Overview
IBM ILOG CPLEX CP Optimizer uses constraint programming technology to solve detailed scheduling problems and other combinatorial optimization problems not easily solved using mathematical programming technology.
IBM ILOG CPLEX CP Optimizer handles the complexity of real-world scheduling problems for personnel, machines or process steps. IBM ILOG CPLEX CP Optimizer constraint programming solver enables model-and-run problem solving with robust constraint propagation and search algorithms.
Support business goals by optimizing earliness and tardiness costs, duration costs and non-execution costs
Model the work breakdown structure of the schedule, task dependencies as well as multiple production modes
Model finite capacity resources and reservoirs
Model setup times to compute schedules that define the best possible sizes for batches
Find optimized solutions to combinatorial optimization problems affecting your operations
IBM ILOG CPLEX CP Optimizer is a component of IBM ILOG CPLEX Optimization Studio, which combines and simplifies IBM’s product offerings for optimization model development, solving, and deployment. It offers in a single package, all the functionality that was previously available among an array of product and component configurations, making all tools and technologies available during prototyping and development.
IBM ILOG CPLEX Optimizer provides a complimentary optimization technology based on mathematical programming that provides flexible, high-performance solving linear programming, quadratic programming, quadratically constrained programming and mixed integer programming problems.
Latest version: IBM ILOG CPLEX Optimization Studio 12.4 offers improved access to predictive analytics tools with the connector to IBM SPSS Modeler. The connector enables CPLEX Optimization Studio to read data directly from SPSS Modeler streams and to visualize SPSS streams in CPLEX Optimization Studio's integrated development environment. This makes an integrated modeling environment for prescriptive and predictive analytics available to professionals using multiple advanced techniques. The CPLEX Optimizers offer significant performance gains both for mathematical- and constraint-programming. Additional enhancements include computation of dual values for second-order cone constraints in quadratic models, used especially in finance applications, and deterministic parallel search for constraint programming models, used especially in detailed scheduling. A new component, IBM ILOG CPLEX Enterprise Server, provides the capability to deploy OPL projects in enterprise environments using a client-server architecture, separating the computationally intensive algorithms of the CPLEX Optimizers onto dedicated hardware to alleviate the burden on more routine data handling and user interface tasks on database servers and desktop clients.
IBM ILOG CPLEX CP Optimizer Benefits
Constraint programming is invaluable when dealing with the complexity of many real-world sequencing and scheduling problems. Whether you are scheduling people, machines or process steps, you need constraint programming when there are too many operating constraints and individual business rules for solutions based on linear algebra.
IBM's constraint programming technology systematically eliminates possibilities in order to reduce the size of the "search space," rapidly identifying feasible solutions that can then be optimized. You can model your real scheduling and sequencing problems instead of simplifying them for an mathematical programming model.
IBM ILOG CPLEX CP Optimizer Features
IBM ILOG CPLEX CP Optimizer has many advanced features to help you save time and increase efficiency.
Modeling features for detailed scheduling problems
Optional tasks:
For modeling activities or processes that may or may not be executed in the final schedule
Tasks can be grouped to match the work breakdown structure of a problem
Precedence constraints:
Can model dependencies between tasks
Can include a delay
Can be applied to group of intervals
Expression on interval properties:
Express typical scheduling costs such as tardiness costs, completion costs or total duration
The presence status of an optional interval can be used to express completion costs or resource costs
Can be applied to group of intervals
Finite capacity reservoir and resources:
Specify limits on the number of tasks that can be performed in parallel with a common resources
Set constraints on inventory levels
Set-up times and batches
Calendars:
State that some tasks cannot start or end on some dates
State that some tasks that cannot overlap some dates
Define resource breaks
State that resource productivity changes over time
Resource states
Modeling features for discrete combinatorial optimization problems
Arithmetic linear and non-linear constraints
Logical constraints
Specialized constraints and expressions:
The all-different constraint: enforces uniqueness for each variable in an array
The pack constraint: packs items into containers with finite capacity in one dimension (time, weight, budget etc.)
The lexicographic constraint: enforces a lexical ordering between groups of decision variables and is convenient to break symmetries
The count expression
The standard deviation expression
Compatibility and incompatibility constraints:
Define possible assignments for arrays of decision variables. They can be used, for instance, to model allowed transitions in a sequencing problem.
Optimization engine features
Solves a large range of problems with default settings
Tested on an extensive library of models
A tunable search engine
A fast feasible solution generator, for use in multi-model architectures such as column generation
