|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|
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
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.
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.
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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