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Construction of a stadium: project planning

Description
  • Project planning problem modeled with linear constraints, demonstrating the effect of constraint propagation (b1stadium_ka.mos).
  • Alternative formulation as scheduling problem with precedence constraints, modeled with task objects (b1stadium2_ka.mos).
Further explanation of this example: 'Xpress Kalis User Guide', Section 5.2 Precedences

b1stadiumka.zip[download all files]

Source Files

Data Files





b1stadium2_ka.mos

(!****************************************************************
   Mosel Example Problems
   ======================
   
   file b1stadium2_ka.mos
   ``````````````````````
   Construction of a stadium
   (See "Applications of optimization with Xpress-MP",
        Section 7.1 Construction of a stadium)
   - Alternative formulation using tasks -

   *** This model cannot be run with a Community Licence 
       for the provided data instance ***

   (c) 2008 Artelys S.A. and Fair Isaac Corporation
       
*****************************************************************!)

model "B-1 Stadium construction (CP)"
 uses "kalis"
 
 declarations
  N = 19                              ! Number of tasks in the project
                                      ! (last = fictitious end task)
  TASKS = 1..N
  ARC: dynamic array(range,range) of integer  ! Matrix of the adjacency graph
  DUR: array(TASKS) of integer        ! Duration of tasks
  HORIZON : integer                   ! Time horizon

  task: array(TASKS) of cptask        ! Tasks to be planned
  bestend: integer
 end-declarations

 initializations from 'Data/b1stadium.dat'
  DUR ARC
 end-initializations

 HORIZON:= sum(j in TASKS) DUR(j)

! Setting up the tasks
 forall(j in TASKS) do
  setdomain(getstart(task(j)), 0, HORIZON)   ! Time horizon for start times
  set_task_attributes(task(j), DUR(j))       ! Duration
  setsuccessors(task(j), union(i in TASKS | exists(ARC(j,i))) {task(i)})
 end-do                                      ! Precedences
          
 if not cp_propagate or not cp_shave then
  writeln("Problem is infeasible")
  exit(1)
 end-if 

! Since there are no side-constraints, the earliest possible completion
! time is the earliest start of the fictitious task N
 bestend:= getlb(getstart(task(N)))
 getstart(task(N)) <= bestend
 writeln("Earliest possible completion time: ", bestend)

! For tasks on the critical path the start/completion times have been fixed
! by setting the bound on the last task. For all other tasks the range of
! possible start/completion times gets displayed.
 forall(j in TASKS) writeln(j, ": ", getstart(task(j)))

! Complete enumeration: schedule every task at the earliest possible date
 if cp_minimize(getstart(task(N))) then
  writeln("Solution after optimization: ")
  forall(j in TASKS) writeln(j, ": ", getsol(getstart(task(j))))
 end-if
end-model

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