Multiobjective optimization of buffer capacity allocation in multiproduct unreliable production lines using improved adaptive NSGA-II algorithm

Duan, Jianguo; Zhang, Qinglei; Li, Haochen

doi:10.48129/kjs.v48i1.7789

Cited by 4 publications

(2 citation statements)

References 19 publications

(10 reference statements)

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“…Во многих публикациях отмечена возрастающая актуальность к задаче оптимизации производственных процессов. В [3][4][5] отмечена тенденция увеличения сложности серийных производственных линий и связанных с этим производственных затрат в современных отраслях с большими объемами производства. В [6] отмечено, что в последние годы увеличивается турбулентность внешней среды, в которой действуют компании, проявляющейся, в частности, в усилении динамичности рынков и глубоких изменениях в структуре потребительского спроса.…”

Section: Temporal Graphs Have Been Proposed To Reflect the Dynamics O...unclassified

Using Periodic Fuzzy Graphs in Production Equipment Control Problems

Bozhenyuk,

Knyazeva,

Kosenko

et al. 2023

IZVESTIYA SFedU. ENGINEERING SCIENCES

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Section: Temporal Graphs Have Been Proposed To Reflect the Dynamics O...unclassified

Using Periodic Fuzzy Graphs in Production Equipment Control Problems

Bozhenyuk,

Knyazeva,

Kosenko

et al. 2023

IZVESTIYA SFedU. ENGINEERING SCIENCES

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“…The results for various numerical case studies were compared against the great deluge algorithm (GDA), and NLTA gave better results. In another research study, the NSGA-II algorithm was used for the multi-objective optimization of the buffer capacity allocation in multiproduct unreliable production lines [26]. Gao [27] presented a bottleneck detection-based tabu search algorithm for optimizing the BAP.…”

Section: Introductionmentioning

confidence: 99%

Development and Numerical Optimization of a System of Integrated Agents for Serial Production Lines

et al. 2023

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In modern high-volume industries, the serial production line (SPL) is of growing importance due to the inexorable increase in the complexity of manufacturing systems and the associated production costs. Optimal decisions regarding buffer size and the selection of components when designing and implementing an SPL can be difficult, often requiring complex analytical models, which can be difficult to conceive and construct. Here, we propose a model to evaluate and optimize the design of an SPL, integrating numerical simulation with artificial intelligence (AI). Numerous studies relating to the design of SPL systems have been published, but few have considered the simultaneous consideration of a number of decision variables. Indeed, the authors have been unable to locate in the published literature even one work that integrated the selection of components with the optimization of buffer sizes into a single framework. In this research, a System of Integrated Agents Numerical Optimization (SIGN) is developed by which the SPL design can be optimized. A SIGN consists of a components selection system and a decision support system. A SIGN aids the selection of machine tools, buffer sizes, and robots via the integration of AI and simulations. Using a purpose-developed interface, a user inputs the appropriate SPL parameters and settings, selects the decision-making and optimization techniques to use, and then displays output results. It will be implemented in open-source software to broaden the impact of the SIGN and extend its influence in industry and academia. It is expected that the results of this research project will significantly influence open-source manufacturing system design and, consequently, industrial and economic development.

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Data-driven hybrid algorithm with multi-evolutionary sampling strategy for energy-saving buffer allocation in green manufacturing

Shi,

Gao

2024

Proceedings of the Institution of Mechanical Engineers, Part B:

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Buffer allocation, which is an important research topic in manufacturing system design, typically focuses on system performance and cost. However, few previous studies have been performed to investigate energy-saving buffer allocation, which can decrease operational energy consumption in green manufacturing. Furthermore, the computational efficiency of solving the buffer allocation problem requires further investigation. This paper proposes a data-driven hybrid algorithm based on multi-evolutionary sampling strategies for solving energy-saving buffer allocation that can maximize the throughput rate and minimize energy consumption. Two evolutionary sampling strategies, that is, global search and surrogate-assisted local search, are integrated to balance exploitation and exploration. In addition, a database containing historical data pertaining to buffer allocation solutions is used to develop surrogate models that can rapidly predict the throughput and energy consumption and improve the evaluation efficiency of the local search strategy. Experimental results demonstrate the efficacy of the proposed algorithm. This study contributes to an efficient buffer allocation and presents a new perspective on energy-saving measures for green manufacturing designs.

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Multiobjective optimization of buffer capacity allocation in multiproduct unreliable production lines using improved adaptive NSGA-II algorithm

Cited by 4 publications

References 19 publications

Using Periodic Fuzzy Graphs in Production Equipment Control Problems

Using Periodic Fuzzy Graphs in Production Equipment Control Problems

Development and Numerical Optimization of a System of Integrated Agents for Serial Production Lines

Data-driven hybrid algorithm with multi-evolutionary sampling strategy for energy-saving buffer allocation in green manufacturing

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