JOpt.TourOptimizer
When creating software solutions for Logistics Dispatch, you may encounter various challenges, including those related to staff dispatching for mobile services, sales representatives, or other workforce issues; managing truck shipment allocations for daily logistics and transportation needs, which involves scheduling and optimizing routes; addressing concerns in waste management and district planning; and tackling a variety of highly constrained problem sets. If your product lacks an automated optimization engine to address these complexities, JOpt can be an invaluable addition, providing you with the tools to reduce costs, save time, and optimize workforce efficiency, allowing you to focus on your primary business objectives. The JOpt.TourOptimizer is a versatile component designed to tackle Vehicle Routing Problems (VRP), Capacitated Vehicle Routing Problems (CVRP), and Time Windowed Vehicle Routing Problems (VRPTW), making it suitable for any route optimization tasks in logistics and related sectors. Available as either a Java library or a Docker container that incorporates the Spring Framework and Swagger, this solution is tailored to facilitate seamless integration into your existing software ecosystem.
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TinyPNG
TinyPNG (by Tinify) is a free image optimization solution trusted by developers, designers, and businesses worldwide. Using smart lossy compression, it reduces JPEG, PNG, WebP, and AVIF file sizes by up to 80% without sacrificing quality. Accelerating load times, boosting SEO, and lowering bandwidth costs.
Easily compress, convert, and resize images through a user-friendly web interface or integrate with your stack via our robust API. Official SDKs are available for Python, Node.js, PHP, Java, Ruby, and .NET. We also offer a WordPress plugin and a growing ecosystem of third-party integrations.
Tinify eliminates complexity, no confusing settings, no guesswork. Whether you're optimizing a small catalog or managing millions of files, it delivers consistent, scalable results. Every plan starts with a generous free tier, and our responsive support team is ready to assist.
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Bokeh
Bokeh streamlines the creation of standard visualizations while also catering to specific and unique needs. It provides users the ability to share plots, dashboards, and applications either on web platforms or directly within Jupyter notebooks. The Python ecosystem is rich with a variety of powerful analytical tools, such as NumPy, Scipy, Pandas, Dask, Scikit-Learn, and OpenCV, among many others. Featuring an extensive array of widgets, plotting options, and user interface events that activate real Python callbacks, the Bokeh server is essential for linking these tools to dynamic and interactive visualizations displayed in web browsers. Moreover, the Microscopium initiative, led by researchers at Monash University, harnesses Bokeh's interactive features to assist scientists in uncovering new functionalities of genes or drugs by allowing them to explore extensive image datasets. Another significant tool in this ecosystem is Panel, which focuses on producing polished data presentations and operates on the Bokeh server, enjoying support from Anaconda. Panel simplifies the process of building custom interactive web applications and dashboards by effortlessly connecting user-defined widgets to a variety of components, including plots, images, tables, or text. This seamless integration not only enhances the overall user experience but also cultivates an atmosphere that promotes effective data-driven decision-making and thorough exploration of complex datasets. Ultimately, the combination of these tools empowers users to engage with their data in innovative and meaningful ways.
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broot
The ROOT data analysis framework is a prominent tool in High Energy Physics (HEP) that utilizes its own specialized file format (.root) for data storage. It boasts seamless integration with C++ programs, and for those who prefer Python, it offers an interface known as pyROOT. Unfortunately, pyROOT faces challenges with compatibility for Python 3.4, which has led to the development of a new library called broot. This streamlined library is designed to convert data contained in Python's numpy ndarrays into ROOT files, organizing data by creating a branch for each array. The primary goal of this library is to provide a consistent method for exporting numpy data structures to ROOT files efficiently. Additionally, broot is crafted to be both portable and compatible across Python 2 and 3, as well as with ROOT versions 5 and 6, requiring no modifications to the existing ROOT components—only a standard installation is sufficient. Users will appreciate the straightforward installation process, as they can either compile the library once or install it conveniently as a Python package, making it an attractive option for data analysis tasks. This user-friendly approach is likely to encourage an increasing number of researchers to incorporate ROOT into their data analysis routines. Overall, the accessibility and functionality of broot enhance the versatility of using ROOT in various research settings.
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