Optimization of production control policies in failure-prone homogenous transfer lines

Lavoie, Philippe; Kenné, Jean‐Pierre; Gharbi, Ali

doi:10.1080/07408170802375760

Cited by 14 publications

(5 citation statements)

References 19 publications

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“…As stated above, so far, there is no exact solution for failure-prone machine systems with the large of the transfer lines. The simulation method therefore represents a significant advantage in terms of analysis of the performance of the system, as can be seen in Lavoie et al (2009), Kenne and Gharbi (2001). Other papers focus on the performance parameters of transfer lines (i.e., lines including production rate and average buffer levels); that is the case in Dallery et al (1989), Ciprut et al (2000), Gershwin (2008, 2005), and Tan and Gershwin (2009), where long lines are decomposed into two-machine lines (flowshop system) in the case of identical machines.…”

Section: Literature Reviewmentioning

confidence: 95%

Feedback optimal control of dynamic stochastic two-machine flowshop with a finite buffer

Diep¹,

Kenné²,

Dao³

2010

IJIEC

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This paper examines the optimization of production involving a tandem two-machine system producing a single part type, with each machine being subject to random breakdowns and repairs. An analytical model is formulated with a view to solving an optimal stochastic production problem of the system with machines having up-downtime non-exponential distributions. The model developed is obtained by using a dynamic programming approach and a semi-Markov process. The control problem aims to find the production rates needed by the machines to meet the demand rate, through a minimization of the inventory/shortage cost. Using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-JacobiBellman equation, which depends on time and system states, and ultimately, leads to a feedback control. Consequently, the new model enables us to improve the coefficient of variation (CVup/down) to be less than one while it is equal to one in Markov model. Heuristics methods are used to involve the problem because of the difficulty of the analytical model using several states, and to show what control law should be used in each system state (i.e., including Kanban, feedback and CONWIP control). Numerical methods are used to solve the optimality conditions and to show how a machine should produce.

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Section: Literature Reviewmentioning

confidence: 95%

Feedback optimal control of dynamic stochastic two-machine flowshop with a finite buffer

Diep¹,

Kenné²,

Dao³

2010

IJIEC

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“…Since general distribution function is assumed for all time-dependent parameters of production system, to estimate the production rate based on these three variables, this paper uses simulation and experimental design. Discrete-event simulation is a very effective way of estimating almost any system performance given that the input data is accurate [38]. Computer simulation offers the advantage of tracing complex system processes (considering any distribution functions and complex relationships among the stations, elements, components, and sections), providing timely information on operating characteristics, and for these reasons, it has been adopted in this research.…”

Section: Index Of Mutation Mpmentioning

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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“…An approximation method has been used with a special version of a multi‐objective genetic algorithm. Lavoie et al (2009) use a combined discrete/continuous simulation modeling, design of experiments and response surface methodology to optimize a set of transfer lines, with one parameter per machine, for up to seven machines. They report that the last buffer in a production line is more important than the others.…”

Section: Literature Reviewmentioning

confidence: 99%

A nonlinear model for optimizing the performance of a multi-product production line

Abdul‐Kader¹,

Ganjavi²,

Baki³

2011

International Transactions in Operational Research

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This paper examines the measures of performance and in particular addresses the throughput of an automated production line processing multiple products. The line is composed of a sequence of workstations connected in series with finite buffers in between. We explore the effects of buffer size on attenuating the impact of line blocking and starvation that can cause a reduction in the output. Such effects are analyzed through a nonlinear mathematical programming model and the implications are examined. The aim of the model is to achieve the best performance subject to available workstation capacity without overexpenditure on buffer size. Single and multi‐objective optimizations are carried out in the paper. A numerical example of a production line with a given configuration of workstations; workstation capacity; and job mix is presented to demonstrate the model and its application. A discussion of the impact of buffer size on maximum throughput is also provided. The paper is concluded with a discussion on the decision‐making implications.

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Optimization of production control policies in failure-prone homogenous transfer lines

Cited by 14 publications

References 19 publications

Feedback optimal control of dynamic stochastic two-machine flowshop with a finite buffer

Feedback optimal control of dynamic stochastic two-machine flowshop with a finite buffer

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

A nonlinear model for optimizing the performance of a multi-product production line

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