IBM ILOG CPLEX CP Optimizer

最適化されたスケジュールの計算

IBM ILOG CP Optimizer は、制約プログラミングを使用して、数理計画法の手法では簡単に解決できない詳細スケジューリングの問題や組み合わせの問題を解決します。

制約プログラミングは、人員、機械、またはプロセスのステップで実際に発生する複雑なスケジューリングの問題を扱います。IBM の第 2 世代制約プログラミング・ソルバーである IBM ILOG CP Optimizer を使用すると、堅固な制約伝搬と検索アルゴリズムによって問題解決をモデル化して実行できます。

• 早期コストと遅延コスト、持続期間コスト、および非実行コストを最適化することによって、ビジネス目標をサポートします。
• スケジュールの作業明細構造、作業の依存関係、および複数の実動モードをモデル化します。
• 有限キャパシティーのリソースとタンクをモデル化します。
• セットアップ時間をモデル化して、バッチの最適なサイズを定義するスケジュールを計算します。
• 運用に影響を及ぼす組み合わせの最適化の問題に対する最適解を求めます。

特長とメリットを表示する (英文)

サポートされるオペレーティング・システム (英文)

製品について

• 製品情報を調べる

ご購入 IBM ILOG CPLEX CP Optimizer

詳細の表示詳細の非表示

• 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.

• 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

• Can model dependencies between tasks

• Can include a delay

• Can be applied to group of intervals

• 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

• Specify limits on the number of tasks that can be performed in parallel with a common resources

• Set constraints on inventory levels

• 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

• 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

• Define possible assignments for arrays of decision variables. They can be used, for instance, to model allowed transitions in a sequencing problem.

• Intro to CP

• CP vs. MP

• Practical Scheduling Examples

• Detailed Scheduling

• IBM ILOG CPLEX CP Optimizer Spec Sheet (PDF, 221KB)

• Learn more

• Library

• Discussion Forums

• Academic Initiative

• Partners

• DeveloperWorks