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Sequencing jobs on a bottleneck machine Description Sequencing jobs on a bottleneck machine: consecutive solving with 3 different objectives;
Source Files By clicking on a file name, a preview is opened at the bottom of this page.
Data Files b4seq_ka.mos
(!******************************************************
CP Example Problems
===================
file b4seq_ka.mos
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Sequencing jobs on a bottleneck machine
(See "Applications of optimization with Xpress-MP",
Section 7.4 Sequencing jobs on a bottleneck machine)
*** 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-4 Sequencing (CP)"
uses "kalis"
forward procedure print_sol
forward procedure print_sol3
declarations
NJ = 7 ! Number of jobs
JOBS=1..NJ
REL: array(JOBS) of integer ! Release dates of jobs
DUR: array(JOBS) of integer ! Durations of jobs
DUE: array(JOBS) of integer ! Due dates of jobs
rank: array(JOBS) of cpvar ! Number of job at position k
start: array(JOBS) of cpvar ! Start time of job at position k
dur: array(JOBS) of cpvar ! Duration of job at position k
comp: array(JOBS) of cpvar ! Completion time of job at position k
rel: array(JOBS) of cpvar ! Release date of job at position k
end-declarations
initializations from 'Data/b4seq.dat'
DUR REL DUE
end-initializations
MAXTIME:= max(j in JOBS) REL(j) + sum(j in JOBS) DUR(j)
MINDUR:= min(j in JOBS) DUR(j); MAXDUR:= max(j in JOBS) DUR(j)
MINREL:= min(j in JOBS) REL(j); MAXREL:= max(j in JOBS) REL(j)
forall(j in JOBS) do
1 <= rank(j); rank(j) <= NJ
0 <= start(j); start(j) <= MAXTIME
MINDUR <= dur(j); dur(j) <= MAXDUR
0 <= comp(j); comp(j) <= MAXTIME
MINREL <= rel(j); rel(j) <= MAXREL
end-do
! One posistion per job
all_different(rank)
! Duration of job at position k
forall(k in JOBS) dur(k) = element(DUR, rank(k))
! Release date of job at position k
forall(k in JOBS) rel(k) = element(REL, rank(k))
! Sequence of jobs
forall(k in 1..NJ-1) start(k+1) >= start(k) + dur(k)
! Start times
forall(k in JOBS) start(k) >= rel(k)
! Completion times
forall(k in JOBS) comp(k) = start(k) + dur(k)
! Set the branching strategy
cp_set_branching(split_domain(KALIS_SMALLEST_DOMAIN, KALIS_MIN_TO_MAX))
!**** Objective function 1: minimize latest completion time ****
if cp_minimize(comp(NJ)) then
print_sol
end-if
!**** Objective function 2: minimize average completion time ****
declarations
totComp: cpvar
end-declarations
totComp = sum(k in JOBS) comp(k)
if cp_minimize(totComp) then
print_sol
end-if
!**** Objective function 3: minimize total tardiness ****
declarations
late: array(JOBS) of cpvar ! Lateness of job at position k
due: array(JOBS) of cpvar ! Due date of job at position k
totLate: cpvar
end-declarations
MINDUE:= min(k in JOBS) DUE(k); MAXDUE:= max(k in JOBS) DUE(k)
forall(k in JOBS) do
MINDUE <= due(k); due(k) <= MAXDUE
0 <= late(k); late(k) <= MAXTIME
end-do
! Due date of job at position k
forall(k in JOBS) due(k) = element(DUE, rank(k))
! Late jobs: completion time exceeds the due date
forall(k in JOBS) late(k) >= comp(k) - due(k)
totLate = sum(k in JOBS) late(k)
if cp_minimize(totLate) then
writeln("Tardiness: ", getsol(totLate))
print_sol
print_sol3
end-if
!-----------------------------------------------------------------
! Solution printing
procedure print_sol
writeln("Completion time: ", getsol(comp(NJ)) ,
" average: ", getsol(sum(k in JOBS) comp(k)))
write("\t")
forall(k in JOBS) write(strfmt(getsol(rank(k)),4))
write("\nRel\t")
forall(k in JOBS) write(strfmt(getsol(rel(k)),4))
write("\nDur\t")
forall(k in JOBS) write(strfmt(getsol(dur(k)),4))
write("\nStart\t")
forall(k in JOBS) write(strfmt(getsol(start(k)),4))
write("\nEnd\t")
forall(k in JOBS) write(strfmt(getsol(comp(k)),4))
writeln
end-procedure
procedure print_sol3
write("Due\t")
forall(k in JOBS) write(strfmt(getsol(due(k)),4))
write("\nLate\t")
forall(k in JOBS) write(strfmt(getsol(late(k)),4))
writeln
end-procedure
end-model
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