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

   file sludge2.mos
   ````````````````
   Sludge problem illustrating recursion
   -- Formulation as NLP problem --
   
     Example discussed in section 11.2.1 of
     J. Kallrath: Business Optimization Using Mathematical Programming -
     An Introduction with Case Studies and Solutions in Various Algebraic 
     Modeling Languages. 2nd edition, Springer Nature, Cham, 2021 

   author: S. Heipcke, June 2018, rev. June 2023

   (c) Copyright 2020 Fair Isaac Corporation
  
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.
    You may obtain a copy of the License at
 
       http://www.apache.org/licenses/LICENSE-2.0
 
    Unless required by applicable law or agreed to in writing, software
    distributed under the License is distributed on an "AS IS" BASIS,
    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    See the License for the specific language governing permissions and
    limitations under the License.

*********************************************************************!)

model 'sludge2'
  uses "mmxnlp"

  declarations
    I= 2                        ! Number of sources
    J= 2                        ! Number of processing areas
    K= 3                        ! Number of destinations (end points)
    C= 2                        ! Number of components
    RI=1..I
    RJ=1..J
    RK=1..K
    RC=1..C

    LAM: array(RC,RJ) of real   ! Assumed fraction of c at j (initial guess)
    COST: array(RJ,RK) of real  ! Per tonne disposal cost from j to k
    MAXD: array(RC,RK) of real  ! Maximum tonnes disposal of c at k
    FRACT: array(RC,RI) of real ! Fraction of c in material from i
    AVAIL: array(RI) of real    ! Availability at i
  end-declarations

  AVAIL::[100, 150]
  FRACT::[0.1, 0.2,
          0.2, 0.15]
  COST::[2, 3, 1,
         1, 5, 0.5]
  MAXD::[20.0, 20.0, 10.0,
         15.0, 15.0, 15.0]
  LAM::[0.20, 0.15,             ! Starting guesses for LAM(c,j)
        0.12, 0.18]

  declarations
    x: array(RI,RJ) of mpvar    ! Amount (tonnes) sent from i to j
    y: array(RJ,RK) of mpvar    ! Amount (tonnes) sent from j to k
    t: array(RJ) of mpvar       ! Throughput at j (tonnes)
    q: array(RC,RJ) of mpvar    ! Quantity of component c passing through j
    lam: array(RC,RJ) of mpvar  ! Concentration of component c passing through j
  end-declarations

  ! Disposal cost
  Cost:=sum(j in RJ,k in RK) COST(j,k)*y(j,k)

  ! Get rid of all sludge at i
  forall(i in RI)
    Arid(i):= sum(j in RJ) x(i,j) = AVAIL(i)

  ! Mass balance into j
  forall(j in RJ)
    Tpx(j):= sum(i in RI) x(i,j) = t(j)

  ! Mass balance out of j
  forall(j in RJ)
    Tpy(j):= sum(k in RK) y(j,k) = t(j)

  ! Define quantities
  forall(c in RC,j in RJ)
    Q(c,j):= q(c,j) = sum(i in RI) FRACT(c,i)*x(i,j)

  ! Limits on disposal: not too much c at k
  forall(c in RC,k in RK)
    Mx(c,k):= sum(j in RJ) lam(c,j)*y(j,k)<= MAXD(c,k)

  ! Throughput quantities
  forall(c in RC,j in RJ) t(j)*lam(c,j) = q(c,j)

  ! Start values
  forall(c in RC,j in RJ) setinitval(lam(c,j), LAM(c,j))
  
  ! In this example we will use a local solver, since it can be time consuming to solve it to global optimality
  setparam("xprs_nlpsolver", 1)

  ! Solve the problem
  setparam("XNLP_verbose",true)
  minimise(Cost)
  writeln("Solution: ", getobjval)
  forall(i in RI,j in RJ) write(" x(", i, ",", j, "):", x(i,j).sol)
  writeln
  forall(j in RJ,k in RK) write(" y(", j, ",", k, "):", y(j,k).sol)
  writeln
  forall(c in RC,j in RJ) write(" lam(", c, ",", j, "):", lam(c,j).sol)
  writeln

end-model