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Mining and process industries

Description
Problem name and type, featuresDifficulty
A‑1 Production of alloys: Blending problem *
formulation of blending constraints; data with numerical indices, solution printout, if-then, getsol
A‑2 Animal food production: Blending problem *
formulation of blending constraints; data with string indices, as, formatted solution printout, use of getsol with linear expressions, strfmt
A‑3 Refinery : Blending problem **
formulation of blending constraints; sparse data with string indices, dynamic initialization, dynamic arrays, finalize, create, union of sets
A‑4 Cane sugar production : Minimum cost flow (in a bipartite graph) *
ceil, is_binary
A‑5 Opencast mining: Minimum cost flow **
encoding of arcs, solving LP-relaxation only
A‑6 Production of electricity: Dispatch problem **
inline if, is_integer


Further explanation of this example: 'Applications of optimization with Xpress-MP', Chapter 6: Mining and process industries (blending problems)

mosel_app_1.zip[download all files]

Source Files

Data Files





a2food.mos

(!******************************************************
   Mosel Example Problems
   ======================
    
   file a2food.mos
   ```````````````
   Food production for farm animals

   (c) 2008 Fair Isaac Corporation
       author: S. Heipcke, Feb. 2002
*******************************************************!)

model "A-2 Animal Food Production"
 uses "mmxprs"
 
 declarations
  FOOD = 1..2                    ! Food types
  COMP = 1..3                    ! Nutritional components
  RAW  = {"oat", "maize", "molasses"}   ! Raw materials
  
  P: array(RAW,COMP) of real     ! Composition of raw materials (in percent)
  REQ: array(COMP) of real       ! Nutritional requirements
  AVAIL: array(RAW) of real      ! Raw material availabilities
  COST: array(RAW) of real       ! Raw material prices
  PCOST: array(set of string) of real  ! Cost of processing operations
  DEM: array(FOOD) of real       ! Demands for food types

  use: array(RAW,FOOD) of mpvar  ! Quantity of raw mat. used for a food type
  produce: array(FOOD) of mpvar  ! Quantity of food produced
 end-declarations

 initializations from 'a2food.dat'
  P REQ PCOST DEM
  [AVAIL, COST] as 'RAWMAT'
 end-initializations
 
! Objective function
 Cost:= sum(r in RAW,f in FOOD) COST(r)*use(r,f) +
        sum(r in RAW,f in FOOD|r<>"molasses") PCOST("grinding")*use(r,f) +
        sum(r in RAW,f in FOOD) PCOST("blending")*use(r,f) +
        sum(r in RAW) PCOST("granulating")*use(r,1) +
        sum(r in RAW) PCOST("sieving")*use(r,2)

! Quantity of food produced corresponds to raw material used
 forall(f in FOOD) sum(r in RAW) use(r,f) = produce(f)

! Fulfill nutritional requirements
 forall(f in FOOD,c in 1..2) 
  sum(r in RAW) P(r,c)*use(r,f) >= REQ(c)*produce(f)
 forall(f in FOOD) sum(r in RAW) P(r,3)*use(r,f) <= REQ(3)*produce(f)

! Use raw materials within their limit of availability
 forall(r in RAW) sum(f in FOOD) use(r,f) <= AVAIL(r)

! Satisfy demands
 forall(f in FOOD) produce(f) >= DEM(f)

! Solve the problem
 minimize(Cost)

! Solution printing
 writeln("Total cost: ", getobjval)
 write("Food type"); forall(r in RAW) write(strfmt(r,9))
 writeln("  protein  lipid   fiber")
 forall(f in FOOD) do
  write(strfmt(f,-9))
  forall(r in RAW) write(strfmt(getsol(use(r,f)),9,2))     
  forall(c in COMP) write("   ", 
   strfmt(getsol(sum(r in RAW) P(r,c)*use(r,f))/getsol(produce(f)),3,2),"%")
  writeln
 end-do
 
end-model

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