A data-driven ensemble algorithm of black widow optimizer and simulated annealing algorithms for multi-objective buffer allocation in production lines

Gao, Sixiao; Liu, Hui

doi:10.1177/09544054231157153

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…The most common optimization criteria consider maximizing the average throughput rate (Gao, 2022;Gao, Higashi, Kobayashi, Taneda, Rubrico & Ota, 2020;Gao & Liu, 2023;Kassoul, Cheikhrouhou & Zufferey, 2023;Köse, Demir, Tunal & Eliiyi, 2015;Kose & Kilincci, 2015;Koyuncuoğlu & Demir, 2021;Lin & Chiu, 2018;Nahas, Nourelfath & Gendreau, 2014;Narasimhamu, Reddy & Rao, 2014;Patchong & Kerbache, 2017;Wang, Song, Shin & Moon, 2014), minimizing the total buffer size (Li, 2013;Weiss & Stolletz, 2015), minimizing the total cost of allocation (Magnanini, Terkaj & Tolio, 2022;Nahas, 2017;Nahas & Nourelfath, 2018;Ouzineb, Mhada, Pellerin & El Hallaoui, 2018;Tiacci, 2022), among others (Alfieri, Matta & Pastore, 2020;Hernández-Vázquez, Hernández-González, Jiménez-García, Hernández-Ripalda & Hernández-Vázquez, 2019;Hernández-Vázquez, Hernández-González, Hernández-Vázquez, Figueroa-Fernández & Cancino de la Fuente, 2022b;Koyuncuoğlu & Demir, 2023;Shaaban & Romero-Silva, 2021;Shao, Moroni, Li, & Xu, 2022;Xi, Smith, Chen, Mao, Zhang & Yu, 2021;Zhou, Liu, Yu & Tao, 2018).…”

Section: Buffer Allocation Problemmentioning

confidence: 99%

Optimization of the OEE indicator through meta-models’ simulation in the buffer allocation problem

Hernández Vázquez,

Hernández González

et al. 2024

JIEM

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Purpose: The buffer allocation problem (BAP) arises in the design of production systems; it involves analyzing and defining the optimal distribution of buffers within a production line. This paper presents a BAP formulation in a parallel series line from a cup sublimation process with unreliable operating conditions. The main objective of this study is to develop a new BAP solution proposal, considering the optimization of the OEE indicator used in Lean Manufacturing.Design/methodology/approach: The BAP was analyzed under an optimization approach from two different criteria: firstly, the maximization of the OEE indicator (Overall Equipment Effectiveness) utilized in Lean Manufacturing, as well as the maximization of the average production rate (Throughput). The case study involves unreliable operating conditions. Process times, and timeframes between failures and repairs, consider normal distribution functions. The evaluation method employed in the study includes the use of simulation meta-models built from experiment designs and production line simulations; on the other hand, the nonlinear GRG algorithm is used to solve the mathematical models.Findings: In the study carried out, it is shown that the OEE indicator can be affected when more buffers are allocated than necessary, hence it is important to calculate and establish the best configuration for them through an analysis such as the one proposed in this document.Research limitations/implications: The research is limited to a case study of an unreliable production line from a cup sublimation process.Practical implications: The proposed solution established in this study can be used in other production lines with configurations different from the one analyzed, considering the optimization criterion of the OEE indicator. Originality/value: Seeking that the allocation of buffers within the production line improves the OEE indicator is something new in the literature, therefore, the results achieved in this research become even more relevant.

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Section: Buffer Allocation Problemmentioning

confidence: 99%

Optimization of the OEE indicator through meta-models’ simulation in the buffer allocation problem

Hernández Vázquez,

Hernández González

et al. 2024

JIEM

View full text Add to dashboard Cite

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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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A data-driven ensemble algorithm of black widow optimizer and simulated annealing algorithms for multi-objective buffer allocation in production lines

Cited by 2 publications

References 30 publications

Optimization of the OEE indicator through meta-models’ simulation in the buffer allocation problem

Optimization of the OEE indicator through meta-models’ simulation in the buffer allocation problem

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

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