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Maximo Scheduler Optimization leverage IBM® ILOG® CPLEX® Optimization Studio (COS) and the CP Optimizer solver. IBM® ILOG® CPLEX® Optimization Studio uses decision optimization technology to optimize your business decisions, develop and deploy optimization models quickly, and create real-world applications that can significantly improve business outcomes. https://www.ibm.com/products/ilog-cplex-optimization-studio IBM ILOG CPLEX Optimization Studio is a prescriptive analytics solution that enables rapid development and deployment of decision optimization models using mathematical and constraint programming. 2 solvers are included: CPLEX Optimizer for mathematical programming (linear, mixed-integer and quadratic programming), addressing problems like long term production/maintenance planning e.g. assigning work to monthly time buckets https://www.ibm.com/analytics/cplex-optimizer CP Optimizer for constraint programming and constraint-based models, addressing efficiently scheduling problem e.g. deciding start and end time (at minute level for workorders) https://www.ibm.com/analytics/cplex-cp-optimizer Maximo Scheduler Optimization uses CP Optimizer for the out of the box scheduling optimization models: GS (Graphical Scheduling), GA (Graphical Assignment) , GSLP (Graphical Scheduling Large Project). CP Optimizer Provides modeling features specialized to scheduling like intervals decision variables (for activities) and cumul functions (for finite capacity resources) Support business goals by optimizing earliness and tardiness costs, duration costs Model the work breakdown structure of the schedule, task dependencies, setup/travel times… CP Optimizer benefits includes a compact and maintainable formulations for complex scheduling problems, a highly optimized solution search: a continuously improving automatic search algorithm that is complete, anytime, efficient and scalable
Decision Optimization Java models can now be deployed in Watson Machine Learning. By using the Java worker API, you can create optimization models with OPL, CPLEX, and CP Optimizer Java APIs. You can now easily create your models locally, package them and deploy them on Watson Machine Learning by using the boilerplate that is provided in the public Java worker GitHub . For more information, see Deploying Java models for Decision Optimization in Cloud Pak for Data as a Service, or Deploying Java models for Decision Optimization in watsonx.ai