(!******************************************************
   Mosel Example Problems
   ====================== 

   file capbgt.mos
   ```````````````
   TYPE:         Capital budgeting
   DIFFICULTY:   1
   FEATURES:     simple MIP problem
   DESCRIPTION:  Among 8 projects under consideration we wish 
                 to choose the most profitable ones. Each project 
                 requires a capital investment and a commitment of 
                 skilled personnel. The (discounted) return of each 
                 project is known. The available capital and the 
                 number of skilled personnel are limited. 
   FURTHER INFO: Similar problem:
                 `Applications of optimization with Xpress-MP', 
                 Section 13.6 `Choice of expansion projects'.
 
    (c) 2001 Dash Associates 
       authors: Y. Colombani & S. Heipcke
*******************************************************!)

model Capbgt
 uses "mmxprs"                   ! Use Xpress-Optimizer

 declarations
  RPROJ = 1..8                   ! Range of possible projects
  CAPAVL = 478                   ! Availability of capital
  PERAVL = 106                   ! Availability of skilled personnel
  CAP: array(RPROJ) of real      ! Capital required for project p
  PER: array(RPROJ) of real      ! Personnel required for project p
  RET: array(RPROJ) of real      ! Return from project p
  x: array(RPROJ) of mpvar       ! Variables indicating whether a project 
                                 ! is chosen  
 end-declarations

!  PROJ   1    2    3    4    5    6    7    8
 CAP := [104,  53,  29, 187,  98,  32,  75, 200]
 PER := [ 22,  14,   7,  36,  24,  10,  20,  41]
 RET := [124,  75,  42, 188, 108,  56,  88, 225]
 
! Objective: maximize the return
 MaxReturn:= sum(i in RPROJ) RET(i)*x(i)  

! Limit on capital used by all projects
 LimCap:= sum(i in RPROJ) CAP(i)*x(i) <= CAPAVL

! Limit on personnel used by all projects
 LimPers:= sum(i in RPROJ) PER(i)*x(i) <= PERAVL

! Variables are 0/1
 forall(i in RPROJ) x(i) is_binary
   
! Solve the problem
 maximize(MaxReturn)                

! Solution printing
 writeln("Solution:\n Objective: ", getobjval)
 forall(i in RPROJ)  write(" x(", i, "): ", getsol(x(i)))
 writeln
 
end-model