Optimal planning of buffer sizes and inspection station positions

Ouzineb, Mohammed; Mhada, Fatima Zahra; Pellerin, Robert; Hallaoui, Issmail El

doi:10.1080/21693277.2017.1422812

Cited by 9 publications

(5 citation statements)

References 55 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

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show abstract

“…Other performance measurements are also used: maximize the profit in a serial system with failures and with different service times [13]; minimizing the total number of spaces (variant two of the BAP) [14]; minimizing the assignment cost [15,16] and minimizing inventory cost of assembly systems with failures [17]. Minimizing Cycle time is only reported for unreliable serial lines [7].…”

Section: Previous Workmentioning

confidence: 99%

Buffer allocation problem in a shoe manufacturing line: A metamodeling approach

Vázquez

Hernández-González

Vázquez

et al. 2021

Rev.Fac.Ing.Univ.Antioquia

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Footwear production is subject to the variability inherent in any process, and producers often need to apply tools that allow them to make the right decisions. This work documents the process to optimize the buffer allocation in a shoe manufacturing line minimizing the cycle time in the system, applying a metamodeling approach. It was found that the Front sewing operation, and the interaction between the Lining sewing operation and the assembly operation have the greatest effect on the flow time of the product within the process; the optimum assignment of spaces follows a non-uniform arrangement on the line saturating the slower stations; the cycle time follows a non-linear behavior vs. the total number of spaces (N) in the line. For a certain value of N, the cycle time reaches a minimum value.

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“…Kang and Ju [11] determined an optimal maintenance policy for an SPL consisting of a series of machines, with each pair separated by an infinite buffer using Markov decision models. Ouzineb et al [12] studied the BAP and location of inspection stations for unreliable SPLs. The aim was to fulfill the customer requirements at a minimum cost by optimizing the buffer size and minimizing the number of inspection stations by selecting suitable locations.…”

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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Optimal planning of buffer sizes and inspection station positions

Cited by 9 publications

References 55 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

Buffer allocation problem in a shoe manufacturing line: A metamodeling approach

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

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