Performance evaluation of flow lines with non-identical and unreliable parallel machines and finite buffers

Diamantidis, Alexandros; Lee, Jun-Ho; Papadopoulos, Chrissoleon T.; Li, Jingshan; Heavey, Cathal

doi:10.1080/00207543.2019.1636322

Cited by 16 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the variables of such modified stations are estimated using the DDX algorithm in [2]. The decomposition approaches exhibited robust and fast solution characteristics [5,6]. On other hand, aggregation methods count on replacing a two-machineone-buffer sub-line by a single equivalent machine.…”

Section: Related Backgroundmentioning

confidence: 99%

“…If buffer ℬ 𝑖 becomes empty such that the production rates are forced to follow ℛ 𝑖+1 =ℛ 𝑖 and ℳ 𝑖+1 becomes starved. Hence, for the possible sequence of starved machines (i.e., ℳ 𝑖+2 ,ℳ 𝑖+3 ,…), the following procedure is considered repetitively up stream (6) untill a non-full buffer ℬ 𝑖+𝑘 is attained.…”

Section: Continuous Flow Modelmentioning

confidence: 99%

See 1 more Smart Citation

Optimized Buffer Allocation and Repair Strategies for Series Production Lines

Nahas¹,

Nourelfath²,

Abouheaf³

2022

Int J Ind Eng Manag

View full text Add to dashboard Cite

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.

show abstract

Section: Related Backgroundmentioning

confidence: 99%

Section: Continuous Flow Modelmentioning

confidence: 99%

Optimized Buffer Allocation and Repair Strategies for Series Production Lines

Nahas¹,

Nourelfath²,

Abouheaf³

2022

Int J Ind Eng Manag

View full text Add to dashboard Cite

show abstract

“…These methods are applied if a huge state space makes the numerical solution of Markov chains too slow or impossible (Weiss, Schwarz, and Stolletz 2018). The decomposition method (Liberopoulos 2018;Diamantidis et al 2019;Shaohui et al 2019) is based on a decomposition of the n-machine line into a set of (n-1) two-machine lines where the buffer between the upstream machine and the downstream machine has the same capacity. On the other hand, the aggregation methods (Zhao et al 2017) replace a two-machine line with an equivalent single machine, and then reduce iteratively the number of machines until obtaining only one machine.…”

Section: Literature Reviewmentioning

confidence: 99%

Buffer allocation design for unreliable production lines using genetic algorithm and finite perturbation analysis

Kassoul

Cheikhrouhou

Zufferey

2021

International Journal of Production Research

View full text Add to dashboard Cite

The buffer allocation problem in production lines is an NP-hard combinatorial optimisation problem. This paper proposes a new hybrid optimisation approach (using simulation) relying on genetic algorithm (GA) and finite perturbation analysis (FPA). Unlike the infinitesimal perturbation analysis, which deals with small (infinitesimal variation) perturbations for estimating gradients of the performance measure, FPA deals with larger (finite) or more lasting perturbations. It is an extension specifically dedicated to discrete decision variables and applicable to most discrete-event dynamic systems. The proposed method allows a global search using GA, with refinement in specific solution-space regions using FPA. The main objective is to maximise the average production rate of a production line with unreliable machines, by allocating the total buffer capacity in locations between machines. Extensive numerical experiments show that: (1) the proposed hybrid GA-FPA method clearly outperforms the state-of-the-art methods from the literature; (2) combining FPA and GA is beneficial when compared to employing GA or FPA independently.

show abstract

“…Decomposition methods have been used to analyze serial lines with parallel stations at each stage (Diamantidis et al 2020) and assembly/disassembly systems (Jeong andKim 1999, Patchong andWillaeys 2001). The approximation methods for queueing networks have been extended to various settings, e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

Supervised-learning-based approximation method for multi-server queueing networks under different service disciplines with correlated interarrival and service times

Khayyati

Tan

2021

International Journal of Production Research

View full text Add to dashboard Cite

Developing e cient performance evaluation methods is important to design and control complex production systems e↵ectively. We present an approximation method (SLQNA) to predict the performance measures of queueing networks composed of multi-server stations operating under di↵erent service disciplines with correlated interarrival and service times with merge, split, and batching blocks separated with infinite capacity bu↵ers. SLQNA yields the mean, coe cient of variation, and first-lag autocorrelation of the inter-departure times and the distribution of the time spent in the block, referred as the cycle time at each block. The method generates the training data by simulating di↵erent blocks for di↵erent parameters and uses Gaussian Process Regression to predict the inter-departure time and the cycle time distribution characteristics of each block in isolation. The predictions obtained for one block are fed into the next block in the network. The cycle time distributions of the blocks are used to approximate the distribution of the total time spent in the network (total cycle time). This approach eliminates the need to generate new data and train new models for each given network. We present SLQNA as a versatile, accurate, and e cient method to evaluate the cycle time distribution and other performance measures in queueing networks.

show abstract

Performance evaluation of flow lines with non-identical and unreliable parallel machines and finite buffers

Cited by 16 publications

References 34 publications

Optimized Buffer Allocation and Repair Strategies for Series Production Lines

Optimized Buffer Allocation and Repair Strategies for Series Production Lines

Buffer allocation design for unreliable production lines using genetic algorithm and finite perturbation analysis

Supervised-learning-based approximation method for multi-server queueing networks under different service disciplines with correlated interarrival and service times

Contact Info

Product

Resources

About