Author: Dr. Kostja Siefen
Every mathematical optimization implementation project is a unique journey, which begins with an initial idea of how mathematical optimization could be used to address a business problem and ends with the actual implementation of a mathematical optimization application that delivers a solution to that business problem.
As a Technical Account Manager, my job is to guide companies on their journeys from idea to implementation, to advise them on which pathways to take and which pitfalls to avoid, and to help ensure they reach and realize their ultimate objective: the successful deployment of mathematical optimization in their organization.
Although each of these implementation journeys is different, they all – generally speaking – follow a sequence of similar steps. In the first blog in this series, I highlighted the first stages of the journey from project kick-off and scoping discussions to the development of a project roadmap.
In this blog, I would like to move on to the next leg of the journey: Designing a deployment architecture for your mathematical optimization application.
Once you have determined which business problem you will use mathematical optimization to address, discussed and defined your project roadmap, and aligned all relevant stakeholders around a common vision for your mathematical optimization solution, you are ready to begin conceptualizing your deployment architecture.
It is essential to create a detailed blueprint of your deployment architecture, which will serve as the foundation on which your application will be built and used. To design this blueprint, you will need a “solution architect” (which could be someone from within your organization or an external consultant) who has the necessary technical and business expertise to transform your vision for the solution into an actual application design.
When designing the deployment architecture for your mathematical optimization application, these are the key factors to consider:
The entire project team should meet and discuss these and other factors, decide on what the right deployment architecture should be, and then begin to design and develop this architecture.
Of course, this process takes time and effort and requires a solution architect (either from within your company or an external expert), but it’s vital that you ensure that you are able to conceptualize and ultimately construct a deployment architecture that is the right size and structure for your organization. Gurobi frequently helps companies here as an external advisor.
The deployment architecture serves as the foundation on which your application will be built and run. By designing, developing, and deploying an architecture that fits the needs of your organization and fulfills the requirements of your application, you are setting the stage for a successful mathematical optimization implementation.
Perhaps the most important decision that you have to make during the process of designing a deployment architecture is which mathematical optimization solver you want to use in your application – and I will delve into this topic in the next blog in this series.
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