Information Science Abstracts

The ever-changing times demand a resilient industry that quickly adapts to the oncoming wave of new technology; however, a strong social environment that comprises organic social values is needed for a thriving human society. Industry 4.0 has become the standard for applications in recent years, as its technologies are rapidly being implemented and positively impacting every sector. However, these developments are still unable to achieve the desired outcomes and have neglected the environment by prioritizing machines over humans. Therefore, the Industry 5.0 (I5.0) revolution is a call to bring forth the ideas of sustainability into practice, integrate human values with technology, and is considered a step forward for achieving sustainable development goals. Hence, the present study proposes a framework for analyzing I5.0 enablers for achieving sustainability by integrating human values with technology. To achieve the objectives, this study was conducted in three different phases: 1) through extensive review, the list of criteria and enablers was identified; 2) Pythagorean fuzzy Delphi was used to validate the enablers against criteria; 3) a hybrid approach of Pythagorean fuzzy analytical hierarchy process-combined compromise solution was employed to calculate the weights of selected criteria and determine the ranks of enablers. A sensitivity analysis was then performed to check the robustness of this framework. The findings show that the criterion of personal customization obtained the highest weight followed by human-machine collaboration, which will create a smart cognitive environment for humans. Human intelligence is used in more important places, which will help society focus on developing social values. The authors also put forward seven propositions about adopting I5.0 enablers for best results, taking examples from existing case studies and helping industrialists adopt I5.0 methods.

In this paper, we consider the online uniform machine scheduling problem on mprocessors when speed si=1 for i =k +1, ..., mand si=s, s >1, for i =1, ..., k. The objective is to minimize makespan. We propose a parametric scheme with the worst-case performance 2.618 when 1 1 when the ratio m/k tends to infinity.

Business processes are a main part of any organization therefore it is essential to improve their execution. Analysis of real process data can provide useful insights. Process mining techniques can be applied to event logs containing data related to business process execution to discover business processes and their behaviour therefore improving decision support. This paper presents an approach to discover probabilistic belief network from event logs, which focuses on domain-specific data contained in the logs for the analysis of business process behaviour. For evaluation purposes, the approach is applied to predict the business process execution. Experiments presented in the paper showcase practical application of the approach for synthetic and real-life logs. Obtained results prove that the approach is suitable for follow-up activity prediction and the nature of the approach allows for it to be extended for other use cases, such as anomaly detection or business process simulation.