A Novel Hybrid Aquila Optimizer for Energy-Efficient Hybrid Flow Shop Scheduling

Utama, Dana Marsetiya; Primayesti, Meri Dines

doi:10.2139/ssrn.4008763

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Selain itu, beberapa pendekatan lain termasuk A-Novel Teaching Learning-Based Optimization Algorithm [37], Particle Swarm Optimization [28], Genetic Algorithm [38], dan Lagrangian Relaxation Algorithm [39], juga dikemukakan sebagai solusi potensial pada permasalahan minimasi konsumsi energi dalam HFSSP. Uraian tersebut menunjukan bahwa prosedur metahuristik merupakan prosedur yang populer untuk menyelesaikan masalah HFSSP [40,41].…”

Section: Pendahuluanunclassified

Minimasi Konsumsi Energi Pada Permasalahan Hybrid Flow Shop Scheduling Dengan Algoritma Salp Swarm

Hidayat,

Utama,

Amallynda

2023

jitiuntar

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The scarcity and possible depletion of energy sources in the future is due to the high energy consumption in the manufacturing industry. One of the efforts to deal with this problem is scheduling. This article investigates the Hybrid Flow Shop Scheduling Problem (HFSSP) to minimize energy consumption. A metaheuristic algorithm procedure is proposed, inspired by the Salp swarm in the ocean, the Salp Swarm Algorithm. The SSA algorithm is proposed to determine the optimal sequencing considering minimal energy consumption. An interesting finding of this study is that the higher the population and iterations, the lower the energy consumption. In addition, the optimization results show that the total energy consumption is due to variations in setup and removal energy consumption and idle machine energy consumption. From the numerical results, the SSA algorithm can produce scheduling with minimal energy consumption compared to the heuristic procedure.

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Section: Pendahuluanunclassified

Minimasi Konsumsi Energi Pada Permasalahan Hybrid Flow Shop Scheduling Dengan Algoritma Salp Swarm

Hidayat,

Utama,

Amallynda

2023

jitiuntar

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“…In recent years, worldwide, the energy demand has increased significantly. One of the most critical resources for the industrial sector is energy [1][2][3]. Excessive energy use accounts for half of the world's total energy consumption [4].…”

Section: Introductionmentioning

confidence: 99%

A Modified Mountain Gazelle Optimizer for Minimizing Energy Consumption on No-Wait Permutation Flow Shop Scheduling Problem

2024

IJIES

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In the context of growing global energy demand, the industrial sector has become one of the significant contributors to the world's energy consumption. To face this challenge, scheduling has been identified as one of the potential methods to reduce energy consumption in industrial operations. This article introduces the mountain gazelle optimizer (MGO) algorithm as a solution to solve the no-wait flow shop scheduling problem with a focus on the main objective of minimizing energy consumption. The research involves comparing the performance of the MGO algorithm with popular algorithms such as grey wolf optimizer (GWO), particle swarm optimization (PSO), and genetic algorithm (GA). In addition, this research also compares the proposed MGO algorithm with the latest advanced algorithms, such as coati optimization algorithm (COA) and fire hawk optimizer (FHO). In this work, the six algorithms were tested on three different scheduling cases by repeating the process 30 times, using a population of 200 and 200 iterations to minimise energy consumption. The performance comparison between these algorithms was analyzed using the One-Way ANOVA statistical test. Based on the results, MGO outperforms GWO, PSO, GA, COA, and FHO algorithms in solving scheduling problems, with the primary function of minimizing energy consumption in Cases 1, 2, and 3. In addition, based on the convergence curve, the MGO algorithm has a better convergence curve compared to GWO, PSO, GA, COA, and FHO; it shows that the intestinal algorithm can reach the optimal solution faster and more stable during iterations than the GWO, PSO, GA, COA, and FHO algorithms. This finding confirms that the MGO algorithm has the potential to be an effective alternative in the scheduling optimization process, significantly reducing energy consumption in the industrial sector.

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Energy-efficient scheduling model and method for assembly blocking permutation flow-shop in industrial robotics field

Kong,

Wu,

Zhang

et al. 2024

Artif Intell Rev

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Implementing green and sustainable development strategies has become essential for industrial robot manufacturing companies to fulfill their societal obligations. By enhancing assembly efficiency and minimizing energy consumption in workshops, these enterprises can differentiate themselves in the fiercely competitive market landscape and ultimately bolster their financial gains. Consequently, this study focuses on examining the collaborative assembly challenges associated with three crucial parts: the body, electrical cabinet, and pipeline pack, within the industrial robot manufacturing process. Considering the energy consumption during both active and idle periods of the industrial robot workshop assembly system, this paper presents a multi-stage energy-efficient scheduling model to minimize the total energy consumption. Two classes of heuristic algorithms are proposed to address this model. Our contribution is the restructuring of the existing complex mathematical programming model, based on the structural properties of scheduling sub-problems across multiple stages. This reformation not only effectively reduces the variable scale and eliminates redundant constraints, but also enables the Gurobi solver to tackle large-scale problems. Extensive experimental results indicate that compared to traditional workshop experience, the constructed green scheduling model and algorithm can provide more precise guidance for the assembly process in the workshop. Regarding total energy consumption, the assembly plans obtained through our designed model and algorithm exhibit approximately 3% lower energy consumption than conventional workshop experience-based approaches.

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A Novel Hybrid Aquila Optimizer for Energy-Efficient Hybrid Flow Shop Scheduling

Cited by 4 publications

References 13 publications

Minimasi Konsumsi Energi Pada Permasalahan Hybrid Flow Shop Scheduling Dengan Algoritma Salp Swarm

Minimasi Konsumsi Energi Pada Permasalahan Hybrid Flow Shop Scheduling Dengan Algoritma Salp Swarm

A Modified Mountain Gazelle Optimizer for Minimizing Energy Consumption on No-Wait Permutation Flow Shop Scheduling Problem

Energy-efficient scheduling model and method for assembly blocking permutation flow-shop in industrial robotics field

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