A new aggregation algorithm for performance metric calculation in serial production lines with exponential machines: design, accuracy and robustness

Bai, Yishu; Tu, Jiachen; Yang, Ming; Zhang, Liang; Denno, Peter

doi:10.1080/00207543.2020.1757777

Cited by 34 publications

(3 citation statements)

References 28 publications

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“…Due to its main approach that considers only full and empty buffer states, instead of all buffer states as in other methods from the literature, the state space cardinality of the Markov chain representation of the system is reduced drastically. Results from numerical experiments demonstrate a high accuracy with extensively reduced computational time when compared to other methods from the literature, such as the decomposition and aggregation methods [41], [42].…”

Section: Nj Jmentioning

confidence: 81%

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

2022

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The current context of rising ecological awareness and high competitiveness, reveals a strong necessity to integrate the sustainability paradigm into the design of production systems. The buffer allocation problem is of particular interest since buffers absorb disruptions in the production line. However, despite the rich literature addressing the BAP, there are no studies that use a multi-objective framework to deal with energetic considerations. In this study, the energy-efficient buffer allocation problem (EE-BAP) is studied through a multi-objective resolution approach. The multi-objective problem is solved to optimize two conflicting objectives: maximizing production throughput and minimizing its energy consumption, under a total storage capacity available. The weighted sum and epsilon-constraint methods as well as the elitist non-dominated sorting genetic algorithm (NSGA-II) are adapted and implemented to solve the EE-BAP. The obtained solutions are analyzed and compared using different performance metrics. Numerical experiments show that epsilon-constraint outperforms the NSGA-II when considering comparable computational time. The Pareto solutions obtained are trade-offs between the two objectives, enabling decision making that balances productivity maximization with energy economics in the design of production lines.

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Section: Nj Jmentioning

confidence: 81%

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

2022

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“…Three commonly used approximation methods for larger production lines are the generalized expansion method [18], [19], the decomposition method [20]- [22], and the aggregation method [23], [24]. The generalized expansion method utilizes queuing models and applies to serial production lines and split and merge configurations, accommodating reliable machines and random distributed service times.…”

Section: Literature Reviewmentioning

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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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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“…Xia et al [31] decomposed the original long line into several small decoupled subsystems and added relation condition variables between the subsystems. Bai et al [32] proposed a new aggregation-based iterative algorithm to calculate the performance metrics of a multimachine serial line by representing it using a group of virtual two machine lines. In this paper, based on the approximate decomposition method, the influence factors are introduced according to the importance of different machines in the series system to obtain the optimal maintenance strategy for each machine.…”

Section: Introductionmentioning

confidence: 99%

A New Maintenance Optimization Model Based on Three‐Stage Time Delay for Series Intelligent System with Intermediate Buffer

Liu

et al. 2021

Shock and Vibration

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For the maintenance problem of intelligent series system with buffer stock, a preventive maintenance model based on the threestage time delay theory is proposed. Firstly, the intelligent series system is decomposed into n − 1 virtual series systems by using approximate decomposition method. The impact factor is introduced to establish the failure rate and maintenance rate model of each virtual machine. Secondly, a preventive maintenance model based on the three-stage time delay theory is proposed for each virtual series system. The machine state from normal operation to failure stage is divided into three steps: initial defect, serious defect, and fault, and different distribution functions are defined in different stages to simulate the degradation process of the machine. Based on the three-stage time delay theory, the machine cost ratio model was established by taking the machine monitoring time and buffer stock as decision variables and the minimum unit time cost rate as objective function. Finally, the rationality and validity of the model are verified by an example analysis, which provides a basis for the maintenance of the intelligent series system.

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A new aggregation algorithm for performance metric calculation in serial production lines with exponential machines: design, accuracy and robustness

Cited by 34 publications

References 28 publications

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

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

A New Maintenance Optimization Model Based on Three‐Stage Time Delay for Series Intelligent System with Intermediate Buffer

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