Optimization of Buffer Allocation in Manufacturing System Using Particle Swarm Optimization

Narasimhamu, K. L.; Reddy, V. Venugopal; Rao, C.S.P.

doi:10.15866/iremos.v8i2.5666

Cited by 5 publications

(4 citation statements)

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“…Regarding PSO, there are only two known papers addressing the BAP [7], [8], both focusing on small production lines (8 machines). Lin and Chiu [42] hybridizes PSO with OCBA but for the Resource Allocation Problem.…”

Section: Literature Reviewmentioning

confidence: 99%

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Adaptive Dynamic Jumping Particle Swarm Optimization for Buffer Allocation in Unreliable Production Lines

Kassoul,

Cheikhrouhou,

Zufferey

2023

IEEE Access

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Over the past half-century, the Buffer Allocation Problem (BAP) in production lines has remained a topic of continuous interest and research. In this context, the BAP refers to determining the optimal allocation of buffers along a production line to maximize efficiency and productivity. This paper presents a novel approach to address the BAP in unreliable serial production lines. The objective is to maximize the production rate of the production line, which directly influences its overall performance and profitability. The proposed approach introduces an adaptive simulation-optimization methodology, APSO, that leverages the particle swarm optimization (PSO) algorithm. PSO is a metaheuristic optimization technique inspired by the behavior of bird flocking or fish schooling, where particles explore the solution space to find optimal solutions. The novelty of this approach lies in integrating a jumping strategy into the PSO algorithm's velocity equation. The jumping strategy incorporates logarithmic and exponential functions into the velocity equation of the PSO algorithm, utilizing dynamic parameters. This modification enables the algorithm to quickly converge towards (or very close to) optimal solutions. By incorporating this jumping strategy, the proposed approach enhances the algorithm's exploration-exploitation balance, efficiently navigating complex solution spaces and overcoming local optima. To evaluate the effectiveness of the proposed method, extensive numerical experiments are conducted using various instances of production lines, ranging from 3 to 100 machines. Additionally, benchmark algorithms from the existing literature are employed for comparison purposes. The obtained results from these experiments serve as empirical evidence to demonstrate the efficiency and accuracy of the proposed approach. The results indicate that the proposed adaptive approach outperforms the benchmark algorithms regarding efficiency and solution quality.

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Section: Literature Reviewmentioning

confidence: 99%

“…for the first subswarm 𝑁 1 , do Calculate 𝜉 and 𝜁 using Equations ( 6) and (7). Calculate the velocity 𝑣 𝑖 for each particle using Equation (8).…”

Section: 𝑤 = 𝑑 (mentioning

confidence: 99%

Adaptive Dynamic Jumping Particle Swarm Optimization for Buffer Allocation in Unreliable Production Lines

Kassoul,

Cheikhrouhou,

Zufferey

2023

IEEE Access

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“…Other approaches relied on the Approximate Dynamic Programming (ADP) heuristic structures [4]. Over the last decade, meta-heuristics have been proposed to tackle the buffer allocation optimization problems such as GA [1], TS [3], Particle Swarm Optimization (PSO) [23], Variable Neighborhood Search (VNS) [31] and Non-Linear Threshold Algorithm (NLTA) [21], [22]. Additionally, hybrid search mechanisms that fuse together more than one heuristic are also considered such as a combination of SA and GA in [16] and TS and Nested Partition Algorithm (NPA) in [24].…”

Section: Related Backgroundmentioning

confidence: 99%

Optimized Buffer Allocation and Repair Strategies for Series Production Lines

Nahas¹,

Nourelfath²,

Abouheaf³

2022

Int J Ind Eng Manag

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This paper proposes an efficient approach for solving the buffer allocation problem with limited number of repair resources. In this problem, the objective is to maximize the throughput of a series production line through solving the buffer allocation optimization problem and electing the best repair polices. This is done using a simulation model to estimate the throughput combined with a self-adjusting search heuristic approach. The total count of repairmen is constrained to be strictly less than or equal to the count of repairable machines. Therefore, a self-adjusting search algorithm that is based on a nonlinear threshold accepting criterion is employed to reach the best production line configurations in terms of the buffer-levels and repair policies. The effectiveness of the combined simulation-search solution mechanism is tested using three different scenarios with 8 different repair policies. The numerical results showed that not only the proposed approach can find a near optimal solution in a limited and acceptable time, but also the overall production system performance can be improved by selecting the best repair policy.

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“…Recientemente en el estudio del BAP, las técnicas metaheurísticas han reportado con mucho éxito soluciones de mayor calidad en tiempos relativamente cortos. Algunas de las técnicas utilizadas son: Búsqueda Tabú (Costa et al, 2015), Recocido Simulado (Spinellis and Papadopoulos, 2000a, b), (Spinellis et al, 2000), Algoritmos Genéticos (Spinellis and Papadopoulos, 2000b), (Cruz et al, 2012), (Nahas et al, 2014), (Nahas and Nourelfath, 2018) Colonia de Hormigas (Nahas et al, 2009), (Nahas and Nourelfath, 2018) y Optimización por Enjambre de Partículas (Narasimhamu et al, 2014(Narasimhamu et al, , 2015.…”

Section: Introductionunclassified

Enfoque híbrido metaheurístico AG-RS para el problema de asignación del buffer que minimiza el inventario en proceso en líneas de producción abiertas en serie

Hernández-Vázquez

Hernández-González

Jiménez-García

et al. 2019

Rev. iberoam. autom. inform. ind.

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El problema de asignación del buffer (BAP, por sus siglas en inglés) es clasificado como un problema de optimización combinatorio NP-Duro en el diseño de las líneas de producción. Éste consiste en definir la asignación de lugares de almacenamiento (buffers) dentro de una línea de producción, con el fin de aumentar al máximo la eficiencia del proceso. Los métodos de optimización que han sido reportados con mayor éxito en los últimos años son las técnicas metaheurísticas. En este trabajo, se propone un enfoque híbrido que utiliza las técnicas metaheurísticas de: Algoritmos Genéticos (AG) y Recocido Simulado (RS), con el objetivo de determinar los buffers requeridos que minimicen el promedio de inventario en proceso (<em>WIP</em>, por sus siglas en inglés) en líneas de producción abiertas en serie <em>M/M/1/K</em>. La evaluación se realiza con un método analítico de descomposición. Los resultados obtenidos demuestran la eficiencia computacional del algoritmo híbrido propuesto con respecto a un RS o AG estándar.

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Optimization of Buffer Allocation in Manufacturing System Using Particle Swarm Optimization

Cited by 5 publications

References 0 publications

Adaptive Dynamic Jumping Particle Swarm Optimization for Buffer Allocation in Unreliable Production Lines

Adaptive Dynamic Jumping Particle Swarm Optimization for Buffer Allocation in Unreliable Production Lines

Optimized Buffer Allocation and Repair Strategies for Series Production Lines

Enfoque híbrido metaheurístico AG-RS para el problema de asignación del buffer que minimiza el inventario en proceso en líneas de producción abiertas en serie

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