The hybrid whale optimization algorithm: A new metaheuristic algorithm for energy-efficient on flow shop with dependent sequence setup

Utama, Dana Marsetiya; Widodo, Dian Setiya; Ibrahim, Muhammad Faisal; Hidayat, Khoirul; Baroto, Teguh; Yurifah, Aminatul

doi:10.1088/1742-6596/1569/2/022094

Cited by 16 publications

(18 citation statements)

References 15 publications

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“…The light intensity of firefly x is represented with l and is proportional to the solution's objective function to be solved f(x). The attractiveness function is presented in equation (12). To obtain the value of the firefly's attractiveness, the calculation is conducted with distance (r).…”

Section: I( X1 ) = F(x)mentioning

confidence: 99%

“…HFSS is one of the problems in NP-hard problems, which is also the generalization of the flow shop scheduling problem [8] [9] [10] [11]. In the flow shop scheduling problems, a series of jobs are ordered, and each job employs one machine in each stage [12] [13]. However, in HFSS, a series of jobs have similar production orders, but each stage's job might require one or more machines [14] [15].…”

Section: Introductionmentioning

confidence: 99%

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Energy-Aware Scheduling in Hybrid Flow Shop using Firefly Algorithm

Jabari

Hasan

2021

JTI

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Nowadays, the industrial sector takes up a significant portion of the world's total energy consumption. This sector is responsible for half of the total energy consumed in the world. Therefore, efficiency in the industrial sector becomes an essential issue. One of the main factors triggering the high energy consumption in this sector is that many machines are left idle. Idle machines during the manufacturing process require electricity and other energies. This research aimed to develop a firefly algorithm that can minimize the energy consumption in the hybrid flow shop scheduling problem. This algorithm is used to determine the optimum order of the jobs. The ultimate goal is to minimize energy consumption. The experiment on the algorithm was conducted by employing iteration and population variations. The research results show that population and iteration affect the quality of the hybrid flow shop scheduling solution.

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Section: I( X1 ) = F(x)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy-Aware Scheduling in Hybrid Flow Shop using Firefly Algorithm

Jabari

Hasan

2021

JTI

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“…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].…”

Section: Introductionmentioning

confidence: 99%

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

Utama

Widodo

2021

Bulletin EEI

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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.

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“…2). Several previous studies have shown that WOA has been effectively used in solving problems such as Inventory (Khalilpourazari et al, 2019), scheduling (Abdel-Basset et al, 2018;Utama et al, 2020c), Prediction (Osama et al, 2017), and Allocation (Yan et al, 2018). 3).…”

Section: Introductionmentioning

confidence: 99%

“…This research offers a Local search procedure to improve algorithm performance. It is a popular procedure to improve performance algorithm to solve the NP-Hard problem (Pop et al, 2013;Stodola, 2020;Utama et al, 2020c).…”

Section: Introductionmentioning

confidence: 99%