IBM ILOG CPLEX CP Optimizer

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 IBMs 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 12.3 offers access to predictive analytics tools by providing a connector to IBM SPSS Modeler. The ability to execute Modeler streams directly from CPLEX Studio makes an integrated modeling environment for prescriptive and predictive analytics available to professionals using multiple advanced techniques. CPLEX Studio also extends its reach to developers using Linux on Intel compatible processors by making available the IDE on this platform. The CPLEX Optimizers offer significant performance gains both for mathematical- and constraint-programming. Additional enhancements include support for solving very large models (> 2 billion non-zero elements), capabilities to solve quadratic programs with non-convex objective functions, and capabilities to solve scheduling problems with non-convex objectives and constraint programs with multiple objective functions.

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

 

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