Hybrid Genetic Algorithm with Multiparents Crossover for Job Shop Scheduling Problems

Research Article
Hybrid Genetic Algorithm with Multiparents Crossover for
Job Shop Scheduling Problems
Noor Hasnah Moin, Ong Chung Sin, and Mohd Omar
Institute of Mathematical Sciences, Faculty of Science, University of Malaya, 50603 Kuala Lumpur, Malaysia
Correspondence should be addressed to Noor Hasnah Moin; noor hasnah@um.edu.my
Received 13 June 2014; Revised 8 September 2014; Accepted 8 September 2014
Academic Editor: Shifei Ding
Copyright © 2015 Noor Hasnah Moin et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
The job shop scheduling problem (JSSP) is one of the well-known hard combinatorial scheduling problems. This paper proposes a
hybrid genetic algorithm with multiparents crossover for JSSP. The multiparents crossover operator known as extended precedence
preservative crossover (EPPX) is able to recombine more than two parents to generate a single new offspring distinguished from
common crossover operators that recombine only two parents. This algorithm also embeds a schedule generation procedure to
generate full-active schedule that satisfies precedence constraints in order to reduce the search space. Once a schedule is obtained,
a neighborhood search is applied to exploit the search space for better solutions and to enhance the GA. This hybrid genetic
algorithm is simulated on a set of benchmarks from the literatures and the results are compared with other approaches to ensure
the sustainability of this algorithm in solving JSSP. The results suggest that the implementation of multiparents crossover produces
competitive results.
1. Introduction
The job shop scheduling problem (JSSP) is one of the well-
known hard combinatorial scheduling problems and it has
been studied extensively due its practical importance. During
the past three decades, optimization strategies for JSSP
ranging from exact algorithms (mathematical programming)
to approximation algorithms (metaheuristics) have been
proposed [1]. Exact methods which are based on exhaustive
enumeration and decomposition method guarantee global
convergence and have been successfully applied to small
instances. But, for the moderate and large scale instances,
they require very high computational time to be either
ineffective or inefficient [2]. Therefore, a lot of researchers
focused their attention on approximation methods. Meta-
heuristics is one of the approximation methods that were
proposed in the literatures to deal with JSSP which include
tabu search (TS) [3], simulated annealing (SA) [4], genetic
algorithm (GA) [5], and discrete artificial bee colony (DABC)
[6].
In recent years, since the first use of GA based algorithm
to solve the JSSP proposed by Davis [7], various GA strategies
are introduced to increase the efficiency of GA to find the
optimal or near optimal solutions for JSSP [8]. In the GA
strategies, hybridization of GA with local search methods
provides good results in solving the problems where GA
capitalizes on the strength of the local search in locating the
optimal or near optimal solutions. For example, Gonc¸alves
et al. [9] and Zhang et al. [10] embedded the local search
procedure of Nowicki and Smutnicki [11] into GA due
to the effectiveness of the local search that increases the
performance of GA. Qing-Dao-Er-Ji and Wang [12] proposed
new crossover operator and mutation operator together with
local search in improving local search ability of GA.
Additionally, the structure of the GA can be modified
and enhanced to reduce the problems often encountered
in GA. Yusof et al. [13] implemented a migration operator
in GA by using parallelization of GA (PGA) to find the
near optimal solutions. Watanabe et al. [14] proposed a GA
with search area adaption and a modified crossover operator
for adapting to the structure of the solutions space. Ripon
et al. [15] embedded heuristic method in the crossover to
reduce the tail redundancy of chromosome. Particularly,
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Volume 2015,Article ID 210680, 12 pages
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2 Mathematical Problems in Engineering
recombination operators, especially crossover operators, play
important roles in the structure of GA.
Crossover between two parents is traditionally adopted in
GA [8] for JSSP but the GA can be modified accordingly to
suit the problem at hand including selecting several numbers
of parents for the crossover operation which is known as
multiparents crossover.
The application of multiparents recombination can be
found in different research areas. M¨uhlenbein and Voigt [16]
proposed gene pool recombination (GPR) in solving discrete
domain problems. Eiben and van Kemenade [17] introduced
the diagonal crossover as the generalization of uniform
crossover in GA for numerical optimization problems. Wu
et al. [18] proposed multiparents orthogonal recombination
to determine the identity of an unknown image contour. The
crossover operators that were used in those areas show the
good search ability of the operator but are very much problem
dependent.
The above literatures indicated the ascendency of mul-
tiparents crossover over two parents’ crossover. Although
multiparents crossover has been used in different fields, to
the best of our knowledge, only limited numbers are applied
to combinatorial scheduling problems and none has been
proposed for JSSP. In particular, Eiben et al. [19] proposed
multiparent for the adjacency based crossover (ABC) and
Ting et al. [20] developed multiparent extension of partially
mapped crossover (MPPMX) for the travelling salesman
problems. Although the experimental results point out that
ABC of multiparents has no tangible benefit, MPPMX
shows significant improvement in the use of multiparents
in crossover. In other words, one would expect that, by
biasing the recombination operator, the performance of the
GA would improve.
In this paper, we propose extended precedence preser-
vative crossover (EPPX) as a multiparent crossover. EPPX
is based on the precedence preservative crossover (PPX)
proposed by Bierwirth et al. [21]. Because of its ability to pre-
serve the phenotypical properties of the schedules. EPPX as
crossover operator will retain this advantage in the GA. EPPX
is used in GA in conjunction with neighborhood search to
solve JSSP. The rest of the paper is organized as follows. JSSP
and the different types of schedules are described in detail
in the next section. In Section 3, we present our approach
to solve the JSSP: chromosome representation, schedule gen-
eration procedure, neighborhood search procedure, and GA
with multiparents crossover. Section 4 provides experimental
results and analysis. The conclusions are drawn in Section 5.
2. Problem Definition
2.1. Job Shop Scheduling Problem (JSSP). The JSSP can be
defined as a set of 𝑛 jobs that need to be processed on a set
of 𝑚 machines. A job consists of a set of operations 𝐽, where
𝑂𝑖𝑗 represents the 𝑗th (1 ≤ 𝑗 ≤ 𝐽) operation of the 𝑖th (1 ≤
𝑖 ≤ 𝑛) job. The technological requirement for each operation
processing time is denoted as 𝑝𝑖𝑗 and a set of machines is
denoted by 𝑀𝑘 (1 ≤ 𝑘 ≤ 𝑚).
Table 1: Example for 3-job and 3-machine problem.
Job
Operation routing
1 2 3
Processing time
1 3 3 2
2 1 5 3
3 3 2 3
Machine sequence
1 M1 M2 M3
2 M1 M3 M2
3 M2 M1 M3
Precedence constraint of the JSSP is defined as [22]
operation 𝑗th must finish before operation 𝑗th + 1 in the job.
A job can visit a machine once only. Only one operation at
a time for one time is allowed to be processed in a machine.
It is assumed that the delay time for the job transfer machine
will be neglected and operation allocation for machine will
be predefined. Preemption of operations is not allowed.
There are no precedence constraints among the operations of
different jobs.
The main objective of JSSP is to find the minimum
makespan for the scheduling. The finish time of job 𝑖 with
last operation, 𝐽, is represented by 𝐹𝑖𝐽. The time for the whole
schedule to complete or the makespan is also the maximum
finish time of a set of the jobs 𝑖. Therefore, the makespan is
expressed as follows:
Makespan = max (𝐹𝑖𝐽) . (1)
Let 𝐺(𝑘) be the set of operations being processed in
machine 𝑘 and let
𝑋 𝑂𝑖 𝑗,𝑘 = {
1 if 𝑂𝑖𝑗 has been assigned to machine 𝑘
0 otherwise.
(2)
The conceptual model of the JSSP is shown as below:
Minimize {max (𝐹𝑖𝐽)} (3)
𝐹𝑖𝑗 ≤ 𝐹𝑖𝑗+1 − 𝑝𝑖𝑗+1, 𝑗 = 1, 2, . . . 𝐽, ∀𝑖 (4)
∑
𝑂𝑖 𝑗 𝜖𝐺(𝑘)
𝑋 𝑂𝑖 𝑗,𝑘 ≤ 1, ∀𝑘.
(5)
The objective function represented by (3) minimizes the
maximum finish time in the set of the jobs 𝑖 and therefore
minimizes the makespan. Equation (4) satisfies precedence
relationships between operations and (5) imposes that an
operation can only be assigned to a machine at a time.
Table 1 shows an example of 3 jobs and 3 machines and
their sequences for JSSP.
2.2. Type of Schedules. Three types of feasible schedule are
considered: semiactive, active, and nondelay schedule [22].
In a semiactive schedule, there are no operations that can be
started earlier without altering the sequences of the opera-
tions. In a semiactive schedule the makespan may often be
reduced by shifting an operation to the left without delaying

Mathematical Problems in Engineering 3
Semiactive
Active
Nondelay
Optimal
Feasible
Full-active
Figure 1: Relationship of the schedules.
other jobs. Reassignment of operations is called a permissible
left shift. An active schedule is a schedule with no more
permissible left shifts. Therefore, the set of active schedules is
a subset of semiactive schedules. In a nondelay schedule, no
machine is kept idle at a time when it could begin processing
other operations and hence the set of nondelay schedules is a
subset of active schedules. In addition the search space can
be further reduced by implementing a full-active schedule
introduced by Zhang et al. [10]. A full-active schedule is
defined as a schedule where there is no more permissible left
or right shift. Optimal solution of the scheduling always lies
in the full-active schedule. Figure 1 illustrates the interaction
of the schedules.
In order to generate the full-active schedule, we employ a
scheduling approach called iterative forward-backward pass
[23] in Section 3.1.2 which performs a kind of local search that
can be used to introduce heuristic improvement into genetic
search.
3. Hybrid Genetic Algorithm
GA is a stochastic search optimization technique that mim-
ics the evolutionary processes in biological systems. This
approach begins with a population, which represents a set
of potential solutions in the search space. Each individual
in the population is assigned a value by GA according to
a problem specific objective function. The individuals will
attempt to combine the good features in each individual
in the population using a reproduction operator step such
as crossover or mutation in order to construct individuals
which are better suited than previous individuals. Through
this evolution process, individuals that are less fit tend to be
replaced by fitter individuals to generate a new population
which eventually the desired optimal solutions will be found.
3.1. Schedule Generator Procedure
3.1.1. Chromosome Representation and Decoding. Represen-
tation of JSSP in chromosome is classified by Cheng et al.
[22] into two approaches: direct and indirect. The direct
approach directly encodes and decodes the representation
into schedule while the indirect approach needs a sched-
ule builder to encode and decode the chromosome into
Permutation with
repetition
1 2 3 3 2 2 3 1 1
Machine 1
Machine 2
Machine 3
1 2 3
3 2 1
2 3 1
Figure 2: Permutation with repetition representation for 3 jobs and
3 machines.
the schedule. The indirect approach is adopted in this study
to represent the individual.
The chromosome in this paper is represented as operation
based representation. In this representation, 𝑖 represents the
number of jobs 𝑖 = 1, 2, 3 . . . , 𝑛 and is repeated according the
total number of operations in the job. Figure 2 illustrates the
representation of 3 jobs and 3 machines. The chromosome
is represented as [1 2 3 3 2 2 3 1 1], where jobs 1, 2, and 3
are represented as numbers 1, 2, and 3 in the chromosome,
respectively. The number of occurrences in each chromo-
some depends on the number of operations required. The
chromosome is scanned from left to right and at the 𝑗th
occurrence of a job number refers to the 𝑗th operation in the
technological sequence of this job. The chromosome created
is always feasible and legal.
A scheduling can be built by constructing a schedule
builder that performs a simple local search to decode the
genes of the chromosome from left to right to a list of ordered
operations. The first operation in the list is scheduled first,
then the second operation, and so on. The operation will
always be shifted to the left until time is equal to zero or
inserted into a blank time interval between operations to
find the earliest completion time. The process is repeated
until all operations are scheduled. A schedule generated by
the procedure can be guaranteed to be an active schedule
[22]. Figure 3(a) illustrates the scheduling encoded by fol-
lowing operation sequences in the chromosome [1 1 3 ⋅ ⋅ ⋅ ].
Applying the process will enable the job to find possible
earlier start time before being appended as last operation
in the machine (Figure 3(b)) and the chromosome encoded
is transformed to [1 3 1 ⋅ ⋅ ⋅ ]. In this representation, two
or more chromosomes decoded may be translated into an
identical schedule.
3.1.2. Schedule Generation Procedure. The procedure used
to construct full-active schedule is based on a schedul-
ing scheme called iterative forward-backward pass. This
approach was proposed by Lova et al. [23] which had
been shown to produce significant improvement in reducing
makespan in the other field of scheduling problems.
The algorithm described in Section 3.1.1 uses a forward
pass approach to generate an active schedule with the
makespan max(𝐹𝑖𝐽) in the schedule in which the operations
are able to shift left until time is equal to zero. Backward pass
is a reverse process of the forward pass where the operations

M1 J1
M2 J1 J3
M3
0 2 4 6 8 10
(a) Before left shifting
M1 J1
M2 J3 J1
M3
0 2 4 6 8 10
(b) After left shifting
Figure 3: Permissible left shift for semiactive schedule.
in a schedule start from end of the schedule and end at the
beginning of the schedule min(𝐹𝑖1), in which the operations
are able to shift right until time is equal to max(𝐹𝑖𝐽).
The iterative forward-backward pass approach can be
described with the following steps.
Step 1. An active schedule chromosome is generated by
forward pass with maximum makespan max(𝐹𝑖𝐽).
Step 2. Apply backward pass on the chromosome from Step 1.
Chromosome is scanned starting from right to left to generate
a schedule with start time max(𝐹𝑖𝐽) (Figure 4). Through the
right shifting in the schedule, we can obtain the beginning of
the schedule min(𝐹𝑖1) and the makespan of this schedule is
given as
𝐵𝐶max = max (𝐹𝑖𝐽) − min (𝐹𝑖1) . (6)
𝐵𝐶max and min(𝐹𝑖1) denote makespan and minimum time,
respectively, in this new schedule. The new schedule is
encoded into a new chromosome with makespan 𝐵𝐶max.
Step 3. If 𝐵𝐶max < max(𝐹𝑖𝐽), the makespan obtained from the
schedule in Step 2 is less than the makespan in the schedule of
Step 1, there is improvement of the schedule makespan, and
then the new chromosome is used in Step 1; otherwise the
active schedule chromosome and the makespan generated by
forward pass are maintained. Steps 1 and 2 are repeated until
there is no further improvement on the schedule (Figure 5).
In this iterative function, the makespan of both processes
is mutually restricted and hence the makespan of new
solution generated either is lesser or remains unchanged.
The last schedule generated by forward pass is equivalent to
the last schedule generated by backward pass with the same
makespan and they are sharing the same critical path.
Figure 6 shows the steps where each chromosome gener-
ated by the GA employed the schedule generator procedure.
The procedure starts from generating the full-active schedule
by schedule generation procedure and then applying the
neighborhood search to improve the schedule obtained. After
improvement of the schedule ends, the corresponding quality
of the makespan is obtained. The neighborhood search is
presented in the next section.
3.1.3. Neighborhood Search Procedure. Reduction of the
search space does not guarantee the optimal solution will
be found. Therefore we use a neighborhood search as an
exploitation mechanism to decrease the makespan. This
mechanism is restricted to searching the possible solutions in
a critical path that consists of longest sequences of operation
in a schedule. Swapping the operations on the path by using
neighborhood search significantly reduces the total length of
the makespan [11].
Instead of the swap operation which is determined
deterministically as in [11], we modify the operation such
that we chose the operations to be swapped randomly in
a critical block. The neighborhood search starts with the
identification of the critical path in the schedule generated
by the scheduling process. Operations on the critical path
are called critical operations. A critical block consists of a
maximal sequence of adjacent critical operations that are
processed on the same machine [11]. Our neighborhood is
defined as the random swap between two jobs in a critical
block that contains two or more operations. No swap is made
if the critical block contains only one operation.
All possible moves of the operations are predetermined
(Figure 7). A swap of the operations is accepted if it improves
the makespan; otherwise the operations remain unchanged.
Once the swap is accepted, the new critical path must be
identified. The procedure is repeated and stops if there is no
swap that can improve the makespan. Pseudocode for the
neighborhood search is presented in Algorithm 1.
3.2. Hybrid GA. In our proposed hybrid GA, the search
methods will be based on intensification and diversification
mechanisms. The neighborhood search acts as an intensi-
fication mechanism that exploits the better solution in an
individual and GA functions as diversification mechanism
that explores the search space to provide different individuals
for the local search. Structure of the hybrid GA that is based
on a standard GA may be represented by the pseudocode in
Algorithm 2.
Population is initialized randomly with a set of param-
eters and a particular chromosome which is often referred
to as an individual. Each individual is evaluated by a fitness
function and the parents selected for the recombination,
which favors fitter individuals. The selected chromosomes

M1 J1
M2 J3 J1
M3
0 Max (FiJ)
Chromosome = [. . . 3 1 1]
(a) Before backward pass
M1 J1
M2 J1 J3
M3
0 Max (FiJ)
Chromosome = [. . . 1 3 1]
(b) After backward pass
Figure 4: Backward pass.
while New solution accepted = true
New solutions accepted = false
Determine the critical path, critical block in New schedule
List out the possible swaps of the operations
for 𝑘 = 1 to total of possible swaps do
Swap a pair of operations
New schedule generated and makespan recalculated (New makespan)
if New makespan < Current makespan
Current makespan = New Makespan
New solution accepted = true
end if
end for
end while
Algorithm 1: Pseudocode for neighborhood search.
Forward pass
Backward pass
Improvement
Yes
Use chromosome
from forward pass
No
Use chromosome
from backward pass
Figure 5: Iterative forward-backward pass.
are recombined using mechanisms of crossover and mutation
to produce offspring. These offspring are then evaluated and
then elitism strategy is applied to the new offspring with best
fitness to replace the worst individuals in the previous popu-
lation to generate a new population. Termination criterions
of the GA in JSSP are set to terminate once the GA had
achieved the optimal solution (if have) or maximum number
of generations is reached.
3.2.1. Fitness and Selection Method. In this paper, we use
nonlinear ranking to rank the evaluated chromosomes and
each chromosome competes with the others and the selected
chromosome survives to the next generation based on the
fitness value (objective function). Chromosome with greater
fitness indicates the greater probability to survive. The highest
ranking chromosome in a population is considered the best
solution. It is noted that the lowest makespan is given the
highest ranking.
Stochastic universal sampling (SUS) is applied to select
the parents for recombination in our GA. SUS is one of the
selection methods that are often used in practice because
they have less stochastic noise and have a constant selection
pressure [24]. This fitness based proportionate selection will
choose the chromosomes process with minimum spread and
zero bias. SUS uses 𝑁 equally spaced pins on wheel, where
𝑁 is the number of selections required. The population is
shuffled randomly and a single random number in the range
[0−∑ 𝑓𝑖/𝑁] is generated in which ∑ 𝑓𝑖 represents the sum of
all the fitness values of the individuals in the population.
3.2.2. Proposed Extended Precedence Preservative Crossover
(EPPX). Generally in natural biological system the reproduc-
tion takes place between two parents (bisexual) or in a single

Evolutionaryprocessingeneticalgorithm
Chromosome
Generate full-active schedule using an iterative
forward-backward pass
Improve the schedule using a neighborhood
search procedure
Feedback of quality (makespan) of full-active
schedule chromosome
Schedule generation
procedure
Schedule improvement
Figure 6: Architecture of schedule generation procedure.
M1
M2
M3
0
Critical operations in critical path
22 242 4 6 8 10 12 14 32
J3
J3 J2
J3 J1 J2
16 18 20
J2 J1
J1
26 28 30
Figure 7: Critical path, critical operations, and possible operations swaps.
Initialize population
Evaluation
while termination criterion = false
Selection
Crossover
Mutation
Evaluation
Reinsertion
End while
Algorithm 2: Pseudocode for a standard GA.
parent (asexual). However from the computational perspec-
tive, there is no restriction on the number of parents to
use. Therefore some of the multiparents crossover operators
are extended from the two-parent crossover operators [17–
20] for recombination. As mentioned before, our proposed
EPPX is an extension of PPX. A crossover mask in the form
of a vector is generated randomly to determine in which
parent the genes, specified in the mask, are to be selected for
recombination. The multiparents will then recombine into a
single offspring (Figure 8(a)). Starting from the first element
on the mask vector, number 1 in the first element of the mask
vector indicates that the first gene in that parent 1 is selected.
In general, the mask vector indicates from which parent the
element is to be selected. In this example, the selected job
(job 3) is selected and eliminated in the other parents (Figures
8(b) and 8(c)). The second element in the mask indicates that
the first element (after deletion) is to be selected also from
parent 1 (Figure 8(c)). The third element in the mask shows
that the first element in parent 3 is selected (Figure 8(d)).
The process continues until all the elements in the mask
have been examined. Algorithm 3 outlines the algorithm for
multiparents crossover for JSSP.
3.2.3. Mutation. Mutation is a genetic operator, analogous to
the biological mutation, which is used to maintain genetic

Parent 1: 3 3 1 1 2 1 2 2 3
Parent 2: 3 2 2 1 1 1 3 3 2
Parent 3: 1 3 2 2 1 1 2 3 3
Vector: 1 1 3 2 3 3 1 1 2
Child: 3 3 1 2 2 1 1 2 3
(a) Example of EPPX
Parent 1: 3 3 1 1 2 1 2 2 3
Parent 2: 3 2 2 1 1 1 3 3 2
Parent 3: 1 3 2 2 1 21 3 3
Vector: 1 1 3 2 3 3 1 1 2
Child: 3
Vector number 1 = select first gene from parent 1
(b) First step—vector number 1; the first gene in
parent 1 is selected and the same job from the other
parents is removed
Parent 1: 3 3 1 1 2 1 2 2 3
Parent 2: 3 2 2 1 1 1 3 3 2
Parent 3: 1 3 2 2 1 1 2 3 3
Vector: 1 1 3 2 3 3 1 1 2
Child: 3 3
(c) Second step—previous selected gene will be
deleted, first gene (after deletion) in parent 1 is
selected, and the job from the other parents are
removed
Parent 1: 3 3 1 1 2 1 2 2 3
Parent 2: 3 2 2 1 1 1 3 3 2
Parent 3: 1 3 2 2 1 1 2 3 3
Vector: 1 1 3 2 3 3 1 1 2
Child: 3 3 1
(d) Repeat—the process will continue until at the
end of the vector
Figure 8: EPPX crossover.
Crossover vector generated randomly
Select three parents: → S1, S2, and S3
for 𝑘 = 1 to length of the chromosome do
Select vector number by position 𝑘th starting from the left element in the vector
case
Vector number 1:
Choose first gene at left most S1
Search same job number at left most in S2 and S3
Remove the first gene in S1 and gene searched in S2 and S3
Vector number 2:
Remove the first gene in S2 and gene searched in S1 and S3
Vector number 3:
Remove the first gene in 𝑆3 and gene searched in S1 and S2
end case
Selected gene insert to new chromosome by sequence from left to right
end for
Algorithm 3: Pseudocode for EPPX (3 jobs and 3 machines).
diversity from one generation of a population of chromo-
somes to the next. In this study, the mutation is applied by
selecting two genes in different positions and different jobs
inside the same chromosome to be swapped. The process will
be repeated if two genes selected are at the same position
or the same job. Figure 9 illustrates the swapping of the two
genes in the chromosome.
4. Experiment Setup and Results
4.1. Experimental Setup. To test the performance of our
hybrid GA, we consider the benchmark from four classes
of different JSSP test problems: instances FT06, FT10, and
FT20 from Fisher and Thompson [25], instances ORB01 to
ORB10 from Applegate and Cook [26], instances ABZ5 to

Before mutation
After mutation
x
x
y
y
Figure 9: Mutation by swapping two genes in the chromosome.
Table 2: Output for different crossover rates and mutation rates.
Mut 01 Mut 02 Mut 03 Mut 04 Mut 05 Mut 06 Mut 07 Mut 08 Mut 09 Mut 10 Average Relative error (%)
Crs 10 971.96 978.20 972.64 968.06 969.28 972.34 971.58 965.46 963.24 968.12 970.09 4.31
Crs 9 967.38 975.94 965.82 968.24 970.30 965.36 963.44 961.14 961.00 960.32 965.89 3.86
Crs 8 971.54 968.00 967.24 965.92 966.10 964.28 964.58 958.92 958.84 959.12 964.45 3.70
Crs 7 973.94 968.32 969.26 963.72 963.48 961.58 958.46 957.84 959.02 958.94 963.46 3.60
Crs 6 972.96 968.32 969.30 965.26 964.02 965.10 964.08 960.16 959.90 959.58 964.87 3.75
Crs 5 971.88 975.96 975.60 969.12 962.58 967.36 960.92 961.60 961.04 957.10 966.32 3.90
Crs 10 represents crossover rate at 1.0, Crs 9 represents crossover rate at 0.9, and so on.
Mut 01 represents mutation rate at 0.1, Mut 02 represents mutation rate at 0.2, and so on.
ABZ9 from Adams et al. [27], and instances YN1 to YN4 from
Yamada and Nakano [28]. The instances that we use were
downloaded from http://people.brunel.ac.uk, OR-Library of
Brunel University.
MATLAB 7.11 R2010b is used to develop the GA and
the simulations are run using workstation Intel Xeon 12 GB
RAM with 2.4 GHz processor. The population size comprises
100 individuals for problems FT06, FT10, ABZ5, ABZ6,
and ORB01–ORB10. In large sized problems (FT20, ABZ7–
ABZ9, and YN1–YN4), their population sizes are increased
to 150. The number of parents for EPPX ranges from 3 to
10. Crossover rate and mutation rate are set to 0.7 and 1.0,
respectively. After the recombination process, elitism strategy
is applied; 10% of the fittest new offspring replaces 10% of
the worst individuals in the previous population to generate a
new population. Each instance is run for 50 times for different
numbers of parents to find out their best solution available.
4.1.1. Maximum Number of Generations. In the multiparent
crossover, the parents will be recombined to generate one
child. Therefore, the relationship of different numbers of par-
ents with different total solutions (offspring) for a population
exists. Thus the total solutions are defined as
total solutions =
MAXGen × population size
number of parents
. (7)
The maximum number of generations is denoted as
MAXGen. To be fair, the total number of generations is
adjusted to make sure that different numbers of parents
for recombination generate approximately the same number
of total solutions by referring to (8). Total solutions for
instances FT06, FT10, ABZ5 and ABZ6, and ORB01–ORB10
after recombination process are set at around 5000 schedules.
In addition, problems FT20, ABZ7–ABZ9, and YN1–YN4
are set at around 10,000 schedules. Among the multipar-
ents crossover, three-parent crossover operator is used as
reference because we consider them as the starting point
of multiparents recombination (more than two parents).
Consider
MAXGen =
total solutions × number of parents
populations size
. (8)
4.1.2. Crossover Rate. In our cases, due to the different
problems sizes and different numbers of parents for recom-
bination, we need to fix crossover rate and mutation rate
to use them for all numbers of parents. Instance FT10 is
selected for the parameters testing because it is considered
as a difficult problem. Among the multiparents crossovers,
three-parent crossovers are used because we consider them as
the starting point of multiparents recombination (more than
two parents).
The dependencies between the crossover and mutation
rates are tested by the GA. The crossover rates are set from
1.0 to 0.5 with varied mutation rates from 0.1 to 1.0. Each case
(example: crossover rate = 1.0, mutation rate = 0.1) will be run
for 100 times and the average of each case will be figured out.
The relative errors are calculated by computing the difference
between the average solutions for each crossover rate and the
optimal solution of FT10 (930).
In Table 2, the relative error for crossover rate at 0.7
appears as the lowest value compared to the other crossover
rates. The frequencies of optimal solutions for FT10 at the
crossover rate 0.7 that obtain from the simulation had shown
the highest occurrences as indicated in Figure 10. Thus, it is
reasonable for us to use the crossover rate at 0.7 to compare
to other values.
4.1.3. Mutation Rate. In the multiparents crossover applica-
tion, especially in the JSSP, there is a lack of information about
the mutation rate values. Hence, we try to find the suitable
mutation rate for our GA. Due to the inconsistencies of the
results between the crossover rates and mutation rates we
obtained from the simulation, Figure 11 plotted the best fit
line for the problems. All lines for the different crossover

0
2
4
6
8
10
12
0.5 0.6 0.7 0.8 0.9 1
Numberofoptimalsolutions
Crossover rate
Frequent of optimal solutions
Figure 10: Frequency of optimal solutions appears (FT10) at differ-
ent crossover rates.
950.00
955.00
960.00
965.00
970.00
975.00
980.00
0 2 4 6 8 10 12
Averageforcrossoverrate
Mutation rate (divide by 10)
Crs 10
Crs 9
Crs 8
Crs 7
Crs 6
Crs 5
Linear (Crs 10)
Linear (Crs 9)
Linear (Crs 8)
Linear (Crs 7)
Linear (Crs 6)
Linear (Crs 5)
Figure 11: Best fit line for crossover with different mutation rates.
rates are decreasing from left to right which means that the
average solutions will get better when the mutation rates
increase. Thus, we conclude that the last mutation rate (1.0)
performs the best when applied in this GA and it will be used
as parameter for other instances and problems.
4.2. Results and Discussions
4.2.1. Comparison with the Other Permutation Crossover
Operator That uses Two Parents. Table 3 lists the GAs using
permutation crossover operator with various hybridization
or modification that had been applied on the FT problem.
The GAs in the list (except EPPX) use only two parents for
crossover operation and the results vary because of different
strategies of hybridization. Initially the earlier papers [21, 29]
was unable to obtain the optimal solutions for FT10 using
the proposed crossover operators. Then later by hybridization
or modification using this method, the optimal results of
FT10 can be reached, including our EPPX. EPPX is able to
perform well in the FT20 as compared to the GP-GA and
IPPX with the deviation of 1.12%. The acceptable ranges of
comparable GAs are up to 7% [21]. Tested results show that
EPPX is able to get the solution within these values and is
considered applicable for solving the JSSP. Instance FT06 can
be solved easily with the number of parents used for crossover
ranging from 3 to 10 parents. For difficult problems such
as FT10, EPPX also performs well because it is able to get
the optimum solution with the number of parents of 3 and
5 in crossover operation. As the variable of JSSP increases
(instance FT20), a deviation between optimal solution and
EPPX which obtains the best solution of 1178 by using 6
parents in EPPX’s crossover operation occurs.
4.2.2. Comparison with Different Algorithms. ORB problem
contains ten 10 × 10 instances. To illustrate the effectiveness,
the proposed multiparent crossover is compared with the
other algorithms such as synergistically combining small
and large neighborhood schemes (SLENP) [31], parallel
genetic algorithms (PGA) [13] with two-parent crossover, and
greedy randomized adaptive search procedure (GRASP) [32].
The relative errors (REs) are calculated based on deviation
between BKS and the best solution found (Best). In EPPX,
different numbers of parents are used for recombination. The
numbers of parents (MP) that achieve the best solutions are
recorded in the table.
Table 4 states the name, size (number of jobs 𝑥 number of
machines), the best known solutions (BKS), the best solutions
found (Best), multiparents that obtain the best solutions
(MP), and relative error (RE) for each test instance. The RE is
calculated from the gap between Best and BKS in percentage.
The effectiveness of the proposed algorithm is analyzed by
referring to the total relative error, total RE, in the last lane.
In the ORB problem, EPPX found the BKS in 5 instances. The
best solutions found are located in the different numbers of
parents for different instances. Total RE provided by EPPX
is lower than GRASP but higher than SLENP and PGA. This
shows that EPPX performs averagely among these methods.
ABZ problem contains five instances with the sizes 10×10
and 20 × 15. ABZ8 and ABZ9 and no optimal solutions
have been known. Table 5 shows the detailed results of
comparison for ORB instances. EPPX is able to perform
better as compared to PGA and GRASP with lower total RE.
YN problem consists of four instances with size of 20 ×
20 that are still open problems. EPPX is compared with
TSSA and PGA in Table 6 because algorithm GRASP did not
provide the results of each instance.
4.2.3. The Performance Evaluation of EPPX. FT, ORB, ABZ,
and YN problems are used to evaluate the performance of
our hybrid GA which incorporates EPPX. Tables 3–6 show
that, in 9 out of 22 cases, the EPPX has been able to find
the best known solution for the corresponding benchmark
instance. Table 4 shows that our results are competitive when
compared to PGA achieving the same solutions in 5 out
of 10 instances. However when the problem size increases
(Tables 5 and 6) the performances of our algorithm are better
with smaller relative error compared to PGA and GRASP.
The computational results indicated that EPPX is capable of

Table 3: Results for FT06, FT10, and FT20 with 𝑛 jobs × 𝑚 machines.
Author(s) Year Crossover operator FT06 (6 × 6) FT10 (10 × 10) FT20 (20 × 5)
Optimum 55 930 1165
Gen et al. [29] 1994 Partial schedule exchange crossover 55 962 1175
Bierwirth et al. [21] 1996 Generalized permutation GP-GA 55 936 1181
Park et al. [30] 2003 Parallel genetic algorithm PGA 55 930 1173
Ripon et al. [15] 2011 Improved precedence preservation crossover IPPX 55 930 1180
2014 Multiparents crossover EPPX 55 930 1178
Table 4: Results for ORB problems.
Instances Size BKS
EPPX SLENP PGA GRASP
Best RE MP Best RE Best RE Best RE
ORB01 10 × 10 1059 1077 1.70 9 1059 0.00 1060 0.09 1070 1.04
ORB02 10 × 10 888 889 0.11 3, 4, 6, 7, 9 888 0.00 889 0.11 889 0.11
ORB03 10 × 10 1005 1022 1.69 7 1005 0.00 1020 1.49 1021 1.59
ORB04 10 × 10 1005 1005 0.00 5, 9 1005 0.00 1005 0.00 1031 2.59
ORB05 10 × 10 887 890 0.34 5 887 0.00 889 0.23 891 0.45
ORB06 10 × 10 1010 1021 1.09 4 1010 0.00 1013 0.30 1013 0.30
ORB07 10 × 10 397 397 0.00 10 397 0.00 397 0.00 397 0.00
ORB08 10 × 10 899 899 0.00 4 899 0.00 899 0.00 909 1.11
ORB09 10 × 10 934 934 0.00 10 934 0.00 934 0.00 945 1.18
ORB10 10 × 10 944 944 0.00 3–7, 9 944 0.00 944 0.00 953 0.95
Total RE 4.93 0.00 2.22 9.32
Table 5: Results for ABZ problems.
Instances Size BKS
EPPX SLENP PGA GRASP
Best RE MP Best RE Best RE Best RE
ABZ5 10 × 10 1234 1234 0.00 3 1234 0.00 1236 0.16 1238 0.32
ABZ6 10 × 10 943 943 0.00 7, 8 943 0.00 943 0.00 947 0.42
ABZ7 20 × 15 656 683 4.12 4 662 0.91 685 4.42 723 10.21
ABZ8 20 × 15 665 701 5.41 3 668 0.45 704 5.86 729 9.62
ABZ9 20 × 15 678 712 5.01 4 688 1.47 723 6.64 758 11.80
Total RE 14.54 2.83 17.08 32.37
Table 6: Results for YN problems.
Instances Size BKS
EPPX SLENP PGA
Best RE MP Best RE Best RE
YN1 20 × 20 884 911 3.05 7 892 0.90 933 5.54
YN2 20 × 20 907 941 3.75 4 911 0.44 944 4.08
YN3 20 × 20 892 928 4.04 4 900 0.90 928 4.04
YN4 20 × 20 968 1011 4.44 6 982 1.45 1018 5.17
Total RE 15.28 3.69 18.82
adapting to more difficult and larger size problems in com-
parison with PGA that only uses two parents for crossover.
The best solutions found are located in the different numbers
of parents for different instances indicated in column MP in
all tables.
Table 7 displays the average computational times for
different numbers of parents for each instance. Optimal
solutions for easy problem (FT06) can be found in shorter
time while, as for harder problem, the algorithm needs more
time to find out the near optimal solutions. It can be seen
that our algorithms require on average less than 15 seconds
CPU time. The CPU times of EPPX, PGA, and GRASP are
calculated per iteration meanwhile SLENP is based on the
time per run.
The computational time for EPPX and PGA which are
based on genetic algorithm is large for harder problems.

Table 7: Average computational time for different number of parents.
Instances Size
Computational time (in second)
EPPX (number of parents)
SLENP PGA GRASP
3 4 5 6 7 8 9 10
FT 06 6 × 6 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.76 0.70
FT 10 10 × 10 0.93 0.72 0.57 0.46 0.41 0.37 0.33 0.30 9.22 1.54 2.90
FT 20 20 × 5 1.19 0.89 0.73 0.61 0.52 0.45 0.40 0.38 2.73 1.52 4.30
ORB01 10 × 10 1.02 0.78 0.64 0.52 0.45 0.38 0.37 0.33 5.96 1.56 2.90
ORB02 10 × 10 0.92 0.70 0.57 0.46 0.41 0.35 0.32 0.29 0.48 1.56 3.80
ORB03 10 × 10 1.07 0.82 0.66 0.55 0.48 0.40 0.38 0.34 7.42 1.56 3.10
ORB04 10 × 10 0.93 0.73 0.59 0.47 0.41 0.36 0.33 0.30 7.58 1.56 3.10
ORB05 10 × 10 0.98 0.81 0.60 0.49 0.43 0.37 0.34 0.30 12.09 1.56 2.80
ORB06 10 × 10 1.11 0.80 0.64 0.53 0.46 0.40 0.37 0.33 9.17 1.56 3.10
ORB07 10 × 10 0.90 0.70 0.55 0.46 0.40 0.35 0.32 0.29 0.28 1.56 3.20
ORB08 10 × 10 1.01 0.75 0.63 0.51 0.46 0.39 0.35 0.33 6.02 1.56 3.10
ORB09 10 × 10 0.92 0.72 0.57 0.47 0.41 0.36 0.33 0.30 0.51 1.56 3.10
ORB10 10 × 10 0.98 0.74 0.59 0.48 0.42 0.36 0.33 0.30 0.18 1.56 2.90
ABZ5 10 × 10 0.84 0.64 0.51 0.41 0.36 0.31 0.29 0.26 3.55 1.78 0.30
ABZ6 10 × 10 0.79 0.61 0.53 0.43 0.38 0.34 0.31 0.28 0.15 1.56 3.10
ABZ7 20 × 15 6.17 4.77 3.78 3.29 2.79 2.42 2.09 1.97 64.82 7.36 17.40
ABZ8 20 × 15 6.45 4.76 3.85 3.21 2.77 2.52 2.05 1.93 55.94 7.28 18.20
ABZ9 20 × 15 5.83 4.38 3.53 2.97 2.49 2.19 1.96 1.79 35.82 7.32 17.10
YN1 20 × 20 12.19 9.32 7.56 6.46 5.39 4.66 4.13 3.79 40.04 7.60 —
YN2 20 × 20 11.72 8.97 7.32 6.00 5.18 4.48 3.95 3.80 62.31 7.60 —
YN3 20 × 20 14.09 10.43 8.56 7.13 6.14 5.28 4.71 4.45 42.18 7.60 —
YN4 20 × 20 10.52 7.75 6.38 5.30 4.51 3.89 3.45 3.26 56.05 7.60 —
However the large computational time displayed by SLENP
and GRASP which are based on single solution is larger when
compared to EPPX and PGA. PGA is expected to perform
better in terms of computational time because the algorithm
is run in parallel. Based on the calculation per iteration, EPPX
is more efficient compared to PGA and GRASP because it
requires less time to do the calculation.
The highlighted time in the EPPX shows the computa-
tional time of the result achieved by different number of
parents. Multiparents are recombined to produce a single
child resulting in a decreasing rate of computational time
with the increasing number of parents. It is proven that
increasing the number of parents in the EPPX may reduce
the calculation time to obtain the optimal result in some
instances.
5. Conclusion
This paper presents a hybrid genetic algorithm with multipar-
ent crossover, EPPX, for the job shops scheduling problem.
The hybrid GA combines genetic algorithm with neighbor-
hood search in which GA explores the population while the
neighborhood search exploits the individual solution to find
better solution. The chromosome represented by operation
based representation is used to generate a full-active schedule
through iterative forward-backward pass which can further
reduce the search space.
In the experimental results, EPPX using multiparent is
able to get the solutions within the acceptable range of GA
values. Results show that the best solutions are obtained from
different numbers of parents for crossover; thus it is proven
that GA is not restricted to two-parent crossover in order to
find the best solution. The number of parents used in EPPX
and GA is dependent on the problem and it may be observed
that the best solutions for different instances are produced by
different numbers of parents.
In future works, suitable ranges for number of parents
for crossover are needed to determine and solve JSSP. It is
also anticipated that the efficiency of GA can be increased
by embedding other local searches into GA. Further research
is necessary to reduce the computational time as a way
to improve the efficiency of hybrid genetic algorithm with
multiparent crossover for JSSP.
Conflict of Interests
The authors declare that there is no conflict of interests
regarding the publication of this paper.
Acknowledgment
This work has been supported by University of Malaya
Research Grant Scheme UMRG-RG116-10AFR.

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