Class Model

OptSeq model class.

• Attbibutes:
  • activities: Dictionary that maps activity names to activity objects in the model.
  • modes: Dictionary that maps mode names to mode objects in the model.
  • resources: Dictionary that maps resource names to resource objects in the model.
  • temporals: Dictionary that maps pairs of activity names to temporal constraint objects in the model.
  • Params: Object including all the parameters of the model.
  • act: List of all the activity objects in the model.
  • res: List of all the resource objects in the model.
  • tempo: List of all the tamporal constraint objects in the model.

Overrides: object.__init__

str(x)

Overrides: object.__str__

(inherited documentation)

Add an activity to the model.

• Arguments:
  • name: Name for new activity. A string object except "source" and "sink." Remark that strings in OptSeq are restricted to a-z, A-Z, 0-9,[],_ and @.
  • duedate(optional): Duedate of activity. A non-nagative integer or string "inf."
  • weight(optional): Panalty of one unit of tardiness. Positive integer.
• Return value: New activity object.
• Example usage:

  >>> a = model.addActivity("act1")

  >>> a = model.addActivity(name="act1",duedate=20,weight=100)

  >>> a = model.addActivity("act1",20,100)

Add a resource to the model.

• Arguments:
  • name: Name for new resource. Remark that strings in OptSeq are restricted to a-z, A-Z, 0-9,[],_ and @.
  • capacity (optional): Capacity dictionary of the renewable (standard) resource.
  • Capacity dictionary maps intervals (pairs of start time and finish time) to amounts of capacity.
  • rhs (optional): Right-hand-side constant of nonrenewable resource constraint.
  • direction (optional): Rirection (or sense) of nonrenewable resource constraint; "<=" (default) or ">=" or "=".
• Return value: New resource object.
• Example usage:

  >>> r=model.addResource("res1")

  >>> r=model.addResource("res1", {(0,10):1,(12,100):2} )

  >>> r=model.addResource("res2",rhs=10,direction=">=")

Add a temporal constraint to the model.

A temporal constraint has the following form:

   predecessor's completion (start) time +delay <=
                   successor's start (completion) time.

Parameter "delay" can be negative.

• Arguments:
  • pred: Predecessor (an activity object) or string "source." Here, "source" specifies a dummy activity that precedes all other activities and starts at time 0.
  • succ: Successor (an activity object) or string "source." Here, "source" specifies a dummy activity that precedes all other activities and starts at time 0.
  • tempType (optional): String that differentiates the temporal type. "CS" (default)=Completion-Start, "SS"=Start-Start, "SC"= Start-Completion, "CC"=Completion-Completion.
  • delay (optional): Time lag between the completion (start) times of two activities.
• Return value: New temporal object.
• Example usage:

  >>> t=model.addTemporal(act1,act2)

  >>> t=model.addTemporal(act1,act2,type="SS",delay=-10)

  To specify the start time of activity act is exactly 50, we use two temporal constraints:

  >>> t=model.addTemporal("source",act,type="SS",delay=50)

  >>> t=model.addTemporal(act,"source",type="SS",delay=50)

Optimize the model using optseq.exe in the same directory.

• Example usage:

  >>> model.optimize()

Output the gantt's chart as a text file.

• Argument:
  • filename: Output file name. Default="optseq.txt."
• Example usage:

  >>> model.write("sample.txt")