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Branch-and-Price for the Generalized Assignment Problem Description The model implements a branch-and-price algorithm that
solves a disaggregated formulation of
the Generalized Assignment Problem (GAP) where columns
represent feasible assignments of batches
to machines. Column generation is applied at every node of the
branch-and-bound tree. The branching algorithm is completely
implemented in Mosel, and the optimizer is used only to solve
the LP relaxation at each node. The model implementation shows the following features of Mosel:
Source Files By clicking on a file name, a preview is opened at the bottom of this page.
Data Files genGapDataD.mos (!************************************************************ Mosel Example Problems ====================== file genGapDataD.mos ```````````````````` Generate random instances of the Generalized Assignment Problem (GAP) of type D (c) 2008 Fair Isaac Corporation author: Hernan Wurgaft, 2007 **************************************************************!) model GenGAPData uses "mmsystem" parameters NM = 5 ! Number of machines NP = 30 ! Number of production batches NI = 6 ! Number of problem instances end-parameters declarations DATAFILE: string RM = 1..NM RP = 1..NP DUR: array(RM,RP) of integer PROFIT: array(RM,RP) of integer CAP: array(RM) of integer end-declarations setrandseed(666) forall(inst in 1..NI) do ! Generate data DATAFILE:= string(text("Dtestx")+text(NM)+"_"+text(NP)+"_"+text(inst)+".dat") forall(i in RM) do tot:=0 forall(j in RP) do DUR(i,j):= integer(round((100*random)+0.5)) ! Random integer in [1,100] tot+=DUR(i,j) e1:= integer(round((20*random)))-10 ! Random integer in [-10,10] PROFIT(i,j):= 111-DUR(i,j)+e1 end-do CAP(i):=integer(round(.8*tot/NM)) end-do maxprofit:=0 forall(i in RM,j in RP) do if PROFIT(i,j)>maxprofit then maxprofit:=PROFIT(i,j) end-if end-do maxprofit+=1 forall(i in RM,j in RP) PROFIT(i,j):=maxprofit-PROFIT(i,j) ! Write data to file initializations to DATAFILE NM NP DUR CAP PROFIT end-initializations end-do end-model | |||||||||||||||
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