Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm

Amiri, Maghsoud; Mohtashami, Ali

doi:10.1007/s00170-011-3802-8

Cited by 57 publications

(51 citation statements)

References 52 publications

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“…Chan and Ng (2002) compared buffer allocation strategies for maximized the production rate in serial production line. Amiri and Mohtashami (2012) presented a multi-objective formulation of the buffer allocation problem in a serial line in which unreliable machines, finite buffer and exponential service time were assumed. They developed a meta-model for estimating production rate based on discrete event simulation, and used genetic algorithm combined to line search method to solve the multi-objective model, maximizing production rate and minimizing buffer size, and determining the optimal (or near optimal) size of each buffer storage.…”

Section: Figure 1 Example Of Crankshaftmentioning

confidence: 99%

A Simulation Based Study on Increasing Production Capacity in a Crankshaft Line Considering Limited Budget and Space

Wang¹,

Song²,

Shin³

et al. 2014

Journal of Korean Institute of Industrial Engineers

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In this paper, we discussed a problem for improving the throughput of a crankshaft manufacturing line in an automotive factory in which the budget for purchasing new machines and installing additional buffers is limited. We also considered the constraint of available space for both of machine and buffer. Although this problem seems like a kind of buffer allocation problem, it is different from buffer allocation problem because additional machines are also considered. Thus, it is not easy to calculate the throughput by mathematical model, and therefore simulation model was developed using ARENA ® for estimating throughput. To determine the investment plan, a modified Arrow Assignment Rule under some constraints was suggested and it was applied to the real case.

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Section: Figure 1 Example Of Crankshaftmentioning

confidence: 99%

A Simulation Based Study on Increasing Production Capacity in a Crankshaft Line Considering Limited Budget and Space

Wang¹,

Song²,

Shin³

et al. 2014

Journal of Korean Institute of Industrial Engineers

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“…In a new one, I proposed a methodology for buffer allocation problem in unreliable production systems [7] regarding general distribution function for time-dependent parameters of production system. Aksoy and Gupta consider optimal management of remanufacturing systems with server vacations.…”

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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“…Several research works [15,21] can be cited as applications of simulation models to search the solution of BAP. The study of the transference lines without restrictions in the stations and finite temporal intermediate buffer was performed by Hillier and So in 1991 and Hillier et al in 1993 [33,34].…”

Section: Complexitymentioning

confidence: 99%

“…The typical methods applied in this area include Tabu Search [11][12][13], Simulated Annealing [14], Genetic Algorithms [15], and Ant Colony Optimization [16]. In order to search for the best solution space, a recent tendency is to hybridize metaheuristics with other methods like nested partitions [17], the Branch and Bound method [18], and the local search [15]. These hybrid search methods have an advantage over the traditional ones because they can jump over local optimal solutions in the search of the global optimal.…”

Section: Related Workmentioning

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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Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm

Cited by 57 publications

References 52 publications

A Simulation Based Study on Increasing Production Capacity in a Crankshaft Line Considering Limited Budget and Space

A Simulation Based Study on Increasing Production Capacity in a Crankshaft Line Considering Limited Budget and Space

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

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

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