Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization

Abdullah, Arif; Rashid, Mohd Fadzil Faisae Ab; Ponnambalam, S. G.; Ghazalli, Zakri

doi:10.1108/aa-06-2018-091

Cited by 13 publications

(8 citation statements)

References 54 publications

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“…With these features, for example, compared with other metaheuristic methods, MFO yielded better results in economic dispatch problems [29], [30], software fault prediction datasets [31], and energy efficient modeling for assembly sequence planning [32]. Moreover, MFO was modified and improved for gaining better performances applied to solve problems as discussed in [33]- [40].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

Kigsirisin

Miyauchi

2021

IEEE Access

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This study proposes a novel binary optimization method for solving unit commitment (UC) problems. Developed from moth-flame optimization (MFO), the proposed method is called binary alternative MFO (BAMFO). Originally, each MFO moth is restricted to fly toward a predetermined flame (solution), causing it to be trapped in the local optimum of that flame and reducing its performance. To address these drawbacks, BAMFO represents four alternative characteristics for moths to discover better flames all over the search space. The exploration and exploitation performances of BAMFO via these characteristics are balanced by a random parameter competing with the criteria of each characteristic. A repair strategy working with the priority list is created to adjust any unit status from the BAMFO simulation to satisfy all UC constraints. Then, Lambda-iteration method is adopted to solve economic dispatch problems. The proficiency of BAMFO is verified in various scales of unit systems for short-term operational scheduling. The results show that BAMFO yields better solutions than do other methods. The Wilcoxon signed rank test was conducted to prove the predominance of BAMFO.

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Section: Introductionmentioning

confidence: 99%

Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

Kigsirisin

Miyauchi

2021

IEEE Access

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“…Abdullah et al [144] developed an assembly sequence planning model in order to achieve energy efficiency. The objective function considered is the weighted sum of the number of tool changes, of the number of assembly direction changes and of energy consumption in idle mode.…”

Section: Assembly Linementioning

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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“…The proposed MFO methodology, entrusted with controlling the NN controller's weight and bias parameters compared with various evolutionary and gradient-based approaches, demonstrates its clear superiority. Abdullah et al [23] have developed an MFO-based approach for the optimization of energy usage at assembly stations. The idle energy in the assembly sequencing problems is optimized using MFO-based controller, which performs much better than the genetic algorithm (GA), Particle Swarm Optimization (PSO), and ACO controllers in terms of robustness, feasibility, and computational time.…”

Section: Introductionmentioning

confidence: 99%

Navigation for multi-humanoid using MFO-aided reinforcement learning approach

Kashyap

Parhi²,

Kumar³

2022

Robotica

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The given article emphasizes the development and modeling of a hybrid navigational controller to optimize the path length and time taken. The proposed navigational controller is developed by hybridizing the metaheuristic moth–flame optimization (MFO) approach and the reinforcement learning (RL) approach. Input parameters like obstacle and target locations are fed to the MFO controller that implements a proper navigational direction selection. It forwards to the RL controller, which exercises further refinement of the output turning angle around obstacles. The collaboration of the global MFO approach with the local-based RL approach helps to optimize the path traversed by the humanoid robot in an unknown environment. The major breakthrough in this article is the utilization of humanoid robots for navigation purposes between various checkpoints. The humanoid robots are placed in a cluttered environment and assigned specific target positions to complete the assigned tasks. In the case of a multi-humanoid robot system, to avoid self-collision, it requires a Petri-Net controller to be configured in the navigation system to prevent deadlock situations and enhance the smooth completion of tasks without inter-collision among the humanoid robots. Simulations and real-time experiments are undertaken using different controllers involving single- and multi-humanoid robot systems. The robustness of the proposed controller is also validated in dynamic environment. Comparisons are carried with an established navigational controller in a similar environmental setup, which proves the proposed hybrid controller to be robust and efficient.

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Energy efficient modeling and optimization for assembly sequence planning using moth flame optimization

Cited by 13 publications

References 54 publications

Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

Short-Term Operational Scheduling of Unit Commitment Using Binary Alternative Moth-Flame Optimization

A Literature Review of Energy Efficiency and Sustainability in Manufacturing Systems

Navigation for multi-humanoid using MFO-aided reinforcement learning approach

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