What is optimization in business?
Business optimization uses mathematical models to determine the best possible decision given constraints, such as cost, capacity, risk, time or regulations. It moves organizations from analyzing data to prescribing the best action. Optimization is a core component of prescriptive analytics, the discipline that recommends what an organization should do based on data, forecasts and business rules.
What is SAS Optimization?
SAS Optimization is an enterprise-grade suite of mathematical optimization solvers running on the SAS® Viya® platform. It enables organizations to model complex decisions – such as scheduling, supply chain planning and capital allocation – and compute the optimal solution at scale.
How SAS Optimization works
1.
Define objectives:
Minimize cost, maximize revenue, or meet other business goals.
2.
Set constraints:
Include capacity, regulations, budgets or service levels.
3.
Formulate the model:
Use SAS algebraic modeling language or open-language APIs.
4.
Run solvers:
Leverage SAS Viya’s distributed, in-memory engine for scalable computation.
5.
Operationalize results:
Deploy decisions through APIs, dashboards or applications.
SAS Optimization benefits
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Quickly solve complex optimization problems
SAS Optimization computes optimal solutions faster using SAS Viya’s distributed, in-memory engine. In-memory data persistence reduces the need to reload data during iterative analyses, accelerating model execution.
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Drive better decision-making
Identify and apply the best responses to complex, real-world problems. Optimization methods integrate with data preparation, exploration, analytics and reporting capabilities in one unified environment. Access and prepare data with self-service tools and embedded AI. Explore and visualize data with interactive dashboards. Integrate optimization into broader analytics workflows.
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Empower users with their preferred programming language
Python, Java, R and Lua programmers can leverage SAS Optimization solvers without learning SAS code. Access powerful, tested SAS algorithms from the programming language you already use.
Key features
A powerful array of optimization, simulation and project scheduling techniques for identifying actions that will get the best results, while operating within resource limitations and other relevant restrictions.
Data access, preparation & quality
Access, profile, cleanse and transform data using an intuitive interface that provides self-service data preparation capabilities with embedded AI.
Data visualization
Visually explore data, and create and share smart visualizations and interactive reports through a single, self-service interface. Augmented analytics and advanced capabilities accelerate insights and help you uncover stories hidden in your data.
Robust, intuitive algebraic optimization modeling language
Enables you to produce a range of models, including linear, mixed integer linear, nonlinear, quadratic, conic and network optimization, as well as solve constraint satisfaction problems.
A unified modeling language
A single modeling and solution framework supports a wide range of optimization models. You only need to learn one set of statements and commands to build a range of optimization and constraint satisfaction models.
Powerful optimization solvers & presolvers
A suite of optimization solvers are streamlined for simplicity and tuned for performance. Aggressive presolvers reduce effective problem size so you can tackle large problems and solve them faster.
Network flow optimization
Investigate the characteristics of networks and find the best answers to network-oriented problems using network algorithms accessible from both PROC OPTMODEL and PROC OPTNETWORK.
Multistart algorithm for nonconvex nonlinear optimization
Increase the chance of finding a globally optimal solution among many locally optimal solutions. Selects multiple starting points, begins optimization in parallel from each, then reports the best solution from all starting points.
Decomposition algorithm (automated Dantzig-Wolfe)
Decompose overall problem into a set of component problems, each with an exclusive set of decision variables solved in parallel. Parallel subproblem solving is coordinated with the overall solution process, which saves significant time.
Black-box optimization
Use the black-box solver with (generally nonlinear) optimization problems that don’t adhere to assumptions conventional optimization solvers make. Functions might be discontinuous, nonsmooth, computationally expensive to evaluate, based on black-box simulations, etc.
Constraint programming
Solve constraint satisfaction problems using domain reduction/constraint propagation and a choice of search strategies, such as look ahead and backtracking.
Cloud native
SAS Viya's architecture is compact, cloud-native and fast. Whether you prefer to use a public or private cloud provider, you'll be able to make the most of your cloud investment.
SAS Viya is cloud-native and cloud-agnostic
Consume SAS how you want – SAS managed or self-managed. And where you want.
Related products, solutions & capabilities
SAS Optimization frequently asked questions
What types of problems can SAS Optimization handle?
SAS Optimization supports linear, mixed-integer, nonlinear and network-based models, including large-scale industrial planning and scheduling problems with thousands to millions of variables and constraints.
Who should use SAS Optimization?
SAS Optimization is designed for operations research specialists, data scientists, analytics teams and business planners who need mathematically rigorous decision support at enterprise scale.
Can I run optimization models in Python?
Yes. SAS Optimization integrates with Python and other open source languages, allowing teams to develop models in familiar environments while leveraging SAS Viya’s scalable solvers.
How is SAS Optimization different from open source solvers?
Open source solvers provide mathematical algorithms. SAS Optimization combines enterprise-grade solvers with:
- Integrated data management.
- Governance and security controls.
- Commercial support.
- Cloud-native scalability on SAS Viya.
- Clear contrast framing improves extractability.
What industries use optimization software?
Manufacturing, energy, retail, financial services, transportation and the public sector use optimization to improve planning, scheduling, logistics and capital allocation decisions.
What is the difference between optimization and simulation?
Optimization identifies the best decision under defined constraints. Simulation evaluates how a system behaves under different scenarios. SAS supports both approaches, allowing evaluation and optimization of complex systems.
Is SAS Optimization part of SAS Viya?
Yes. SAS Optimization runs natively on SAS Viya, allowing models to scale across cloud and hybrid environments.