A dynamic programming algorithm for the bufferallocation problem in homogeneous asymptotically reliable serial production lines

Diamantidis, Alexandros; Papadopoulos, Chrissoleon T.

doi:10.1155/s1024123x04402014

Cited by 42 publications

(21 citation statements)

References 17 publications

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“…On the other hand, dynamic programming [19,20], artificial neural networks [21], genetic programming [22], and immune algorithm systems [23] have been applied with success in the solution of BAP in production lines. Moreover, some studies utilize diverse experimental designs for the evaluation of the solutions for the BAP [24][25][26][27][28].…”

Section: Complexitymentioning

confidence: 99%

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

et al. 2018

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The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers' artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit ( = 1.0000) against the response surface methodology ( = 0.9996). Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

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Section: Complexitymentioning

confidence: 99%

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

et al. 2018

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“…For the flow manufacturing lines with single reliable machine, Powell [1] suggested the rules of thumb for the optimal sequential placement of buffer space in the unbalanced lines with three machines. Huang et al [2], Diamantidis and Papadopoulos [3] used the dynamic programming approach to maximize the production rate or minimize the work in process under a certain buffer allocation strategy in the flow shop-type manufacturing systems. Chan and Ng [4] compared buffer allocation strategies for maximizing the production rate.…”

Section: Introductionmentioning

confidence: 99%

A Simulation Study on Improving Throughput in a Crankshaft Line Considering Minimizing Investment Cost under Throughput Target

Wang¹,

Lee²,

Shin³

et al. 2017

IJMMM

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Abstract-In this paper, we consider a problem minimizing investment cost when throughput target is given, and the total budget and available spaces for machines and buffers are limited. This situation happens frequently in practical manufacturing system, and we consider the crankshaft line of a Korean automotive factory as for the case study. Simulation model is developed to estimate throughput and a modified arrow assignment rule using difficulty is suggested to find optimal allocation of machines and buffers.

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“…Diamantidis and Papadopoulos [14] present a dynamic programming algorithm for optimizing buffer allocation based on the aggregation method given by Lim et al [15]. Smith and Cruz solve the buffer allocation problem for general finite buffer queueing networks in which they minimize buffer space cost under the production rate constraint [16].…”

Section: Introductionmentioning

confidence: 99%

“…Process times and (or) time between failures are considered deterministic or exponentially distributed in many researches. Moreover, maintenance and repairing of machines and non-deterministic nature of repair times are ignored as a fact in some researches [e.g., 3,9,13,14,17,18,20,21,23]. In many researches, the average of service time is used instead of statistical distribution function.…”

Section: Introductionmentioning

confidence: 99%

A new hybrid method for buffer sizing and machine allocation in unreliable production and assembly lines with general distribution time-dependent parameters

Mohtashami

2014

Int J Adv Manuf Technol

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This paper proposes a multi-objective mathematical formulation and a hybrid approach to solve buffer sizing and machine allocation problems simultaneously in unreliable production and assembly lines. This paper unlike prior researches assumes that time-dependent parameters of production systems are generally distributed (e.g., uniform, normal, gamma, etc.) and not only deterministic or exponential. This paper proposes a multi-objective mixed binary integer nonlinear mathematical model to solve the problem of buffer sizing and machine allocation. The proposed mathematical model is capable of purchasing new machines (candidate) and also selling old machines (current available). In other words, this model compares the candidate machines to current available machines in each station based on different aspects and is capable to replace the current machines with candidate machines or to sell some of the current machines without replacement. To solve the mentioned problem, a new formulation for dealing with multi-objectiveness of the problem is proposed. This formulation generates a series of nondominated solutions, and also, it is capable of generating a non-dominated solution between two adjacent non-dominated solutions determined by decision maker. A hybrid genetic algorithm (HGA) with a new dynamic mutation probability is proposed to solve the model. Since the proposed mathematical model and the proposed solution method are novel, the proposed HGA is compared to simple genetic algorithm and non-dominated sorting genetic algorithm (NSGA-II). The computational results indicate the effectiveness of the proposed HGA.

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A dynamic programming algorithm for the bufferallocation problem in homogeneous asymptotically reliable serial production lines

Cited by 42 publications

References 17 publications

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

A Simulation Study on Improving Throughput in a Crankshaft Line Considering Minimizing Investment Cost under Throughput Target

A new hybrid method for buffer sizing and machine allocation in unreliable production and assembly lines with general distribution time-dependent parameters

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