An energy-efficient flow shop scheduling using hybrid Harris hawks optimization

Bulletin of Electrical Engineering and Informatics
Vol. 10, No. 3, June 2021, pp. 1154~1163
ISSN: 2302-9285, DOI: 10.11591/eei.v10i3.2958  1154
Journal homepage: http://beei.org
An energy-efficient flow shop scheduling using hybrid Harris
hawks optimization
Dana Marsetiya Utama1
, Dian Setiya Widodo2
1
Department of Industrial Engineering, University of Muhammadiyah Malang, Indonesia
2
Department of Manufacturing Technology, Vocational Faculty, University of 17 Agustus 1945 Surabaya, Indonesia
Article Info ABSTRACT
Article history:
Received Jul 22, 2020
Revised Oct 12, 2020
Accepted Mar 17, 2021
The energy crisis has become an environmental problem, and this has
received much attention from researchers. The manufacturing sector is the
most significant contributor to energy consumption in the world. One of the
significant efforts made in the manufacturing industry to reduce energy
consumption is through proper scheduling. Energy-efficient scheduling
(EES) is a problem in scheduling to reduce energy consumption. One of the
EES problems is in a flow shop scheduling problem (FSSP). This article
intends to develop a new approach to solving an EES in the FSSP problem.
Hybrid Harris hawks optimization (hybrid HHO) algorithm is offered to
resolve the EES issue on FSSP by considering the sequence-dependent setup.
Swap and flip procedures are suggested to improve HHO performance.
Furthermore, several procedures were used as a comparison to assess hybrid
HHO performance. Ten tests were exercised to exhibit the hybrid HHO
accomplishment. Based on numerical experimental results, hybrid HHO can
solve EES problems. Furthermore, HHO was proven more competitive than
other algorithms.
Keywords:
Energy-efficient scheduling
Flow shop
Harris hawks
Metaheuristic
This is an open-access article under the CC BY-SA license.
Corresponding Author:
Dana Marsetiya Utama
Department of Industrial Engineering
University of Muhammadiyah Malang
Jl. Raya Tlogomas 246 Malang, 65144, Indonesia
Email: dana@umm.ac.id
1. INTRODUCTION
Recently, an environmental problem has received much attention from researchers. One of those
problems is the energy crisis. The consumption of non-renewable energy causes the depletion of reserves of
energy resources. At present, the energy supply is dominated by fossil fuels [1]. The total world energy use
half is consumed in the industrial sector [2]. The industry of manufacturing is the biggest consumer of fuel
energy. Energy consumption is dominated by non-renewable energy, which causes climate change.
Therefore, to reduce energy consumption, manufacturing companies also must involve [2]. Increased energy
consumption and energy crisis encourage manufacturing companies to minimize energy consumption during
the production process [3]. Generally, during the manufacturing process, energy consumption is large. Idle
time consumes most of the energy in the production process [4]. Therefore, proper scheduling is proposed to
solve this problem to reduce energy consumption. Researchers call this problem energy-efficient scheduling
(EES) [5]. EES is a scheduling problem that focuses on energy-efficient in the manufacturing process [6].
EES has received attention in recent years because it can reduce energy consumption without investment
costs [7].

Bulletin of Electr Eng & Inf ISSN: 2302-9285 
An energy-efficient flow shop scheduling using hybrid Harris hawks optimization (Dana Marsetiya Utama)
1155
Flow shop scheduling problem (FSSP) is one of the exciting issues in the EES. This problem has an
n-job that is done at the m-stage in the same order [8]. Garey, et al. [9] claimed that the FSSP problem could
not be solved in polynomial time if the number of machines was more than 2. Therefore, FSSP is categorized
as the NP-Hard problem [9, 10]. Several sophisticated algorithms have been proposed to resolve EES on
FSSP. Prior study shows that the heuristic and metaheuristic procedure has attracted many researchers [11].
Some of these algorithms include the genetic algorithm (GA) [12], Nahwas Enscore Ham (NEH) algorithm
[13], Campble Dudeck Smith (CDS) [14], ant colony optimization (ACO) [15], and Hybrid Metaheuristic
[10]. While several studies have been conducted on the EES problem at FSSP, no studies have been found
that consider the sequence-dependent setup. Setup time has an essential role in scheduling [16]. The sequence
job also affects idle time [17]. High idle time increases energy consumption. Therefore, the right sequence
can reduce energy consumption [18].
Over the past few years, flow shop scheduling problems have become an essential topic for energy
efficiency. To our knowledge, there are no studies that discuss EES on FSSP by considering the sequence-
dependent setup using hybrid Harris hawks optimization (HHO). HHO algorithm is a new algorithm of
metaheuristic that imitates Harris hawks animals' prey hunting behavior [19]. Heidari, et al. [19] were
researchers that introduced the HHO algorithm to solve a continuous optimization problem. This procedure
effectively employed to complete the issue of the photovoltaic models [20], [21], manufacturing operation
[22], [23], and color image segmentation [24]. These are our motives for using HHO to solve the EES on the
FSSP problem. Therefore, this article aims to develop a new approach to solving an energy-efficient
scheduling (EES) in FSSP with the sequence-dependent setup. This research offers a hybrid Harris hawks
optimization (hybrid HHO) algorithm. This study carries out numerical tests and comparisons by the state-of-
the-art algorithm to test the algorithm performance. This research has two main contributes; 1) In to the EES
field on FSSP, hybrid HHO is a new competitive algorithm for solving EES problems in flow shop
scheduling with the sequence-dependent setup; and 2) this research investigated the parameter influence in
the EES problems.
2. PROPOSED ALGORITHM
We proposed hybrid HHO, which combined HHO with strategies swap and flip in the stage local
search. The exploration and the exploitation phase are two main strategies for the HHO algorithm in search
of prey. Hard besiege, soft besiege, hard besiege with progressive rapid lived, and soft besiege with
progressive rapid lived are four main behaviors of the HHO algorithm. These behaviors are in the
exploitation phase. This algorithm was used to solve continuous problems. Therefore, we developed the
HHO algorithm to minimize energy consumption in FSSP with the sequence-dependent setup time. FSSP
was considered as an NP-hard combinatorial problem that can be solved by discrete search space. We
propose four stages in the Hybrid HHO algorithm. These stage includes; 1) The random population of rabbits
and large rank value (LRV), 2) Exploration phase, 3). Exploitation phase, and 4) Local search phase with flip
and swap. Pseudocode hybrid HHO (proposed procedure) is shown in Algorithm 1. A description of the
stages of the Hybrid HHO algorithm is explained in the following section.
Algorithm 1 Pseudocode of Hybrid HHO algorithm
Inputs: Maximum iterations t and the population size N
Outputs: ﬁtness value and the rabbit location
random population initialization Xi(i = 1,2,...,N)
for iteration 1 to t
ﬁtness value calculation of hawks = 𝑋∗
Set Xrabbit as the rabbit location (best location)
for (each hawk (Xi)) do
Reform the first energy E0 and jump strength J (E0=2rand()-1, J=2(1-rand())
Update the E using (3))
if (|E|≥ 1) then (Exploration phase)
The location vector update using (1)
if (|E| < 1) then (Exploitation phase)
if (r ≥0.5 and |E|≥ 0.5 )then (The vector location update of soft
besiege using (4))
else if (r ≥0.5 and |E| < 0.5 ) then (The location vector update of hard
besiege using (6))
else if (r <0.5 and |E|≥ 0.5 ) then (The location vector update of soft
besiege with progressive rapid dives using (20)
else if (r <0.5 and |E| < 0.5 ) then (The location vector update of hard
besiege with progressive rapid dives using (10)
assure no repetitive number in the same s rabbit position 𝑥𝑡+1
Apply LRV on each rabbit position 𝑥𝑡+1
for 𝑖 = 0: 𝑛

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Bulletin of Electr Eng & Inf, Vol. 10, No. 3, June 2021 : 1154 – 1163
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Perform swap on the rabbit position 𝑥𝑡+1
if (evaluate (𝑥𝑡+1
) < evaluate (𝑋∗
))
𝑋∗
= 𝑥𝑡+1
end if
end for
do a flip operation on a random rabbit position 𝑥𝑟
for 𝑖 = 0: 𝑛
if (evaluate (𝑥𝑟) < evaluate (𝑋∗
))
𝑋∗
= 𝑥𝑟
end if
end for
𝑋∗
= Perform local search strategy on the best rabbit position 𝑋∗
Evaluate the rabbit position
Update 𝑋∗
if there is a better solution in the population
Return Xrabbit
2.1. The random population of rabbit and large rank value
The position of the initial rabbit population was generated randomly. This research proposed that
population positions had to ensure that no numbers were repeated. Figure 1 shows the illustration of the
initialization of the rabbits' random population. In Figure 1 there were the same numbers in the population. It
resulted in a population that could not be converted into a discrete search space. In converting continuous
numbers to discrete search space, this research proposed converting rabbit positions (continuous value) to job
permutations by applying LRV. LRV is seen as a successful way to convert continuous values to job
sequence of permutation [25]. In LRV, continuous values were ranked from the largest to the smallest values.
Figure 2 describes the Illustration of the LRV application.
[
0.12 0.29 0.11
0.61 0.43 0.88
0.55 0.72 0.72
0.81
0.76
0.94
] [
0.12 0.33 0.81
0.61 0.82 0.43
0.55 0.76 0.94
0.81
0.43
0.94
]
Accepted population Rejected population
Figure 1. Illustration of rabbit random population initiation
Figure 2. Illustration of LRV application
2.2. Exploration phase
In this phase, Harris hawk perched randomly in several locations. They were waiting for their rabbit
prey. This behavior is modeled in (1):
𝑋(𝑡 + 1) = {
𝑋𝑟𝑎𝑛𝑑(𝑡)−𝑟1|𝑋𝑟𝑎𝑛𝑑(𝑡)−2𝑟2𝑋(𝑡)| 𝑞≥0.5
(𝑋𝑟𝑎𝑏𝑏𝑖𝑡(𝑡)−𝑋𝑚(𝑡))−𝑟3(𝐿𝐵+𝑟4(𝑈𝐵−𝐿𝐵)) 𝑞<0.5
(1)
X (t+1) showed the vector position of the eagle in the further iteration. The current eagle position vector is
depicted as X (t). X_rabbit (t) indicated the rabbit position. UB and LB projected the upper bound and lower
bound variable limits. r1, r2, r3, r4, and q were random values in the range (0.1). This number was updated in

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every iteration. Xrand (t) formulated a randomly picked eagle of the current population. 𝑋𝑚(𝑡) described the
average position of the current eagle population. 𝑋𝑚(𝑡) was formulated with (2).
𝑋𝑚(𝑡) =
1
𝑁
∑ 𝑋𝑖(𝑡)
𝑁
𝑖=1 (2)
𝑋𝑖(𝑡) was the location of each eagle in the iteration t. Furthermore, N was the total number of eagles. The
HHO algorithm was transferred from exploration to exploitation. That behavior was based on prey energy
(rabbit). The energy of prey (rabbit) was drastically reduced during escape behavior. In (3) showed the prey
energy formula (rabbit).
𝐸 = 2𝐸0 (1 −
𝑡
𝑇
) (3)
E indicated the energy coming out of the prey. T formulated the maximum number of iterations. 2𝐸0
displayed the initial state of energy of the rabbit.
2.3. Exploitation phase
In this section, the exploitation phase strategies of HHO are explained. Harris hawk performed four
strategies: hard besiege, soft besiege, hard besiege with progressive rapid dives, and soft besiege with
progressive rapid dives. In the soft besiege strategy, when the rabbits still had enough energy, they tried to
escape from Harris hawk. These eagles then surrounded it gently, and they made the rabbit more tired.
Furthermore, they did a surprise pounce. This behavior was formulated in (4) and (5).
𝑋(𝑡 + 1) = ∆𝑋(𝑡) − 𝐸|𝐽𝑋𝑟𝑎𝑏𝑏𝑖𝑡(𝑡) − 𝑋(𝑡)| (4)
∆𝑋(𝑡) = 𝑋𝑟𝑎𝑏𝑏𝑖𝑡(𝑡) − 𝑋(𝑡) (5)
∆X (t) showed the difference between the rabbit position vector and the current location in iteration t. r5
described random numbers in the range (0.1). During the runaway procedure, the rabbit random jump
strength was formulated as J=2(1-r5). The value of J changed randomly at each iteration. The second
strategy was hard besieged. Rabbits were already exhausted, and they had low runaway energy. Harris
hawk did not surround rabbits, and they did pounce shocks. A new position on the hard besiege strategy
was formulated in (6).
𝑋(𝑡 + 1) = 𝑋𝑟𝑎𝑏𝑏𝑖𝑡(𝑡) − 𝐸|∆𝑋(𝑡)| (6)
The third strategy was soft besiege with progressive rapid dives. Rabbits were assumed to have
enough energy to escape successfully because Harris hawk built a soft siege before the shock pounced. To
make a soft siege, the eagle could evaluate the next step based on (7). The current position was updated using
(6). In this strategy, Harris hawks did irregular, sudden, and fast dives to get close to its prey. Harris hawks
dive pattern was formulated in (8).
𝑌 = 𝑋𝑟𝑎𝑏𝑏𝑖𝑡(𝑡) − 𝐸|𝐽𝑋𝑟𝑎𝑏𝑏𝑖𝑡(𝑡) − 𝑋(𝑡)| (7)
𝑍 = 𝑌 + 𝑆 𝑥 𝐿𝐹(𝐷) (8)
LF indicated the levy fight function. S showed a random vector of size 1xD. D described the dimensions of
the problem. The LF function was calculated using (9).
𝐿𝐹(𝑥) = 0.01 𝑥
𝑢𝑥𝜎
|𝜈|
1
𝛽
, 𝜎 = (
𝛤(1+𝛽)𝑥𝑠𝑖𝑛𝑠𝑖𝑛 (
𝜋𝛽
2
)
𝛤(
1+𝛽
2
)𝑥 𝛽 𝑥 2(
𝛽−1
2
)
)
1
𝛽
(9)
𝑈 and 𝜈 were random values in the range (0.1), β was the default constant of 1.5. Therefore, the strategy of
Soft besieges with progressive rapid dives was shown in (10). Y and Z were obtained using (7) and (8).
𝑋(𝑡 + 1) = {𝑌 𝑖𝑓 𝐹(𝑌) < 𝐹(𝑋(𝑡)) 𝑍 𝑖𝑓 𝐹(𝑍) < 𝐹(𝑋(𝑡)) (10)

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The fourth strategy was hard to besiege with progressive rapid dives. This behavior was carried out
if the rabbit did not have enough energy to escape. Harris hawk does hard besiege. Furthermore, they surprise
shocking rabbits. In (10) shows the rule in hard besiege conditions. Y and Z were obtained using the new
rules in (7) and (8). Xm(t) was obtained by using (2).
2.4. Local search
The swap and flip are two are local search procedures proposed to improve the HHO performance.
The swap illustration can is seen in Figure 3. In the swap operation, two positions were chosen randomly, and
they were then swapped. Furthermore, the flip procedure was done by reversing the randomly selected job
sequence. The flip operation was illustrated in Figure 4. In hybrid HHO, each iteration t, swap, and flip
operations were repeated by the number of jobs.
Figure 3. Swap illustration Figure 4. Flip illustration
3. RESEARCH METHOD
3.1. Model assumptions
Flow shop scheduling problem (FSSP) is a scheduling problem to complete the problem on the m-
machine in the same sequence of the n-job. FSSP on this issue considers sequence-dependent setup. Some
assumptions in FSSP with dependent setup time are; (1) the sequence of jobs (n=1,2,3, ..., j) done on
machines (m=1,2,3, ..., i) is the same, (2) start time for all machines are t=0, (3) the setup time in each job in
the machine is dependent sequence job, (4) machine finish is when the last job is finished on each machine,
which means each machine that stops independently, (5) setup time is separate from processing time, (7)
every job when it starts processing to completion should not be interrupted, and (6) setup time for moving
from job j to job k on the machine i is Sijk (where j=k, Sij shows the setup time for j job if the job is the first
job in sequence). The objective function in this model aims to minimize the Total Consumption of Energy
produced (TCE). Notations in the problem total energy consumption of PFSP employed in this paper is as
follows:
𝑗 : Machines index, 𝑗 = 1,2 … , 𝑚
𝑖 : Jobs index, i=1,2 …,n
𝑚 : total machines
𝑛 : total jobs
𝑆𝑖 : Setup time of job 𝑖 in the first sequence on every machine(hour)
𝑃𝑖, 𝑗 : processing time of job sequence 𝑖 on machines 𝑗 (hour)
𝑆𝑖,𝑖+1 : Setup time for sequence switching 𝑖 to 𝑖 + 1 in all machines (hour)
𝑆𝑒𝑗 : machine j index of energy consumption setup (watt/hour)
𝑃𝑒𝑗 : machine j index of energy consumption processing (watt/hour)
𝐼𝑒𝑗 : machine j index of energy consumption when idle (watt/hour)
𝑇𝑗 : finish time of machines 𝑗 (hour)
𝐶𝑖, 𝑗 : finish time of job sequence 𝑖 at on machines 𝑗
𝑆𝑗 : total setup time of machines 𝑗 (hour)
𝐵𝑗 : total busy time of machines 𝑗 (hour)
𝐼𝑗 : total idle time of machines 𝑗 (hour)
𝑇𝐸𝐶 : total energy consumption (watt)

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In this problem, the objective function in this study is minimizing total energy consumption (TEC).
The EES FSSP issue was developed by Li, et al. [26]. The EES FSSP model to reduce energy consumption is
as follows:
Objective function 𝑍=min 𝑇𝐸𝐶 (11)
Subject to :
𝐶1,1 = 𝑆1 + 𝑃1,1 (12)
𝐶1,𝑗 = ( 𝐶1,𝑗−1, 𝑆1) + 𝑃1,𝑗 ,𝑗 = 2 . . 𝑚 (13)
𝐶𝑖,1 = 𝐶𝑖−1,1 + 𝑆𝑖−1,𝑖 + 𝑃𝑖,1 ,𝑖 = 2 . . 𝑛 (14)
𝐶𝑖,𝑗 = (𝐶𝑖,𝑗−1, 𝑆𝑖−1,𝑖 + 𝐶𝑖−1,𝑗) + 𝑃𝑖,𝑗 ,𝑖 = 2 . . 𝑛, 𝑗 = 2 . . 𝑚 (15)
𝐵𝑗 = ∑𝑛
𝑖=1 𝑃𝑖,𝑗 , ∀ 𝑗 = 1. . 𝑚 (16)
𝑆𝑗 = ∑𝑛
𝑖=2 𝑆𝑖−1,𝑖 + 𝑆1 , ∀ 𝑗 = 1. . 𝑚 (17)
𝑇𝑗 = 𝑚𝑎𝑥 (𝐶𝑖,𝑗) , ∀ 𝑖 = 1 . . 𝑛, 𝑗 = 1. . 𝑚 (18)
𝐼𝑗 = 𝑇𝑗 − 𝐵𝑗 − 𝑆𝑗 ,∀ 𝑗 = 1. .𝑚 (19)
𝑇𝐸𝐶 = ∑𝑚
𝑗=1 (𝐵𝑗. 𝑃𝑒𝑗 + 𝐼𝑗. 𝐼𝑒𝑗 + 𝑆𝑗. 𝑆𝑒𝑗) (20)
Formula (11) describes the objective function, which minimizing energy consumption in the FSSP
model. The finish time of sequence one on machine 1 formulates in constraint (12). Constraint (13) explains
the settlement time of job sequence one the machine 2 to m. The settlement time of sequence i from machine
1 describes in constraint (14). The finish time of job sequence i in machine j is shown in constraint (15).
Constraint (16) describes a total processing time machine. The total of setup time is stipulated constraint (17).
Constraint (18) explains the completion time of machine j from permutation. Whereas constraint (19)
indicates the total idle time of j machine permutation. Finally, constraint (20) describes the permutation TEC
(objective function).
3.2. Data and experimental setup
Experimental data from previous studies were used. Some processing time data were based on
research conducted by Carlier [27], Heller [28], and Reeves [29]. The setup time for the first order job was
generated from uniform random numbers (5,10). The setup time for moving jobs sequence i to i+1 was
generated from uniform random numbers (5,10). Uniform random data of consumption of process energy,
setup, and idle time series were as follows: uniform (10,20), uniform (5,10), and uniform (1,5). Table 1
portrayed a list of problems from numerical experiments.
Table 1. List of problem
Problem No. Job Machine Instance from
Case 1 13/4 13 4 Carlier [27]
Case 2 10/6 10 6 Carlier [27]
Case 3 100/10 100 10 Heller [28]
Case 4 20/10 20 10 Heller [28]
Case 5 20/15 20 15 Reeves [29]
Case 6 30/10 30 10 Reeves [29]
Case 7 30/15 30 15 Reeves [29]
Case 8 50/10 50 10 Reeves [29]
Case 9 75/20 75 20 Reeves [29]
Case 10 75/20 75 20 Reeves [29]
This research conducted several experiments to determine the hybrid HHO parameters' effect on
energy consumption and computing time. Two parameters were used in this experiment include population
and iteration. Iteration included three levels, namely iteration 5, 25, and 50. Furthermore, the population
consisted of three levels, namely the population 10, 25, and 50. Each data repeatedly experimented for nine
times. We used 10 case data jobs and machines as shown in Table 1. Therefore, the experiments were carried
out as many as 90 times. Each experiment recorded energy consumption and computational time.

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Furthermore, we also tested the effect of parameters on the structure of energy consumption. The
structure of energy consumption comprised process energy consumption, setup energy consumption, and idle
energy consumption. In this experiment, we implemented case 3, case 8, case 9, and case 10. Furthermore,
the best parameters of the results of the experiment were compared with some previous algorithms, including
GA [12], NEH [13], CDS [14], ACO [15], and hybrid metaheuristic [10]. The experiment was done by
Matlab R16 software on a Windows 10 Intel® Core ™ i5-8250U RAM 4 GB processor. The performance of
the Hybrid HHO algorithm was measured by the Efficiency Index (EI). EI formula was presented in (21).
EIP=
𝑝𝑟𝑜𝑝𝑜𝑠𝑒𝑑 𝑎𝑙𝑔𝑜𝑟𝑖𝑡ℎ𝑚
𝑎𝑛𝑜𝑡ℎ𝑒𝑟 𝑎𝑙𝑔𝑜𝑟𝑖𝑡ℎ𝑚
× 100% (21)
4. RESULTS AND DISCUSSION
4.1. The comparison of various parameters toward energy consumption
The results of the hybrid HHO parameter experiment on energy consumption were shown in
Table 2. It reflected that the higher the number of iterations resulted in smaller energy consumption.
Furthermore, the lower the number of iterations would lead to greater energy consumption. In population
parameters, the higher the population resulted in lower energy consumption. Furthermore, the lower
population produced higher energy consumption. Overall these findings were consistent with the findings
reported by Tang, et al. [30]. It is now generally accepted that population and proper iteration reduce FSSP
energy consumption. For the case of small jobs, the best parameter uses population numbers and small
iterations. Conversely, for large jobs, the population and iteration used are high.
4.2. The comparison of various parameters toward computation time
Table 2 also displayed the result of the comparison of multiple parameters toward computation time.
In iteration parameters, significant iterations increase computational time. Conversely, a small iteration
decreases computational time. Furthermore, in the iteration parameter, a large population increases
computational time. Conversely, a small population decreases computing time. The number of jobs affects
the computational time. Experimental results showed that large jobs increase computational time.
Tabel 2. The results of the experiment parameter variations on energy consumption and computation time
Population problem
Total Energy Consumption (watt) Computation Time (second)
Iteration Iteration
5 25 50 5 25 50
5
Case 1 13/4 349339 346882 351027 0.033146 0.039977 0.072365
Case 2 10/6 478592 486000 481260 0.039030 0.039906 0.066424
Case 3100/10 117640 113443 113253 0.054764 0.168875 0.325313
Case 4 20/10 22572 22218 21687 0.008835 0.048976 0.101677
Case 5 20/15 265563 264676 265199 0.032797 0.057065 0.120249
Case 6 30/10 276188 275944 274736 0.015781 0.058442 0.116066
Case 7 30/15 425637 425101 422070 0.016743 0.070520 0.150442
Case 8 50/10 446092 440156 438365 0.017363 0.077573 0.161033
Case 9 75/20 1200753 1199676 1186720 0.032500 0.165406 0.326697
Case 10 75/20 1297543 1293532 1291371 0.032946 0.163298 0.345988
25
Case 1 13/4 349545 348105 347698 0.013922 0.079200 0.155404
Case 2 10/6 483814 481991 479060 0.017884 0.078955 0.159360
Case 3100/10 117612 116467 108669 0.052846 0.304258 0.577329
Case 4 20/10 21994 21563 21559 0.019935 0.118536 0.221414
Case 5 20/15 265674 263438 262466 0.024908 0.130414 0.268450
Case 6 30/10 275748 275221 272654 0.022985 0.128296 0.250404
Case 7 30/15 422169 421397 416485 0.026573 0.142162 0.295109
Case 8 50/10 445739 443778 441927 0.030262 0.165092 0.322063
Case 9 75/20 1197514 1185167 1184112 0.060237 0.288490 0.633256
Case 10 75/20 1302907 1287244 1282054 0.056205 0.300946 0.591204
50
Case 1 13/4 347548 347118 347101 0.025392 0.130725 0.299974
Case 2 10/6 483931 481680 478735 0.027437 0.160287 0.308441
Case 3100/10 116110 116098 110511 0.091227 0.489995 1110880
Case 4 20/10 22407 22225 21353 0.037658 0.217429 0.417839
Case 5 20/15 263945 262178 261877 0.041895 0.255801 0.494134
Case 6 30/10 275412 273453 273099 0.043486 0.226931 0.477967
Case 7 30/15 424132 423583 420924 0.048858 0.262267 0.588634
Case 8 50/10 445371 444536 442709 0.057114 0.308542 0.646978
Case 9 75/20 1201292 1192656 1189724 0.099352 0.570180 1166938
Case 10 75/20 1300412 1299754 1296874 0.100612 0.574883 1039619

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4.3. The comparison of various parameters toward structure energy consumption
Experimental results of the effect of parameters on structure energy consumption were presented in
Table 3. It showed that FSSP energy consumption with the sequence-dependent setup was influenced by
setup and idle time. Overall, these findings are consistent with the study results by Utama, et al. [10].
Furthermore, the higher iteration and population would decrease the energy consumption setup and idle.
Conversely, the smaller iteration and population would increase the energy consumption setup and idle.
Table 3. The parameter influence the structure energy consumption (watt)
Population problem
Iteration 5 Iteration 25 Iteration 50
proses setup idle proses setup idle proses setup idle
10
Case 3 100/10 64100 51496 2044 64100 47372 1971 64100 47185 1968
Case 8 50/10 399421 27180 19491 399421 21415 19320 399421 19716 19228
Case 9 75/20 1052381 87361 61011 1052381 86311 60984 1052381 73463 60876
Case 10 75/20 1119568 85016 92959 1119568 81136 92828 1119568 79025 92778
25
Case 3 100/10 64100 51482 2030 64100 50343 2024 64100 42617 1952
Case 8 50/10 399421 26954 19364 399421 25144 19213 399421 23398 19108
Case 9 75/20 1052381 84146 60987 1052381 71899 60887 1052381 70942 60789
Case 10 75/20 1119568 90525 92814 1119568 74928 92748 1119568 69800 92686
50
Case 3 100/10 64100 50004 2006 64100 49985 2013 64100 44491 1920
Case 8 50/10 399421 26843 19107 399421 26101 19014 399421 24334 18954
Case 9 75/20 1052381 87947 60964 1052381 79488 60787 1052381 76690 60653
Case 10 75/20 1119568 88230 92614 1119568 87639 92547 1119568 84854 92452
4.4. The comparison of algorithms
Table 4 indicated the other EIP algorithms against the proposed algorithm. The proposed algorithms
were compared with including GA [12], NEH [13], CDS [14], ACO [15], and hybrid metaheuristic [10]. The
results suggested that the proposed algorithm was more competitive than other algorithms. This result was
proven by the EIP value <100%. The values of EIP including GA [12], NEH [13], CDS [14], ACO [15] and
Hybrid Metaheuristic [10] were as follows 98.79%, 97.81%, 97.73%, 98.79%, and 99.00%. Therefore, it was
clear that the Hybrid HHO algorithm significantly improves the FSSP energy efficiency solution's quality.
Hybrid HHO is an efficient algorithm for solving EES in FSSP with the sequence-dependent setup.
Table 4. EIP algorithm comparison
problem GA [12] NEH [13] CDS [14] ACO [15] Hybrid Metaheuristic [10]
Case 1 13/4 99,73% 99,46% 98,11% 99,75% 99,98%
Case 2 10/6 99,99% 98,39% 99,99% 99,54% 99,97%
Case 3100/10 94,69% 92,94% 94,31% 93,53% 94,97%
Case 4 20/10 98,40% 94,29% 93,03% 97,05% 97,64%
Case 5 20/15 99,67% 98,22% 97,95% 99,96% 99,18%
Case 6 30/10 99,51% 98,15% 98,83% 99,37% 99,26%
Case 7 30/15 98,12% 99,06% 98,31% 99,81% 99,82%
Case 8 50/10 98,39% 99,06% 98,99% 99,56% 99,97%
Case 9 75/20 99,54% 98,97% 98,71% 99,49% 99,37%
Case 10 75/20 99,92% 99,52% 99,13% 99,81% 99,80%
Average 98,79% 97,81% 97,73% 98,79% 99,00%
5. CONCLUSION
This study discussed the energy-efficient scheduling (EES) in FSSP with the sequence-dependent
setup to minimize energy consumption. This study succeeds in proposing the hybrid Harris hawk
optimization (hybrid HHO) algorithm to solve the EES in the FSSP with the sequence-dependent setup
problem. The higher the number of iteration and populations can reduce energy consumption. However, these
produced significant computation time. Setup and idle time are time structures that influence to reduce
energy consumption. Furthermore, the proposed procedure was tested with several algorithms. The test
outcome demonstrated that the hybrid HHO algorithm was more effective than other procedures. Further
research should be aimed at developing procedures and dynamic job arrival. Future studies also need to
develop the hybrid HHO procedure with other metaheuristic procedures to produce more best results.

 ISSN: 2302-9285
1162
ACKNOWLEDGEMENTS
The authors would like to thank Muhammadiyah Malang Research University Directorate for
assistance in conducting the research. We would also like to thank the Laboratory for using their facilities by
the Department of Industrial Engineering Optimisation.
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BIOGRAPHIES OF AUTHORS
Dana Marsetiya Utama is currently a Lecturer and a Researcher in the Industrial Engineering
Department, University of Muhammadiyah Malang indonesia. His research interests include
optimization engineering, scheduling, and modeling. He received the bachelor's degree in
industrial engineering from Trunojoyo University, in 2008, and the master's degree in industrial
engineering from the University of brawijaya, in 2011.
Dian Setiya Widodo is currently a Lecturer and a Researcher in the department of
Manufacturing Technology, Vocational Faculty, University of 17 Agustus 1945 Surabaya
Indonesia. His research interests include optimization engineering, scheduling, and modeling. He
received the bachelor's degree in industrial engineering from Trunojoyo University, in 2011, and
the master's degree in industrial engineering from the University of brawijaya, in 2014.

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