Dana Marsetiya Utama, Dian Setiya Widodo, Wahyu Wicaksono, Leo Rizki Ardiansyah

Corresponding email: dana@umm.ac.id

Corresponding email: dana@umm.ac.id

**Published at : ** 25 Apr 2019

**Volume :** IJtech
Vol 10, No 2 (2019)

**DOI :** https://doi.org/10.14716/ijtech.v10i2.2194

Utama, D.M., Widodo, D.S., Wicaksono, W., Ardiansyah, L.R., 2019. A New Hybrid Metaheuristics Algorithm for Minimizing Energy Consumption in the Flow Shop Scheduling Problem.

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Dana Marsetiya Utama | Department of Industrial Engineering, University of Muhammadiyah Malang, Jl. Raya Tlogomas 246 Malang, 65144, Indonesia |

Dian Setiya Widodo | Department of Manufacturing Technology, Vocational Faculty, University of 17 Agustus 1945 Surabaya, Jl. Semolowaru No. 45 Surabaya, 60118, Indonesia |

Wahyu Wicaksono | Department of Industrial Engineering, University of Muhammadiyah Malang, Jl. Raya Tlogomas 246 Malang, 65144, Indonesia |

Leo Rizki Ardiansyah | Department of Manufacturing Technology, Vocational Faculty, University of 17 Agustus 1945 Surabaya, Jl. Semolowaru No. 45 Surabaya, 60118, Indonesia |

Abstract

In this study, we discuss the
problem of permutation flowshop scheduling problem (PFSP) to reduce total
energy consumption (TEC). We offer a new hybrid meta-heuristic algorithm for
solving the problem. The paper aims to combine the cross entropy and genetic
algorithm (CEGA) with the simulated annealing (SA) algorithm. The CEGA is
applied to find the best initial solution inside the SA algorithm and the
proposed algorithm is compared to previous tests of the famous NSGA-II and
GA-SA algorithm. During study of the numerical test, the proposed algorithm
genuinely useful is compared certain efficient algorithms of the from previous
research.

Algorithm; Energy consumption; Flow shop, Meta-heuristic

Introduction

Recently, Total
Energy Consumption (TEC) in the manufacturing sector has received much
attention from experts. This has been focused on highly TEC in the
manufacturing sector. TEC in this
sector requires almost half of the total energy needs in country. In the USA, it requires 33% of the total electricity of the country (Evans, 2003), while in Germany it requires 47% of electricity from
all energy requirements (Dai et al., 2013). The electricity consumption of the sector
needs fossil fuels for electricity generation; therefore, experts consider such
consumption to be a problem because of the decreasing availability of these
fuels. Some experts have made efforts to minimize TEC, one of which is scheduling, which refers to the
arrangement of resources (machines) to complete the job (Surjandari et
al., 2015). Generally, the
goal of scheduling is to minimize completion time (Thawongklang &
Tanwanichkul, 2016). However, some
experts are now using scheduling to reduce TEC.

Several researchers have researched flow shop scheduling problems to reduce TEC. Zhang et al. (2014), Brundage et al. (2014) and Zanoni et al. (2014) have succeeded in minimizing TEC in simple flow shop problems, using a heuristic algorithm as a solution. Besides, heuristic algorithms are explicitly used to solve specific problems. In recent years, some meta-heuristic algorithm have also been used to solve the classic flow shop problem in order to minimize TEC. These algorithms include simulated annealing (SA) (Iqbal & Al-Ghamdi, 2018); a genetic algorithm (GA) (Liu et al., 2017); and particle swarm optimization (PSO) (Tang et al., 2016). In hybrid flow shop problems, several studies to minimize TEC have been conducted by Luo et

al. (2013), Dai
et al. (2013) and Liu and Huang (2014), who used meta-heuristic algorithms to solve energy
consumption problems. In this article, we focus on the Permutation Flow-Shop Scheduling Problem (PFSP). Researchers claim that
a solution to this problem cannot be found in polynomial time. Therefore, PFSP
is considered an NP-Hard problem (Garey et al., 1976; Sayadi et al.,
2010). Because of the
importance of this problem, several efforts have been made by experts to
develop algorithms to minimize TEC.

In
recent years, SA, Cross-entropy (CE) and GA algorithms have been used to solve
scheduling problems. The SA algorithm is a meta-heuristic algorithm, which were
first introduced by Kirkpatrick et al. (1983) for optimization. However, this algorithm is now
used in most PFSP scheduling problems (Pinedo, 2016). Like the SA, GA is also a meta-heuristic
algorithm based on mimicking natural selection and recombination (Holland,
1992). CE is another
meta-heuristic algorithm applied to rare event simulations, continuous
optimization, and combinatorial optimization (Deng, 2006). This algorithm is useful in solving
complex combinatorial optimization problems (De Boer et al.,
2005). In recent years, some
experts have used meta-heuristic algorithms to solve PFSP, and some simple
meta-heuristics have been applied
to reduce TEC. However, classic meta-heuristics need a long time if used in
large cases (Santosa et al., 2011). Recently, some hybrid meta-heuristic alternatives have
been developed to solve PFSP. These algorithms include a hybrid GA with SA (Dai et al., 2013); a hybrid GA with TS (Sukkerd and Wuttipornpun, 2016); a hybrid of
ABC and TS (Li and Pan, 2015); and a hybrid
of CE and GA (Santosa et al., 2011).

Although many hybrid meta-heuristic algorithms have been developed to solve PFSP problems, they still display certain weaknesses, namely the long computing time for large-scale problems and optimal local solutions. Although they do need a long computation time, hybrid meta-heuristics give better performance compared to simple meta-heuristics. Many meta-heuristic algorithms have good global search capabilities, while some have local search capabilities. At present, few papers focus on minimizing TEC in PFSP. To our knowledge, none integrate CE and GA (CEGA) with SA. Therefore, this paper aims to combine CEGA with SA to reduce TEC, an approach we term CEGASA. This algorithm follows the rules for fixed energy consumption (FEC) Li et al. (2011). Hence, the paper focuses on minimizing TEC by following FEC rules. The remainder of this paper is organized as follows: Part 2 explains problem discription, example problem, proposes algorithms, and describes the experimental procedure. Section 3 then presents the computational experiments, experimental parameters, and comparison algorithms. Finally, the the conclusion is made in section 4.

Conclusion

We have discussed the problem of PFSP in reducing energy
consumption and offer the CEGASA algorithm to solve this problem. The algorithm
has been compared with other algorithms and numerical experiments have proven
that it achieves optimum energy consumption. Some other research areas could be
studied in future work. We propose that the CEGASA be used as an initial
solution for other meta-heuristic algorithms, and ultimately be applied to the
reduction of energy consumption in more complex PFSPs.

Acknowledgement

The authors would like to thank the Directorate of the
Research University of Muhammadiyah Malang for support in conducting the
research. We would also like to thank the Department of Industrial Engineering
Optimation Laboratory for use of their facilities.

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