ProcessScheduler

A a 100% pure python library to compute resource-constrained task schedules. It is distributed under the terms of the GNU General Public License v3 or later.

Contents:

• Introduction
  • What’s inside
  • Download/install
  • Development version
• Scheduling problem
  • Time slots as integers
  • Mapping integers to datetime objects
• Task
  • Common base Task model
  • FixedDurationTask class
  • ZeroDurationTask class
  • VariableDurationTask class
  • Advanced parameters
• Task Constraints
  • Single task temporal constraints
  • Two tasks temporal constraints
  • $n$ tasks temporal constraints
  • Advanced tasks constraints
  • Logical task constraints
• Resource
  • Worker
  • CumulativeWorker
  • Advanced parameters
• Resource assignment
  • Single resource assignment
  • Multiple resources assignment
  • Alternative resource assignment
• Resource Constraints
  • WorkLoad
  • ResourceUnavailable
  • DistinctWorkers
  • SameWorkers
  • ResourceTasksDistance
• Buffer
  • Buffer constraints
  • Example
• First order logic constraints
  • Logical operators
  • Logical Implication - Conditional expressions
• Indicator
  • Indicator class
  • Builtin indicators : Resource cost, utilization, number of tasks assigned
• Optimization
  • Objective
  • Builtin optimization objectives
  • Available solvers : incremental and optimize
  • Single objective optimization
  • Multiple objective optimization using the incremental solver (default)
  • Weighted objectives
  • Multiple objective optimization using the optimize solver (default)
  • Lexicon priority ('lex', default)
  • Lexicon priority ('box')
  • Pareto priority ('pareto')
• Problem solving
  • Define the solver
  • Solve
  • Find another solution
  • Run in debug mode
  • Render to a Gantt chart
• Use case: flowshop scheduling
  • Imports
  • Create the scheduling problem
  • Create the 3 machines M1, M2 and M3
  • Create jobs J1, J2, J3 and J4 - related tasks
  • Assign resources
  • Constraint: release dates
  • Constraints: precedences
  • Add a makespan objective
  • Solution, plot the schedule
• Use case: formula one pitstop
  • Imports
  • Create the scheduling problem
  • Create the 16 available resources
  • Create tasks and assign resources
  • Task precedences
  • First solution, plot the schedule
  • Second solution: add a makespan objective
  • Third solution: constraint workers
• Use case: software development
  • Step 1. Import the module
  • Step 2. Create the scheduling problem
  • Step 3. Create tasks instances
  • Step 4. Create tasks time constraints
  • Step 5. Create resources
  • Step 6. Assign resources to tasks
  • Step 7. Add a total cost indicator
  • Step 8. Solve and plot using plotly
• API documentation
  • Problem
  • Tasks
  • Resources
  • Constraints
  • Solver
  • Solution

Indices and tables

• Index

• Module Index

• Search Page