{"id":408,"date":"2024-04-05T08:16:35","date_gmt":"2024-04-05T06:16:35","guid":{"rendered":"https:\/\/optano-homepage.orconomy.local\/?page_id=408"},"modified":"2025-12-05T13:23:25","modified_gmt":"2025-12-05T12:23:25","slug":"glossary","status":"publish","type":"page","link":"https:\/\/optano.com\/en\/media\/glossar\/","title":{"rendered":"OPTANO Glossary - Technical terms for optimization & analytics explained"},"content":{"rendered":"
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API <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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API<\/strong> stands for Application Programming Interface<\/strong> and refers to a programming interface. APIs are used to connect third-party programs to software at source code level. When programming, APIs standardize the data transfer between an application and individual program parts, the modules. The aim of APIs is to simplify programming through modularization. For this purpose, individual modules are encapsulated from the actual code and communication takes place exclusively via the API.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

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APS<\/strong>-systems are software solutions for detailed production and supply chain planning. They use complex algorithms to create optimal production plans, taking into account capacities, material availability and delivery times.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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C# (c sharp) is an object-oriented programming language that was developed in the early 2000s as a native programming language for Microsoft's .NET platforms.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Descriptive analytics <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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The descriptive or descriptive analysis<\/strong> serves as a summary of the past. It asks the question \u201eWhat happened?\u201c. Existing information from various systems and databases is processed in such a way that complexity is reduced. The results are reports, diagrams, dashboards and also key figures that support people in interpretation and decision-making.<\/p>

Typical applications are<\/p>

  • Reporting of key figures<\/li>
  • Condition monitoring<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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    Diagnostic Analytics <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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    The Diagnostic or explanatory analysis<\/strong> also works on the basis of historical data. It asks the question \u201eWhy did it happen?\u201c. It looks for causes and their effects on company processes and thus offers deeper insights into specific problems. Data analysis helps to identify correlations and recognize patterns. Domain knowledge is often required in order to draw conclusions and recognize correlations. Specialist knowledge such as entrepreneurial thinking, industry knowledge and methodological expertise play a decisive role. The expert is therefore responsible for deriving recommendations for action.
    Typical applications are<\/p>

    • Identification of operational disruption effects<\/li>
    • Finding the causes of quality defects<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
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      Digital twin (digital twin) <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      A digital twin<\/strong> is the virtual representation of a real system. In optimization, it is used to simulate, analyse and improve processes before changes are implemented in reality.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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      Decision variables <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      Decision variables<\/strong> are the variables that are adjusted in the model in order to optimize the target function. They represent concrete decisions, such as production quantities, transportation routes or personnel deployment.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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      Integer optimization <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      With the Integer optimization<\/strong> decision variables may only assume integer values. This method is used when decisions are discrete, for example when selecting locations or planning shift staffing.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

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      Gurobi <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      Gurobi<\/strong> is a powerful optimization solver that solves mathematical models to efficiently handle complex decision problems. It supports various methods such as linear programming (LP), mixed-integer programming (MILP), quadratic and quadratically constrained programming (QP, QCP) as well as their mixed-integer variants. As a Gurobi Premier Service Partner, OPTANO can already look back on many years of experience in working with Gurobi.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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      Heuristics <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      Heuristic procedures<\/strong> provide good, but not necessarily optimal solutions to complex problems. They are used when exact methods take too long or are impractical. Examples include greedy algorithms or metaheuristics such as genetic algorithms.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

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      Gurobi <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      Gurobi<\/strong> is a powerful optimization solver that solves mathematical models to efficiently handle complex decision problems. It supports various methods such as linear programming (LP), mixed-integer programming (MILP), quadratic and quadratically constrained programming (QP, QCP) as well as their mixed-integer variants. As a Gurobi Premier Service Partner, OPTANO can already look back on many years of experience in working with Gurobi.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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      Artificial intelligence (AI) <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      Artificial intelligence<\/strong> refers to the ability of computer systems to perform tasks that normally require human intelligence. This includes recognizing patterns, learning from data, making decisions and processing natural language. AI is used in many areas to automate processes, make predictions and solve complex problems efficiently.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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      Linear optimization \/ Linear programming <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      The linear optimization<\/strong> or linear programming<\/strong> is the most important sub-discipline of operations research. It deals with the optimization of linear objective functions over a set that is restricted by linear equations and inequalities. Linear optimization models consist of a linear objective function, which is minimized or maximized, and a large number of linear restrictions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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      Mathematical model <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      A mathematical model<\/strong> is the formal representation of a real-world problem using mathematical structures such as equations, variables and restrictions. It is used to describe complex relationships and enables the analysis and calculation of optimal solutions for decision-making processes<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

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      Mathematical optimization <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      Mathematical optimization<\/strong> is a method for determining the best solution for a problem under given conditions. Decision variables, target variables and restrictions are mapped in a mathematical model. The aim is to find an optimal solution, taking all restrictions into account.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

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      Machine learning (ML) <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      Machine learning<\/strong> is a branch of artificial intelligence in which algorithms learn from data in order to recognize patterns and make predictions or decisions. Instead of being explicitly programmed, the system improves through experience and is used in applications such as forecasting, classification and optimization.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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      Operations Research (OR) <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      Operations Research<\/strong> is a scientific discipline that uses mathematical models, algorithms and analytical methods to solve complex decision-making problems. The aim is to optimize processes such as planning, production or logistics and to use resources efficiently. OR is used in many industries to enable well-founded, data-based decisions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

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      OPTANO Modeling <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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      OPTANO Modeling<\/strong> is a .NET API for creating mathematical optimization models. It enables the modeling of linear, mixed-integer and partially quadratic programs in a familiar programming environment. The API is easy to use, saves resources and supports fast switching between solvers.<\/p>

      OPTANO Modeling is available for free download as a NuGet Gallery package.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

      \n\t\t\t\t\n\t\t\t\t\t
      OPTANO platform <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
      \n\t\t\t\t\t
      \n\t\t\t\t
      \n\t\t\t\t
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      The OPTANO platform<\/strong> is a technology platform used to create optimization systems for model-based planning and optimization of complex processes. It combines mathematical optimization, advanced analytics and artificial intelligence methods to support data-based decisions. The platform enables integration into existing IT systems, offers functions for scenario analyses and provides recommendations for the efficient use of resources.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

      \n\t\t\t\t\n\t\t\t\t\t
      Optimization solver <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
      \n\t\t\t\t\t
      \n\t\t\t\t
      \n\t\t\t\t
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      A Optimization solver<\/strong> is software that solves mathematical models to find the best solution for a given problem under certain conditions. It uses specialized algorithms for methods such as linear programming, mixed-integer programming or non-linear optimization.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

      \n\t\t
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      P<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
      \n\t\t\t\t
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      \n\t\t\t\t\n\t\t\t\t\t
      Predictive analytics <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
      \n\t\t\t\t
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      The Predictive analysis or forecast analysis<\/strong> works with foresight. The aim here is to derive models from historical data and use them to predict unknown events or deviations from the norm in the future. Forecasting future trends and conditions enables early intervention and optimization of business processes. Predictive analytics also requires domain knowledge to derive recommendations for action. However, predictions of trends and forecasted values create transparency, which makes it easier to weigh up risks and forms an important basis for making decisions.<\/p>

      Typical applications are<\/p>

      • Resource analysis<\/li>
      • Predictive maintenance<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t
        \n\t\t\t\t\n\t\t\t\t\t
        Prescriptive analytics <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
        \n\t\t\t\t\t
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        The Prescriptive or prescriptive analysis<\/strong> has the task of specifying decision options in order to achieve the forecast results and, if necessary, minimize future risks and maximize opportunities. It asks the question \u201eWhat measures should be taken?\u201c. The aim of prescriptive analytics is to directly offer recommendations for action or - as the highest degree of automation - the direct execution of actions and optimizations in fully automated companies. Prescriptive analytics therefore shows options for action and predicts the effects of certain decisions on the future without the decisions actually being made. Methods such as simulations, mathematical optimization, algorithms and machine learning processes are used for this purpose.<\/p>

        Typical applications are<\/p>

        • Procurement of goods to avoid delivery bottlenecks and high stock levels<\/li>
        • Route planning for sustainable logistics<\/li><\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
          \n\t\t\t\t
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          R<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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          Restrictions <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
          \n\t\t\t\t\t
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          <\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
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          Restrictions<\/strong> are the rules and limits that must be adhered to in the optimization model. They can be formulated as equations or inequalities and define the permissible solution space. Examples include capacity limits, budget limits or legal requirements.<\/p>

          Figuratively speaking, you have a rucksack with a maximum load capacity (e.g. in kg) and a number of items, each with a weight (e.g. in kg) and value (e.g. in euros.) The value of the selected items should be maximized, taking into account the load capacity of the rucksack.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

          \n\t\t\t\t\n\t\t\t\t\t
          Knapsack problem <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
          \n\t\t\t\t
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          The Backpack problem<\/strong> (knapsack problem)<\/strong> is a model combinatorial optimization problem. From a set of objects, for each of which a weight and a utility value are defined, the optimal combination of objects is to be compiled. The aim is to maximize the value of the selected objects, whereby this subset must not exceed a predefined weight limit.<\/p>

          Figuratively speaking, you have a rucksack with a maximum load capacity (e.g. in kg) and a number of items, each with a weight (e.g. in kg) and value (e.g. in euros.) The value of the selected items should be maximized, taking into account the load capacity of the rucksack.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

          \n\t\t\t\t
          \n\t\t\t\t
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          S<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t\t\t\t
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          Simulation <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
          \n\t\t\t\t\t
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          <\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
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          Simulations<\/strong> are used to analyze the effects of decisions under uncertainty. They complement optimization models by running through scenarios and making risks visible.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

          \n\t\t\t\t\n\t\t\t\t\t
          Solver <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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          <\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
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          A Solver<\/strong> is a software component that solves mathematical models. It processes variables, objective functions and restrictions and calculates optimal or near-optimal solutions. Solvers are the central tool in mathematical optimization and operations research.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

          \n\t\t\t\t\n\t\t\t\t\t
          Scenarios <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
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          <\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
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          Scenarios<\/strong> are realistic assumptions about possible future developments. They are used to map uncertainties in planning and to compare different options for action and their effects.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

          \n\t\t\t\t\n\t\t\t\t\t
          Scenario analysis <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
          \n\t\t\t\t\t
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          <\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
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          With the Scenario analysis<\/strong> or what-if analysis, various future scenarios are created and their effects considered in order to derive recommendations for action and decisions.
          Scenario analysis is a very good method for comparing different solutions and assessing risks. When comparing different solution approaches, scenarios and key figures can be determined to support the decision-making process. This makes it easier to assess potential risks and their impact during risk analysis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

          \n\t\t\t\t
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          T<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
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          Traveling Salesman Problem (TSP) <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
          \n\t\t\t\t
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          <\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
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          The Traveling Salesman Problem (TSP)<\/strong> or the travelling salesman problem is an optimization problem in operations research and theoretical computer science - more precisely, a route planning problem. The task is to choose the sequence for visiting different cities in such a way that each station is only visited once, the travel distance of the traveler is as short as possible and the last city is the same as the first.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

          \n\t\t\t\t
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          U<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
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          UX\/UI design <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
          \n\t\t\t\t
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          <\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
          \n\t\t\t\t
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          UX design<\/strong> refers to the term \u201euser experience design\u201c, while UI Design<\/strong> stands for \u201eUser Interface Design\u201c.<\/p>

          The UX design<\/strong> focuses on the user's overall experience and therefore on user-friendliness. The goal of UX design is to create efficient, intuitive and pleasant experiences for the user. UX design focuses on determining which elements relate to each other and ensuring a uniformly organized navigation.<\/p>

          UI design<\/strong> is a sub-area within the user experience and contributes to the user-friendliness of an application by graphically designing the user interface in such a way that the user can interact effortlessly and intuitively. The layout of the application interface is the focus of UI design. It includes all visual, interactive elements such as typography, icons, buttons, color schemes and responsive design.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

          \n\t\t\t\t\n\t\t\t\t\t
          OPTANO Modeling <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
          \n\t\t\t\t\t
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          OPTANO Modeling<\/strong> is a .NET API for creating mathematical optimization models. It enables the modeling of linear, mixed-integer and partially quadratic programs in a familiar programming environment. The API is easy to use, saves resources and supports fast switching between solvers.<\/p>

          OPTANO Modeling is available for free download as a NuGet Gallery package.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

          \n\t\t\t\t\n\t\t\t\t\t
          OPTANO platform <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
          \n\t\t\t\t\t
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          The OPTANO platform<\/strong> is a technology platform used to create optimization systems for model-based planning and optimization of complex processes. It combines mathematical optimization, advanced analytics and artificial intelligence methods to support data-based decisions. The platform enables integration into existing IT systems, offers functions for scenario analyses and provides recommendations for the efficient use of resources.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t\t

          \n\t\t\t\t\n\t\t\t\t\t
          Optimization solver <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
          \n\t\t\t\t\t
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          A Optimization solver<\/strong> is software that solves mathematical models to find the best solution for a given problem under certain conditions. It uses specialized algorithms for methods such as linear programming, mixed-integer programming or non-linear optimization.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

          \n\t\t\t\t
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          W<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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          What-if analysis \/ What-if analysis <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
          \n\t\t\t\t
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          With the What-if analysis (what-if analysis)<\/strong> In a scenario analysis, different future scenarios are created and their effects considered in order to derive recommendations for action and decisions.
          The what-if analysis is a very good method for comparing different solutions and assessing risks. When comparing different solution approaches, scenarios and key figures can be determined to support the decision-making process. This makes it easier to assess potential risks and their impact during the risk analysis.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

          \n\t\t\t\t
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          Z<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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          Target function <\/div><\/span>\n\t\t\t\t\t\t\t\n\t\t\t<\/i><\/span>\n\t\t\t<\/i><\/span>\n\t\t<\/span>\n\n\t\t\t\t\t\t<\/summary>\n\t\t\t\t
          \n\t\t\t\t
          \n\t\t\t\t
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          The Target function<\/strong> is the core of an optimization model. It describes mathematically what is to be optimized - for example, cost minimization or profit maximization. All decisions in the model are aimed at improving the value of this function under given conditions.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/details>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"

          In the OPTANO glossary you will find understandable explanations of terms from optimization, data science, planning and software development<\/p>","protected":false},"author":3,"featured_media":9672,"parent":406,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-408","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/optano.com\/en\/wp-json\/wp\/v2\/pages\/408","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/optano.com\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/optano.com\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/optano.com\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/optano.com\/en\/wp-json\/wp\/v2\/comments?post=408"}],"version-history":[{"count":56,"href":"https:\/\/optano.com\/en\/wp-json\/wp\/v2\/pages\/408\/revisions"}],"predecessor-version":[{"id":11551,"href":"https:\/\/optano.com\/en\/wp-json\/wp\/v2\/pages\/408\/revisions\/11551"}],"up":[{"embeddable":true,"href":"https:\/\/optano.com\/en\/wp-json\/wp\/v2\/pages\/406"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/optano.com\/en\/wp-json\/wp\/v2\/media\/9672"}],"wp:attachment":[{"href":"https:\/\/optano.com\/en\/wp-json\/wp\/v2\/media?parent=408"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}