When it comes to commercial solvers and advanced solving algorithms, modelers have tons of options to choose from. But while more options sound like a good thing, many of these options present the same hurdles to modelers and/or their companies. If you’re trying to develop expertise in machine learning, simulation, and optimization - these hurdles can be quite challenging to overcome. We’ve listed the most common hurdles that modelers may encounter with solver solutions, and how Optilogic leaps over them.
Hurdle One: Cost
The Problem: Purchasing optimization, simulation, and other advanced solving technologies can cost tens to hundreds of thousands of dollars, making it financially infeasible for small to medium sized businesses who could benefit from the technology. But even if the initial cost of the technology can be absorbed, there’s the cost of tuning the solver, support hours, and licensing across teams. And let’s not forget the cost of hardware provisioning. If you plan on optimizing complex models, you’ll need high performing software with a greater capacity for solving - which often runs up the cost. For financially conscious businesses, this hurdle makes it difficult to model, optimize, and innovate.
The Optilogic Solution: We offer a no-cost solution to our cloud modeling studio. Users can sign up for an account and have access to 40 hours of workspace runtime and 40 hours of solver time - all for free. Users can also search our library of pre-configured problems for both optimization and simulation techniques - to use and configure in their workspace for their specific problem.
Hurdle Two: Expertise
The Problem: While there’s many different ways to optimize, it’s often difficult to know what the right approach should be for your specific problem. Developing models across solving techniques such as simulation, optimization, and machine learning in Python can take specialized skills that are not readily available in a team. These highly specialized machine learning skills for tuning models or for advanced analytics requires applied research techniques typically found in PhD Operations Research team members. Additionally, it takes time and deep knowledge to understand what constraints to adjust, what outputs to pay attention to, and how to take your optimized model to the next level.
The Optilogic Solution: You don’t need to worry if your team doesn’t have the expertise to get started in optimization. We’ve got experts who’ve dedicated their careers to all things machine learning at the ready to help users get started. Support on Python programming, optimization model setup, and simulation approach are just a few key areas where our team can help.
But you don’t need to solely rely on our experts. We also have an in-app, preconfigured optimization and simulation library, along with our pre-tuned and configured solvers so modelers can get a jump start in using advanced solving techniques.
The Optilogic Community will also be a feature for modelers to connect with other modelers, crowdsource problem solving, and fill the knowledge gaps that build out advanced solving models. This feature is not yet released and will be coming soon.
Hurdle Three: Inflexibility
The Problem: With out-of-the-box optimization and simulation tools, modelers may get a simple solution that works for common problems. However, each modeler’s objective is unique. Often, these tools can’t fully cover the unique use case that you or your modelers need to optimize for. Many optimization tools also contain limitations such as how much you can change a constraint or data structure.
The Optilogic Solution: Optilogic is designed to be completely configurable. Programmers proficient in Python can take any of our example models and tailor them their specific needs - adding custom constraints and additional data elements. Your Optilogic model can then be used in any solution that can make an API call. You can upload new data, run models, and download results to power visualizations completely through automation.
Speaking of flexibility, we’re completely cloud-based - making it easier for you and your team to collaborate on a single model in real time and from anywhere.
Hurdle Four: Scalability
The Problem: While it’s a good problem to have, many companies start with individuals working on one or two projects, but then grow to multiple modelers working on a number of projects requiring more solvers, memory, and data storage. This makes it imperative that a solver solution not only have a central hub for analysts to share models and insights, but the power to support hundreds of models at once.
The Optilogic Solution: As the need for solving multiple problems or dozens of scenarios for a project, Optilogic’s Andromeda solver architecture enables concurrent solving. This means no additional hardware or software is required. With the full power of the Microsoft Azure cloud behind us, users can link their models to apps anywhere in the world, creating a centralized space for all of their models.
From prototyping to deployment, we’ve got your end-to-end modeling needs covered, no matter the complexity of your model. Get started for free today. The world is yours to optimize.
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