This paper introduces BigVRSP, an instance generator tool for long-haul transportation problems, designed to support numerical experiments in real-life pickup and delivery and truck assignment applications. The software automates the process of building realistic scenarios by combining either historical operational data or artificially generated ones with user preferences through command-line interface (CLI) parameters. Implemented as a series of Python scripts with CLI, it generates structured text datasets as CSV files, for both trucks and customer requests. The tool is openly available in a public repository on Gitlab and can be easily integrated into workflows in many different logistics contexts.
The shift in acoustic environmental studies from noise mitigation to a soundscape-centric approach, formalized by the ISO 12913 series, has highlighted the requirement for sophisticated analytical tools to map human perception. This paper presents SoundGraphy, an open-source Python-based software designed to enhance the characterization and visualization of soundscape data. The architecture of SoundGraphy is established upon a functional tripod: a user-friendly graphical user interface (GUI) that democratizes complex analysis for researchers without programming expertise, an advanced data-filtering and representation system incorporating bivariate Kernel Density Estimation (KDE) and radar plots, and the novel integration of the Structural Summary Method (SSM). SoundGraphy is distributed as a standalone executable, removing technical barriers such as manual dependency management. Ultimately, this tool provides a robust framework for objectifying subjective acoustic experiences, bridging the gap between raw data and nuanced human perceptual outcomes across diverse international contexts.
