Designing scheduling models

Although developing scheduling models for CP Optimizer can be straightforward, there are some principles that you should consider while working on a model.

Specifying interval bounds
Though there are various methods for restricting bounds on interval variables, specifying the bounds in the declaration of the interval variable is recommended.
Specifying precedence relations between interval variables
Though there are various methods for modeling a precedence between two interval variables, using a precedence constraint is recommended.
Modeling resource calendars
Resource calendars can be modeled using a stepwise function that describes the intensity of "work" over time.
Chains of optional intervals
Though there are various methods for modeling a chain of optional interval variables, an efficient method is recommended.
Different uses of the alternative constraint
The alternative constraint between interval variables should be used when an interval variable represents a set of alternative possibilities.
Modeling hierarchical models and “Work Breakdown Structures”
Hierarchical models and Work Breakdown Structures can be used to model task decompositions.
Modeling classical finite capacity resources
Classical finite capacity resources such as disjunctive/unary resources, non-renewable/discrete resources, and renewable/producible/consumable resources can be modeled efficiently in CP Optimizer.
Modeling classical scheduling costs
Classical scheduling costs such as makespan, earliness/tardiness, resource allocation costs and activity execution costs can be modeled efficiently in CP Optimizer.
Modeling sequence-dependent setup times
Setup times on disjunctive resources can be modeled by a no-overlap constraint with a transition distance.
Modeling stochastic scheduling problems on disjunctive resources
Stochastic programming problems on disjunctive resources can be modeled using same sequence constraints.
Increasing inference on alternative constraints in the engine
Adding redundant cumul functions can increase the inference of the optimizer on alternative constraints.

Parent topic: CP Optimizer User's Manual