A Genetic Algorithm to Solve the Hybrid Flow Shop Scheduling Problem with Subcontracting Options and Energy Cost Consideration

Schulz, Sven

doi:10.1007/978-3-319-99993-7_23

Cited by 9 publications

(6 citation statements)

References 12 publications

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“…EEHFSP has been the subject of many studies that attempted to provide solutions for reducing energy consumption. Several studies have previously discussed this problem under two stages, including Schulz [16] which proposed a Genetic Algorithm (GA), and Du, et al [17] which introduced Ant Colony Optimization (ACO) algorithm. Tao, et al [18] also presented the Discrete Imperialist Competitive Algorithm in the two stages of EEHFSP.…”

Section: A R T I C L E I N F O Abstractmentioning

confidence: 99%

“…The comparison with other variants of the algorithm was also presented and the algorithms used were PSO [19], GA [16], and ACO [17]. A total of 9 variants of problems that were randomly generated were employed based on the uniform distribution range [1.8, 12.3] for the processing time.…”

Section: Experimental Settingmentioning

confidence: 99%

“…Constraint (15) illustrates that the job completion time in the previous stage is considered, as well as the initial setup time for each job, processing time, and removal time. Furthermore, constraint (16) shows that when the machine completes a job, it is positioned to do the next, and constraint (17) models that each job is only processed on 1 machine in each phase, where variable 𝑌 𝑖𝑗𝑘 represents a binary number 0 and 1. At each stage of the job to be processed, n is formulated in Constraint (18), and the TEC are modeled in equations ( 19) to (23).…”

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confidence: 99%

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A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

Utama

Salima

Widodo

2022

Int. J. Adv. Intell. Informatics

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The manufacturing sector consumes most of the global energy and had been in focus since the outbreak of the energy crisis. One of the proposed strategies to overcome this problem is to implement appropriate scheduling, such as Hybrid Flow Shop Scheduling. Therefore, this study aims to create a Hybrid Archimedes Optimization Algorithm (HAOA) for solving the Energy-Efficient Hybrid Flow Shop Scheduling Problem (EEHFSP). It is hoped that this helps to provide new insights into advanced HAOA methods for resolving the EEHFSP as the algorithm has the potential to be a more efficient alternative. In this study, three stages of EEHFSP were considered in the problem as well as a sequence-dependent setup and removal times in the second stage. Experiments with three population variations and iterations were presented for testing the effect of HAOA parameters on energy consumption. Furthermore, ten job variations are also presented to evaluate the performance of the HAOA algorithm and the results showed that HAOA iteration and the population did not affect the removal and processing of energy consumption, but impacted that of setup and idle. The comparison of these ten cases revealed that the proposed HAOA produced the best total energy consumption (TEC) when compared to the other algorithms.

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Section: A R T I C L E I N F O Abstractmentioning

confidence: 99%

Section: Experimental Settingmentioning

confidence: 99%

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confidence: 99%

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A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

Utama

Salima

Widodo

2022

Int. J. Adv. Intell. Informatics

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“…A significant number of the algorithms in EE-HFS make use of the problem. In the past five years, an algorithm based on genetic algorithms (GA) has been used to solve the EE-HFS problem [6] and then improvised with a Paretobased method using a similar algorithm [7]. Other evolutionary algorithms (EA) were applied in similar years by Jiang and Zhang and Gong et al to tackle the emotional EE-HFS problem [8,9].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Flow Shop Scheduling Problem with Energy Utilization using Non-Dominated Sorting Genetic Algorithm-III (NSGA-III) Optimization

Nik Mu'Tasim,

Ab. Rashid

2023

Int. J. Automot. Mech. Eng.

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Hybrid flow shop scheduling (HFS) is an on sought problem modelling for production manufacturing. Due to its impact on productivity, researchers from different backgrounds have been attracted to solve its optimum solution. The HFS is a complex dilemma and provides ample solutions, thus inviting researchers to propose niche optimization methods for the problem. Recently, researchers have moved on to multi-objective solutions. In real-world situations, HFS is known for multi-objective problems, and consequently, the need for optimum solutions in multi-objective HFS is a necessity. Regarding sustainability topic, energy utilization is mainly considered as one of the objectives, including the common makespan criteria. This paper presents the existing multi-objective approach for solving energy utilization and makespan problems in HFS scheduling using Non-Dominated Sorting Genetic Algorithm-III (NSGA-III), and a comparison to other optimization models was subjected for analysis. The model was compared with the most sought algorithm and latest multi-objective algorithms, Strength Pareto Evolutionary Algorithm 2 (SPEA -II), Multi-Objective Algorithm Particle Swarm Optimization (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D). The research interest starts with problem modelling, followed by a computational experiment with an existing multi-objective approach conducted using twelve HFS benchmark problems. Then, a case study problem is presented to assess all models. The numerical results showed that the NSGA-III obtained 50% best overall for distribution performance metrics and 42% best in convergence performance metrics for HFS benchmark problems. In addition, the case study results show that NSGA-III obtained the best overall convergence and distribution performance metrics. The results show that NSGA-III can search for the best fitness solution without compromising makespan and total energy utilization. In the future, these multi-objective algorithms’ potential can be further investigated for hybrid flow shop scheduling problems.

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“…Xiang, et al [20] designed a model to solve three-stage HFSS, which involved three machines in stage 1, 2 machines in stage 2, and 3 machines in stage 3 to solve the HFSS problems proposed GA algorithm to find the solutions. Several other algorithms to solve HFSP by considering energy efficiency which has been proposed so far include A-novel Teaching-learning-based Optimization Algorithm [21], two-level imperialist competitive algorithm [7], Particle swarm optimization [22], Genetic Algorithm [23], Lagrangian Relaxation Algorithm [24], and Improved Genetic Algorithm [25].…”

Section: Introductionmentioning

confidence: 99%

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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A Genetic Algorithm to Solve the Hybrid Flow Shop Scheduling Problem with Subcontracting Options and Energy Cost Consideration

Cited by 9 publications

References 12 publications

A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

A novel hybrid archimedes optimization algorithm for energy-efficient hybrid flow shop scheduling

Hybrid Flow Shop Scheduling Problem with Energy Utilization using Non-Dominated Sorting Genetic Algorithm-III (NSGA-III) Optimization

Energy-Aware Scheduling in Hybrid Flow Shop using Firefly Algorithm

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