RS) rack following a Depth-First storage and a Depth-First relocation strategy. We propose an analytical model based on a novel approach that utilises the Markov chain stochastic steady-state model. To verify the analytical model, a numerical simulation is developed. We also derive an empirical model using first- and second-order polynomial functions that are accurately fitted with regression equations and examined with MAPE and RMSE prediction accuracy measurements from a large-scale simulation study. The empirical model enables a straightforward calculation of the expected number of location movements of shuttle carriers and the attached satellite vehicles from which the AVS/RS throughput performance can be calculated. We present threefold and sixfold deep AVS/RS case study scenarios with an equal number of storage locations and estimate the cycle times. The evaluation of the case study results reveals that the analytical and empirical models achieve less than 2% error in the case of a dual command cycle time prediction compared to the simulation results. This proves that our approach allows an accurate estimation of multiple-depth AVS/RS throughput performance.

The outbreak of COVID-19 caused many changes in people’s life. One of the most significant is the travel behaviour and transport mode choice. This study focus on the changes that the inhabitants of Vienna made in their travel choices because of the virus. The same research about spatial modelling the transport mode choice of commuters in Vienna was completed in 2019 and is a topic addressed in our previous work. Based on our developed methodology, this article indicates that public transport is not a dominant transport mode choice as it was before the virus outbreak. The main result of this paper is geographically defined areas of application of individual alternatives shown on the final map of modal shift in Vienna, which could provide theoretical support for policymakers and transportation planners. For the city of Vienna, we found that the area of the city where cars are now used has increased, which certainly has a negative impact on air quality and life in the city. The advantage of the methodology is that it can also be applied to other cities in the world.

When introducing new technologies, companies are repeatedly faced with choosing between solutions from different providers. Regardless of all the good technical characteristics of the technology, if it is chosen inappropriately, it can prove to be a cost driver instead of something that brings added value to the system. Aware of this, we considered selecting a real-time location system (RTLS) based on Ultra-wideband technology in the indoor work environment. In practice and theory, it has been proven that the introduction of the RTLS can have highly positive effects on performance and business sustainability indicators. When reviewing the literature, it was noticed that authors solely focus on the technical properties of the systems and prices when giving guidelines on selecting the optimal RTLS. This article aims to provide advanced guidelines for UWB RTLS selection, proposing a phased selection process which is the main novelty proposed and investigated in this research. The guidelines are based on fragmented recommendations in the scientific literature that have been identified, gathered, considered, and reasonably allocated to the advanced performance-oriented phased selection process. In practice, this approach enables decision-makers to choose the most efficient and most appropriate UWB RTLS for specific logistics systems.

Climate change, the main accelerator of which has been consumerism and the industry driving it, soon will force companies to make changes that will intensify their activities in the natural environment. The Covid-19 pandemic, Due to their specificity...

Emergency Remote Teaching (ERT) aims to swiftly adapt conventional face-to-face educational methods to alternative (typically virtual) formats during crises. The recent COVID-19 pandemic accentuated the vulnerability of traditional educational systems, revealing limitations in their ability to effectively withstand such unprecedented events, thereby exposing shortcomings in the adopted ERT strategies. The goal of this study is to discuss the establishment of resilient, sustainable, and healthy educational systems in non-crisis times, which will enable teachers and students to make a smoother and less stressful transition to Emergency Remote Teaching (ERT) when necessary. A comprehensive hybrid approach, combining quantitative (interviews) and qualitative (online survey) methods has obtained data from 276 professors in 29 countries. These data have been used to identify a range of challenges related to ERT and their perceived level of difficulty. The methodological and social challenges (overshadowed by technical issues at the beginning of the crisis) identified in this research—such as the lack of personal contact or poor feedback from students—have been found to be the most demanding. From the collected insights regarding the perceived level of difficulty associated with the identified challenges, the present study aims to contribute to making higher education systems more robust in non-crisis times.