Radosław Rudek, Agnieszka
Wielgus
Metaheuristic algorithms for
scheduling on parallel machines with
variable setup times
Ekonomiczne Problemy Usług nr 122, 369-377
2016
IS S N : 1 8 9 6 -3 8 2 X | w w w .w n u s .e d u .p l/p l/e d u / DOI: 10.18276/epu.2016.122-35 | strony: 371-379
RADOSŁAW RUDEK
W r o c ł a w U n i v e r s i t y o f E c o n o m i c s
AGNIESZKA WIELGUS
W r o c ł a w U n i v e r s i t y o f T e c h n o l o g y
METAHEURISTIC ALGORITHMS FOR SCHEDULING ON PARALLEL MACHINES
WITH VARIABLE SETUP TIMES
Abstract
I n o r d e r to m e e t g r o w in g d e m a n d s o f th e m a r k e t m o d e r n m a n u f a c tu r in g a n d s e r v ic e e n v ir o n m e n t s m u s t o f f e r a n in c r e a s i n g ly b r o a d r a n g e o f s e r v ic e s o r p r o d u c t s a s w e ll a s e n s u r e t h e i r r e q u i r e d a m o u n t a n d s h o r t le a d tim e s . I t c a n b e d o n e b y th e a p p l i c a t i o n o f u n iv e r s a l m a c h i n e s o r w o r k e r s w h i c h a r e a b le to p e r f o r m d if f e r e n t ta s k s . O n th e o th e r h a n d , h u m a n a c tiv ity e n v ir o n m e n t s a r e o f t e n a f f e c te d b y le a r n in g . T h e r e f o r e , in th is p a p e r , w e a n a ly s e r e l a te d p r o b le m s , w h i c h c a n b e e x p r e s s e d a s th e m a k e s p a n m i n i m i z a ti o n s c h e d u l in g p r o b l e m o n id e n t ic a l p a r a l l e l m a c h i n e s w i t h v a r i a b le s e t u p ti m e s a f f e c te d b y l e a r n in g o f w o r k e r s . T o p r o v i d e a n e f f ic i e n t s c h e d u le , w e p r o p o s e m e t a h e u r is tic a lg o r ith m s . T h e i r p o te n t ia l a p p li c a b il it y is v e r i f i e d n u m e r ic a lly .Keywords:
s c h e d u lin g , p a r a l le l m a c h in e s , s e tu p tim e s , le a r n in g .Introduction
M o d e r n m a n u f a c t u r i n g a n d s e r v i c e e n v i r o n m e n t s t o m e e t g r o w i n g d e m a n d s o f t h e m a r k e t m u s t o f f e r a n i n c r e a s i n g l y b r o a d r a n g e o f s e r v i c e s o r p r o d u c t s , e n s u r e t h e i r r e q u i r e d a m o u n t a n d s h o r t l e a d t i m e s . H o w e v e r , t h i s c a u s e s s i g n i f i c a n t i n c r e a s e i n t h e c o m p l e x i t y o f t h e m a n a g e m e n t p r o c e s s ( P i n e d o , 2 0 1 2 ) . I t l e a d s to a r e d u c t i o n i n t h e e f f i c i e n c y o f a v a i l a b l e r e s o u r c e s u t i l i z a t i o n ( w o r k e r s , m a c h i n e s , e tc .) . T h e r e f o r e , t h e i n c r e a s e o f m a n u f a c t u r i n g / s e r v i c e o r g a n i z a t i o n c o m p e t i t i v e n e s s is a l s o a s s o c i a t e d w i t h t h e u s e o f e f f i c i e n t m e t h o d s s u p p o r t i n g t h e m a n a g e m e n t , t h a t370
Metaheuristic algorithms for scheduling on parallel machines...
w i l l b e a b l e t o m e e t t h e s e c h a n g e s . T h i s is e s p e c i a l l y e v i d e n t i n p r o j e c t m a n a g e m e n t a n d p r o d u c t i o n , w h e r e a p l a n o r a s c h e d u l e is d e t e r m i n e d s u c h t h a t i t o p t i m i z e s s o m e g i v e n c r i t e r i o n ( A l l a h v e r d i , 2 0 1 5 ; B i s k u p , 2 0 0 8 ; P i n e d o , 2 0 1 2 ) . O f t e n t h e r e is a n e e d t o p r o v i d e m u l t i p l e p r o d u c t s o r s e r v i c e s o n c o m m o n r e s o u r c e s s u c h a s u n i v e r s a l C N C m a c h i n e s o r w o r k e r s b e i n g a b l e t o p e r f o r m d i f f e r e n t j o b s . T h u s , s u c h e n v i r o n m e n t s r e q u i r e s e t u p s r e l a t e d w i t h p r e p a r a t i o n s o f u n i v e r s a l m a c h i n e s t o p r o c e s s d i f f e r e n t p r o d u c t s ( e . g . , t o o l c h a n g e s , p r o g r a m m i n g , e t c . ) o r r e a r r a n g e m e n t s o f w o r k i n g p l a c e s . D u r i n g t h e a n a l y s i s o f m o d e l s o f s c h e d u l i n g p r o b l e m s , i t c a n b e o b s e r v e d t h a t t h e y s i m u l t a n e o u s l y e v o l v e t o b e t t e r d e s c r i b e t h e p h e n o m e n a e x i s t i n g i n r e a l - l i f e . I n r e c e n t y e a r s s o m e a d d i t i o n a l f a c t o r s a f f e c t i n g s e t u p t i m e s / c o s t s h a v e b e e n t a k e n i n t o a c c o u n t , s u c h a s l e a r n i n g o f a h u m a n w o r k e r ( K u o , H s u , a n d Y a n g , 2 0 1 1 ) . N a m e l y , t y p i c a l h u m a n a c t i v i t y e n v i r o n m e n t s a s w e l l a s a u t o m a t i z e d m a n u f a c t u r i n g r e q u i r e h u m a n s u p p o r t f o r m a c h i n e s , w h i c h is n e e d e d d u r i n g a c t i v i t i e s s u c h a s o p e r a t i n g , c o n t r o l l i n g , s e t u p , c l e a n i n g , m a i n t a i n i n g , f a i l u r e r e m o v a l , e tc . U s u a l l y , h u m a n s i n c r e a s e t h e i r e f f i c i e n c y w i t h t h e n u m b e r o f r e p e t i t i o n s o f t h e s a m e o r s i m i l a r a c t i v i t i e s ( B i s k u p , 2 0 0 8 ) . I t c a n r e s u l t i n d e c r e a s i n g o f p r o c e s s i n g t i m e s o f s e t u p s ( A l l a h v e r d i , 2 0 1 5 ) . T h i s p h e n o m e n a o f l e a r n i n g c a n b e i l l u s t r a t e d b y F i g u r e 1. F ig . 1. S e tu p p ro c e s s in g tim e s d e p e n d in g o n th e n u m b e r o f p e rf o rm e d s e tu p s (u n its) S o u rce: F o llo w in g (B is k u p 2 0 0 8 ). T h e r e f o r e , i n t h i s p a p e r , w e w i l l a n a l y s e s u c h p r o b l e m s t h a t c a n b e e x p r e s s e d a s s c h e d u l i n g o n p a r a l l e l m a c h i n e s ( e . g . , h u m a n w o r k e r s o r C N C m a c h i n e s ) w i t h s e t u p t i m e s o f m a c h i n e s u n d e r m i n i m i z a t i o n o f t h e m a x i m u m c o m p l e t i o n t i m e ( m a k e s p a n ) . A d d i t i o n a l l y , w e w i l l t a k e i n t o c o n s i d e r a t i o n t h a t s e t u p t i m e s c a n b e a f f e c t e d b y l e a r n i n g o f h u m a n w o r k e r s . T o s o l v e t h e c o n s i d e r e d p r o b l e m , w e w i l l p r o p o s e m e t a h e u r i s t i c a l g o r i t h m s t h a t a r e b a s e d o n a s i m u l a t e d a n n l e a l i n g t e c h n i q u e .The remainder o f this paper is organized as follows. The problem is form u
lated in the next section, whereas its solution algorithms are given subsequently,
followed by their numerical analysis. The last section concludes the paper.
1. Problem formulation
There is given a set
J= {1,...
,n }o f
njobs (tasks) to be completed by a set
M= {Mi,..,
M m}o f
midentical machines (e.g., workers or CNC machines). It is
assumed that jobs and machines are available at time zero. We assume that job
preemption is not allowed and no precedence constraints between jobs exist. M ore
over, each job is characterized by is processing time
pj.Following practical cases, jobs can belong to different families. It means that
if they are from the same family, they require the same sets o f production facilities
(e.g., tools, working place arrangement, etc.). However, a rearranging (setup) of
a machine (preparation o f tools, working place) is needed whenever there occurs
a switching between jobs belonging to different families, which on the other hand is
related with additional time. Let
F=
{ I f:
f= 1, ...,
b }be a set o f
bfamilies. Each job
je
Jbelongs to only one family
I fe F. Namely, if job
jbelongs to family
If,then it
is denoted by
gj= f Thereby, if job
kis processed ju st before jo b
jon the same m a
chine
M iand
kbelongs to a different family than j , i.e.,
g k 4 gj,then a setup o f m a
chine
M iis required. In other words, a preparation o f a machine is needed to start
job j , where
g j= fIt is related with a setup time
Sfrelated with family
If.If job
jis
performed as the first job on machine
M i, then a setup related with its family is
needed. In this paper, we consider setups that are sequence independent, thereby
a setup o f a family does not depend on the previously performed family (Allah
verdi, 2015).
In many real-life cases, setup times are not constant values, but they can vary
due to the learning effect. It means that preparing a machine (or working place) to
process jobs belonging to the same family is less time consuming if such prepara
tions were done before. Following (Biskup, 2008), the setup time o f family
Sf(v;) is
described by a non-decreasing function dependent on the num ber
v io f setups re
lated with family
I fperformed on machine (by worker)
M i(or by workers related
with this machine) as follows:
s f ( v i
) = Sf V “ >
(1)
where
Sfis a normal setup time related with family
If, if the setup is performed for
the first time. The parameter
ais a learning index (a slope o f the learning curve)
that depends on the learning rate
L R (a= log2LR), which is defined as rate o f each
redoubling the output
L R=
Sf(2vi)/sf
(vi).For the popular 80% hypothesis the learn
ing index is calculated as follows
a= log2LR = -0.322 (Biskup, 2008).
372
Metaheuristic algorithms for scheduling on parallel machines...
Let us now formulate the considered problem. A t first, the schedule o f jobs
can
be
unambiguously
defined
by
their
permutations
(sequences)
n= {^i, ...,
n u...,
Km],where
x,denotes the permutation o f jobs on machine
M iand
^i(k) is the index o f the kth job in permutation
n,for
i= 1,...,m. Moreover,
n iis the
number o f jobs assigned to machine (worker)
M i.On this basis, the completion time
o f job
x ,(k ),i.e., scheduled as the kth in permutation
xon machine
M i,can be de
fined as follows:
C (i)
( k )
_
с
(i ) ( k - 1 ) +p
щ (k )+ s
щ (k-1) ,щ ( k У i 5v a
(
2
)
where C
S
o) = 0 (for
i= 1 ..., m) and
s Mt ( k - I Xk,(
k)
I S^i ( k ), g Ki (k ) ^ g Ki ( k-1) .
= < is
[0,
otherwise
equal to the setup time
s n(k) o f job
k,(k) if the previous job
k,(k-1) belongs to
a different family, otherwise
sn(^k_Y)„(k) = 0 if both jobs belong to the same family.
Note that
s Mi (k) =
s fif
g Mi(
k) =
fand s o
^
.(i) =
s„t
(i) for
i= 1, ..., m . Recall that
the parameter v
iis the number o f previous setups on machine
M ,.The objective is to find such a schedule o fjo b s
%that minimizes the maximum
completion time among all machines (makespan):
C m axO ) = . m a x { C £ (n ) } .
i=1,...,m
z
(3)
Thereby the problem can be formally defined as
TV* =
a rg m in {C max( 0 } ,
(4)
where n is the set o f all possible schedules.
For convenience the problem will be used according to the standard three field
notation scheme as follows Pm|SLE|Cmax (i.e., Pm - parallel identical machines,
SLE - Setup Learning Effect, Cmax - the maximum completion time criterion,
called the makespan).
2. Algorithms
The considered problem Pm|SLE|Cmax is at least NP-hard, since its classical
version without setup times is at NP-hard (Pinedo, 2012). To solve it, we will pro
pose some metaheuristic algorithms based on simulated annealing (Kirkpatrick,
Gelatt, and Vecchi, 1983; Rudek, 2013).
In both algorithms, we use a representation o f a solution, where a set o f indi
ces is used {{1,..., n}, {n+1, ..., n+m-1}} such that indices {1,...,n} refer to jobs,
whereas {n+1,..., n+m-1} are markers to separate jobs on particular machines. It is
illustrated in the following example.
E x a m p le 1
Given
n= 6 jobs and
m= 3 machines. On this basis, the following set is con
structed {{1, ...,6},{7, 8}}, where {1,...,6} are indices o f jobs and {7,8} are used to
separate them on particular machines. Therefore, a representation o f a schedule
%= (1, 2, 3, 7, 4, 8, 5, 6), which is equivalent to (1, 2, 3, 8, 4, 7, 5, 6), refers to the
following schedules o fjo b s on machines
= (1, 2, 3),
%2= (4),
n 3= (5, 6).
The primary simulated annealing (SA) algorithm (e.g., Rudek, 2013) starts
with an initial solution rcmit. In each iteration o f the algorithm, a new solution
n newis
obtained on the basis o f an old solution rcold. It is done by the interchanging o f two
randomly chosen jobs. This new solution is accepted with the probability
m a x
j
l, e x p j^ - Cmax
^ Cmax
^n °id)
jj
,
(5)
where
T = T /(1+ X )is a temperature parameter, which decreases in each iterations.
The param eter
Xis calculated as follows:
T - T
Д J 0 J N
^ T T 5
0 N(
6
)
where
T 0is an initial value o f T, and TN is its final value, whereas
Nis the assumed
number o f iterations o f SA. The value o f
TNis close to zero and the initial value of
T0is calculated according to Algorithm 1.
Finally, if a new solution rc
newis better than the best already found ft
*, i.e.,
^
m a x) > C
m a x n e w)
’ th en ft= ft
n ™.
374
Metaheuristic algorithms for scheduling on parallel machines...
In this paper, we also extend the classical SA by a variable neighbourhood
search (denoted by SAV), where swap and insert moves are applied. Its formal
description is given as follows.
Initial solution
Kinit o f SA and SAV is provided by a list scheduling algorithm
(LSA) that on the basis o f a list (with randomly sequenced jobs) assigns jobs to the
first available machine.
3. Numerical analysis
In this section, we will analyse numerically the algorithms described in the
previous section.1 During simulations, the following problem sizes were considered
«g
{10, 100,500} and m
e{2, 5, 10}.
1
All of them were coded in C++ and simulations were run on PC,
CPU Intel® Core™ i7-2600K 3.40 GHz and 8 GB RAM.
T a b le 1
M e a n a n d m a x im u m re la tiv e e rr o rs o f th e a n a ly s e d a lg o r ith m s (r u n n in g tim e s 5 0 0 m s )
n m b sj L S A SA S A V
m ean m ax m ean m ax m ean m ax
10 2 [1,2] [1, 5] 17.87 2 9 .60
0.00
0.00
0.00
0.00
[1,10] 25.63 48.010.00
0.00
0.00
0.00
[1,20] 37.35 99.150.00
0.00
0.00
0.00
[1,5] [1, 5] 12.57 28.410.00
0.00
0.00
0.00
[1,10] 28 .4 6 52.730.00
0.00
0.00
0.00
[1,20] 34.69 54.930.00
0.00
0.00
0.00
100 2 [1,10] [1, 5] 16.69 2 1 .86 0.47 2.320.10
0.82
[1,10] 29 .1 2 36.67 1.04 3.300.16
1.45
[1,20] 58.97 78.211.13
3.37
1.38 6.39 [1,50] [1, 5] 11.84 16.150.19
0.77
0.21 0.83 [1,10] 21 .3 0 30.69 0.24 1.910.13
0.67
[1,20] 37.53 4 3 .50 0.44 1.570.24
1.10
5 [1,10] [1, 5] 20 .4 8 2 4 .900.19
0.84
0.45 2.54 [1,10] 34.38 51.090.23
1.03
0.68 2 .3 7 [1,20] 71.04 90.69 1.55 7.301.38
3.94
[1,50] [1, 5] 14.26 2 0 .22 0.34 2.590.32
1.12
[1,10] 28 .5 9 35.64 0.75 2.040.54
1.89
[1,20] 45 .6 6 62.76 0.76 2.650.37
2.13
10 [1,10] [1, 5] 2 1 .9 7 2 8 .58 0.71 2.700.53
2.24
[1,10] 35.62 54.20 0.51 1.740.30
1.68
[1,20] 67.40 101.41 0.98 4.760.60
4.00
[1,50] [1, 5] 17.22 30.23 0.33 1.340.30
1.18
[1,10] 2 7 .8 7 34.180.19
1.64
1.27 2.73 [1,20] 51.88 68 .3 7 2.25 4.590.30
1.66
500 2 [1,50] [1, 5] 13.10 16.49 0.78 1.550.00
0.02
[1,10] 24 .4 9 30.22 1.25 2.780.11
0.57
[1,20] 4 4 .0 7 55.66 0.75 2.840.30
1.57
[1,250] [1, 5] 9.23 10.80 0.23 0.840.05
0.29
[1,10] 15.33 18.33 0.56 1.410.11
0.65
[1,20] 28.35 34.50 0.99 3.080.21
1.49
5 [1,50] [1, 5] 14.58 18.890.07
0.56
0.82 2.68 [1,10] 26 .0 8 33.400.10
0.47
0.63 1.89 [1,20] 43.65 54.360.27
1.80
1.55 3.94 [1,250] [1, 5] 9.55 12.490.07
0.41
0.61 1.53 [1,10] 16.63 2 2 .0 70.32
1.49
0.54 1.60 [1,20] 32.01 38.180.22
1.15
1.31 5.23 10 [1,50] [1, 5] 16.83 2 1 .28 0.52 4.020.47
1.22
[1,10] 25.13 37.190.44
2.80
1.10 5.47 [1,20] 49.15 59.82 1.56 5.100.88
4.14
[1,250] [1, 5] 10.64 14.270.16
0.46
0.46 1.76 [1,10] 18.68 2 7 .960.56
2.45
0.67 2.82 [1,20] 33.99 4 3 .660.41
2.66
1.15 2.75 S o u rce: o w n w o rk .376
Metaheuristic algorithms for scheduling on parallel machines...
For each pair o f
nand m, 100 different random instances were generated from
the uniform distribution over the integers in the following ranges o f parameters: the
processing times
p je {1,..., 10}; the setup times
SjG{1,...,5}, {1,...,10}, {1,..., 20};
the num ber o f families
be {0.1n, 0.5n}; the learning index
a= -0.322 that refers to
a popular learning rate 80% (Biskup, 2008). The parameters o f simulated annealing
(SA and SAV) are chosen empirically as follows:
I te r M a x= 1 0 ,
TN= 0.0001,
N= 280000n"0 7 and the stop condition is set to be 500 ms.
Similarly as in (Rudek, 2013), the algorithms are evaluated according to the
relative error £ calculated as follows:
S A
(I
) =
C
m a x(
1
) ~
C
m a x(
1
) x 100%,
C x(
I
)
(7)
where Cmax(
I) is the criterion value obtained by algorithm
A e{SA, SAV, TS} for
instance
Iand
C
max(
I
) is the optimal (
n
= 10) or best found criterion value for
instance
I. The mean and maximum relative errors o f the algorithms are given in
Table 1.
It can be seen in Table 1 that SAV provided similar results as SA. Moreover,
the algorithms SA and SAV provided optimal solutions for all instances
n= 10, and
they are significantly better than LSA. It is worth mentioning that we have im ple
mented and analysed different tabu search algorithms (based on insert and swap
moves). However, they were overwhelmed by SAV and duo to page limit their
descriptions as well as numerical analysis were omitted in this paper.
Conclusions
In this paper, we expressed some problems as the makespan minimization
scheduling problem on identical parallel machines with non-increasing setup times
dependent on the number o f previous setups. W e also proposed metaheuristic algo
rithms based on simulated annealing. The numerical analysis showed that the algo
rithms are efficient to solve the problem.
Our future work will focus on the analysis of parallel scheduling problems
under other objectives as well as the construction o f other metaheuristic algorithms.
Acknowledgement
The research presented in this paper has been partially supported by the Polish
National Science Centre under grant no. DEC-2012/05/D/HS4/01129 (algorithms)
and by the Polish Ministry o f Science and Higher Education under Iuventus Plus
Programme (No. IP2014 040673) (models/analysis).
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6 . R u d e k , R . ( 2 0 1 3 ) . O n s in g le p r o c e s s o r s c h e d u l in g p r o b l e m s w i t h l e a r n in g d e p e n d e n t o n th e n u m b e r o f p r o c e s s e d jo b s . A p p lie d M a th e m a tic a l M o d e llin g, 3 7 , p p .
1 5 2 3 - 1 5 3 6 .
ALGORYTMY METAHEURYSTYCZNE DLA PROBLEMU
HARMONOGRAMOWANIA ZADAŃ NA IDENTYCZNYCH MASZYNACH
RÓWNOLEGŁYCH ZE ZMIENNYMI CZASAMI PRZEZBROJEŃ
Streszczenie
W s p ó ł c z e s n e p r z e d s ię b i o r s t w a p r z e m y s ło w e i u s ł u g o w e a b y s p r o s ta ć r o s n ą c y m w y m a g a n io m r y n k o w y m , m u s z ą o f e r o w a ć c o r a z s z e r s z y a s o r ty m e n t o f e r o w a n y c h u s ł u g lu b p r o d u k tó w , a t a k ż e z a p e w n ić i c h w y m a g a n ą ilo ś ć i s z y b k o ś ć r e a liz a c ji. M o ż n a te g o d o k o n a ć p o p r z e z z a s t o s o w a n ie u n iw e r s a ln y c h m a s z y n lu b p r a c o w n ik ó w , k tó r z y p o tr a f ią r e a liz o w a ć r ó ż n e z a d a n ia . Z d r u g ie j s tr o n y , is tn i e n ie c z y n n ik a lu d z k ie g o p o w o d u je w y s tę p o w a n ie e f e k t u u c z e n ia . W z w ią z k u z t y m w n in ie js z e j p r a c y a n a li z o w a n y j e s t p o w ią z a n y p r o b l e m h a r m o n o g r a m o w a n i a n a i d e n t y c z n y c h m a s z y n a c h r ó w n o le g ł y c h p r z y k r y t e r i u m m i n im a li z a c ji d łu g o ś c i u s z e r e g o w a n i a o r a z p r z y z m i e n n y c h c z a s a c h p r z e z b r o j e ń w y n ik a ją c y c h z e f e k t u u c z e n i a p r a c o w n ik ó w . W c e l u o p r a c o w a n ia e f e k ty w n e g o h a r m o n o g r a m u z a p r o p o n o w a n o a lg o r y tm y m e t a h e u r y s ty c z n e . Z a k r e s i c h z a s t o s o w a n ia z o s t a ł z w e r y f i k o w a n y w o p a r c i u o a n a liz ę n u m e r y c z n ą .