Energy-Saving Scheduling in a Flexible Flow Shop Using a Hybrid Genetic Algorithm

Huang, Rong‐Hwa; Yu, Shun-Chi; Chen, Po‐Han

doi:10.4236/jep.2017.810066

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…The mathematical model was solved using a hybrid evolutionary algorithm. The minimization of energy consumption costs considering three possible machine states (standby, shut-down, and operation) was studied and solved with a hybrid genetic algorithm in [81]. Hasani and Hosseini [82] considered, in the bi-objective scheduling problem, production cost and energy consumption as the two targets to be minimized and implemented a makespan constraint.…”

Section: Flexible Flow Shopmentioning

confidence: 99%

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

Renna

Materi

2021

Applied Sciences

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Climate change mitigation, the goal of reducing CO2 emissions, more stringent regulations and the increment in energy costs have pushed researchers to study energy efficiency and renewable energy sources. Manufacturing systems are large energy consumers and are thus responsible for huge greenhouse gas emissions; for these reasons, many studies have focused on this topic recently. This review aims to summarize the most important papers on energy efficiency and renewable energy sources in manufacturing systems published in the last fifteen years. The works are grouped together, considering the system typology, i.e., manufacturing system subclasses (single machine, flow shop, job shop, etc.) or the assembly line, the developed energy-saving policies and the implementation of the renewable energy sources in the studied contexts. A description of the main approaches used in the analyzed papers was discussed. The conclusion reports the main findings of the review and suggests future directions for the researchers in the integration of renewable energy in the manufacturing systems consumption models.

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Section: Flexible Flow Shopmentioning

confidence: 99%

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

Renna

Materi

2021

Applied Sciences

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“…It helps to reduce peak loads and decrease the demand for applied energy sources [20]. Research of Huang et al [21] also shows that optimizing various engine conditions under time-use rates can significantly reduce energy costs in timely delivery. At the same time, Zhang [22] research shows that both individual and total factory electricity costs can be minimized.…”

Section: Scheduling Parametersmentioning

confidence: 99%

Flow Shop Scheduling Based on Palmer-Neh, Gupta-Neh and Dannenbring-Neh Algorithms to Minimize the Energy Cost

Masrikhan

Kurniawati

2021

Sinergi

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In the manufacturing industry, the most widely used equipment is equipment that uses electricity. Electricity cost is one of the highest operational production costs after labor cost. So, it is very important to save and optimize the use of electrical equipment. One of the manufacturing industries is Taru Martani, Ltd. This research aims to minimize the energy cost by proposing three hybrid algorithms, namely Palmer-NEH, Gupta-NEH, and Dannenbring-NEH methods. Some scheduling evaluation is done using the Efficiency Index (EI) and Relative Error (RE) parameters. It is concluded that the Palmer-NEH and Gupta-NEH methods are the best methods with the lowest energy cost compared with company's actual method and the Dannenbring-NEH method. Based on the Palmer-NEH and Gupta-NEH methods, both methods can save the makespan up to 399.13 minutes or 6.65 hours compared with the company's actual method. With these methods, the company is also able to save the production cost by Rp. 818,043.00.

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“…In addition, a hybrid iterated greedy algorithm [33], an efficient multi-objective algorithm [34], and an improved Jaya algorithm [35] has been studied in the multi-objective field. Many experts have also applied energy-aware models [36][37][38], turn off/on scheme [39], different types of machines [40], and so on.…”

Section: Introductionmentioning

confidence: 99%

Discrete imperialist competitive algorithm for the resource-constrained hybrid flowshop problem with energy consumption

Tao

Huang

et al. 2020

Complex Intell. Syst.

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The resource-constrained hybrid flowshop problem (RCHFS) has been investigated thoroughly in recent years. However, the practical case that considers both resource-constrained and energy consumption still has rare research. To address this issue, a discrete imperialist competitive algorithm (DICA) was proposed to minimize the makespan and energy consumption. In the proposed algorithm, first, each solution was represented by a two-dimensional vector, where one vector represented the scheduling sequence and another one showed the machine assignment. Then, a decoding method considering the resource allocation was designed. Finally, we combined DICA and the simulated annealing algorithm (SA) to improve the performance of the proposed approach. Furthermore, we tested the proposed algorithm based on a randomly generated set of real shop scheduling system instances and compared with the existing heuristic algorithms. The results confirmed that the proposed algorithm can solve the RCHFS with high efficiency.

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Energy-Saving Scheduling in a Flexible Flow Shop Using a Hybrid Genetic Algorithm

Cited by 8 publications

References 26 publications

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

Flow Shop Scheduling Based on Palmer-Neh, Gupta-Neh and Dannenbring-Neh Algorithms to Minimize the Energy Cost

Discrete imperialist competitive algorithm for the resource-constrained hybrid flowshop problem with energy consumption

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